{"title":"基于大数据的风机齿带断裂故障预测算法","authors":"Zhihe Yang","doi":"10.1109/ICSAI.2018.8599400","DOIUrl":null,"url":null,"abstract":"In order to accurately predict the fracture fault of fan tooth belt, the NARIMA method is proposed in this paper. The method is based on ARIMA model, and effectively combines the run length stationary test method, differential stationary processing method, linear minimum variance prediction algorithm, etc.. The model is used to fit the time series of the fracture fault of fan tooth belt, and the model is used to predict the fracture fault of fan tooth belt. It is found that the NARIMA model can well fit the given time series, and the predicted values are in line with the actual situation and trend. The test results show the effectiveness of the proposed algorithm.","PeriodicalId":375852,"journal":{"name":"2018 5th International Conference on Systems and Informatics (ICSAI)","volume":"120 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Prediction Algorithm For the Fan Tooth Belt Fracture Fault Based on Big Data\",\"authors\":\"Zhihe Yang\",\"doi\":\"10.1109/ICSAI.2018.8599400\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to accurately predict the fracture fault of fan tooth belt, the NARIMA method is proposed in this paper. The method is based on ARIMA model, and effectively combines the run length stationary test method, differential stationary processing method, linear minimum variance prediction algorithm, etc.. The model is used to fit the time series of the fracture fault of fan tooth belt, and the model is used to predict the fracture fault of fan tooth belt. It is found that the NARIMA model can well fit the given time series, and the predicted values are in line with the actual situation and trend. The test results show the effectiveness of the proposed algorithm.\",\"PeriodicalId\":375852,\"journal\":{\"name\":\"2018 5th International Conference on Systems and Informatics (ICSAI)\",\"volume\":\"120 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 5th International Conference on Systems and Informatics (ICSAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAI.2018.8599400\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 5th International Conference on Systems and Informatics (ICSAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI.2018.8599400","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Prediction Algorithm For the Fan Tooth Belt Fracture Fault Based on Big Data
In order to accurately predict the fracture fault of fan tooth belt, the NARIMA method is proposed in this paper. The method is based on ARIMA model, and effectively combines the run length stationary test method, differential stationary processing method, linear minimum variance prediction algorithm, etc.. The model is used to fit the time series of the fracture fault of fan tooth belt, and the model is used to predict the fracture fault of fan tooth belt. It is found that the NARIMA model can well fit the given time series, and the predicted values are in line with the actual situation and trend. The test results show the effectiveness of the proposed algorithm.
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