A reliable rolling bearing fault diagnosis method based on Titan

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

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.

An Interpretable and Calibrated Energy-Based Model for Bearing Fault Diagnosis and Out-of-Distribution Detection

Predictive maintenance of rotating machinery in industrial settings requires bearing fault diagnosis that is both accurate and auditable by maintenance engineers. Methods that achieve high classification accuracy typically operate as closed-box deep learning models, while methods that provide interpretability rarely report probability calibration, out-of-distribution (OOD) detection, or pairwise statistical significance. This paper addresses this gap with an Energy-Based Model (EBM) trained via Stochastic Gradient Langevin Dynamics on physics-informed features, in which the energy gradient with respect to each input feature provides intrinsic interpretability without post-hoc surrogates. A single forward pass simultaneously yields classification logits, a calibrated probability output, and a free-energy score for OOD detection. On three benchmark datasets (CWRU, MFPT, Paderborn) under file-level splitting and across five training seeds, the method attains 93.24% accuracy on Paderborn with an Expected Calibration Error of 0.017, less than half that of a Random Forest baseline. The same energy score separates inputs drawn from datasets absent at training time and preserves accuracy under additive feature noise. A leave-one-operating-condition-out evaluation bounds the operating envelope, in which three of four unseen conditions transfer with moderate loss and the lowest-speed condition does not. The results indicate that classification, probability calibration, OOD detection, noise robustness, and feature-level interpretability can be jointly delivered by a single energy-based model on bearing fault diagnosis benchmarks, without auxiliary classifiers or post-hoc explanation modules.

Motor-Current-Based Bearing Fault Detection Under Unseen Operating Conditions

Reliable motor-current-based bearing diagnosis requires evaluation on unseen physical bearings and operating conditions. This study uses the Paderborn University benchmark, acquired from a 425 W permanent-magnet synchronous motor (PMSM) test rig, to evaluate time–frequency deep transfer learning under strict bearing-level grouping. Four representations—continuous wavelet transform (CWT), short-time Fourier transform (STFT), wavelet synchrosqueezed transform (WSST), and Fourier synchrosqueezed transform (FSST)—are combined with pretrained CNN backbones across four binary targets: aged-only A/B and artificial-plus-aged C/D, with mixed-fault bearings excluded/included within each pair. The workflow includes pooled-condition candidate discovery, exploratory Main-split leave-one-operating-condition-out (LOCO) screening, and a retrospective multi-split LOCO audit. The audit contains 288 crossed condition–split–seed evaluations. Because pooled test summaries and Main-split LOCO results informed later stages, these evaluations provide descriptive robustness evidence rather than an independent post-selection test. Target A achieved the highest all-split mean balanced accuracy (0.736 for CWT–EfficientNetB0). The pairs for Targets B and C were near-ties, and the Target D ordering reversed when Main was excluded. Across the eight audited candidates, mean sensitivity ranged from 0.618 to 0.948, whereas specificity ranged from 0.092 to 0.564. Target D combined approximately 0.89 sensitivity with an approximately 0.90 false-alarm rate. Thus, operating condition, fault-class composition, bearing split, and error-cost priorities all affect model interpretation. A matched current-domain baseline audit added 336 evaluations using handcrafted-feature RBF–SVM and Random Forest models and a compact raw-current 1D-CNN. The results show that instability is broader than the TF–CNN pipeline but is not uniform across model families: TF candidates were clearly stronger for Targets A and C, feature-based models were stronger for Target B, and Target D remained mixed and protocol-sensitive. A complementary bearing-level source-group analysis quantified six healthy/fault-source categories across the 37 current features; among the eight features with the largest mean between-group variance fraction, only spectral entropy and dominant power fraction preserved the same mixed-versus-non-mixed contrast direction across all four operating conditions. An additional matched Target D reference held the binary target, physical-bearing split, held-out condition, seed, fourth-order FSST representation, ResNet-50 backbone, and training settings fixed while changing the sensing channel. Across 24 matched runs, vibration showed descriptively higher mean balanced accuracy (0.642 vs. 0.503) and specificity (0.476 vs. 0.146), while sensitivity was slightly lower (0.807 vs. 0.859); the comparison does not establish universal modality superiority. Pooled-condition performance is useful for candidate discovery, but credible condition-generalization claims require explicit separation of exploratory selection and confirmatory testing. The numerical findings are specific to the evaluated PMSM benchmark and do not establish universal performance across electric-machine types.

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.

BEARING EARLY FAULT DETECTION AND REMAINING USEFUL LIFE ESTIMATION WITH KNOWLEDGE-INFORMED MACHINE LEARNING

Hierarchical Approach to Rolling Bearing Failure Detection Using Spectrogram Images Classification

Early detection of rolling bearing faults is essential for preventing failures in rotating machinery. Although vibration-based analysis is widely used for fault diagnosis, it requires additional sensors, such as accelerometers, which increases system cost and complexity. Motor current signals, inherently available in industrial drive systems, offer a cost-effective alternative; however, their indirect sensitivity to mechanical defects makes reliable fault diagnosis challenging. This paper proposes a hierarchical motor current-based diagnostic framework that integrates domain knowledge with data-driven image analysis. Raw current signals are transformed into time-frequency spectrograms, which provide enhanced representations of faults compared to pure time or frequency-domain features. The proposed approach consists of three stages. First, bearing health is characterized using a rule-based method to compute the characteristic frequencies associated with different fault types, incorporating domain knowledge into the diagnosis process. Second, this information is used to guide a hierarchical classification strategy that separates health assessment from fault localization. Finally, global image descriptors extracted from the spectrograms are employed to classify the bearing condition as healthy or faulty and to identify the fault location (internal or external). By explicitly combining physics-informed fault characterization with image-based machine learning, the proposed approach improves interpretability while maintaining competitive diagnostic performance. The effectiveness of the method is validated on a benchmark bearing dataset, demonstrating its potential for cost-effective industrial condition monitoring.