Condition-Based Monitoring and Novel Fault Detection Based on Incremental Learning Applied to Rotary Systems

Abstract

Thanks to the development of new technologies such as sensor networks and advanced computational power, the research field of condition-based monitoring (CBM) has drawn increasing attention in manufacturing. With the aim of enhancing equipment reliability, leading to a reduction in maintenance costs, one of the most crucial challenges dealing with CBM is the detection or prediction of unseen/uncharacterized event during manufacturing system operation. Therefore, the identification of novel fault conditions and learning of new patterns are believed to be an important and mandatory thrust in CBM research. In this work, a convolutional neural networks and autoencoder (CNN-AE) based incremental learning method is presented. It applies CNN-AE to identify various types and severities of faults under scenarios of previously unseen fault conditions. In this method, the spectrograms generated from raw sensory signals are acquired and labelled along with working condition information via inspection. A model composed of two sub-models for novel fault identification is then constructed and trained using the time-frequency spectrograms of available conditions of operation from a rotary system. One sub-model based on CNN is for known fault classification and the other sub-model based on AE is for novelty detection, where the two sub-models share an architecture for improving efficiency and accuracy. Finally, incremental learning is performed to retrain the model with the data identified as a novel fault condition. The performance of this method is validated via an experimental case study conducted on a fault machinery simulator.