Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning

• URL: http://arxiv.org/abs/2508.07536v1
• Date: Mon, 11 Aug 2025 01:32:09 GMT
• Title: Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
• Authors: Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman,
• Abstract summary: This study proposes a physics-informed multimodal convolutional neural network (CNN) with a late fusion architecture.<n>The model incorporates a novel physics-informed loss function that penalizes physically implausible predictions.<n>Experiments on the Paderborn University dataset demonstrate that the proposed physics-informed approach consistently outperforms a non-physics-informed baseline.
• Score: 0.46085106405479537
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and interpretable bearing fault classification is critical for ensuring the reliability of rotating machinery, particularly under variable operating conditions where domain shifts can significantly degrade model performance. This study proposes a physics-informed multimodal convolutional neural network (CNN) with a late fusion architecture, integrating vibration and motor current signals alongside a dedicated physics-based feature extraction branch. The model incorporates a novel physics-informed loss function that penalizes physically implausible predictions based on characteristic bearing fault frequencies - Ball Pass Frequency Outer (BPFO) and Ball Pass Frequency Inner (BPFI) - derived from bearing geometry and shaft speed. Comprehensive experiments on the Paderborn University dataset demonstrate that the proposed physics-informed approach consistently outperforms a non-physics-informed baseline, achieving higher accuracy, reduced false classifications, and improved robustness across multiple data splits. To address performance degradation under unseen operating conditions, three transfer learning (TL) strategies - Target-Specific Fine-Tuning (TSFT), Layer-Wise Adaptation Strategy (LAS), and Hybrid Feature Reuse (HFR) - are evaluated. Results show that LAS yields the best generalization, with additional performance gains when combined with physics-informed modeling. Validation on the KAIST bearing dataset confirms the framework's cross-dataset applicability, achieving up to 98 percent accuracy. Statistical hypothesis testing further verifies significant improvements (p < 0.01) in classification performance. The proposed framework demonstrates the potential of integrating domain knowledge with data-driven learning to achieve robust, interpretable, and generalizable fault diagnosis for real-world industrial applications.

Related papers

• Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols [123.73663884421272]
  Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.<n>We establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets.<n>By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research.
  arXiv  Detail & Related papers  (2026-02-28T05:41:57Z)
• Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Control [2.7222301668137483]
  Reliable spacecraft attitude control depends on accurate prediction of attitude dynamics.<n>For spacecraft with complex dynamics, obtaining accurate physics-based models can be difficult, time-consuming, or computationally heavy.<n>This work explores Physics-Informed Neural Networks (PINNs) for modeling spacecraft attitude dynamics.
  arXiv  Detail & Related papers  (2026-02-17T19:08:48Z)
• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• Noisy Analysis of Quantum SMOTE on Condition Monitoring and Fault Classification in Industrial and Energy Systems [0.5505634045241289]
  Imbalanced machine learning models are a fundamental issue in industrial condition monitoring and fault classification pipelines.<n>This work presents a detailed benchmarking and investigation of classical classifiers under class imbalance mitigation.<n>The results show that QSMOTE consistently corrects distributional skew and significantly enhances the performance of non-linear classifiers.
  arXiv  Detail & Related papers  (2026-01-16T16:44:38Z)
• Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration [56.074760766965085]
  PRISM achieves a dynamics-aware framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.<n>Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
  arXiv  Detail & Related papers  (2026-01-12T05:43:20Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Migration as a Probe: A Generalizable Benchmark Framework for Specialist vs. Generalist Machine-Learned Force Fields [1.572216094651749]
  Machine-learned force fields (MLFFs) are transforming computational materials science by enabling ab initio-level accuracy at molecular dynamics scales.<n>Yet their rapid rise raises a key question: should researchers train specialist models from scratch, fine-tune generalist foundation models, or use hybrid approaches?<n>We introduce a benchmarking framework using defect migration pathways, evaluated through elastic band trajectories, as diagnostic probes.<n>Fine-tuned models substantially outperform from-scratch and zero-shot approaches for kinetic properties but show partial loss of long-range physics.
  arXiv  Detail & Related papers  (2025-08-27T13:24:41Z)
• Learning Robust Satellite Attitude Dynamics with Physics-Informed Normalising Flow [2.7222301668137483]
  We investigate the benefits of incorporating Physics-Informed Neural Networks into the learning of spacecraft attitude dynamics.<n>We train several models on simulated data generated with the Basilisk simulator.<n>We find that PINN-based models consistently outperform their purely data-driven counterparts in terms of control accuracy and robustness.
  arXiv  Detail & Related papers  (2025-08-11T10:50:49Z)
• PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environments [3.4802474792943805]
  We introduce the Physics-Informed Wind-Adaptive Network (PI-WAN), which embeds physical constraints directly into the training process for robust quadrotor dynamics learning.<n>Specifically, PI-WAN employs a Temporal Convolutional Network (TCN) architecture that efficiently captures temporal dependencies from historical flight data.<n>By incorporating real-time prediction results into a model predictive control (MPC) framework, we achieve improvements in closed-loop tracking performance.
  arXiv  Detail & Related papers  (2025-07-01T14:48:22Z)
• Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis [5.937203351551678]
  This study critically examines the performance of physics-informed machine learning (PIML) approaches for traffic flow modeling.<n> Experiments reveal that physical residuals can degrade the performance of LWR- and ARZ-based PIML models.<n>We identify the Courant-Friedrichs-Lewy (CFL) condition as a key indicator of dataset suitability for PIML.
  arXiv  Detail & Related papers  (2025-05-16T17:55:06Z)
• Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks [4.46753539114796]
  Bearing faults are a leading cause of machinery failures.<n>This study utilizes Kolmogorov-Arnold Networks to address these challenges.<n>It produces lightweight models that deliver explainable results.
  arXiv  Detail & Related papers  (2024-12-02T09:40:03Z)
• Physics-Informed Deep Learning of Rate-and-State Fault Friction [0.0]
  We develop a multi-network PINN for both the forward problem and for direct inversion of nonlinear fault friction parameters. We present the computational PINN framework for strike-slip faults in 1D and 2D subject to rate-and-state friction. We find that the network for the parameter inversion at the fault performs much better than the network for material displacements to which it is coupled.
  arXiv  Detail & Related papers  (2023-12-14T23:53:25Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse [49.1501082763252]
  This paper presents HFEDMS for incorporating practical FL into the emerging Industrial Metaverse. It reduces data heterogeneity through dynamic grouping and training mode conversion. Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics. Experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices.
  arXiv  Detail & Related papers  (2022-11-07T04:33:24Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Influence Functions in Deep Learning Are Fragile [52.31375893260445]
  influence functions approximate the effect of samples in test-time predictions. influence estimates are fairly accurate for shallow networks. Hessian regularization is important to get highquality influence estimates.
  arXiv  Detail & Related papers  (2020-06-25T18:25:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.