Vibration signals captured with an accelerometer carry essential information about Rolling Element Bearings (REBs) faults in rotating machinery, and the envelope spectrum has proven to be a robust tool for their diagnosis at an early stage of development. In this paper, Cepstrum Pre-Whitening (CPW) has been applied to REBs’ signals to enhance and extract health-state condition indicators from the preprocessed signals’ envelope spectra. These features are used to train some of the state-of-the-art Machine Learning (ML) algorithms, combined with time-domain features such as basic statistics, high-order statistics and impulsive metrics. Before training, these features were ranked according to statistical techniques such as one-way ANOVA and the Kruskal–Wallis test. A Convolutional Neural Network (CNN) has been designed to implement the classification of REBs’ signals from a Deep Learning (DL) point of view, receiving raw time signals’ greyscale images as inputs. The different ML models have yielded validation accuracies of up to 87.6%, while the CNN yielded accuracy of up to 77.61%, for the entire dataset. In addition, the same models have yielded validation accuracies of up to 97.8%, while the CNN, 90.67%, where signals from REBs with faulty balls have been removed from the dataset, highlighting the difficulty of classifying such faults. Furthermore, from the results of the different ML algorithms compared to those of the CNN, frequency-domain features have proven to be highly relevant condition indicators combined with some time-domain features. These models can be potentially helpful in applications that require early diagnosis of REBs faults, such as wind turbines, vehicle transmissions and industrial machinery.
En este artículo se presenta una propuesta de procedimiento que incorpora el diagnóstico de fallos desde la fase de diseño de un equipo de desfibrilación ventricular. Lo anterior permite resolver un grupo de limitaciones que están presentes actualmente en el diseño de sistemas electrónicos. El procedimiento propuesto utiliza el concepto de diseño basado en diagnóstico, la técnica de composición de autómatas híbridos para el modelado y diagnóstico basado en el conocimiento de los expertos. Finalmente se diseña el sistema con el diagnosticador ya incorporado. El procedimiento utilizado puede ser extendido a otros tipos de sistemas. Palabras clave: Sistema de diagnóstico de fallos (SDF), diseño basado en diagnóstico, autómatas híbridos, autómatas cronometrados, composición de autómatas, árboles de decisión.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.