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Health Monitoring of Industrial Equipment Based on a Single Output Parameter Using a Bayesian Two-Sample Test in Hilbert Space | ||
| Journal of Data Science and Modeling | ||
| دوره 3، شماره 1 - شماره پیاپی 5، اسفند 2024، صفحه 281-300 اصل مقاله (327.9 K) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2026.86633.1072 | ||
| نویسندگان | ||
| Mohammad Mehdi Abdollahi؛ M. Bameni Moghadam* | ||
| Statistics Dept., Faculty of Statistics, Mathematics and Computer, Allameh Tabataba'i University | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Bayesian hypothesis testing؛ two-sample test؛ Hilbert space؛ industrial equipment؛ anomaly detection | ||
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آمار تعداد مشاهده مقاله: 177 تعداد دریافت فایل اصل مقاله: 96 |
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