Enhancing Industrial Fault Diagnosis: A Probabilistic Expert System With LLM-Augmented Validation

Abstract

Keeping industrial utility equipment reliable is a central problem in industrial predictive maintenance, especially for assets whose faults are reflected in coupled pressure, temperature, flow, power, and efficiency signals. Pure data-driven models can learn complex patterns but are difficult to audit, while conventional rule-based expert systems are interpretable but brittle when symptoms are incomplete. This paper proposes a Hybrid Probabilistic Expert System that combines a deterministic rule base, a Pattern Matching with Scoring (PMS) inference engine, and a Large Language Model (LLM) fallback for low-confidence cases. Diagnostic rules covering seven industrial asset types are organized by physical coupling structure, and PMS converts partial rule matches into normalized confidence scores. We also report a constrained Monte-Carlo coverage analysis to distinguish rule-space reachability from high-confidence diagnosability. On the controlled synthetic benchmark with a mock-LLM fallback, the hybrid system achieved a fault-only accuracy of 0.946 and a macro-F1 of 0.855, and repeated runs yielded a mean hybrid fault-only accuracy of 0.9464 with a 95% confidence interval of 0.0026. In an API-backed real-LLM experiment using DeepSeek v4-pro on a 100-sample synthetic subset, the hybrid system achieved 0.96 accuracy, compared with 0.95 for the expert system and 0.93 for the Real LLM alone. To improve external validity, we further evaluated the method on public LBNL FDD and MetroPT time-series benchmarks after converting public time-series scenarios and windows into abnormal-parameter predicates. On 104 public benchmark samples, the expert system achieved 0.808 accuracy, the Real LLM achieved 0.702, and the hybrid system achieved 0.779 with a false-alarm rate of 0.071. These results indicate that confidence-based expert-first routing can preserve interpretability while providing a controlled pathway for LLM assistance when symbolic evidence is weak or incomplete.