Enhanced Rotating Machinery Fault Diagnosis Using Holo-Hilbert Spectrum Analysis and Machine Learning

Machine learning-driven fault detection in rotating machinery: A predictive maintenance framework

A new predictive maintenance model that incorporates multi-domain feature extraction, and hybrid feature selection approach and optimized ensemble classifier to facilitate high-fidelity fault detection in four operational modes is proposed, affirming its suitability for deployment in industrial cyber-physical monitoring systems.

ENHANCING FAULT CLASSIFICATION IN ROTATING MACHINERY USING A HYBRID MODEL WITH FOURIER-BASED ACTIVATION FUNCTION

Accurate diagnosis of faults in rotating industrial machinery is very critical for operational efficiency, reducing downtime, and avoiding costly failures and the associated economic losses. This study makes use of a hybrid machine learning model designed to classify known fault types which include imbalance, overhang, underhang (bearing), and horizontal faults based on vibration signal data. Traditional hybrid models often struggle to capture the complex, oscillatory nature of vibration data using standard activation functions like ReLU, which can lead to poor representation of non-linear and periodic fault dynamics. The novelty in the proposed approach is the incorporation of the Fourier function as an activation function within the model architecture. This models periodic and non-linear behaviors, enhancing the detection of subtle patterns missed by standard functions. This function captures both periodic and non-linear behaviours in the data, enhancing the model’s ability to distinguish between subtle fault patterns that traditional activation functions might overlook. The model's performance was evaluated using cross-validation, which revealed high precision, recall, and F1-scores (all above 0.90) for imbalance and bearing faults. However, horizontal faults posed more classification challenges, with F1-scores ranging from 0.73 to 0.76, likely due to spectral overlaps and noise. Among the classifiers assessed, XGBoost achieved the lowest error rate at 0.0067, followed closely by Random Forest at 0.007 and CNN at 0.008. Notably, the hybrid model outperformed existing methods, such as Abbasi’s approach, which achieved 92% accuracy, by reaching a peak accuracy of 99% in fold three. These results underscore the effectiveness of incorporating the Fourier activation function into machine learning models, as it enhances the ability to capture complex fault dynamics and improves diagnostic accuracy in industrial applications.

Machine learning-based feature-driven model generation and evaluation for multi-fault bearing diagnosis using XGBoost and time-domain statistical features of vibration data

Accurate and timely detection of bearing faults is critical for ensuring the reliability and safety of rotating machinery in industrial environments. This study investigates the application of machine learning (ML) techniques for classifying ten different types of bearing fault using features extracted from vibration signals. Among the various classifiers evaluated, the extreme gradient boosting (XGBoost) algorithm demonstrates superior performance. Through a combination of standard signal preprocessing, hyperparameter optimisation via GridSearchCV and comprehensive model evaluation using accuracy, F1 score and confusion matrix analysis, the optimised XGBoost model achieves a high classification accuracy of 95.48%. Notably, fault types such as IR_007_1, IR_014_1 and OR_007_6_1 are classified with perfect precision and recall. While minor misclassifications occur, primarily between ball and outer race (OR) faults due to overlapping signal characteristics, the overall confusion matrix underscores the strong generalisation and class discrimination capabilities of the model. Additionally, feature importance analysis highlights kurtosis, standard deviation (SD) and mean as the most influential statistical descriptors, with kurtosis emerging as the dominant factor for distinguishing impulsive fault characteristics. These findings affirm the efficacy of XGBoost in bearing fault classification and emphasise the diagnostic value of carefully selected time-domain features. The results suggest strong potential for deploying such models in real-time condition monitoring and predictive maintenance systems. Future research may explore integrating deep learning or hybrid ensemble methods to further enhance diagnostic accuracy in more complex or noisy fault scenarios.

Fault Diagnosis of Automotive Gasoline Engines Using Time–Frequency Vibration Image Analysis

Mode entropy knowledge machine: a fully automated bearing fault diagnosis model for complex operating conditions

Accurate diagnosis of rolling bearing faults is critical to the reliability of industrial equipment. However, rolling bearings often operate under complex operating conditions, and with data imbalances and noise interference, fault diagnosis of them remains extremely challenging. To address these issues, a novel mode entropy knowledge machine (MEKM) framework for robust bearing fault diagnosis is proposed in this study. For MEKM, the mode entropy space is firstly constructed to decompose the vibration signal into intrinsic mode components, and the noise-resistant feature extraction and dimensionality reduction are realized by principal component analysis. Secondly, a fast classifier based on extreme learning machines is introduced, and its parameters are automatically adjusted through a particle swarm optimization to establish an adaptive extreme learning machine diagnosis model, ensuring optimal generalization under different load and speed levels. Then, a collaborative optimization paradigm is developed to coordinate mode entropy features and classifier parameters through fully automated learning, in which entropy-driven feature characterization guides the iterative refinement of decision boundaries, while classifier feedback dynamically improves the selectivity of entropy features. Finally, validation is performed on bearings with multiple operating conditions, and the results indicated that the MEKM outperformed conventional deep learning methods in terms of diagnostic accuracy and generalization ability. The work provides a theoretical basis and an industrially feasible solution for health monitoring of mechanical equipment.

Research on Fault Feature Extraction and Intelligent Diagnosis Technology of Motor Drive in Embedded Systems

Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.