A self-regulating heating cable (SRHC), which has the advantages of ease of use and economic feasibility, is widely used in various industrial processes to prevent the freezing of pipes and maintain temperature. However, if the SRHC, a heating element, loses its temperature control ability due to internal and external factors, it may ignite and cause a fire. Various sensors can be used to prevent these problems; however, there is a limit to their use because they are ineffective and expensive. In this study, we attempted to solve this problem by applying electrical signature analysis technology, which is widely used for diagnosing electric equipment, to SRHC. We attempted to predict the fire and detect the cause by acquiring and analyzing the current supplied to the SRHC. Current values were acquired from SRHCs that are experiencing series arc faults, local heating, or tracking phenomena, which are known as the causes of fires in SRHC, and these data were analyzed to predict the fire and to determine the cause. In particular, in order to maintain the economic advantage of the SRHC system, a signal processing method that can achieve the above objective from an electrical signal obtained at a low sampling frequency was proposed. In addition, the difference between signal waveforms of two consecutive cycles was used to analyze in order to minimize the impact of the SRHC's used environment or manufacturers. As a result, it was possible to predict the fire and detect the cause of the fire of the SRHC only with the frequency distribution table which is made of 83 data points acquired and processed with a low sampling frequency of 5 kHz.
{"title":"Prediction of Ignition Possibility of Self-Regulating Heating Cable Using Current Signature Analysis","authors":"Dong-Young Lim, Young-Cheon Yu, Seung-Wook Jee","doi":"10.1002/fam.70022","DOIUrl":"https://doi.org/10.1002/fam.70022","url":null,"abstract":"<p>A self-regulating heating cable (SRHC), which has the advantages of ease of use and economic feasibility, is widely used in various industrial processes to prevent the freezing of pipes and maintain temperature. However, if the SRHC, a heating element, loses its temperature control ability due to internal and external factors, it may ignite and cause a fire. Various sensors can be used to prevent these problems; however, there is a limit to their use because they are ineffective and expensive. In this study, we attempted to solve this problem by applying electrical signature analysis technology, which is widely used for diagnosing electric equipment, to SRHC. We attempted to predict the fire and detect the cause by acquiring and analyzing the current supplied to the SRHC. Current values were acquired from SRHCs that are experiencing series arc faults, local heating, or tracking phenomena, which are known as the causes of fires in SRHC, and these data were analyzed to predict the fire and to determine the cause. In particular, in order to maintain the economic advantage of the SRHC system, a signal processing method that can achieve the above objective from an electrical signal obtained at a low sampling frequency was proposed. In addition, the difference between signal waveforms of two consecutive cycles was used to analyze in order to minimize the impact of the SRHC's used environment or manufacturers. As a result, it was possible to predict the fire and detect the cause of the fire of the SRHC only with the frequency distribution table which is made of 83 data points acquired and processed with a low sampling frequency of 5 kHz.</p>","PeriodicalId":12186,"journal":{"name":"Fire and Materials","volume":"50 1","pages":"94-107"},"PeriodicalIF":2.4,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/fam.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}