Jul 2026· The 2026 International Conference on Optical Communication and Intelligent Algorithms (OCIA 2026)· Vol 14301, pp. 143012D - 143012D-15· 0 citations· 11 references
Engineering
TL;DR
It is confirmed that traditional machine learning models, optimized through manual feature engineering, can provide a ‘high-precision, low-risk’ solution for bearing fault diagnosis and offers significant reference value for the intelligent operation of aero-engines and other industrial equipment.
Abstract
To ensure the safe operation of aircraft engine bearings under extreme conditions such as high temperatures, high pressures, and high-speed rotation, and to address their susceptibility to failure, this study explores a machine learning-based bearing fault diagnosis method. The core of the research lies in enhancing diagnostic accuracy through effective feature engineering strategies: first, multidimensional features are extracted from both the time and frequency domains of bearing vibration signals; subsequently, key features are selected using variance analysis and the Gini coefficient, with Principal Component Analysis employed for dimensionality reduction to retain core information. Performance comparisons of models including One-Dimensional convolutional neural networks, logistic regression, random forests, and gradient-boosted trees demonstrated that random forests combined with Gini coefficient feature selection achieved optimal results. This approach attained an exceptionally high accuracy of 0.9872 on the test set while exhibiting robust generalisation capabilities. This research confirms that traditional machine learning models, optimized through manual feature engineering, can provide a ‘high-precision, low-risk’ solution for bearing fault diagnosis. It offers significant reference value for the intelligent operation and maintenance of aero-engines and other industrial equipment.
The near-perfect linear separability indicates that the dataset’s binary, controlled-laboratory labelling rather than intrinsic bearing-degradation physics drives the clean classification, and validation on 500–1000 or more samples with progressive-degradation labelling is essential before any operational claim can be supported.
P. Pugazhendi, Vinoth Vishwanathan, Aadil Arshad Ferhath et al.· Engineering Research Express· 0 citations
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.
M. I. Quamar, Abdulrazaq Nafiu Abubakar, Ali Nasir· Journal of Vibration Enginee...· 0 citations
The findings affirm the efficacy of XGBoost in bearing fault classification and emphasise the diagnostic value of carefully selected time-domain features, as well as suggesting strong potential for deploying such models in real-time condition monitoring and predictive maintenance systems.
A. Bhende· Insight - Non-Destructive Te...· 0 citations
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.
Hongchuang Tan, Yiheng Su, Jiang Ding et al.· Journal of Dynamics Monitori...· 0 citations
The results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.
A. Oner, Meral Bayraktar· Italian National Conference...· 0 citations
The increasing demand for reliability in wind turbine systems makes early bearing fault detection under variable-speed conditions a persistent challenge. This paper proposes a hybrid methodology for classifying degradation stages and estimating a relative RUL-related degradation indicator for bearings by integrating synthetic data modeling, feature selection, and a combined unsupervised–supervised learning approach. Synthetic vibration signals are generated through logistic-curve interpolation with pink noise, enabling controlled degradation simulation. Features from time, frequency, and time–frequency domains were ranked using Mutual Information, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was employed to identify progressive wear stages. Cluster centers serve as anchors for mapping degradation into RUL percentages, while classification ensures stage consistency. Experimental results demonstrate six well-defined clusters for inner-race faults (Silhouette 0.5190), three moderate clusters for outer-race faults (0.2339), and overlapping patterns for rolling element faults (–0.1527), with zero RUL deviation in the best case. The proposed framework combines real and synthetic data to enhance generalization while reducing computational cost, offering a reliable and scalable solution for predictive maintenance and assessment of degradation progression in wind turbine bearings.
Gustavo Gomes Do Valle, Benjamin Soudhan, Meisam Mahdavi et al.· IEEE Access· 0 citations