A deep learning approach for fault detection and RUL estimation in bearings

F. M. Bono; S. Cinquemani; S. Chatterton; P. Pennacchi

doi:10.1117/12.2607084

18 April 2022 A deep learning approach for fault detection and RUL estimation in bearings

F. M. Bono, S. Cinquemani, S. Chatterton, P. Pennacchi

Proceedings Volume 12049, NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World; 1204908 (2022) https://doi.org/10.1117/12.2607084
Event: SPIE Smart Structures + Nondestructive Evaluation, 2022, Long Beach, California, United States

Abstract

This paper presents a deep learning approach for detecting early fault in bearings. The identification of bearings defects represents an important problem in the field of rotating machines. Sudden failures may occur, leading to breakdown of the machinery. For this reason, the prediction of possible faults has become a major issue in the study of bearing elements. Different fault diagnosis techniques have been developed during the years based on aggregated parameters (i.e. features) that are computed starting from time domain, frequency domain or time frequency domain analysis, relying on prior knowledge about signal processing. These approaches present major limitations, that can be overcome by adopting a convolutional LSTM (long short-term memory) neural network model. In this case, a more complex architecture is built, and the algorithm can identify effective features from accelerometer signal, that could not be considered by a manual computation approach. The algorithm has been applied on data obtained from a complex test rig to assess bearings failure on high speed trains. The outcome of this work indicates that the adopted approach leads to satisfactory performances.

Citation Download Citation

F. M. Bono, S. Cinquemani, S. Chatterton, and P. Pennacchi "A deep learning approach for fault detection and RUL estimation in bearings", Proc. SPIE 12049, NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World, 1204908 (18 April 2022); https://doi.org/10.1117/12.2607084

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