# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
###### Abstract  
Predictive maintenance is essential for improving the efficiency of equipment and reducing downtime in industrial operations. This study investigates the application of machine learning in predictive maintenance, specifically emphasizing data preprocessing and classification techniques using the Teager-Kaiser Energy Operator (TKEO) method, which captures dynamic variation in signals. The effectiveness of TKEO was compared against conventional methods, using the Case Western Reserve University (CWRU) dataset, with vibration data collected from bearings operating under different load conditions. Different data segmentation lengths (2400 and 12,000 samples) were evaluated to assess the impact of segment size on classification accuracy. The study also investigated the effects of various feature selection strategies by comparing four- and six-feature combinations. Advanced classifiers, including support vector machines and random forests, demonstrated that TKEO effectively improved model accuracy in the capture of fault-related signal dynamics. These findings offer new insights to support reliable predictive maintenance in industrial settings and provide a new perspective for future research into active vibration control, where vibration signal analysis, feature extraction, and mathematical modeling play key roles in optimizing control algorithms and enhancing the efficiency of adaptive control systems. Teager-kaiser energy operator method; predictive maintenance; vibration signals; machine learning; classification +
Footnote †: journal: Applied sciences  
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fail to capture sudden malfunctions [2]. This is where the importance of machine learning methods becomes evident. [PERSON] et al. [3] defined and described fault diagnostics using artificial intelligence and gave examples of approaches to failure prediction through systems engineering. By considering a variety of data generated during the operation of bearings, such as vibrations, temperature, and pressure, machine learning can identify abnormal patterns and predict potential failures. However, applying machine learning to the predictive maintenance of bearings goes beyond merely feeding data into a model for training. This is where expertise becomes crucial. A deep understanding of mechanical engineering principles is required to explain and comprehend the operational mechanisms of bearings, including load conditions and different types of faults, among other factors [4]. Only a profound knowledge of these underlying mechanisms enables the extraction of relevant features of the data that relate to the health of bearings, thus facilitating the creation of reliable predictive models. In recent years, many fault diagnosis methods based on machine learning and deep learning have been proposed. Significant progress has been made, particularly with the wide use of benchmark datasets such as the Case Western Reserve University (CWRU) bearings dataset. However, these methods still have several shortcomings that need to be improved and optimized. [PERSON] et al. (2019) [5] proposed a deep-stacking least-squares support vector machine (DSLS-SVM), which combined deep learning with a traditional SVM. While their approach demonstrated extremely high accuracy (up to 99.9%), its requirements included complex parameter selection in high-dimensional feature space. Moreover, its computational cost and the model's complexity make it impractical for real-time applications in resource-limited environments. [PERSON] et al. (2023) [6] introduced a lightweight deep learning model (Lite CNN) to reduce input dimensionality and optimize model architecture. Their method achieved an average classification accuracy of 99.92%, demonstrating superior computational efficiency. However, Lite CNN's performance is heavily dependent on dataset-specific preprocessing (e.g., fixed segment lengths), limiting its adaptability to variable operating conditions (e.g., fluctuating loads or speeds).