Authors: Pooja Kamat; Rekha Sugandhi; Satish Kumar
Addresses: Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, India; MIT School of Engineering, MIT-ADT University, Loni-Kalbhor, Pune, Maharashtra, India ' MIT School of Engineering, MIT-ADT University, Loni-Kalbhor, Pune, Maharashtra, India ' Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, India
Abstract: Artificial intelligence (AI) and its sub-domains of machine learning and deep learning have kindled the interests of both industry practitioners and academicians. Its contribution to the manufacturing industry in making intelligent predictions about a machinery's health and its working has seen a huge surge in the research carried in recent years. Nowadays, AI in manufacturing is popularly applied for the efficient fault detection of machinery using data analytics. Traditional fault predictive classification and further diagnosis have pitfalls such as low prediction accuracy, poor feature extraction and susceptibility to noise. To overcome these disadvantages, this paper proposes the deep-learning-based hybrid autoencoders (AE) - long-short-term memory (LSTM) framework for fault detection. The dimensionality reduction with automatic latent feature extraction by autoencoders and temporal feature consideration by LSTM help to achieve high fault diagnosis accuracy. The empirical results show that fault detection of roll bearings based on the proposed hybrid AE-LSTM deep learning technique achieved superior results in comparison to the traditional K-means clustering technique.
Keywords: bearings; fault detection; deep learning; autoencoder; long-short-term memory; LSTM; K-means.
International Journal of Modelling, Identification and Control, 2021 Vol.38 No.1, pp.88 - 103
Received: 21 Jan 2021
Accepted: 24 Mar 2021
Published online: 04 Apr 2022 *