CORRELATION AND REDUNDANCY ANALYSIS OF STATISTICAL FEATURES FOR PERMANENT MAGNET SYNCHRONOUS MOTOR FAULT DETECTION

An FEM-Informed Statistical Feature Extraction and Comparative Machine Learning Framework for Dynamic Eccentricity Fault Diagnosis in Interior Permanent Magnet Synchronous Motors

Data-driven diagnosis of permanent magnet synchronous motor demagnetization faults using machine learning and statistical process control

A physics-guided hybrid diagnostic architecture has been developed for assessing demagnetization in PMSM that provides a scalable, interpretable, and cost-effective solution for predictive maintenance and real-time fault diagnosis of PMSMs.

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

Reliable fault diagnosis in rotating machinery is challenging due to the nonlinear and non-stationary nature of vibration signals. Although time–frequency analysis is widely used, it cannot capture the cross-scale coupling between amplitude-modulated (AM) and frequency-modulated (FM) components that carry essential diagnostic information. This study applies Holo-Hilbert Spectrum Analysis (HHSA) to extract amplitude–frequency modulation features and integrates them with six machine learning classifiers to identify four fault conditions. Random Forest, K-Nearest Neighbors, and Logistic Regression achieve accuracies of up to 99.95%, yielding higher accuracy than Fast Fourier Transform-based features. The proposed framework employs an HHSA-based feature extraction pipeline that effectively captures AM–FM coupling in nonlinear vibration signals. It also provides higher discriminative capability than traditional spectral approaches and maintains robustness across multiple classifiers. This method offers high diagnostic accuracy and strong potential for industrial predictive maintenance. Future work will focus on improving computational efficiency and evaluating the framework under more diverse and realistic operating conditions. Received: 10 September 2025 | Revised: 20 April 2026 | Accepted: 10 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The VBL-VA001 datasets that support the findings of this study are openly available at https://doi.org/10.1007/s42417-023-00959-9, reference number [44]. Author Contribution Statement Van-Trung Nguyen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Ba-Tan Le: Investigation, Data curation, Writing - original draft, Writing - review & editing. Van-Phuong Dao: Methodology, Validation, Writing - review & editing.

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

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.

A robust multi-class bearing fault diagnosis framework using envelope analysis, cepstrum prewhitening and machine learning

The proposed framework provides an effective balance between diagnostic accuracy, robustness, interpretability, and computational efficiency, making it a promising solution for intelligent condition monitoring and predictive maintenance of rotating machinery.