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Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM)

Rohan, Ali

Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM) Thumbnail


Authors

Ali Rohan



Abstract

Most methodologies for fault detection and diagnosis in prognostics and health management (PHM) systems use machine learning (ML) or deep learning (DL), in which either some features are extracted beforehand (in the case of typical ML approaches) or the filters are used to extract features autonomously (in the case of DL) to perform the critical classification task. In particular, in the fault detection and diagnosis of industrial robots where the primary sources of information are electric current, vibration, or acoustic emissions signals that are rich in information in both the temporal and frequency domains, techniques capable of extracting meaningful information from non-stationary frequency-domain signals with the ability to map the signals into their constituent components with compressed information are required. This has the potential to minimise the complexity and size of traditional ML- and DL-based frameworks. The deep scattering spectrum (DSS) is one of the approaches that use the Wavelet Transform (WT) analogy for separating and extracting information embedded in a signal’s various temporal and frequency domains. Therefore, the primary focus of this work is the investigation of the efficacy and applicability of the DSS’s feature domain relative to fault detection and diagnosis for the mechanical components of industrial robots. For this, multiple industrial robots with distinct mechanical faults were studied. Data were collected from these robots under different fault conditions and an approach was developed for classifying the faults using DSS’s low-variance features extracted from input signals. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis for simple and complex classification problems with a classification accuracy of 99.7% and 88.1%, respectively. The results suggest that, similarly to other ML techniques, the DSS offers significant potential in addressing fault classification challenges, especially for cases where the data are in the form of signals.

Citation

Rohan, A. (2022). Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM). Sensors, 22(23), Article 9064. https://doi.org/10.3390/s22239064

Journal Article Type Article
Acceptance Date Nov 15, 2022
Online Publication Date Nov 22, 2022
Publication Date Dec 1, 2022
Deposit Date Nov 25, 2022
Publicly Available Date Dec 6, 2022
Journal Sensors
Electronic ISSN 1424-8220
Publisher MDPI AG
Peer Reviewed Peer Reviewed
Volume 22
Issue 23
Article Number 9064
DOI https://doi.org/10.3390/s22239064
Keywords Article, deep scattering spectrum, wavelet scattering network, prognostics and health management (PHM), fault detection and diagnosis
Public URL https://nottingham-repository.worktribe.com/output/14040514
Publisher URL https://www.mdpi.com/1424-8220/22/23/9064

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