{'fraction_non_alphanumeric': 0.010073260073260074, 'fraction_numerical': 0.0, 'mean_word_length': 5.391812865497076, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Online Fault Diagnosis System for Electric Powertrains Using Advanced Signal Processing and Machine Learning

Online condition monitoring and fault diagnosis systems are necessary to prevent unexpected downtimes in critical electric powertrains. The machine learning algorithms provide a better way to diagnose faults in complex cases, such as mixed faults and/or in variable speed conditions. Most of studies focus on training phases of the machine learning algorithms, but the development of the trained machine learning algorithms for an online diagnosis system is not detailed. In this study, a complete procedure of training and implementation of an online fault diagnosis system is presented and discussed. Aspects of the development of an online fault diagnosis based on machine learning algorithms are introduced. A developed fault diagnosis system based on the presented procedure is implemented on an in-house test setup and the reliably detected results suggest that such a system can be widely used to predict multiple faults in the power drivetrains under variable speeds online.