Hierarchical Approach to Rolling Bearing Failure Detection Using Spectrogram Images Classification

Multi-Class Fault Detection and Diagnosis of Rolling Bearings: a Machine Learning Approach

Results show that using statistical vibration features with ensemble classifiers is a good way to diagnose multi-class bearing faults and establishes a comprehensive benchmark for ML- and DL-based rolling bearing FDD.

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.

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.

A reliable rolling bearing fault diagnosis method based on Titan

TitanDiag, a recently proposed architecture for long-context language modelling, tackles a similar challenge of maintaining performance across varying contexts by adapting this mechanism to fault diagnosis for rolling bearings.

Incipient fault diagnosis of rolling element bearings using vibration, sound and tribological analysis: A literature review

Rolling element bearings are used in rotating machines in aviation, chemical, and nuclear industries. A failure to detect faults in the rolling bearing causes unexpected breakdown of rotating machines. Detecting bearing defects early on is still a challenge since micro- faults have less energy. Early-stage defects from fatigue, misalignment, overload and poor lubrication create low-energy signals that can be masked. This article summarises some key measurement methods, i.e. vibration and acoustic signal measurement, tribological parameter analysis, wear debris analysis, and thermal measurement, in rolling element bearings. All the methods will be evaluated with respect to a sensing mechanism, signal processing algorithm, micro-defect sensitivity, and operational constraints of the anti-friction bearing. To deal with the drawbacks of single sensors, we demonstrate the use of recent advancements in multi-sensor data fusion and machine learning models through examples where kinetic, acoustic and chemical metrics are fused for improved classification outcomes. In summary, multi-sensor data fusion greatly improves the accuracy of early fault detection, and it can greatly enhance predictive maintenance systems. Keywords: Vibration, Acoustic, Wear Debris, Tribology, Condition Monitoring, Bearing

Recent trends on bearing faults diagnosis of IM using vibration signal: a critical review