Jul 2026· International Conference on Computer Aided Design· pp. 1-5· 0 citations· 22 references
Abstract
Rolling Element Bearing (REB) failures represent a major challenge in the maintenance of rotating machinery, as they compromise the reliability and operational continuity of systems. The analysis of vibration signals at the bearing level provides an effective approach for the early detection of anomalies and their classification, thereby helping to anticipate breakdowns and enhance equipment safety. In this work, the the well-known bearing dataset of Case Western Reserve University (CWRU) is utilized to perform fault classification using four machine learning algorithms: K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). The study particularly addresses feature extraction in the time domain, such as root mean square (RMS), standard deviation, kurtosis, and other statistical indicators.
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
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.
Premsagar D Patil· Materials Research Proceedin...· 0 citations
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.
Qianxi Ye, Pengfang Gao· The 2026 International Confe...· 0 citations
Experimental results indicate that XGBoost achieves the highest accuracy in identifying unbalance, outperforming the neural network and the Bayesian model and for misalignment detection, however, the three methods exhibit comparable performance, underscoring the limitations of ML models that rely solely on vibration indicators for this fault type.
A. Marzougui, A. Hachem, T. Mazoyer· Insight - Non-Destructive Te...· 0 citations
Accurate detection of faults within rotary machinery components is vital for assuring the structural reliability of equipment in manufacturing facilities and power generation systems. Automatic fault detection is an increasingly common approach whereby data acquired from sensors are analyzed by machine learning algorithms to distinguish between normal and faulty component states. Vibration signatures recorded from healthy and faulty ball bearings, taken from the Case Western Reserve University (CWRU) 12k Drive End database, were utilized in the present study. These vibration signatures were segmented into windows of 0.1 s duration. Standard statistical time-domain features (root mean square, kurtosis, peak value and crest factor) were extracted from each sample. A random forest (RF) classifier was trained on vibration data from healthy bearings and several types of faulty bearings, partitioned into training (70% of data) and testing (30%) sets. The RF classifier distinguished healthy from faulty bearings with 98.8% accuracy across ball, inner race, outer race and normal categories. Feature importance analysis indicated that RMS (50% importance) and peak value (28% importance) were the most influential contributors, together comprising about 78% of the decision making. The RF model further recorded a recall of 1.00 for the normal baseline category, indicating that healthy bearings were identified with zero false negatives. The results demonstrate that ensemble machine learning, combined with statistically significant time-domain features, provides a highly accurate and physically interpretable solution for industrial condition monitoring, serving as a foundation for more advanced rotor-dynamic fault detection.
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
