Xing Zhou, Long Cheng, Yanzhen Tang, Zhengqiang Pan, Quan Sun
{"title":"Model identification of lithium-ion batteries in the portable power system","authors":"Xing Zhou, Long Cheng, Yanzhen Tang, Zhengqiang Pan, Quan Sun","doi":"10.1109/ICPHM.2016.7542843","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542843","url":null,"abstract":"Portable Power System (PPS) supplies energy for electronic devices outdoors. The lithium-ion batteries are adopted as a kind of ideal energy storage unit for the PPS. In order to monitor batteries, parameter identification should be performed on batteries. To achieve the tradeoff between the accuracy and simplicity, the first order RC model is used as the fundamental model for lithium-ion batteries. Under the working condition, an online parameter identification method, which is combined with the recursive least square (RLS), is proposed in a batch-type working manner. Although the RLS-based identification method can only be applicable to time-invariant systems, the combination of the RLS method and the proposed batch-type working manner can work well to identify the model parameters during the operation of PPS.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121516434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of micro-flaws in metallic material based on a self-organized data-driven approach","authors":"Xudong Teng, Yuantao Fan, Sławomir Nowaczyk","doi":"10.1109/ICPHM.2016.7542868","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542868","url":null,"abstract":"Evaluating the health condition of a material that could potentially contain micro-flaws is a common and important application within the field of non-destructive testing. Examples of such micro-defects include dislocation, fatigue cracks or impurities and are often hard to detect. The ability to precisely measure their type, size and position is a prerequisite for estimating the remaining useful life of the component. One technique that was shown successful in the past is based on traditional ultrasonic testing methods. In most cases, inner micro-flaws induce slight changes of acoustic wave spectrum components. However, these changes are often difficult to detect directly, as they tend to exhibit features that are most naturally analyzed using statistical and probabilistic methods. In this paper we apply Consensus Self-Organizing Models (COSMO) method to detect micro-flaws in metallic material. This approach is essentially an unsupervised deviation detection method based on the concept of “wisdom of the crowd”. This method is used to analyze the spectrum of acoustic waves received by the transducer attached on the surface of material being analyzed. We have modeled a steel board with micro-cracks and collected time-series of acoustic echo response, at different positions on material's surface. The experimental results show that the COSMO method is able to detect and locate micro-flaws.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129371033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Intermittent fault diagnosis of industrial systems in a model-checking framework","authors":"Abderraouf Boussif, M. Ghazel","doi":"10.1109/ICPHM.2016.7542874","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542874","url":null,"abstract":"In this paper, a formal verification approach for diagnosability analysis of intermittent faults is proposed. In this approach, the industrial systems are abstracted as discrete-event systems (DES) and modeled by finite state automata (FSA), then a model-checking framework is set to deal with diagnosability issues. Intermittent faults are defined as faults that can automatically recover once they occur. We first revisit two existing definitions of diagnosability of intermittent faults, regarding the occurrence of faults and their normalization (i.e., disappearance of faults). Then, necessary and sufficient conditions are developed based on the twin plant construction, and reformulated as linear temporal logic (LTL) formulas in order to use model-checking for actual verification. A benchmark is used to illustrate the contributions discussed and to assess the efficiency and the scalability of the proposed approach.1","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130137920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}