Predictive maintenance AI for energy and utilities. On the asset. Not in the cloud.
Energy assets drive value, but only when they're online. Unplanned downtime is expensive, and maintenance visits to distributed assets are costly. Logic-Based Networks run directly on the sensor, controller hardware or the asset itself, enabling accurate and timely maintenance and health predictions without the need for continuous data transmission.
Distributed assets. Selective coverage.
Water utilities managing hundreds of pump stations, wind operators maintaining turbines across remote sites, transmission network operators watching substations — energy utilities and their suppliers share a common challenge: the assets they need to monitor are geographically distributed, expensive to visit, and can't afford unplanned downtime.
Traditional predictive maintenance programmes typically rely upon scheduled site visits supplemented by emergency maintenance and a small number of monitoring systems covering only the highest-value assets. AI driven predictive maintenance reduces downtime but comes with the significant costs of communication infrastructure. Logic-Based Networks (LBNs) reduce the infrastructure and communication burden.
| Approach | Periodic site visits | Selective permanent monitoring | Continuous at-asset LBN |
|---|---|---|---|
| Asset coverage | All assets, infrequently | Highest-value assets only | All monitored assets, continuously |
| Alert lead time | Between visit intervals | Real-time (covered assets) | Real-time (all monitored assets) |
| Communication requirement | None | Continuous data stream | Occasional status packet |
| Scales with asset count | Linearly (visit cost) | Linearly (comms + compute cost) | Yes — one model per asset type |
| Remote/off-grid deployment | Yes | Constrained by connectivity | Yes — no cloud dependency |
Many asset classes. One deployment pattern.
Engineered through ModelMill, LBNs can be trained to be offer unique models for individual assets, or broadly across an entire asset of infrastructure. The model landscape matches your asset landscape, never forcing a generic threshold or an averaged-out approach typically of other utility maintenance AI models.
Water treatment and distribution pumps
Centrifugal pumps in water utilities operate continuously under varying load. Cavitation, bearing wear, and impeller damage each produce a characteristic acoustic signature detectable weeks before structural failure. LBNs trained on pump acoustics data run on the MCU in the pump control panel, generating maintenance alerts without transmitting audio data.
Wind turbine drivetrains
Main bearings, gearboxes, and generator bearings in wind turbines are subject to complex variable loading that accelerates wear. Replacing a main bearing on a multi-megawatt turbine requires a crane and a multi-day outage; detecting bearing degradation early enough to schedule replacement in a planned maintenance window significantly reduces cost. Vibration monitoring LBNs run on the turbine controller's existing hardware.
HVAC and cooling infrastructure
Data centres, hospitals, and industrial facilities depend on HVAC equipment for operational continuity. Chiller compressors, cooling tower fans, and air handling unit drives are subject to bearing and motor faults that degrade system efficiency before they cause failure. Continuous monitoring on the HVAC controller hardware provides early detection without additional infrastructure.
Substations and grid equipment
Transformer health, circuit breaker contact wear, and switchgear condition monitoring are established disciplines in electricity transmission and distribution. LBNs add automated continuous classification to thermal, acoustic, and partial discharge monitoring systems already installed on critical grid assets — without requiring a connection back to a central data platform.
Wastewater and treatment plant
Blowers, centrifuges, and mixers in wastewater treatment carry high-value rotating equipment in corrosive environments. Aggressive operating conditions and regulatory requirements for continuous operation make predictive maintenance economically compelling — and the absence of reliable cloud connectivity makes on-device inference the only viable delivery mechanism.
Continuous monitoring on battery or harvested power.
LBNs' 52× energy efficiency over neural network equivalents enables a class of AI model deployment impossible with conventional approaches: continuous monitoring on battery-powered or energy-harvesting sensor nodes.
For a pump monitoring node powered by a coin cell or a small solar panel, the inference energy budget determines operational lifetime. At the LBN energy profile of 455µJ per inference, continuous monitoring on a 220mAh coin cell can sustain operation for months. The same node running a neural network inference workload would require battery replacement within weeks.
This matters for the practical economics of wide-area monitoring in utilities. An energy-harvesting sensor node that requires no maintenance beyond occasional firmware updates has a total cost of ownership profile that scales to hundreds or thousands of monitoring points. A node that requires quarterly battery replacement does not.
Battery-powered nodes
Remote pump stations, underground valve chambers, and pipeline monitoring points without mains power access. LBN inference at 455µJ per classification sustains multi-year operation on a coin cell — no power cable, no repeated site visits for battery replacement.
Energy-harvesting nodes
Vibration energy harvesters, small solar panels, and thermal gradient harvesters can supply a continuous LBN monitoring node indefinitely. The low inference energy budget means the harvester does not need to sustain peak compute loads — it supplies a steady trickle comfortably above the LBN operating threshold.
Minimal communication overhead
Because classification happens on-device, the only data transmitted is the health state and alert — a short packet sent on an event basis or at a low polling interval. This is compatible with the narrowband, intermittent wireless links common in utility infrastructure: LoRaWAN, NB-IoT, and satellite links carry status packets without difficulty.
Sixty days. Your data. Your assets. Your model.
The Literal Labs 60-day PdM Pilot Programme takes your existing sensor data to develop a validated, ready-to-deploy predictive maintenance model — built to your assets, tuned to your failure modes, and tested against your own benchmark.
- Fixed 60-day execution timeline with defined milestones
- Time to Value typical within 6 months of deployment
- Limited spaces available
We align to your business targets and review your sample data.
We develop a predictive maintenance AI model based on your data and defined success criteria.
Performance comparisons produced against common or your own baselines.
Joint review of results and agreement on next steps — field trial or deployment.