# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
###### Abstract
[PERSON] and [PERSON] (2024) [7] systematically compared traditional ML classifiers (logistic regression, SVM, random forests) for diagnosis of faulty bearings. Their SVM model achieved 99.22% test accuracy, highlighting the effectiveness of machine learning in this domain. However, their work focused solely on ideal noise-free conditions, leaving robustness to industrial noise unverified. [PERSON] et al. (2023) [8] applied envelope analysis combined with ML classifiers (decision trees, KNN) for fault diagnosis, reporting 100% accuracy for certain fault types (e.g., inner race defects). However, their method failed to detect faults affecting ball bearings, due to the limitations of envelope analysis in capturing transient frequency modulations. Although these methods have achieved promising results, they share the following common limitations:  
* High computational overhead (e.g., DSLS-SVM's parameter tuning, Lite CNN's GPU dependency);
* Sensitivity to noise (e.g., degradation in SVM performance with contaminated signals);
* Limited generalizability to diverse fault types and operating conditions. These issues not only hinder real-time performance but also restrict industrial adoption [9]. Our work addresses these gaps by proposing a TKEO-enhanced machine learning framework that leverages the TKEO to amplify fault-related transient features. [PERSON] et al. (2013) [10] demonstrated the effectiveness of the feature ex traction method based on the TKEO in diagnosis of faulty bearings. TK features not only outperform time-domain features and traditional AM features but also eliminate the complexity of manually selecting band-pass filter parameters. However, the TKEO model used in their study demonstrated relatively low performance, with an accuracy of around 85%. [PERSON] et al. [11] proposed a method combining TKEO and SSA to detect faults in rolling bearings, enhancing the pulse characteristics in vibration signals to identify characteristic fault frequencies (e.g., BPFO, BPFI), with a detection rate of up to 98.6%. This method focused on frequency extraction and detection, indirectly distinguishing fault types by capturing different frequencies rather than directly classifying them. [PERSON] et al. [12] combined TKEO statistical features with AI for classifying bearing fault, integrating analysis of vibration and acoustic signals. Experiments showed that TKEO significantly improved classification accuracy, achieving 100% across all models on the CWRU dataset. However, this study addressed only binary classification (healthy/rolling element fault) without distinguishing inner and outer race faults. Additionally, achieving 100% classification using 11 parameters increased the computational complexity. [PERSON] et al. [13] employed DSAE for automated high-dimensional feature extraction. After TKEO processing, data were converted into image format for classification using both DSAE and ANN models, achieving 99.5% accuracy on the test dataset. However, that study used only 400 samples (100 per category), limiting the complexity of the data and rendering the results insufficient for generalization to practical operational conditions. Therefore, in the current study, we decided to start from the perspective of simplifying models and feature extraction. We combined the Teager-Kaiser energy operator (TKEO) with machine learning classifiers (SVC and RFC) to simplify the model by optimizing feature extraction and selection of data segment length. We aimed to improve the accuracy and robustness of fault diagnosis while reducing computational costs. TKEO can effectively reveal instantaneous energy changes in signals and is particularly suitable for capturing transient anomalies such as instantaneous impacts or vibrations during fault occurrences. Traditional methods rely mainly on frequency-domain features, while TKEO, by extracting nonlinear features and high-frequency changes, can more sensitively identify complex fault patterns.