Counterfactual Enabled Neuro-Symbolic Digital Twins for Intelligent Industrial Maintenance

Industrial predictive maintenance is a critical challenge in modern manufacturing, where unexpected equipment failures cause significant economic losses through downtime, repair costs, and disrupted production. Conventional maintenance approaches, whether reactive or schedule-based, are becoming inadequate to manage the high-dimensional sensor information of the IoT-enabled machineries. The paper presents a novel hybrid neuro-symbolic digital twin that builds upon Remaining Useful Life (RUL) estimation by combining temporal transformers, physics-informed constraints, and counterfactual reasoning. The model integrates complementary approaches into a single and interpretable predictive system. A temporal transformer backbone is a model of long-range dependencies in multivariate sensor time-series data allowing the detection of gradual patterns of degradation that are frequently overlooked by traditional models of recurrence. The learning objective contains physics-informed constraints that guarantee that predictions are consistent with the principles of thermodynamic, mechanical, and material fatigue, connecting data-driven learning to domain knowledge. A Conditional Variational Autoencoder (CVAE) produces counterfactual failure events, simulating alternative histories of operation, under hypothetical conditions. This mechanism increases data diversity, enables interpretable diagnostics, and reinforces neuro-symbolic reasoning. A method of improving maintenance policies with the help of multi-objective reinforcement learning (MORL) is applied to minimize downtime, unnecessary maintenance, and equipment life. The results of the experiments on 24,042 sensor measurements of CNC machines, pumps, compressors, and robotic arms show good results. The framework has attained RMSE of 21.52 h and a 0.918 score, $R^2$ which is 25.1 percent better as compared to the baseline models. The accuracy of prediction of failures was 94.2 percent, and the maintenance policies were optimized to achieve a reduction in equipment failures by 51.7 percent compared to the rule-based scheduling, which is based on the fact that CVAE-generated counterfactual state transitions were used to train the Q-learning agent, which indicates the practicality of the framework.