Nonlinear weight learning model for incipient fault detection and degradation modelling and its interpretability for fault diagnosis

A Few-Label Contrastive Transfer Fault Diagnosis Method for Rolling Element Bearings

Anomaly Detection of Rolling Element Bearings Based on Contrastive Representation

A novel fault detection method for rotating machinery based on self-supervised contrastive representations

Incrementally Contrastive Learning of Homologous and Interclass Features for the Fault Diagnosis of Rolling Element Bearings

Few-label learning for fault diagnosis based on contrastive representations

Incremental Novelty Identification From Initially One-Class Learning to Unknown Abnormality Classification

A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks

Sparse Autoencoder-Based Multi-Head Deep Neural Networks for Machinery Fault Diagnostics With Novelty Detection

Abstract Novelty detection is a challenging task for the machinery fault diagnosis. A novel fault diagnostic method is developed for dealing with not only diagnosing the known type of defect, but also detecting novelties, i.e. the occurrence of new types of defects which have never been recorded. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that it is able to accurately diagnose known types of defects, as well as to detect unknown defects, outperforming other state-of-the-art methods.