  In fault detection and diagnosis of prognostics and health management (PHM)
systems, most of the methodologies utilize machine learning (ML) or deep
learning (DL) through which either some features are extracted beforehand (in
the case of ML) or filters are used to extract features autonomously (in case
of DL) to perform the critical classification task. Particularly in the fault
detection and diagnosis of industrial robots where electric current, vibration
or acoustic emissions signals are the primary sources of information, a feature
domain that can map the signals into their constituent components with
compressed information at different levels can reduce the complexities and size
of typical ML and DL-based frameworks. The Deep Scattering Spectrum (DSS) is
one of the strategies that use the Wavelet Transform (WT) analogy to separate
and extract the information encoded in a signal's various temporal and
frequency domains. As a result, the focus of this work is on the study of the
DSS's relevance to fault detection and daignosis for mechanical components of
industrail robots. We used multiple industrial robots and distinct mechanical
faults to build an approach for classifying the faults using low-variance
features extracted from the 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.
