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However, in order to address the challenges of advanced manipulation and maintenance during emergencies, there has been limited research on timely alarming for individual critical process parameters. This paper proposes a method based on the combination of power spectral density and statistical characteristics, which can quickly and accurately diagnose large\u2010scale trend changes and short\u2010term nonstationary abnormal trends in process parameters. First, the method employs incremental data from historical records of critical process parameters for volatility analysis. Second, the historical data of critical process parameters are segmented into multiple appropriately sized datasets. We employ a combined analysis of power spectral density and statistical characteristics to extract features from multitude of incremental data. Meanwhile, we have designed a tuning scheme for critical frequencies and their threshold parameters, which can be used for testing and online diagnostics. Experimental validation is performed using actual critical process parameters data from Chinese refineries. The experimental results indicate that the method can detect large\u2010scale trends and short\u2010term nonstationary abnormal trends in process parameters, demonstrating good diagnostic performance.<\/jats:p>","DOI":"10.1049\/sil2\/8178555","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T18:58:04Z","timestamp":1741719484000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Abnormal Diagnosis Method for Process Parameter Fluctuation Based on Power Spectral Density and Statistical Characteristics"],"prefix":"10.1049","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2799-2244","authenticated-orcid":false,"given":"Zhu","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1376-682X","authenticated-orcid":false,"given":"Jiale","family":"Zhan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1466-2542","authenticated-orcid":false,"given":"Qinghe","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Shaokang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"265","published-online":{"date-parts":[[2025,3,9]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2932769"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2023.118594"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.psep.2022.08.035"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-020-1243-2"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.111002"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2022.110983"},{"key":"e_1_2_10_7_2","doi-asserted-by":"crossref","unstructured":"OkadaK. 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