Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

TLDR

Machine health monitoring systems aim to enable predictive maintenance by tracking failures, reducing downtime, and preserving assets, yet conventional methods rely on labor‑intensive handcrafted features that require expert knowledge. This study introduces local feature‑based gated recurrent unit (LFGRU) networks to learn representations directly from raw sensor data. LFGRU combines handcrafted window‑based feature extraction with an enhanced bidirectional GRU that processes the resulting feature sequence, followed by a supervised layer to predict machine condition. Experiments on tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection demonstrate that LFGRU achieves effective and generalizable performance across diverse monitoring tasks.

Abstract

In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.