Zero-Shot Motor Health Monitoring by Blind

13 Pages Posted: 5 Oct 2023

See all articles by Serkan Kiranyaz

Serkan Kiranyaz

Qatar University

Ozer Can Devecioglu

Tampere University

Amir Alhams

Qatar University

Sadok Sassi

Qatar University

Turker Ince

Izmir University of Economics

Osama Abdeljaber

Linnaeus University

Onur Avci

West Virginia University

Moncef Gabbouj

Tampere University

Abstract

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered, e.g., a different speed or load, or for different fault types/severities with sensors placed in different locations. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.

Keywords: Operational Neural Networks, bearing fault detection, 1D Operational GANs, machine health monitoring, Blind Domain Transition

Suggested Citation

Kiranyaz, Serkan and Devecioglu, Ozer Can and Alhams, Amir and Sassi, Sadok and Ince, Turker and Abdeljaber, Osama and Avci, Onur and Gabbouj, Moncef, Zero-Shot Motor Health Monitoring by Blind. Available at SSRN: https://ssrn.com/abstract=4593047 or http://dx.doi.org/10.2139/ssrn.4593047

Serkan Kiranyaz (Contact Author)

Qatar University ( email )

College of Law
Qatar University
Doha, 2713
Qatar

Ozer Can Devecioglu

Tampere University ( email )

Tampere, FIN-33101
Finland

Amir Alhams

Qatar University ( email )

College of Law
Qatar University
Doha, 2713
Qatar

Sadok Sassi

Qatar University ( email )

College of Law
Qatar University
Doha, 2713
Qatar

Turker Ince

Izmir University of Economics ( email )

Turkey

Osama Abdeljaber

Linnaeus University ( email )

Växjö, S-35195
Sweden

Onur Avci

West Virginia University ( email )

PO Box 6025
Morgantown, WV 26506
United States

Moncef Gabbouj

Tampere University ( email )

Tampere, FIN-33101
Finland

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