AI Infrastructure Monitoring

AI infrastructure monitoring is no longer only about server-room temperature, IT equipment status, or data center alarms. The new generation of AI infrastructure depends on a much wider physical ecosystem:

high-density compute rooms,
data halls,
cooling systems,
liquid cooling support loops,
water systems,
backup power assets,
external utility infrastructure,
mechanical rooms,
stormwater networks,
fuel storage,
pressure zones,
and distributed equipment that may sit far outside the core building automation system.

As AI workloads continue to increase compute density, the infrastructure surrounding AI facilities becomes more critical. The limiting factor is not always the availability of GPUs or servers. In many facilities, the limiting factors are cooling capacity, power resilience, water availability, airflow stability, mechanical reliability, backup fuel readiness, site utility performance, and the ability to detect infrastructure problems before they become operational incidents.

This is where Ellenex provides a practical and technically complete monitoring layer for AI infrastructure.

Ellenex supports AI infrastructure monitoring with rugged, low-power, wide-area industrial IoT solutions built around pressure, differential pressure, level, temperature, totaliser and flow interfaces, water quality, environmental monitoring, and flexible sensor interface technologies. By combining these measurement categories with LoRaWAN, NB-IoT, and LTE-M / Cat-M1 connectivity, Ellenex helps operators monitor critical infrastructure assets that are remote, distributed, difficult to wire, expensive to inspect manually, or not fully visible through existing BMS, SCADA, DCIM, or facility systems.

The objective is not simply to collect more data. The objective is to make AI infrastructure more resilient, more efficient, more measurable, and easier to operate.

A high-performance AI environment is only as reliable as the infrastructure that supports it. If cooling water pressure drifts, if airflow becomes unstable, if a filter loads earlier than expected, if a cooling tower basin is not visible, if a fuel tank is lower than assumed, if a remote utility room is unmonitored, or if stormwater infrastructure becomes a site risk, the compute environment is exposed. AI infrastructure monitoring helps operators detect these conditions earlier, understand trends over time, and respond with better operational context.

Ellenex’s AI Infrastructure Monitoring solution is designed around five core infrastructure layers:

Data Hall & Airflow
Cooling Infrastructure
Water & Sustainability
Power Resilience
Site & Utility Infrastructure

Data Hall & Airflow

Pressure, differential pressure, temperature, and airflow-related monitoring for server rooms, data halls, racks, cabinets, cleanrooms, containment zones, AHUs, filters, and ventilation systems.

Cooling Infrastructure

Pressure, differential pressure, temperature, flow interface, level, and water-quality monitoring for chilled water, liquid cooling support systems, pumps, filters, heat exchangers, cooling towers, tanks, and mechanical plant assets.

Water & Sustainability

Water usage, meter digitalization, tank level, storage monitoring, reuse water, water quality, and WUE-supporting visibility for operators that need better control of water consumption and water-related infrastructure.

Power Resilience

Backup fuel level, generator support infrastructure, utility-room conditions, and third-party equipment signal capture for power-adjacent resilience monitoring.

Site & Utility Infrastructure

Stormwater, flood-prone assets, PRV chambers, pipelines, external tanks, remote utility rooms, and distributed site infrastructure that supports data center and AI campus reliability.

Together, these layers form a comprehensive remote monitoring architecture for the physical infrastructure behind AI.

Why AI Infrastructure Monitoring Matters

AI infrastructure is different from conventional digital infrastructure because it concentrates high thermal load, high power demand, and high operational consequence into facilities that must remain stable under increasingly dynamic conditions. Traditional facility monitoring can provide a basic view of rooms and equipment, but it often leaves gaps in the supporting infrastructure that actually determines whether AI compute can operate efficiently and reliably.

Many infrastructure problems do not begin as major failures. They begin as small deviations.

A pressure reading starts to drift. A differential pressure value slowly rises across a filter. A tank level drops faster than expected. A cooling water loop develops abnormal behavior. A remote utility space becomes warmer than normal. A backup fuel tank does not match the expected inventory profile. A stormwater asset begins to rise during a weather event. A pump, valve, filter, or mechanical section operates differently from its baseline.

Without continuous monitoring, these early signals are often missed. Operators may discover them during a scheduled inspection, after a complaint, during a failure investigation, or after a control alarm has already escalated. By then, the cost of response may already be higher.

AI infrastructure monitoring changes the operating model. It provides continuous or near-real-time visibility across critical assets that would otherwise be inspected manually or left outside the main monitoring architecture. Instead of relying only on periodic checks, operators can observe trends, detect abnormal behavior, trigger alerts, and compare asset performance over time.

For AI infrastructure operators, this supports several important outcomes:

It improves resilience by reducing blind spots across support infrastructure.
It improves maintenance efficiency by helping teams prioritize field work based on measured conditions rather than fixed schedules alone.
It supports uptime by identifying infrastructure degradation before it affects compute environments.
It improves water and energy awareness by adding visibility around consumption, cooling support systems, and supporting utilities.
It supports sustainability programs by producing measurable data for water usage, reuse water, tank behavior, and WUE-related inputs.
It improves operational planning by showing where infrastructure is stable, unstable, overused, underperforming, or trending toward service intervention.

This is why AI infrastructure monitoring should not be treated as a secondary facility feature. It is a core operational layer for data centers, AI campuses, high-performance compute sites, edge AI infrastructure, and industrial facilities deploying AI workloads.

Data Hall & Airflow Monitoring

Data hall and airflow monitoring is one of the most important starting points for AI infrastructure visibility. High-density server rooms and AI compute environments depend on stable thermal conditions, reliable airflow, and controlled pressure behavior. Even when the main HVAC or cooling system is functioning, local airflow problems can still create inefficiency, thermal risk, fan stress, contamination risk, or performance instability.

In a data hall, airflow is not simply a comfort variable. It is part of the cooling delivery system. Pressure differences, cabinet airflow, filter loading, air handling unit performance, fan behavior, containment discipline, and temperature gradients all influence whether cooling reaches the right equipment at the right time.

Ellenex supports this layer with differential pressure and temperature monitoring solutions, especially the PDT2 product family for low-range air pressure applications. The PDT2 architecture is highly relevant for AI data centers because it can monitor differential pressure and temperature in environments where airflow stability, clean air delivery, and pressure balance matter.

What Ellenex can monitor in data hall and airflow applications

Ellenex data hall and airflow monitoring can support:

Positive pressure monitoring in server racks and cabinets
Differential pressure between controlled spaces
Pressure monitoring across air filters
Air handling unit performance monitoring
Fan and ventilation system performance monitoring
Cleanroom and controlled-room pressure monitoring
Airflow-related pressure behavior in ducts or controlled zones
Temperature monitoring at selected infrastructure points
Pressure change detection in sensitive environments
Early warning of filter loading, airflow restriction, or ventilation degradation

For AI infrastructure, the key value is not just measuring pressure. The value is understanding whether airflow conditions remain within the expected operating pattern. A stable pressure range may indicate that cooling delivery and containment are behaving as intended. A gradual change may indicate increasing resistance, leakage, filter loading, fan degradation, or a change in room operation. A sudden change may indicate a door state change, fan failure, equipment disturbance, or abnormal airflow event.

Why positive pressure matters in AI infrastructure

Positive pressure can help ensure that controlled air moves in the intended direction and that external contaminants are less likely to enter sensitive spaces. In server racks, cabinets, and controlled environments, pressure behavior can also reflect airflow availability and cooling performance. Monitoring positive pressure helps operators identify problems before they become thermal or reliability issues.

For AI data centers, this matters because cooling loads are high and tolerance for infrastructure uncertainty is low. If a pressure condition changes, operators need to know quickly. If a cabinet, aisle, or controlled space is losing the intended pressure profile, that information can support corrective action before equipment performance or reliability is affected.

Ellenex products for Data Hall & Airflow Monitoring

The most relevant Ellenex product families include:

PDT2 Differential Pressure + Temperature Sensors
For ultra-low-range air pressure, positive pressure, negative pressure, airflow-related monitoring, cleanrooms, filters, fan performance, and controlled environments.
PDS2 / PDG2 Differential Pressure Sensors
For broader differential pressure applications where comparative pressure measurement is required.
TTS2 / TTS3 Temperature Sensors
For temperature visibility in server rooms, utility areas, pipe surfaces, equipment rooms, and supporting spaces.
RS1 / RM1 / RM4 Sensor Interfaces

For connecting existing third-party differential pressure transmitters, temperature probes, analog sensors, Modbus devices, pulse outputs, or other installed instruments into an LPWAN monitoring architecture.

Data Hall & Airflow Monitoring as an AI infrastructure package

For a data hall or AI server-room deployment, Ellenex can package these capabilities into a targeted monitoring solution:

Rack or cabinet pressure monitoring
Room-to-room differential pressure monitoring
AHU filter differential pressure monitoring
Fan and ventilation pressure monitoring
Temperature monitoring at selected points
Wireless LPWAN data transmission
Alerting and trend analysis
Integration with BMS, DCIM, cloud dashboards, or operational workflows

This gives operators a practical way to extend visibility beyond traditional room-level monitoring and into the airflow conditions that affect compute reliability.

Cooling Infrastructure Monitoring

Cooling infrastructure is one of the most critical parts of AI infrastructure. AI workloads generate significant heat, and as compute density rises, cooling systems must work harder and become more complex. Facilities may use air cooling, chilled water, direct-to-chip liquid cooling, rear-door heat exchangers, cooling distribution units, cooling towers, dry coolers, heat exchangers, pumps, filters, strainers, water treatment systems, or hybrid architectures.

The monitoring challenge is that cooling infrastructure is not a single asset. It is a network of mechanical, hydraulic, thermal, and sometimes chemical conditions. Operators need visibility into pressure, differential pressure, temperature, flow, level, water quality, tank status, and equipment behavior.

Ellenex is well suited to this challenge because its product ecosystem covers the main measurement categories required for cooling infrastructure monitoring.

What can be monitored in cooling infrastructure

Ellenex cooling infrastructure monitoring can support:

Chilled-water supply pressure
Chilled-water return pressure
Differential pressure across filters and strainers
Differential pressure across heat exchangers
Pump suction and discharge pressure
Cooling-loop pressure stability
Pipe temperature on supply and return lines
Cooling tower basin level
Makeup water tank level
Water meter pulse or totaliser interfaces
Cooling water quality indicators (Conductivity, Turbidity, pH and ORP)
Abnormal flow or consumption patterns
Remote cooling plant and mechanical-room assets

Cooling infrastructure is especially well suited to pressure and differential pressure monitoring. In many systems, changes in pressure behavior provide early evidence of restriction, fouling, pump degradation, valve position issues, trapped air, filter loading, or abnormal operating states.

Differential pressure in AI cooling systems

Differential pressure is one of the most valuable diagnostic parameters in cooling systems because it shows how one part of the system compares to another. A rising differential pressure across a filter can indicate loading or clogging. A changing differential pressure across a heat exchanger may suggest fouling, scaling, flow restriction, or operating drift. Differential pressure across a pump or loop section can provide useful information about hydraulic performance.

In AI infrastructure, this information matters because cooling performance directly supports compute performance. If a cooling loop becomes unstable or inefficient, thermal risk can increase. Monitoring differential pressure allows operators to identify developing issues earlier and make maintenance decisions based on measured conditions.

Temperature in cooling infrastructure

Temperature data is essential, but it becomes much more powerful when combined with pressure, differential pressure, flow, and level information. A supply temperature alone may show whether cooling is available. A supply and return temperature profile can help operators understand heat transfer. When temperature is paired with pressure and flow-related information, it becomes part of a more complete cooling-performance picture.

Ellenex temperature sensors can be used for pipe temperature, mechanical-room temperature, equipment-area temperature, and other infrastructure points where thermal context is required.

Water quality in cooling infrastructure

Cooling systems that involve water, glycol, treatment chemicals, or tower water may require water-quality visibility. Conductivity, salinity, turbidity, pH, ORP, and dissolved oxygen can all provide useful insight into cooling water condition. These parameters may help operators understand treatment performance, contamination risk, corrosion tendency, scaling potential, or changes in water behavior.

Ellenex water-quality devices can support this layer where water quality is relevant to operational reliability or sustainability reporting.

Ellenex products for Cooling Infrastructure Monitoring

Relevant Ellenex product families include:

PTS2 / PTD2 / PTDH2 / PTS3 Pressure Sensors
For cooling-loop pressure, pump pressure, pipe pressure, and water-side infrastructure monitoring.
PDS2 / PDG2 Differential Pressure Sensors
For filters, strainers, heat exchangers, pumps, and hydraulic comparison points.
TTS2 / TTS3 Temperature Sensors
For pipe temperature, mechanical-room temperature, and thermal support monitoring.
FMS2 Totaliser and Flow Meter Interface
For digitising existing pulse-output meters, water meters, or flow-related devices.
PLS2 / PLD2 / PLS3 / PLM2 / DUS3 / DRC3 Level Sensors
For cooling tower basins, water tanks, storage vessels, reservoirs, and level-related cooling assets.
CSD2 / CTR2 / CPH2 / CDO2 Water Quality Sensors
For conductivity, salinity, turbidity, pH, ORP, dissolved oxygen, and temperature-related water quality monitoring.
RS1 / RM1 / RM4 Sensor Interfaces

For integrating third-party pressure, flow, temperature, Modbus, pulse, analog, or water-quality instruments into a remote monitoring architecture.

Cooling Infrastructure Monitoring as an AI package

An Ellenex cooling infrastructure monitoring package can include:

Pressure monitoring on key loop sections

Differential pressure across filters, strainers, pumps, and heat exchangers

Pipe temperature monitoring

Flow or meter interface monitoring

Tank, basin, and makeup water level monitoring

Water-quality monitoring where required

Threshold alerts and trend analytics

Integration with BMS, SCADA, DCIM, or cloud environments

This package helps operators move from reactive cooling maintenance to data-supported cooling infrastructure management.

Water & Sustainability Monitoring

Water is becoming a strategic issue for AI infrastructure. Data centers and AI facilities may use water directly in cooling systems, indirectly through power generation, or operationally through treatment, reuse, site utilities, tanks, or facility water systems. Even when a facility is designed to minimize water consumption, operators still need accurate visibility into water use, tank behavior, reuse systems, cooling makeup water, blowdown, water quality, and site water risk.

Water monitoring is not only a sustainability function. It is an operational function. If a water supply line loses pressure, if makeup water use rises unexpectedly, if a tank level changes abnormally, if reuse water quality drifts, or if a cooling tower basin is not visible, the data center may face operational risk.

Ellenex has a strong foundation in water infrastructure monitoring, making this one of the most natural extensions into AI infrastructure.

What can be monitored in Water & Sustainability applications

Ellenex water and sustainability monitoring can support:

Cooling makeup water usage
Water meter digitisation
Pulse-output meter monitoring
Tank level monitoring
Reservoir and basin level monitoring
Reuse water monitoring
Water quality at treatment or reuse points
Cooling tower basin level
Blowdown-related monitoring
Water pressure in supply and distribution lines
Stormwater and flood-related water level
Groundwater or well level where relevant
Water usage trends for WUE-related analysis
Abnormal consumption detection

The goal is to create a measurable water layer around the AI facility. Instead of relying only on utility bills, manual tank checks, or disconnected local meters, operators can create a continuous view of water movement, water storage, and water-related risk.

WUE-supporting data

Water Usage Effectiveness, or WUE, depends on reliable water-use data. Ellenex does not need to be positioned as a WUE reporting platform by itself. The stronger and more technically accurate position is that Ellenex provides the field monitoring and telemetry layer that supports WUE-related visibility.

This may include meter interfaces for water consumption, level sensors for storage, water-quality sensors for reuse or treatment points, and pressure sensors for distribution lines. When this data is connected to a central analytics platform, operators gain a more complete understanding of water behavior across the facility.

Water meter digitisation

Many sites already have installed water meters. Replacing them can be expensive, disruptive, or unnecessary. Ellenex’s FMS2 meter interface allows operators to digitise compatible meters and bring their readings into a remote monitoring architecture. This is especially useful for industrial water monitoring, cooling makeup water, reuse water, and utility submetering.

Level monitoring for water assets

Level monitoring is important for tanks, basins, reservoirs, cooling tower basins, stormwater assets, and wells. In AI infrastructure, level visibility supports both operations and resilience. A tank that is too low, too high, or changing unexpectedly can indicate supply risk, overflow risk, abnormal consumption, failed replenishment, or process instability.

Ellenex submersible, ultrasonic, radar, and well-level monitoring options can support different asset types depending on installation conditions and measurement requirements.

Water quality monitoring

Water quality monitoring becomes relevant where cooling water condition, reuse water quality, treatment performance, discharge conditions, or environmental monitoring matter. Ellenex water-quality sensors can support conductivity, salinity, turbidity, pH, ORP, dissolved oxygen, and temperature monitoring. These parameters can help operators understand whether water remains within expected operating conditions and whether treatment or reuse systems need attention.

Ellenex products for Water & Sustainability Monitoring

Relevant product families include:

FMS2 Totaliser and Water Meter Interface
For digitising compatible inline meters and pulse-output meters.
PLS2 / PLC2 / PLD2 / PLS3 / PLM2 / DUS3 / DRC3 Level Sensors
For tanks, reservoirs, basins, wells, open water, and cooling-related water assets.
PTS2 / PTD2 / PTS3 Pressure Sensors
For water supply pressure, distribution pressure, and hydraulic condition monitoring.
CSD2 / CTR2 / CPH2 / CDO2 Water Quality Sensors
For conductivity, salinity, turbidity, pH, ORP, dissolved oxygen, and temperature.
MRS2 Rain Sensor / MSS2 Soil Moisture Sensor / MAS2 Humidity Sensor
For broader site water and environmental context where relevant.
RS1 / RM4 Sensor Interfaces

For connecting existing meters, treatment instruments, third-party water-quality sensors, or legacy water infrastructure signals.

Water & Sustainability Monitoring as an AI package

An Ellenex water and sustainability package can include:

Cooling makeup water monitoring
Water meter digitisation
Tank and basin level monitoring
Reuse water monitoring
Water-quality monitoring
Water pressure monitoring
Stormwater and flood monitoring

Dashboards and alerts for abnormal consumption, storage changes, quality drift, and site water risk

This gives AI infrastructure operators a practical way to manage water as both an operational resource and a sustainability metric.

Power Resilience Monitoring

AI infrastructure is power intensive, but power resilience is not only about grid capacity or electrical switchgear. It is also about the supporting assets that keep the facility operational during power events, outages, curtailments, or abnormal conditions. Backup fuel, generator support systems, remote utility rooms, auxiliary equipment, and third-party signals all contribute to operational resilience.

Ellenex should be positioned carefully in this area. Ellenex does not replace electrical protection systems, UPS systems, generator controllers, or power-quality analyzers. Instead, Ellenex provides the remote monitoring layer for power-adjacent infrastructure: the assets around backup power and utility continuity that are often manually checked, poorly integrated, or difficult to observe remotely.

What can be monitored in Power Resilience applications

Ellenex power resilience monitoring can support:

Backup diesel tank level
Fuel inventory visibility
Remote fuel tank monitoring
Generator support area temperature
Utility room temperature
Auxiliary tank and storage monitoring
Third-party signal capture from installed systems
Pulse outputs from compatible equipment
Modbus or analog signal integration
Cathodic protection monitoring for buried metallic infrastructure where relevant
Remote utility asset visibility
Event-based alerts for abnormal level, temperature, or signal conditions

Backup fuel monitoring

Backup fuel is a critical resilience asset. If the generator is ready but fuel availability is uncertain, resilience planning is incomplete. Manual tank checks can be time-consuming and unreliable, especially across multiple facilities, external tanks, remote edge sites, or large AI campuses.

Ellenex level sensors can provide remote visibility into fuel tank levels, helping operators understand current inventory, depletion trends, replenishment needs, and abnormal drawdown. This reduces dependence on manual inspection and supports more reliable fuel logistics.

Utility room and generator support monitoring

Many utility rooms, generator support areas, and plant rooms have limited remote visibility. Temperature, water ingress, external tank status, or auxiliary signal monitoring can provide useful context for operations teams. Ellenex temperature sensors, level sensors, and sensor interfaces can help bring these points into a central monitoring environment.

Third-party integration for resilience assets

Many backup systems already produce signals through controllers, meters, or industrial communication outputs. Ellenex RS1, RM1, and RM4 sensor interfaces can be used to capture compatible analog, pulse, digital, temperature, and Modbus signals and transmit them through LPWAN or cellular networks.

This is valuable because it allows operators to add remote visibility without replacing existing equipment.

Ellenex products for Power Resilience Monitoring

Relevant product families include:

PLS2 / PLG2 / PLS3 / PLD2 Level Sensors
For backup fuel tanks and storage assets.
TTS2 / TTS3 Temperature Sensors
For utility rooms, generator support rooms, equipment areas, and thermal condition monitoring.
RS1 / RM1 / RM4 Sensor Interfaces
For integrating compatible third-party equipment signals, Modbus devices, analog outputs, pulse outputs, and temperature probes.
ECP2 Cathodic Protection Sensor
For applicable buried infrastructure and corrosion-protection-related monitoring.
EVM2 / ECM2 Electrical Measurement Devices

For selected voltage or current monitoring applications where appropriate and technically validated.

Power Resilience Monitoring as an AI package

An Ellenex power resilience package can include:

Backup fuel tank level monitoring
Remote fuel inventory dashboards
Generator support area temperature monitoring
Utility room monitoring
Third-party controller or signal integration
Alerting for abnormal depletion, low fuel, or environmental conditions
Multi-site visibility across distributed AI infrastructure

This supports one of the most important operating questions in AI infrastructure: are the supporting resilience assets ready when they are needed?

Site & Utility Infrastructure Monitoring

AI infrastructure does not end at the data hall wall. Many operational risks exist in the surrounding site and utility infrastructure. Large data centers and AI campuses depend on external pipes, tanks, stormwater systems, drainage networks, PRV chambers, utility yards, pump stations, underground assets, remote mechanical spaces, and site infrastructure that may not be fully covered by core BMS or DCIM systems.

This is one of the strongest applications for LPWAN remote monitoring. These assets are often distributed, difficult to wire, exposed to harsh environments, or checked manually. They may not justify a full wired automation project, but they still represent operational risk.

Ellenex provides the rugged sensing and connectivity architecture needed to make these assets visible.

What can be monitored in Site & Utility Infrastructure applications

Ellenex site and utility infrastructure monitoring can support:

Stormwater level
Flood-prone site points
Drainage channels
Manholes and underground waterway levels
PRV chamber pressure
Pipeline pressure
Remote water tanks
External utility rooms
Pump station support points
Buried asset monitoring
Rainfall and environmental context
Cathodic protection for applicable assets
External tanks and basins
Remote mechanical areas
Peripheral infrastructure that supports AI facility operation

Stormwater and flood monitoring

Stormwater risk can affect access, safety, operations, and continuity. In some facilities, external water events can cause site disruption before internal systems detect a problem. Remote level monitoring in channels, drains, underground waterways, basins, or flood-prone points helps operators detect rising water and respond earlier.

Ellenex radar, ultrasonic, and submersible level monitoring products can support different types of stormwater and flood monitoring applications depending on the asset and installation environment.

PRV chambers and pipeline pressure

Pressure monitoring in PRV chambers, pipelines, and water distribution points helps operators detect abnormal hydraulic behavior. Pressure drops, transients, instability, or unexpected patterns may indicate leakage, restriction, valve issues, demand anomalies, or system imbalance.

For AI campuses that depend on external water infrastructure, cooling water, process water, or utility distribution networks, this visibility can be important.

Remote utility rooms and external assets

Large AI facilities often include external utility rooms, plant areas, service yards, storage tanks, and support equipment that are not always monitored to the same level as the main building. Ellenex can help instrument these areas using battery-powered LPWAN sensors and interfaces.

Ellenex products for Site & Utility Infrastructure Monitoring

Relevant product families include:

PTS2 / PTD2 / PTS3 Pressure Sensors For pipelines, PRV chambers, pump support points, and water infrastructure.
PDS2 Differential Pressure Sensors For comparative pressure, filters, and pressure-drop applications.
PLS2 / PLS3 / PLM2 / DUS3 / DRC3 Level Sensors For tanks, basins, stormwater channels, wells, reservoirs, and waterways.
MRS2 Rain Sensor For rainfall context and stormwater monitoring.
MSS2 Soil Moisture / MAS2 Humidity Sensors For broader environmental context where relevant.
ECP2 Cathodic Protection Sensor For applicable buried metallic infrastructure.
RS1 / RM4 Sensor Interfaces For external third-party sensors, existing instruments, and legacy site infrastructure.

Site & Utility Infrastructure Monitoring as an AI package

An Ellenex site and utility infrastructure package can include:

Stormwater level monitoring
Flood-prone asset monitoring
Pipeline pressure monitoring
PRV chamber monitoring
External tank and reservoir monitoring
Rainfall and environmental context
Remote utility room monitoring
Buried infrastructure monitoring where relevant
Alerts and trend analytics for external infrastructure risk

This creates an outer layer of infrastructure awareness around the AI facility.

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Ellenex AI Infrastructure Monitoring Packages

Ellenex can present the AI infrastructure solution as a modular package. This makes the offer easier for customers to understand and easier for sales teams to configure.

Package 1: AI Data Hall Monitoring Kit

The AI Data Hall Monitoring Kit is designed for data centers, server rooms, high-density compute spaces, cleanrooms, and AI equipment environments where airflow, pressure, and temperature visibility are critical.

Typical monitoring points include:

Rack or cabinet pressure
Room differential pressure
Cleanroom pressure
AHU filter differential pressure
Fan and ventilation pressure
Selected temperature points

Key products include:

PDT2
PDS2
TTS2 / TTS3
RS1 / RM interfaces

Typical users include:

Data center operators
Facility managers
BMS contractors
HVAC engineers
MEP consultants
AI server-room operators

The value of this package is improved visibility into the airflow and pressure conditions that support reliable cooling delivery.

Package 2: AI Cooling Infrastructure Monitoring Kit

The AI Cooling Infrastructure Monitoring Kit is designed for chilled-water systems, liquid cooling support infrastructure, pumps, filters, heat exchangers, cooling towers, tanks, and mechanical rooms.

Typical monitoring points include:

Cooling loop pressure
Pump suction and discharge pressure
Differential pressure across filters
Differential pressure across heat exchangers
Supply and return temperature
Cooling makeup water
Tank and basin level
Cooling water quality

Key products include:

PTS2 / PTD2 / PTS3
PDS2 / PDG2
TTS2 / TTS3
FMS2
PLS2 / PLS3 / DUS3 / DRC3
CSD2 / CTR2 / CPH2
RS1 / RM interfaces

Typical users include:

Mechanical contractors
Data center engineering teams
Cooling OEM partners
Facility operators
Energy-efficiency consultants
AI campus developers

The value of this package is better visibility into the mechanical and water-side infrastructure that supports AI cooling.

Package 3: AI Water & Sustainability Monitoring Kit

The AI Water & Sustainability Monitoring Kit is designed for operators who need to measure water usage, monitor storage, track reuse water, support WUE-related data, and improve water stewardship.

Typical monitoring points include:

Water usage meters

Cooling makeup water

Reuse water meters

Tank levels

Reservoirs

Cooling tower basins

Water quality points

Stormwater or site water assets

Key products include:

FMS2
PLS2 / PLS3 / PLM2 / DUS3 / DRC3
PTS2 / PTS3
CSD2 / CTR2 / CPH2 / CDO2
MRS2
RS1 / RM interfaces

Typical users include:

Sustainability teams
Data center operators
Water utilities
Industrial park operators
AI campus developers
Facility engineers

he value of this package is reliable water visibility for operational control, sustainability programs, and infrastructure planning.

Package 4: AI Power Resilience Monitoring Kit

The AI Power Resilience Monitoring Kit is designed for backup fuel, generator support infrastructure, utility rooms, and power-adjacent resilience assets.

Typical monitoring points include:

Backup diesel tank level
Remote fuel storage
Generator support room temperature
Utility room temperature
Auxiliary equipment signals
Third-party Modbus or analog signals

Key products include:

PLS2 / PLG2 / PLS3 / PLD2
TTS2 / TTS3
RS1 / RM1 / RM4
ECP2 where relevant

Typical users include:

Data center facility teams
Power resilience teams
Generator service providers
Critical infrastructure operators
Edge AI site operators
Campus utility teams

The value of this package is better readiness and reduced uncertainty around the infrastructure that supports backup power.

Package 5: AI Site & Utility Infrastructure Monitoring Kit

The AI Site & Utility Infrastructure Monitoring Kit is designed for external infrastructure around AI facilities, including stormwater, PRV chambers, pipelines, external tanks, remote utility rooms, and distributed site assets.

Typical monitoring points include:

Stormwater level
Flood-prone areas
Drainage channels
PRV chamber pressure
Pipeline pressure
External tank level
Remote utility room status
Rainfall
Buried infrastructure monitoring where relevant

Key products include:

PTS2 / PTS3 / PDS2
PLS2 / PLS3 / PLM2 / DUS3 / DRC3
MRS2
ECP2
RS1 / RM4

Typical users include:

AI campus operators
Civil infrastructure teams
Water utilities
Site engineers
Municipal partners
Industrial infrastructure operators

The value of this package is an outer layer of visibility around infrastructure that can affect facility resilience.

Designing an AI Infrastructure Monitoring Deployment

A successful AI infrastructure monitoring deployment should be designed around operational risk, not around sensor catalogs. The right starting point is to identify the infrastructure assets that matter most to uptime, cooling performance, water stewardship, and resilience.

Step 1: Identify critical infrastructure layers

The first step is to map the facility into major layers:

Data hall and airflow
Cooling infrastructure
Water and sustainability
Power resilience
Site and utility infrastructure

Each layer should be reviewed for operational blind spots.

Step 2: Identify failure modes and monitoring objectives

For each layer, operators should ask:

What can fail?
What changes before failure?
Which measurement would reveal that change?
How often does the data need to be reported?
Who needs to see the alert?
What action should be taken when a threshold is crossed?

Examples include:

Differential pressure reveals filter loading.
Tank level reveals fuel readiness.
Water meter totals reveal abnormal consumption.
Stormwater level reveals site flood risk.
Pipe pressure reveals hydraulic instability.
Temperature reveals equipment-room thermal risk.

Step 3: Select measurement types

The next step is to select the right measurement categories:

Pressure
Differential pressure
Temperature
Level
Meter interface
Water quality
Rain or environmental context
Third-party signal interface

Step 4: Select connectivity

The connectivity decision should be based on site conditions:

Use LoRaWAN where campus coverage, private network control, and many sensors are required.
Use NB-IoT where assets are remote or geographically dispersed.
Use LTE-M / Cat-M1 where cellular LPWAN is preferred for the application.
Use interfaces where existing instrumentation should be connected rather than replaced.

Step 5: Define alert thresholds and reporting logic

Not every asset requires the same reporting interval or alert logic. Some assets need frequent reporting. Others only need daily values or event-based alerts. Thresholds should be based on operating ranges, asset criticality, and response workflows.

Examples include:

Low fuel alert
High differential pressure alert
Abnormal pressure drop
Rapid level rise
Water quality out-of-range condition
Temperature high alarm
Meter total anomaly
Signal or battery status alert

Step 6: Integrate with operations

Monitoring only creates value when it leads to action. The deployment should define:

Who receives alerts?
What is the escalation path?
What conditions require field dispatch?
What conditions require observation only?
How are events recorded?
How is trend data reviewed?
How is the data integrated into existing systems?

This workflow design is critical for converting telemetry into operational value.

Frequently Asked Questions

1. What is AI infrastructure monitoring?

AI infrastructure monitoring is the continuous remote monitoring of the physical systems that support AI compute environments. It includes monitoring of data halls, server rooms, airflow systems, cooling infrastructure, water systems, backup power support assets, external utility infrastructure, and site conditions.

AI infrastructure monitoring is different from ordinary IT monitoring. IT monitoring focuses on servers, networks, storage, workloads, and software systems. AI infrastructure monitoring focuses on the physical operating environment that makes those systems reliable. This includes pressure, differential pressure, temperature, level, flow-related data, water quality, backup fuel, stormwater, and utility conditions.

For example, an AI data center may already know server utilization, GPU temperature, and network performance. But operators also need to know whether cooling water pressure is stable, whether differential pressure across a filter is rising, whether a fuel tank is at the expected level, whether a cooling tower basin is visible, and whether a remote utility asset is operating normally.

Ellenex supports this layer with LPWAN-enabled industrial sensors and interfaces that can be deployed across data halls, cooling systems, water infrastructure, power resilience assets, and external site utilities.

2. Why is AI infrastructure monitoring important for data centers?

AI infrastructure monitoring is important because AI workloads create higher demands on cooling, power, water, and facility resilience. A conventional data center may already require strong environmental control, but AI workloads can increase rack density, cooling intensity, and operational risk. This makes supporting infrastructure more important.

In a high-density AI environment, small infrastructure changes can become meaningful. A pressure drift across a cooling loop may indicate a developing hydraulic issue. A rising differential pressure across a filter may indicate increasing restriction. An abnormal tank level may indicate fuel, water, or storage risk. A change in room pressure may indicate airflow imbalance. A water-quality change may indicate treatment or cooling-system concerns.

Without remote monitoring, many of these issues are discovered through manual inspections or after they have already affected operations. With AI infrastructure monitoring, operators can detect early signs of abnormal behavior and take action sooner.

Ellenex helps by providing a practical monitoring layer for infrastructure points that are not always covered by existing BMS or DCIM systems, especially remote, distributed, external, underground, or hard-to-wire assets.

3. What parts of AI infrastructure can Ellenex monitor?

Ellenex can monitor a broad range of AI infrastructure assets across five major layers.

The first layer is Data Hall & Airflow, including server room pressure, cabinet pressure, cleanroom pressure, filter differential pressure, AHU performance, fan pressure, and temperature.

The second layer is Cooling Infrastructure, including chilled-water pressure, cooling-loop differential pressure, pump pressure, heat exchanger conditions, filter and strainer differential pressure, supply and return temperature, tank level, water meter interfaces, and cooling water quality.

The third layer is Water & Sustainability, including water usage, water meter digitisation, cooling makeup water, reuse water, tank levels, water pressure, water quality, and WUE-supporting data.

The fourth layer is Power Resilience, including backup diesel tank levels, generator support rooms, utility-room temperature, fuel storage, and integration with selected third-party support systems.

The fifth layer is Site & Utility Infrastructure, including stormwater level, flood-prone assets, PRV chambers, external pipelines, utility rooms, basins, wells, reservoirs, and distributed site assets.

This makes Ellenex suitable for both internal facility monitoring and external infrastructure monitoring around AI facilities.

4. How does Ellenex monitor data hall airflow and positive pressure?

Ellenex monitors data hall airflow and positive pressure using differential pressure and temperature monitoring technologies, especially the PDT2 product family. Differential pressure sensors can measure the pressure difference between two points, which is useful in data halls, cabinets, cleanrooms, ducts, filters, and ventilation systems.

In AI infrastructure, differential pressure monitoring can help operators understand whether airflow is behaving as intended. It can be used to monitor positive pressure in cabinets or controlled spaces, pressure across air filters, fan and ventilation performance, and pressure conditions in sensitive rooms.

Positive pressure monitoring is important because it can help maintain controlled airflow direction and reduce the risk of external contamination entering sensitive areas. It can also provide early evidence of airflow degradation, filter loading, or containment issues.

Ellenex differential pressure monitoring can be combined with temperature monitoring and LPWAN connectivity to provide near-real-time remote visibility. This is especially useful for retrofits, distributed monitoring points, and locations where wiring new instruments into the building system is difficult or expensive.

5. Can Ellenex monitor liquid cooling and chilled-water systems?

Yes. Ellenex can support monitoring of the infrastructure around liquid cooling and chilled-water systems. Ellenex does not provide the chiller, CDU, or liquid cooling equipment itself. Instead, Ellenex provides the monitoring layer that helps operators observe pressure, differential pressure, temperature, level, flow-related data, and water quality across the supporting infrastructure.

Typical monitoring points include chilled-water supply pressure, chilled-water return pressure, differential pressure across filters or strainers, differential pressure across heat exchangers, pump suction and discharge pressure, pipe temperature, cooling makeup water, cooling tower basin level, water meter pulses, and water-quality parameters.

This is valuable because liquid cooling and high-density cooling environments depend on stable mechanical and hydraulic conditions. If pressure changes, if a filter loads, if a tank level becomes abnormal, or if water quality drifts, operators need to know before the issue affects thermal performance.

Ellenex supports these applications with pressure sensors, differential pressure sensors, temperature sensors, level sensors, FMS2 meter interfaces, water-quality sensors, and RS/RM sensor interfaces for third-party device integration.

6. How does Ellenex support water usage, WUE, and sustainability monitoring?

Ellenex supports water usage and sustainability monitoring by providing field-level visibility into water meters, tanks, reservoirs, cooling-related water assets, reuse water systems, and water-quality points. This data can help operators understand how water is being used, where abnormal consumption occurs, and how water-related infrastructure behaves over time.

For WUE-related monitoring, Ellenex can provide the measurement layer that feeds water usage data into broader reporting or analytics systems. This may include digitising existing water meters using FMS2, monitoring tank or basin levels with level sensors, monitoring water pressure in supply lines, and tracking water-quality parameters where water reuse or treatment is involved.

This is especially important for AI infrastructure because water is increasingly tied to cooling strategy, sustainability reporting, site selection, community expectations, and operational risk. Even facilities that minimize water use still need visibility into the water assets they do have.

Ellenex helps operators move from periodic readings and manual checks to continuous remote visibility across water-related infrastructure.

7. How does Ellenex monitor backup power and power resilience assets?

Ellenex supports power resilience monitoring by focusing on the infrastructure around backup power systems, especially backup fuel tanks, utility rooms, generator support areas, and third-party equipment signals.

For example, Ellenex level sensors can monitor backup diesel tank levels and provide remote visibility into fuel inventory. This helps operators understand whether backup fuel is available, whether replenishment is needed, and whether tank levels are changing as expected.

Ellenex temperature sensors can monitor utility rooms or generator support areas. Sensor interfaces can capture compatible third-party signals from installed systems, such as analog outputs, pulse signals, temperature probes, or Modbus devices.

Ellenex does not replace UPS systems, generator controllers, switchgear, or electrical protection systems. Instead, it complements them by monitoring power-adjacent assets that are often checked manually or not fully integrated into a central monitoring environment.

This creates a more complete resilience picture for AI infrastructure operators.

8. Can Ellenex integrate with existing BMS, SCADA, DCIM, or cloud platforms?

Yes. Ellenex solutions can be used as a complementary monitoring layer alongside existing BMS, SCADA, DCIM, cloud, or operational systems. Many AI infrastructure facilities already have strong monitoring in some areas but limited visibility in others. Ellenex helps close those gaps.

The RS1, RM1, and RM4 sensor interface families are especially important for integration because they allow existing instruments and third-party signals to be connected into an LPWAN monitoring architecture. This can include analog signals, pulse outputs, temperature inputs, Modbus devices, and other compatible industrial signals.

This means operators do not always need to replace existing sensors. In many cases, Ellenex can help digitise or connect what is already installed.

The most effective architecture is often hybrid: keep existing BMS, SCADA, or DCIM systems where they already work, deploy Ellenex sensors where visibility is missing, use Ellenex interfaces where existing instruments can be connected, and route data to the systems or dashboards that operations teams already use.

9. Is Ellenex suitable for retrofit AI infrastructure projects?

Yes. Ellenex is highly suitable for retrofit projects because many products are designed around low-power, wireless, LPWAN-enabled deployment. Retrofitting AI infrastructure often requires monitoring points in locations where new wiring would be expensive, disruptive, or impractical.

Examples include external tanks, mechanical rooms, utility yards, rooftops, basements, underground chambers, stormwater assets, PRV chambers, cooling tower areas, remote plant rooms, and edge AI sites.

Ellenex can support retrofit projects in two ways. First, native Ellenex sensors can be installed where measurement is missing. Second, Ellenex sensor interfaces can connect compatible existing devices into a remote monitoring architecture.

This allows operators to deploy monitoring progressively. They can begin with the most critical blind spots, validate the value, and expand across the infrastructure estate over time.

For AI facilities that are upgrading from traditional compute to high-density AI workloads, this phased retrofit model is especially valuable.

10. What should be included in a first AI infrastructure monitoring deployment?

A first AI infrastructure monitoring deployment should focus on the highest-risk and highest-value infrastructure blind spots. The exact configuration depends on the facility, but a strong starting point usually includes data hall airflow, cooling infrastructure, water visibility, backup fuel, and selected site utilities.

A practical first deployment may include:

Positive pressure or differential pressure monitoring in server rooms, cabinets, or controlled zones.
Differential pressure monitoring across AHU filters or cooling system filters.
Cooling-loop pressure and pipe temperature monitoring.
Water meter digitization for cooling makeup water or facility water use.
Tank level monitoring for backup fuel, water storage, or cooling-related basins.
Water-quality monitoring where cooling water, reuse water, or treatment systems matter.
Utility-room temperature monitoring.
Stormwater or flood-prone site monitoring where external water risk exists.
Sensor interfaces for selected third-party equipment or legacy instruments.

The best deployment should be designed around operational questions. What conditions create risk? What measurements reveal those conditions early? Who needs to receive the alert? What action should follow?

Ellenex can help structure the deployment around these questions so that the monitoring system creates practical operational value rather than simply adding more data.

ellenex LPWAN PDT2- differential pressure sensor for data center monitoring

PDS2 with both options of LoRaWAN and Cellular NB IoT / Cat M1 is our flagship product for precise

differential pressure monitoring in clean rooms, and filter performance monitoring of air handling units.

Central Monitoring & Analytics

The real value of AI infrastructure monitoring comes from unifying distributed measurements into a central operational view. A pressure sensor, level sensor, temperature sensor, or meter interface is valuable on its own, but the value increases when operators can compare data across infrastructure layers, detect trends, identify anomalies, and use measured conditions to drive action.

AI infrastructure monitoring should not become another disconnected data silo. It should support a central visibility layer that helps engineering, facility, sustainability, and operations teams understand what is happening across the physical infrastructure estate.

What Central Monitoring & Analytics should provide

A strong central monitoring layer should support:

Asset-level dashboards
Site-level dashboards
Multi-site visibility
Threshold alerts
Event notifications
Historical trends
Rate-of-change analysis
Comparative asset behavior
Alarm history
Battery and connectivity status
Infrastructure health indicators
Integration with existing systems
Data export for reporting and analysis
Operational workflows for maintenance and response

Turning telemetry into decisions

The difference between remote sensing and infrastructure monitoring is decision support.

A single pressure reading is useful, but a pressure trend is more useful. A tank level is useful, but a tank level compared to expected depletion is more useful. A differential pressure value is useful, but a differential pressure trend across a filter is more useful. A water meter total is useful, but abnormal consumption detection is more useful. A temperature point is useful, but temperature combined with pressure, flow, and equipment context is more useful.

Ellenex enables operators to move from isolated telemetry toward infrastructure intelligence.

Analytics examples for AI infrastructure

A central monitoring layer can support many practical use cases:

Detecting gradual filter loading through differential pressure trends
Identifying abnormal cooling loop pressure behavior
Detecting unexpected tank depletion
Comparing water usage across facilities
Flagging cooling water quality drift
Monitoring backup fuel status across multiple sites
Observing stormwater levels during rain events
Identifying repeated pressure instability in utility infrastructure
Detecting changes in cabinet or room pressure behavior
Supporting predictive maintenance workflows
Prioritizing field inspections based on measured asset state

Integration with BMS, SCADA, DCIM and cloud platforms

AI infrastructure environments often already use BMS, SCADA, DCIM, CMMS, cloud dashboards, or enterprise reporting systems. Ellenex monitoring should complement those systems rather than compete with them.

Ellenex sensors and interfaces can be used to fill blind spots and bring remote infrastructure data into the customer’s preferred operational environment. This is particularly valuable where existing systems are strong inside the facility but weak at external, remote, underground, or retrofit points.

The strongest architecture is usually hybrid:

Use existing systems where they already work well.
Add Ellenex sensors where visibility is missing.
Use Ellenex interfaces where existing instruments can be connected.
Route data into dashboards, alerts, APIs, or operational workflows.

This creates a more complete infrastructure picture without forcing a full replacement of existing systems.

Connectivity Architecture for AI Infrastructure Monitoring

AI infrastructure monitoring depends on the right connectivity architecture. The best sensor is only useful if it can transmit data reliably from the location where the measurement is needed. Many critical AI infrastructure assets are located in places where wired connectivity is difficult, expensive, disruptive, or impractical.

Examples include external tanks, rooftops, basements, utility rooms, mechanical yards, cooling towers, PRV chambers, underground waterways, site drainage points, remote plant rooms, and distributed edge facilities. These are exactly the types of assets where LPWAN connectivity can create value.

Ellenex supports low-power wide-area monitoring through LoRaWAN, NB-IoT, and LTE-M / Cat-M1 technologies.

LoRaWAN for campus and private network deployments

LoRaWAN is well suited for many AI infrastructure deployments where the operator controls the site or campus and wants broad wireless coverage across multiple asset types. A private LoRaWAN network can support large numbers of low-power devices across data centers, utility yards, cooling infrastructure, water assets, and external monitoring points.

Typical LoRaWAN-fit applications include:

Data center campus monitoring
Utility yard monitoring
Mechanical plant monitoring
External tank monitoring
Stormwater monitoring
Distributed pressure monitoring
Multiple sensors across a controlled site

LoRaWAN can be especially attractive where recurring carrier connectivity is not preferred, where site owners want more network control, or where many sensors are deployed across a defined estate.

NB-IoT for wide-area remote monitoring

NB-IoT is useful for remote or geographically dispersed assets where building a private network is not practical. For example, distributed AI edge sites, remote tanks, external water infrastructure, backup fuel assets, or standalone utility locations may benefit from cellular LPWAN connectivity.

NB-IoT is typically relevant where:

Assets are geographically scattered
Private gateways are not economical
Coverage exists through cellular networks
Low-power operation is required
Data payloads are small but operationally important

LTE-M / Cat-M1 for cellular LPWAN applications

LTE-M / Cat-M1 can also support remote infrastructure monitoring applications where cellular connectivity is preferred and the application benefits from LTE-M characteristics. It may be suitable for certain mobile, distributed, or infrastructure monitoring scenarios depending on network availability and project requirements.

Hybrid connectivity

In many AI infrastructure projects, the best architecture is not one network. It is a hybrid network.

For example:

LoRaWAN for dense campus monitoring
NB-IoT for remote tanks or off-campus utility assets
LTE-M / Cat-M1 for selected cellular applications
Sensor interfaces for existing instruments
Cloud or platform integration for central visibility

This flexibility is important because AI infrastructure is not uniform. Different assets have different installation conditions, reporting needs, power constraints, and coverage requirements.

Integration with Existing Sensors and Legacy Infrastructure

Many AI infrastructure environments already have installed meters, transmitters, controllers, or monitoring points. The problem is that not all of them are connected to a useful remote monitoring environment. Some assets are local-only. Some require manual reading. Some are wired to isolated systems. Some are not visible to the teams that need the data.

Ellenex sensor interfaces help solve this problem.

The RS1, RM1, and RM4 interface families allow operators to connect existing industrial sensors and signals into an LPWAN monitoring architecture. This can include analog inputs, digital inputs, pulse outputs, temperature probes, Modbus devices, and other common industrial signal types depending on configuration.

Why sensor interfaces are important

Sensor interfaces are critical because they allow Ellenex to support both new and existing infrastructure.

A customer may not need to replace an installed water meter. They may only need to digitise its pulse output.

A customer may not need to replace an installed pressure transmitter. They may only need to connect its signal to a remote monitoring device.

A customer may already have a Modbus-capable device in a utility room. They may only need a practical way to bring selected data into a central dashboard.

This makes Ellenex highly suitable for retrofit projects.

Retrofit value in AI infrastructure

Many AI infrastructure projects are not greenfield builds. They are expansions, upgrades, retrofits, conversions, or hybrid deployments. Existing buildings may be adapted for AI workloads. Existing data centers may be upgraded for higher density. Industrial facilities may add AI compute capacity. Edge sites may be deployed in locations where conventional infrastructure monitoring is limited.

In these environments, installing new wired monitoring everywhere can be too slow, too expensive, or too disruptive. Ellenex provides a practical alternative: add wireless monitoring where needed, integrate existing sensors where possible, and build the visibility layer progressively.

Integration examples

Ellenex interfaces can support use cases such as:

Connecting existing water meters
Capturing pulse-output meter totals
Connecting 4–20 mA pressure transmitters
Reading selected Modbus devices
Connecting Pt100 / Pt1000 temperature probes
Integrating third-party water-quality instruments
Capturing auxiliary signals from utility equipment
Adding remote visibility to legacy plant instrumentation
Connecting remote environmental or infrastructure devices

The result is a flexible monitoring architecture that can work with the infrastructure already in place.

Ellenex Product Architecture for AI Infrastructure Monitoring

Ellenex’s AI infrastructure monitoring solution is built from a set of complementary product categories. Each category addresses a specific measurement need, and together they create a complete infrastructure monitoring stack.

Pressure Monitoring

Pressure monitoring is essential for cooling loops, water systems, pump stations, PRV chambers, pipeline infrastructure, utility systems, and process-related applications. Pressure changes can indicate leakage, restriction, abnormal demand, pump issues, valve behavior, or unstable hydraulic conditions.

Relevant Ellenex pressure product families include:

PTS2
PTC2
PTD2
PTDH2
PTG2
PTS3
PTF2