# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 5 Discussion
### Future Applications and Practical Solutions  
The proposed framework integrates the Teager-Kaiser Energy Operator (TKEO) method with machine learning algorithms (e.g., random forest and support vector machine), demonstrating significant advantages in practical applications. Compared with existing AI-based methods, our approach offers stronger potential and broader applicability, particularly in terms of robustness against noise, low data dependency, computational efficiency, and rapid deployment. These advantages make it highly suitable for real-time monitoring and fault diagnosis in complex industrial environments. Based on these advantages, the proposed method has broad application prospects, especially in the following fields:  
1. Real-Time Vibration Monitoring and Fault Diagnosis in Industrial Equipment  
In noisy industrial environments, real-time monitoring and fault diagnosis are critical for ensuring the safety and efficiency of production. By leveraging TKEO to extract key vibration features, our method achieved stable performance even with limited data and high noise levels. Compared with deep learning approaches (e.g., CNN and LSTM), our framework offers lower computational complexity and faster training speeds, making it highly suitable for real-time industrial diagnostics. 2. Fault Detection in Wind Turbines  
Wind turbines often operate in environments where obtaining large amounts of labeled data is challenging. Our method effectively extracts useful features from small datasets and employs RFC and SVC for efficient fault detection. Unlike deep learning methods (e.g., CNN and LSTM), which require extensive training data and longer training times, our approach reduces data dependency while maintaining high accuracy, making it more practical for real-world applications. Fault Diagnosis in Smart Home Appliances  
Smart home appliances (e.g., air conditioners, refrigerators) require fast and cost-effective fault diagnosis solutions. Our method can operate efficiently on low-resource devices, providing rapid fault identification and diagnosis. Compared with deep learning methods (e.g., DNN), our approach is simpler to implement and has lower computational overhead, enabling quicker responses to appliance failures. Health Management in Smart Grids  
Fault detection in critical grid components (e.g., transformers, switchgear) is essential for maintaining the reliability of power systems. By monitoring vibration signals in real time, our method can identify potential faults and issue early warnings. Compared with traditional methods (e.g., XGBoost), our approach demonstrated superior robustness to noise and faster training speeds, meeting the high demands of real-time performance and cost-effectiveness in power grids. Health Monitoring of Aircraft Engines  
Fault detection in aircraft engines requires high accuracy, real-time performance, and the ability to train effectively with limited labeled data. Through optimized feature selection and data processing strategies, our method achieved high robustness and accuracy with rapid training on small datasets. Compared with deep neural networks, our approach not only reduces data requirements but also provides real-time diagnostics for resource-constrained systems. In addition, the proposed method exhibits strong generalizability and can be applied to the diagnosis of faults in various industrial equipment, such as reciprocating compressors, gearboxes, and inertial devices, indicating its broad application potential. Moreover, when combined with advanced vibration isolation technologies like the inerter, TKEO further enhances the performance of vibration control systems [30]. Its robustness against noise and its computational efficiency also make it an ideal choice for real-time monitoring in complex industrial environments. Conceptualization, X.Y. and O.C. ; Methodology, X.Y. and O.C. ; Software, X.Y. ; Validation, X.Y. ; Formal Analysis, X.Y. ; Investigation, X.Y. ; Resources, O.C. ; Data Curation, X.Y. ; Writing--Original Draft Preparation, X.Y.