Bayesian Computation Statistics
Mohammad Mehdi Abdollahi; M. Bameni Moghadam
Abstract
This study investigates the application of Bayesian two-sample testing in Hilbert space to monitor the health status of industrial equipment. The proposed method evaluates distributional differences between operational data samples to detect faults or anomalies. Unlike traditional multivariate techniques, ...
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This study investigates the application of Bayesian two-sample testing in Hilbert space to monitor the health status of industrial equipment. The proposed method evaluates distributional differences between operational data samples to detect faults or anomalies. Unlike traditional multivariate techniques, our approach pro-vides higher sensitivity to subtle distributional shifts and supports visual insights into posterior distributions. Real-world experiments on industrial sensor outputs demonstrate the method’s effectiveness in early fault detection, reducing mainte-nance costs and downtime. This makes Bayesian two-sample testing in Hilbert space a powerful tool for predictive maintenance strategies.This study investigates the application of Bayesian two-sample testing in Hilbert space to monitor the health status of industrial equipment. The proposed method evaluates distributional differences between operational data samples to detect faults or anomalies. Unlike traditional multivariate techniques, our approach pro-vides higher sensitivity to subtle distributional shifts and supports visual insights into posterior distributions. Real-world experiments on industrial sensor outputs demonstrate the method’s effectiveness in early fault detection, reducing mainte-nance costs and downtime. This makes Bayesian two-sample testing in Hilbert space a powerful tool for predictive maintenance strategies.
