Probabilistic Inference Algorithm for Fault Diagnosis in Industrial Control Systems

Industrial Control Systems (ICS) are required to operate in modern manufacturing, energy, and transportation infrastructures. These systems' growing complexity and integration with cyber-physical components make them susceptible to errors that could endanger reliability, productivity, and safety. These issues could lower system availability. Traditional defect identification techniques rely on deterministic models, which might not fit uncertainty in real-time sensor data and must be considered. To address this limitation, this paper offers PRO-FIDICS, or Probabilistic Fault Inference for Diagnosis in Industrial Control Systems. This was done to prevent the problem. Defect identification in this software is also accomplished using a novel probabilistic inference approach. Apart from being quick and scalable, it was meant to find problems. The stated approach uses Bayesian networks to mimic causal connections between system components. This helps to identify the character of the system. Error risk is calculated from noisy, incomplete data. PRO-FIDICS is a substitute since it employs dynamic Bayesian modeling for temporal reasoning. This makes it possible. Even under ambiguous circumstances, this enables early detection and correction of faults. Using benchmark ICS datasets, the model is validated and found to be more accurate, resilient, and interpretable than present diagnostic methods. This is the outcome of validating work accomplished and creating an intelligent, adaptive diagnostic tool for use in challenging industrial environments. This tool is for harsh work environments. Deployed in Industry 4.0, this technology enhances operational resilience and supports proactive maintenance.

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