{"title":"基于优化递归神经网络门控递归单元(GRU)的机器故障预测分析","authors":"Z. Zainuddin, Emelia A P Akhir, Norshakirah Aziz","doi":"10.1109/AiDAS47888.2019.8970725","DOIUrl":null,"url":null,"abstract":"This paper proposed a technique named Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) to predict the condition of machines by using time series data generated by oil and gas company. The problem raised due to limited research of RNN-GRU in improving the accuracy through hyperparameter tuning. Hence, this paper will provide an optimization method that can improve the accuracy of RNN-GRU in forecasting time series data. The preliminary findings of the experiment conducted shows that RNN-GRU can utilize time series data to predict machine failure with improved high accuracy.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Predictive Analytics For Machine Failure Using optimized Recurrent Neural Network-Gated Recurrent Unit (GRU)\",\"authors\":\"Z. Zainuddin, Emelia A P Akhir, Norshakirah Aziz\",\"doi\":\"10.1109/AiDAS47888.2019.8970725\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposed a technique named Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) to predict the condition of machines by using time series data generated by oil and gas company. The problem raised due to limited research of RNN-GRU in improving the accuracy through hyperparameter tuning. Hence, this paper will provide an optimization method that can improve the accuracy of RNN-GRU in forecasting time series data. The preliminary findings of the experiment conducted shows that RNN-GRU can utilize time series data to predict machine failure with improved high accuracy.\",\"PeriodicalId\":227508,\"journal\":{\"name\":\"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AiDAS47888.2019.8970725\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AiDAS47888.2019.8970725","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictive Analytics For Machine Failure Using optimized Recurrent Neural Network-Gated Recurrent Unit (GRU)
This paper proposed a technique named Recurrent Neural Network-Gated Recurrent Unit (RNN-GRU) to predict the condition of machines by using time series data generated by oil and gas company. The problem raised due to limited research of RNN-GRU in improving the accuracy through hyperparameter tuning. Hence, this paper will provide an optimization method that can improve the accuracy of RNN-GRU in forecasting time series data. The preliminary findings of the experiment conducted shows that RNN-GRU can utilize time series data to predict machine failure with improved high accuracy.