Data-Driven Fault Diagnosis of Rolling Bearings Using Neural Networks Optimized via Bayesian, Particle Swarm, and Genetic Algorithm Methods with Time–Frequency Features
This study presents an applied comparative evaluation of automated rolling bearing fault identification using Artificial Neural Networks (ANNs) optimized through Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Vibration signals were collected from bearings operating under five health conditions (healthy, outer ring fault, inner ring fault, ball fault, and combined faults), at three rotational speeds, and along three measurement directions. The acquired signals were preprocessed using filtering, normalization, and segmentation. Time-domain and Fast Fourier Transform (FFT)-based frequency-domain features were extracted and used to train ANN models. The ANN architectures, including hidden layers, neurons, and activation functions, were optimized using BO, PSO, and GA, resulting in six configurations. Since the problem is a multi-class classification task, performance was assessed using F1-score, Accuracy, precision, and recall. The optimized ANN models were also benchmarked against Support Vector Machine (SVM), K-Nearest Neighbors (kNN), and Random Forest (RF) classifiers using the same feature sets. Results show that FFT -based features consistently outperformed time-domain features, and ANN-PSO with FFT-based features achieved the best performance, with F1-score = 0.982, Accuracy = 0.998, precision = 0.982, and recall = 0.982. This work contributes a systematic applied comparison of ANN optimization strategies rather than a fundamentally new machine-learning architecture, highlighting optimized ANNs as competitive and computationally efficient solutions for bearing fault diagnosis.