Comparative Evaluation of Machine Learning Algorithms for Fault Diagnosis in Automotive Press Lines

Machine learning-based fault detection in low-speed bearings using a multi-environmental dataset

This study provides a systematic robustness evaluation of classical machine learning for vibration-based bearing fault detection in low-RPM internal combustion engines (1000–2000 RPM) across a controlled temperature × humidity grid, a regime underrepresented in benchmark datasets that emphasise high-speed applications. A publicly available dataset from a 658cc engine (–10 °C–45 °C, 0%–100% humidity) was analysed; vibration features were derived from the non-zero channels of a tri-axial acquisition, with the bearing-housing vibration carried primarily by channel Ch3. To prevent temporal leakage, 390 263 continuous measurements were aggregated into 89 steady-state units, each spanning 90 s, yielding a deliberately independence-preserving but low sample-to-feature ratio (89:92). Four algorithms Random Forest, Support Vector Machine, Logistic Regression, and Neural Network were evaluated using stratified 5-fold cross-validation. All models achieved apparent accuracy exceeding 95%, with Random Forest performing best (97.8% ± 2.7%), but no statistically significant differences were found (Friedman test, p = 0.732). Vibration features, particularly crest factor and root mean square, provided the greatest discriminative power, while environmental factors accounted for less than 17% combined importance. The near-perfect linear separability (98.2% with Logistic Regression) indicates that the dataset’s binary, controlled-laboratory labelling rather than intrinsic bearing-degradation physics drives the clean classification. Accordingly, the reported accuracies are apparent upper-bound estimates from an exploratory study, not expected field performance; validation on 500–1000 or more samples with progressive-degradation labelling is essential before any operational claim can be supported.

Artificial Intelligence-Driven Quality Control in Mechanical Manufacturing: Vibration-Based Multiclass Gear Fault Detection Using LightGBM

Artificial intelligence is increasingly used to improve industrial quality control, but its practical value depends on whether models remain accurate under different operating conditions and fault classes. This study evaluates an artificial-intelligence-based workflow for gear quality control using vibration signals measured on a real two-stage reduction gearbox. Two orthogonal vibration channels were analyzed for six health states, three shaft speeds, and two load levels. Because the time-series data were only partly stationary, the dataset was divided chronologically into training and test segments. A 54-feature representation was built from rolling-window statistics and operating variables, and six classifiers were compared: Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBM), random forest, decision tree, multilayer perceptron (MLP), and logistic regression. LGBM achieved the best overall accuracy (0.9728) while maintaining substantially lower training time than several competing nonlinear models. Class-wise precision, recall, and F1-score ranged from 0.95 to 1.00, and the nominal response time for most operating-condition transitions was approximately 0.0998 s. The results show that vibration-based machine learning can support robust, near-real-time fault identification in mechanical manufacturing environments. The study also highlights the importance of chronological validation, feature engineering over multiple time windows, and the trade-off between predictive performance and deployment efficiency. Because the validation dataset originates from one gearbox platform, the results should be interpreted as promising internal evidence rather than as proof of universal industrial robustness.

Machine Learning-Based Predictive Maintenance of a Wire Drawing Machine Using Vibration and Motor Current Signals

Predictive maintenance improves reliability and reduces downtime in modern manufacturing systems. However, many studies rely on laboratory datasets or single-component monitoring, limiting their applicability to complex industrial environments. This study proposes a predictive maintenance framework for a multi-pass wire drawing machine using vibration and motor current signals from a real industrial production line. A Composite Health Index (CHI) is developed to transform multi-motor sensor data into an interpretable machine-level degradation indicator. The extracted features are used to train ensemble machine learning models including Random Forest, Extra Trees, and XGBoost, whose outputs are combined through a weighted hybrid ensemble model. A decision-layer mechanism with smoothing and temporal filtering is applied to reduce false alarms while preserving detection capability. Experimental results show that the model achieves a recall of 0.90 and an F1-score of 0.75, demonstrating its effectiveness for industrial applications.

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

A reliable rolling bearing fault diagnosis method based on Titan

The accuracy of fault diagnosis for rolling bearings degrades sharply when operating conditions shift. Existing high-precision classifiers often experience a drop of over 50% in predictive accuracy when speed or load fluctuates, which seriously jeopardizes the reliability of industrial equipment health monitoring. Titan, a recently proposed architecture for long-context language modelling, tackles a similar challenge of maintaining performance across varying contexts. TitanDiag adapts this mechanism to fault diagnosis. The underlying rationale is that a persistent memory accumulates evidence across operating conditions and stabilizes predictions when the current segment alone is ambiguous. The architecture places Titan's dual-path memory (a long-term store gated by surprise plus a short-term FIFO buffer) inside a Transformer encoder. The multi-view front-end provides three complementary representations for every vibration segment, namely the raw waveform, the Fourier magnitude spectrum, and the continuous wavelet transform scalogram. At inference, Monte Carlo dropout produces per-prediction uncertainty scores that align naturally with Titan's surprise metric. On the CWRU and PU bearing benchmarks, TitanDiag attains 99.25% accuracy on the challenging PU-C2 low-speed condition, where TimeMachine and TSCMamba drop to 41.68% and 60.20%, respectively. The mean error-detection AUROC reaches 0.9668, well above the best baseline of 0.9391, demonstrating that the memory-driven variance inflation produces uncertainty estimates that are closely aligned with actual misclassification patterns.

Machine learning-based bearing failure research

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.