Machinery Fault Detection Using Autoencoder and Online Sequential Extreme Learning Machine

Abstract

Fault detection is one of the most challenging tasks in industrial applications, which aims at identifying the faulty condition deviating from the normal condition of the machine. In this work, a fault detection method is proposed based on autoencoders and online sequential extreme learning machines (OS-ELM). The autoencoder is employed for high-level feature extraction from the monitoring signal and the OS-ELM is developed based on features extracted from signals of normal condition. The fault detection is performed based on i) the updating of OS-ELM using the newly collected data; ii) the quantification of the model modification. The data collected under the faulty condition is expected to significantly modify the OS-ELM model. The proposed fault detection method is validated considering a benchmark bearing case study.