Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers

Version 1 : Received: 30 November 2020 / Approved: 2 December 2020 / Online: 2 December 2020 (11:08:40 CET)

A peer-reviewed article of this Preprint also exists.

Mallak, A.; Fathi, M. Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers. Sensors 2021, 21, 433. Mallak, A.; Fathi, M. Sensor and Component Fault Detection and Diagnosis for Hydraulic Machinery Integrating LSTM Autoencoder Detector and Diagnostic Classifiers. Sensors 2021, 21, 433.

Journal reference: Sensors 2021, 21, 433
DOI: 10.3390/s21020433

Abstract

Anomaly occurrences in hydraulic machinery may lead to massive systems shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications succeeding the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only the machines and their components are prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig is thoroughly achieved. Firstly, we provided a combination of a new architecture of LSTM autoencoders and supervised machine and deep learning methodologies to perform two separate stages of fault detection and diagnosis. The two phases are condensed by: the detection phase using the LSTM autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework is applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of the past decade related work for the two stages separately is successfully conducted in this paper.

Subject Areas

Deep Learning; LSTM Autoencoder; Supervised Learning, Hydraulic Test Rig; Sensor Faults; Component Faults

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