Predictive maintenance

Predictive maintenance is an advanced approach in industrial data analytics that uses sensor data to forecast equipment conditions.

This data typically consists of multivariate time series, capturing parameters such as vibration, temperature, pressure, and other operational metrics. Machine learning and deep learning techniques enable predictive maintenance to address key challenges: diagnosing equipment health, detecting anomalies and early failure signs, and estimating components' remaining useful life (RUL).

By analyzing these patterns, predictive maintenance facilitates proactive maintenance—reducing unplanned downtime, optimizing repair schedules, and preventing costly breakdowns.

Directions:

Graph Neural Networks

GNNs take into account information about the relationships between equipment components 

Self-supervised learning

SSL methods enable learning from unlabeled and sparsely labeled data

Remaining Useful Life

RUL techniques enable the forecasting of the remaining useful life of equipment units

Health Index

HI methods allow for assessing the current health of equipment components

Virtual sensors

VS techniques allow for creating software analogs of missing physical sensors

Embedded AI

Embedded AI enable intelligent functions on edge devices without a constant cloud connection