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In fault detection and diagnosis of prognostics and health management (PHM)systems, most of the methodologies utilize machine learning (ML) or deeplearning (DL) through which either some features are extracted beforehand (inthe case of ML) or filters are used to extract features autonomously (in caseof DL) to perform the critical classification task. Particularly in the faultdetection and diagnosis of industrial robots where electric current, vibrationor acoustic emissions signals are the primary sources of information, a featuredomain that can map the signals into their constituent components withcompressed information at different levels can reduce the complexities and sizeof typical ML and DL-based frameworks. The Deep Scattering Spectrum (DSS) isone of the strategies that use the Wavelet Transform (WT) analogy to separateand extract the information encoded in a signal's various temporal andfrequency domains. As a result, the focus of this work is on the study of theDSS's relevance to fault detection and daignosis for mechanical components ofindustrail robots. We used multiple industrial robots and distinct mechanicalfaults to build an approach for classifying the faults using low-variancefeatures extracted from the input signals. The presented approach wasimplemented on the practical test benches and demonstrated satisfactoryperformance in fault detection and diagnosis for simple and complexclassification problems with a classification accuracy of 99.7% and 88.1%,respectively.

Deep Scattering Spectrum germaneness to Fault Detection and Diagnosis for Component-level Prognostics and Health Management (PHM)