Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals

• URL: http://arxiv.org/abs/2411.06299v2
• Date: Sun, 24 Nov 2024 18:00:49 GMT
• Title: Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals
• Authors: Tito Spadini, Kenji Nose-Filho, Ricardo Suyama,
• Abstract summary: The research leverages sound data from the imbalanced MaFaulDa dataset, aiming to strike a balance between high performance and low resource consumption. We achieved an impressive accuracy of 99.54% and an F-Beta score of 99.52% with just 6 boosting trees at an 8 kHz, 8-bit configuration.
• Score: 0.6144680854063939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study focuses on Intelligent Fault Diagnosis (IFD) in rotating machinery utilizing a single microphone and a data-driven methodology, effectively diagnosing 42 classes of fault types and severities. The research leverages sound data from the imbalanced MaFaulDa dataset, aiming to strike a balance between high performance and low resource consumption. The testing phase encompassed a variety of configurations, including sampling, quantization, signal normalization, silence removal, Wiener filtering, data scaling, windowing, augmentation, and classifier tuning using XGBoost. Through the analysis of time, frequency, mel-frequency, and statistical features, we achieved an impressive accuracy of 99.54% and an F-Beta score of 99.52% with just 6 boosting trees at an 8 kHz, 8-bit configuration. Moreover, when utilizing only MFCCs along with their first- and second-order deltas, we recorded an accuracy of 97.83% and an F-Beta score of 97.67%. Lastly, by implementing a greedy wrapper approach, we obtained a remarkable accuracy of 96.82% and an F-Beta score of 98.86% using 50 selected features, nearly all of which were first- and second-order deltas of the MFCCs.

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