Artificial intelligence evolved as a powerful tool in condition monitoring of the induction motor for early diagnosis of bearing faults. This paper attempted to identify the features to improve the accuracy of diagnosis using the benchmark database of the Case Western Reserve University (CWRU) and the Machinery Fault Prevention Technology (MFPT). In this paper, 18 time-domain features are extracted constituting six proposed time-dependent spectral features (TDSFs) and 12 statistical time-domain features. For the first time, a set of six TDSFs are extracted to diagnose the bearing faults. This involves extracting frequency-domain features using the relationship between Parseval’s theorem and the Fourier transform directly from the time domain. Particle swarm optimization (PSO) and wheel-based differential evolution (WBDE) feature selection algorithms are also implemented to identify four prominent features. It is found that three of the four selected features are TDSF features, and results of selected features revealed the attainment of 90.92% for 48 class identifications of CWRU database and 100% for 17 class identifications of the MFPT database, respectively.

中文翻译：

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感应电机轴承故障各种条件的改进识别

人工智能发展成为感应电机状态监测的强大工具，用于轴承故障的早期诊断。本文尝试使用凯斯西储大学 (CWRU) 的基准数据库和机械故障预防技术 (MFPT) 来识别特征以提高诊断的准确性。在本文中，提取了 18 个时域特征，构成了 6 个提议的时间相关频谱特征 (TDSF) 和 12 个统计时域特征。首次提取了一组 6 个 TDSF 来诊断轴承故障。这涉及使用 Parseval 定理和傅立叶变换之间的关系直接从时域中提取频域特征。还实施了粒子群优化 (PSO) 和基于轮的差分进化 (WBDE) 特征选择算法来识别四个突出特征。发现四个选择的特征中有三个是TDSF特征，选择特征的结果显示，CWRU数据库的48个类识别的达到90.92%，MFPT数​​据库的17个类识别的达到100%。