Aug 2026· Emerging Science Journal· Vol 10, pp. 2032-2060· 0 citations· 33 references
TL;DR
This study establishes that fault discrimination relies on transient impulse morphology rather than bearing characteristic frequencies, a finding invisible to feature-level XAI, and introduces a multi-resolution diagnostic framework bridging deep learning accuracy with physically interpretable vibration analysis for trustworthy deployment in safety-critical industrial environments.
Abstract
Deep learning models for bearing fault diagnosis on the Case Western Reserve University (CWRU) dataset routinely achieve near-perfect accuracy. Yet their decisions remain largely opaque at the signal level, so engineers cannot determine where in the raw vibration waveform the model focuses. This study aims to bridge the interpretability gap in deep learning-based bearing fault diagnosis by developing a signal-level explainability framework for the Wide Deep Convolutional Neural Network (WDCNN) on the Case Western Reserve University (CWRU) dataset. SHAP DeepExplainer is applied directly to raw 2,048-point vibration segments to produce per-sample-point attribution maps; Fault Signature Maps (FSMs) are formalized as class-averaged SHAP fingerprints in three variants (signed, absolute, and variance) and validated via discriminability index, severity monotonicity, and split-half stability, complemented by a three-variant ablation study examining how architectural decisions affect both accuracy and explainability. On the 10-class CWRU dataset under a rigorous temporal split (1,240/310/750 samples), WDCNN achieves 99.87% test accuracy with a macro F1-score of 0.997. FSMs demonstrate high reproducibility (split-half stability = 0.940); severity monotonicity ranges from 8.6% to 17.6%; and ablation reveals that removing Batch Normalization increases FSM discriminability by 2.3× at a 3.6% accuracy cost. As the first work applying SHAP DeepExplainer at full 2,048-point raw signal resolution for WDCNN, this study establishes that fault discrimination relies on transient impulse morphology rather than bearing characteristic frequencies, a finding invisible to feature-level XAI, and introduces a multi-resolution diagnostic framework bridging deep learning accuracy with physically interpretable vibration analysis for trustworthy deployment in safety-critical industrial environments.
An unsupervised hybrid deep learning framework for unknown bearing fault diagnosis and severity assessment using vibration signals that combines Continuous Wavelet Transform, Convolutional Neural Networks, and Long Short-Term Memory autoencoders is presented.
Edris Shamsulhaq, Fikri Arif Wicaksana· Jambura Journal of Electrica...· 0 citations
A novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study, which is an end-to-end trainable diagnostic network after standard signal normalization and segmentation.
Fanlong Zhu, Junyu Lai, Peiwen Lu et al.· Advances in Mechanical Engin...· 0 citations
: Bearing fault diagnosis in industrial deployment must contend with two simultaneous distributional shifts: fault severity increases as damage progresses, and motors operate at loads unseen during training. We define this compound setting as the double domain shift and present a rigorous few-shot benchmark on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets. Six architectures spanning distinct learning paradigms—a multilayer perceptron (MLP), a capsule network (CapsNet), a residual capsule network (ResCaps), a prototypical network (ProtoNet), a modified residual convolutional network (MRCN), and Deep Correlation Alignment (Deep CORAL)—are evaluated under a strict three-way split (support/validation/held-out test) that prevents the data-leakage patterns prevalent in prior CWRU protocols. Models are adapted using K ∈ { 5, 10, 20 } labelled target samples and assessed on three complementary metrics: accuracy, macro F1-score, and Cohen’s κ . A lightweight 1-D convolutional neural network (CNN) with a capsule routing head (CapsNet) leads on 15 of 18 CWRU conditions and on all PU conditions at K ≥ 10 (with MRCN leading at PU Target-A K = 5), achieving macro F1 of 0.860 and κ = 0.818 at K = 5 on the harder CWRU target—with 18,624 parameters (roughly one-third of the MLP baseline) and without any distribution-alignment objective. The multi-metric evaluation reveals findings invisible to accuracy alone: several baselines fall below moderate agreement ( κ < 0.60) at low K , and MRCN’s accuracy–F1 gap of 5.3 percentage points (pp) at K = 10 exposes class-selective failure that accuracy conceals. On PU, a macro F1 of 0.443 at K = 5 on the real inner-race target quantifies the artificial-to-real fatigue transfer gap, and all models produce κ < 0.06 on the outer-race target at K = 5, establishing a realistic lower bound for future work. Wilcoxon signed-rank tests confirm the CapsNet advantage is statistically significant against the weaker baselines in nearly all conditions. Capsule output norms provide interpretable, per-class confidence-like activation scores without requiring post-hoc attribution methods.
Safa Alsafari, Ayman Yafoz· Computer Modeling in Enginee...· 0 citations
These results establish PadéNet-enhanced UDA as an accurate, broadly applicable approach for robust bearing fault diagnosis under varying operating conditions, with a reduced parameter count suited to resource-constrained embedded platforms.
Sertac Kilickaya, Cansu Celebioglu, Murat Askar et al.· Machines· 0 citations
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha· International Journal of Dyn...· 0 citations
Traditional bearing fault diagnosis deep learning models rely on sufficient annotated data and powerful computing hardware, exhibiting poor anti-noise performance and weak generalization under small sample and mixed noise industrial conditions. Moreover, purely data-driven models fail to incorporate bearing contact mechanical laws, leading to low physical interpretability. This work develops a CAMHP framework coupling Hertz contact-based physics-informed neural networks (HPINNs) and physical-guided Comba attention mechanism (CAM). The VMD-HHT-RFE-MI preprocessing pipeline is utilized to purify noisy vibration signals. The main innovations of CAMHP are:(1) Embedding Hertz contact equations into PINN training and makes key mechanical parameters learnable to guarantee physically consistent diagnosis results.(2) Designing a lightweight CAM with dual feedback and block parallelism, realizing O(n) low-complexity temporal feature extraction guided by real-time contact state parameters. Validated on CWRU and JNU bearing datasets, CAMHP reaches 99.89% and 99.73% average diagnosis accuracy respectively. In mixed noise and small sample test scenarios, the proposed method consistently surpasses comparative algorithms with fast inference speed, demonstrating its robustness and interpretability for real-world rotating machinery fault monitoring.