AI & Data

Predictive Maintenance with Machine Learning in Energy Systems

From raw sensor data to early warning: algorithms, workflow and common pitfalls.

1 min readUpdated: October 22, 2026
Predictive Maintenance with Machine Learning in Energy Systems

Photo: Aleksey23 · CC BY-SA 3.0 · Image credits

From reactive to predictive

Reactive maintenance repairs after failure; preventive maintenance follows the calendar; predictive maintenance follows the machine's real condition. Machine learning analyses vibration, temperature, current and pressure and spots small changes long before failure.

A five-step workflow

  1. Acquire data: sensors and PLC; sampling rate matched to the phenomenon (kHz for vibration, seconds for temperature).
  2. Pre-process: denoise, handle gaps, time-align and normalise by operating condition.
  3. Extract features: RMS, kurtosis, spectra, trends and engineered features.
  4. Model: anomaly detection or remaining-useful-life (RUL) prediction.
  5. Operate: alerting, dashboards and maintenance-team feedback to improve the model.
00.30.60.91.204080120160200Time (hours)Anomaly score (0–1)Alarm thresholdFirst sustained alarmAnomaly score
Figure 1 — Sample (synthetic) data: the anomaly score is flat until about hour 110, then rises with wear. A sustained alarm triggers well before failure level.

Choosing the algorithm

MethodData neededInterpretabilityTypical use
Threshold / SPCLittleVery highStarting point
PCA / MahalanobisModerateHighCorrelated sensors
Isolation ForestModerateMediumUnlabelled anomalies
AutoencoderLargeLowComplex signals
LSTM / GRULarge + failure labelsLowRUL prediction
Table 1 — Common anomaly-detection and failure-prediction methods

Common pitfalls

  • No failure labels: failures are rare; start with unsupervised methods.
  • Changing operating conditions: include load and ambient temperature or false alarms multiply.
  • Sensor quality: a good model with a bad sensor still gives bad results.
  • No feedback loop: without recording inspection results the model cannot improve.
Recommendation
Start with one critical asset and 3–5 sensors, set a simple threshold baseline and add the model afterwards; tangible results arrive faster than with a large programme.

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