Home / Current Issue / Paper 1724291
Physics-Coupled Factor Graph Disentanglement: An Explainable Physics-Informed Deep Learning Framework for Rotating Machinery Prognostics
Subject area: Science,Engineering and Technology · Area of research: Deep Learning
Abstract
Rotating machinery prognostics increasingly relies on deep learning, yet the latent representations learned by conventional models remain opaque and often entangle overlapping fault signatures, which undermines operator trust and limits maintenance decision-making. We propose a physics-coupled factor graph disentanglement framework that integrates a temporal convolutional encoder with a differentiable probabilistic graphical model constrained by the governing equations of rotor dynamics and bearing kinematics. Multivariate sensor measurements, including vibration, rotational speed, and temperature, are mapped by the encoder into a structured latent space whose components correspond explicitly to physical degradation factors such as rotor imbalance, shaft misalignment, bearing defect severity, gear mesh stiffness variation, applied load, and speed deviation. These factors then serve as variable nodes in a factor graph, where each factor computes a residual between the physically predicted response and the observed spectral features; for example, the rotor dynamics factor compares a numerically integrated equation of motion against the reconstructed vibration signal, whereas the bearing kinematics factor evaluates energy mismatches at characteristic defect frequencies. The training objective combines the data-driven prediction error with aggregated physics residuals, a correlation-based disentanglement penalty, and a sparsity constraint on factor activations, so that only relevant physical mechanisms are engaged for a given fault scenario. During inference, the dominant residual pathway yields a physically grounded explanation for the diagnosis and remaining useful life prediction. The main contribution lies in coupling mechanical laws with latent factor learning through a differentiable graph, which enables interpretable separation of concurrent faults. This approach offers a principled route toward trustworthy, physics-consistent prognostics for safety-critical rotating equipment.
Keywords
prognostics and health management; physics-informed deep learning; disentangled representation; factor graph; rotating machinery; remaining useful life
References
[1] Han T, Wang X, Meng H, Zio E. Prognostics and health management in the era of foundation models: emerging paradigms and future directions. IEEE Trans Ind Inform. 2026.
[2] Jadaun U, Shriwastava S, et al. Recent advances in bearing remaining useful life prediction: a review of machine learning and deep learning approaches. Int J Sci Res Technol. 2026.
[3] Tang S, Ren Z, Zheng L, Wang J. Explainable artificial intelligence in rotating machinery fault diagnosis: a comprehensive review and emerging trends. Sensors. 2026.
[4] Xie Y, Zheng R, Guo X, Shihan T, Zhu D, et al. Physics-informed cross-domain fault diagnosis for rolling bearings under robustness and interpretability: a novel feature adaptation framework. Eksploat Niezawodn. 2026.
[5] Gupta P, Saluja R. Physics-guided explainable predictive maintenance framework for industrial IoT systems. Sci Rep. 2026.
[6] Jin Z, Gao Y, He D, Yang J, Zhang X, et al. HPI-GWAN: bridging the gap between sensor data and remaining useful life with hidden physics-informed learning. IEEE Sens J. 2026.
[7] Shi C, Li M, Xu S. CAMMA: a physics-informed adaptive Mamba framework for remaining useful life prediction in intelligent predictive maintenance. Expert Syst. 2026.
[8] Kumar A, Wang D. Explainable artificial intelligence based simulation-to-real domain adaptation for robust rotor condition monitoring. Adv Eng Inform. 2026.
[9] Hosseinli A. Physics-informed domain generalization for bearing prognostics under unseen operating conditions. In: PHM Society European Conference. 2026.
[10] Wilson J, Suker T. Explainable source-free domain adaptation for rotating machinery under variable working conditions. SSRN 7534879 [Preprint]. 2026.
[11] Saxena A, Goebel K, Simon D, Eklund N. Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International Conference on Prognostics and Health Management. 2008.
[12] Wang G, Li C, Lv Y, Zhong Z, Shao C, et al. A lightweight intra-class inter-class domain adaptation network approach for diagnosing bearing faults under different operating conditions. Struct Health Monit. 2026.
[13] Deng Y, Du S, Wang D, Shao Y, et al. A calibration-based hybrid transfer learning framework for RUL prediction of rolling bearing across different machines. IEEE Trans Instrum Meas. 2023.
How to cite this paper
@article{1724291,
author = {Mahendra Singh Rajpoot},
title = {Physics-Coupled Factor Graph Disentanglement: An Explainable Physics-Informed Deep Learning Framework for Rotating Machinery Prognostics},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4243-4258},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724291.pdf},
abstract = {Rotating machinery prognostics increasingly relies on deep learning, yet the latent representations learned by conventional models remain opaque and often entangle overlapping fault signatures, which undermines operator trust and limits maintenance decision-making. We propose a physics-coupled factor graph disentanglement framework that integrates a temporal convolutional encoder with a differentiable probabilistic graphical model constrained by the governing equations of rotor dynamics and bearing kinematics. Multivariate sensor measurements, including vibration, rotational speed, and temperature, are mapped by the encoder into a structured latent space whose components correspond explicitly to physical degradation factors such as rotor imbalance, shaft misalignment, bearing defect severity, gear mesh stiffness variation, applied load, and speed deviation. These factors then serve as variable nodes in a factor graph, where each factor computes a residual between the physically predicted response and the observed spectral features; for example, the rotor dynamics factor compares a numerically integrated equation of motion against the reconstructed vibration signal, whereas the bearing kinematics factor evaluates energy mismatches at characteristic defect frequencies. The training objective combines the data-driven prediction error with aggregated physics residuals, a correlation-based disentanglement penalty, and a sparsity constraint on factor activations, so that only relevant physical mechanisms are engaged for a given fault scenario. During inference, the dominant residual pathway yields a physically grounded explanation for the diagnosis and remaining useful life prediction. The main contribution lies in coupling mechanical laws with latent factor learning through a differentiable graph, which enables interpretable separation of concurrent faults. This approach offers a principled route toward trustworthy, physics-consistent prognostics for safety-critical rotating equipment.},
keywords = {prognostics and health management; physics-informed deep learning; disentangled representation; factor graph; rotating machinery; remaining useful life},
month = {October},
}