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.

翻译：本研究聚焦于利用单个麦克风和数据驱动方法对旋转机械进行智能故障诊断，有效诊断了42类故障类型及其严重程度。研究利用不平衡的MaFaulDa数据集中的声音数据，旨在实现高性能与低资源消耗之间的平衡。测试阶段涵盖了多种配置，包括采样、量化、信号归一化、静音去除、维纳滤波、数据缩放、加窗处理、数据增强以及使用XGBoost进行分类器调优。通过分析时域、频域、梅尔频率和统计特征，我们在8 kHz、8比特配置下仅使用6棵提升树就实现了99.54%的准确率和99.52%的F-Beta分数。此外，当仅使用MFCC及其一阶和二阶差分时，我们获得了97.83%的准确率和97.67%的F-Beta分数。最后，通过采用贪婪包装法，我们使用50个精选特征（其中几乎全是MFCC的一阶和二阶差分）实现了96.82%的准确率和98.86%的F-Beta分数。