Equipment Health and Predictive Diagnostics
Vibration + Thermal + ML Anomaly Detection for ISR Hardware
Equipment Health and Predictive Diagnostics is a software module executed from radiation-hardened NOR flash that fuses vibration spectra, motor current signatures, bearing temperatures, and process-output deviations to estimate health states and remaining useful life for up to 50 resource utilization units. The lunar operating regime complicates diagnostic modeling: abrasive regolith dust accelerates mechanical bearing wear, while radiation-induced single-event upsets generate spurious telemetry that requires de-glitching and voting filters. With an anomaly detection latency of five minutes, the module provides a maintenance lead time of two to four weeks to schedule robotic servicing before hardware degradation halts production.
Predictive-maintenance and equipment-health module that fuses vibration spectra, motor currents, bearing temperatures, and process-output deviations into health-state estimates and remaining-useful-life predictions for each piece of ISR equipment.
Purpose
Anticipate equipment wear-out and impending failures with enough lead time for L1-MNT robotic maintenance to be scheduled — preventing cascading failures and downstream production stops.
Context
Receives sensor telemetry from every L3-ISR-* subsystem; outputs health estimates to L3-ISR-CTRL-SCHED (for load balancing) and L1-MNT (for replacement scheduling). Parallels L3-ESS-MGMT-SOH for the storage subsystem.
Principles
- ▸Vibration analysis: spectral peaks at bearing frequencies indicate wear; FFT + envelope analysis identifies early-stage faults
- ▸Motor current signature analysis (MCSA): broken bars, eccentric rotor, bearing faults all show characteristic sidebands
- ▸Thermal signatures: hot-spot detection on motors, bearings, electronics via dedicated thermistors or IR sensors
- ▸Process-output deviation: comparison of observed vs. expected output indicates degraded performance
- ▸Machine-learning anomaly detection (autoencoder, isolation forest) flags abnormal patterns without explicit fault models
- ▸RUL prediction via Particle Filter or LSTM on historical degradation trajectories
Typical implementations
- ▸Industrial CMMS (Computerized Maintenance Management Systems) — terrestrial baseline
- ▸NASA prognostics and health management (PHM) heritage
- ▸Apache Hop / TensorFlow anomaly detection (terrestrial DCS analytics)
- ▸ESA spacecraft health monitoring frameworks
- ▸Mining operation predictive maintenance (Caterpillar Cat Connect)
- ▸Curiosity/Perseverance rover wheel health monitoring (NASA — heritage)
Lunar considerations
- ▸Bearing wear accelerated by dust exposure — diagnostics must track even slow trends
- ▸Long mission generates large historical dataset for ML model training
- ▸Lead time to maintenance must align with L1-MNT capacity — typically 2-4 weeks
- ▸Anomaly detection tolerant of SEU-induced spurious sensor readings (de-glitch + voting)
- ▸Cross-equipment correlation: vibration on hauler chassis when excavator dumps may be normal, in isolation is anomalous
- ▸ML model updates via authenticated DTN with rollback
Specifications
Functional
| primary function | Predictive health monitoring and RUL prediction for ISR equipment |
| inputs | Sensor telemetry from all L3-ISR-* equipment (vibration, current, temperature, output), Historical maintenance records from L1-DVT, Latest ML model from authenticated update |
| outputs | Per-equipment health state estimates, RUL predictions, Maintenance recommendations to L1-MNT, Anomaly alerts to L3-ISR-CTRL-SCHED for load shifting |
| supported models | empirical, physics-based, LSTM anomaly detector, particle filter RUL |
| anomaly detection latency min | 5 |
| rul uncertainty days at 1 month horizon | 5 |
| supported equipment count | 50 |
| maintenance lead time recommendation weeks min | 2 |
Physical
| mass kg | 0.0 |
| dimensions | Software on L3-ISR-CTRL-PROC |
| materials | Code on rad-hard NOR flash |
| operating temperature c | -40, 70 |
Operational
| power consumption w | 1.5 |
| thermal range c | -40, 70 |
| lifetime years | 100 |
| mtbf hours | 500000 |
Interfaces
Provides
- Maintenance recommendations with lead time
- Equipment health for scheduling decisions
- Aggregated ISR fleet health
Requires
- Vibration / motor / temperature sensors
- Hauler health telemetry
- Historical sensor / maintenance database
Cite this entry
Lunar Ark Codex. "Equipment Health and Predictive Diagnostics" (L3-ISR-CTRL-DIAG). Retrieved 10 September 2026, from https://lunarark.com/entry/L3-ISR-CTRL-DIAG
Licensed CC-BY-SA 4.0. You may reuse and adapt this entry with attribution, under the same licence.