Deep learning integrated Bayesian health indicator for cross-machine health monitoring
Taewan Kim, Seung‐Chul Lee
Abstract
Constructing a good health indicator (HI) plays a key role in machinery health monitoring. However, due to the training dataset dependency, most conventional deep-learning-based HIs exhibit inconsistent HI scales and limited cross-domain applicability. This study proposes a deep learning integrated Bayesian health indicator (DBHI) to quantify and evaluate system health states in cross-domain applications accurately. The construction of the DBHI comprises two steps: probability space mapping and probabilistic inference. In the first step, the degradation features are extracted from the data and mapped into a probability space by the proposed adversarial autoencoder model. In the second step, the DBHI is calculated by evaluating the relative risk of the current state based on Bayes’ theorem. The effectiveness of the proposed DBHI was validated on two run-to-failure degradation test datasets. In a cross-domain application, the proposed DBHI achieved good performance without retraining compared to other deep learning-based methods that showed invalid results. The proposed DBHI addresses the limitations of conventional deep-learning-based HIs and provides a more reliable and consistent measure of system health, even in a cross-domain application.