{"title":"Research on the trend prediction method of equipment operation stability based on quantification interval of deterioration degree","authors":"J. Zhanglei, Wu Yapeng, Xu Xiaoli, Zuo Yunbo","doi":"10.1109/ICEMI.2017.8265701","DOIUrl":null,"url":null,"abstract":"Operation of the wind turbine is influenced by ever-changing wind speed and wind direction, and its transmission system running in a state of nonlinear and non-stationary operation. It is difficult and inaccurate to predict the amplitude of the vibration of the transmission system in real time. So the method, the trend prediction method of deterioration about equipment operation stability based on the degree of deterioration and quantification interval, is proposed in this paper. The vibration signal of the running state would be collected about the transmission system of the wind turbine; the concept, “the mean value of 1.5 dimensional spectral band energy”, is proposed. According to the concept of “the mean value of 1.5 dimension spectrum frequency band energy”, corresponding classification standard of vibration signal is established, and the degradation degree of running stability is quantified to obtain the quantification interval about the degree of deterioration. Then the state sequence of deterioration of the operation stability is established. The known observation sequence is predicted about tendency by using the improved superimposed Markov chain prediction method. In order to verify the effectiveness of the proposed method, measured vibration data experiment would be used to do verification test about the windfield wind turbine generator in working condition. The experimental results show that the development trend of the deterioration state could be got though the running stability deterioration trend prediction, which is consistent with the actual running state.","PeriodicalId":275568,"journal":{"name":"2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMI.2017.8265701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Operation of the wind turbine is influenced by ever-changing wind speed and wind direction, and its transmission system running in a state of nonlinear and non-stationary operation. It is difficult and inaccurate to predict the amplitude of the vibration of the transmission system in real time. So the method, the trend prediction method of deterioration about equipment operation stability based on the degree of deterioration and quantification interval, is proposed in this paper. The vibration signal of the running state would be collected about the transmission system of the wind turbine; the concept, “the mean value of 1.5 dimensional spectral band energy”, is proposed. According to the concept of “the mean value of 1.5 dimension spectrum frequency band energy”, corresponding classification standard of vibration signal is established, and the degradation degree of running stability is quantified to obtain the quantification interval about the degree of deterioration. Then the state sequence of deterioration of the operation stability is established. The known observation sequence is predicted about tendency by using the improved superimposed Markov chain prediction method. In order to verify the effectiveness of the proposed method, measured vibration data experiment would be used to do verification test about the windfield wind turbine generator in working condition. The experimental results show that the development trend of the deterioration state could be got though the running stability deterioration trend prediction, which is consistent with the actual running state.