{"title":"基于粒子群算法的空气增压压缩机(ABC)电机故障预测维护","authors":"N. Rosli, Nurul Rawaida ain Burhani, R. Ibrahim","doi":"10.1109/SCORED.2019.8896330","DOIUrl":null,"url":null,"abstract":"Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Booster Compressor (ABC) Motor failure is using Artificial Neural Network (ANN) is presented. However, the optimal weights of the network are one of the parameters that lead to the accuracy of ANN. Therefore, Particle Swarm Optimization (PSO) is proposed to train the weights and bias of ANN. The result presented in this paper is compared with conventional ANN based on Mean Square Error (MSE) and Root Mean Square Error (RMSE)","PeriodicalId":231004,"journal":{"name":"2019 IEEE Student Conference on Research and Development (SCOReD)","volume":"348 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization\",\"authors\":\"N. Rosli, Nurul Rawaida ain Burhani, R. Ibrahim\",\"doi\":\"10.1109/SCORED.2019.8896330\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Booster Compressor (ABC) Motor failure is using Artificial Neural Network (ANN) is presented. However, the optimal weights of the network are one of the parameters that lead to the accuracy of ANN. Therefore, Particle Swarm Optimization (PSO) is proposed to train the weights and bias of ANN. The result presented in this paper is compared with conventional ANN based on Mean Square Error (MSE) and Root Mean Square Error (RMSE)\",\"PeriodicalId\":231004,\"journal\":{\"name\":\"2019 IEEE Student Conference on Research and Development (SCOReD)\",\"volume\":\"348 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Student Conference on Research and Development (SCOReD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCORED.2019.8896330\",\"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 IEEE Student Conference on Research and Development (SCOReD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCORED.2019.8896330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predictive Maintenance of Air Booster Compressor (ABC) Motor Failure using Artificial Neural Network trained by Particle Swarm Optimization
Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Booster Compressor (ABC) Motor failure is using Artificial Neural Network (ANN) is presented. However, the optimal weights of the network are one of the parameters that lead to the accuracy of ANN. Therefore, Particle Swarm Optimization (PSO) is proposed to train the weights and bias of ANN. The result presented in this paper is compared with conventional ANN based on Mean Square Error (MSE) and Root Mean Square Error (RMSE)