Data-Driven Fault Diagnosis of Rolling Bearings Using Neural Networks Optimized via Bayesian, Particle Swarm, and Genetic Algorithm Methods with Time–Frequency Features

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

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.

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.

Intelligent Fault Diagnosis of Rolling Element Bearings Using Statistical Feature Extraction and Random Forest Classifier

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.

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.

Artificial Neural Network-Based Diagnosis of Wind Turbine Blade Faults Using Vibration Analysis at Constant Operational Speed

Wind turbine blade faults, such as surface erosion, cracks, mass imbalance, and twist deformation, significantly compromise operational efficiency and reliability, thereby increasing maintenance costs. This research presents an artificial neural network (ANN)-based diagnostic approach for identifying five distinct fault states in wind turbine blades using vibration signal data collected at a constant operational speed of 1.3 m/s. The dataset, which encapsulates real-world vibration responses under varying fault conditions, was analyzed to extract amplitude features for classification. A balanced dataset of 500 samples per class was used to ensure robust training and evaluation. The ANN model achieved highly reliable performance, with classification accuracies (CA) of 96.21% (crack), 97.12% (erosion), 95.47% (healthy), 96.04% (twist deformation), and 94.38% (mass imbalance). Corresponding F1-scores were 95.32%, 96.51%, 94.45%, 95.18%, and 93.36%, respectively. These results confirm the model's effectiveness in distinguishing between common wind turbine blade faults and healthy conditions. This study demonstrates the potential of ANN-based systems for intelligent fault detection in wind energy systems, aiding in the advancement of condition-based maintenance and operational safety.

An Intelligent Neural Network Based Framework for Enhancing Gearbox Fault Detection and Optimal Fault Recognition in Wireless Sensor Network Environments