Machine Learning for Industrial Anomaly Detection: A Probabilistic Intelligence Approach

This research presents a paradigm shift in industrial monitoring by transitioning from rigid, static threshold systems to a framework of probabilistic intelligence. Modern industrial processes are characterized by inherent stochasticity and natural fluctuations that traditional hard-coded constraints often misidentify as failures, leading to high false alarm rates. By integrating the Univariate Gaussian Probability Density Function (PDF) directly into machine learning architectures, a dynamic mathematical baseline for operational normality is established. The study employs Maximum Likelihood Estimation (MLE) to analyze historical data. This allows the model to learn the unique environmental noise of a system, such as temperature variations in a server room, and calculate a continuous score of suspicion.