{"title":"Bandsaw diagnostics by neurocomputing-two are better than one!","authors":"D. Tuck","doi":"10.1109/ANNES.1995.499501","DOIUrl":null,"url":null,"abstract":"In industrial sawmills, bandsaws must work at a high production rate. Two major factors which limit cutting performance are cracking and instability of the saw blades. This paper describes the results from the development of a diagnostic system which monitors blade vibration and blade tension sensor data to estimate crack length using neurocomputing techniques, to help predict blade failure. It was found that a multi-layered feedforward artificial neural network with two hidden layers produces the most reliable results. The results indicate that this system should enable the detection of cracking in blades while in a running but unloaded (between cuts) state. This may help allow longer run times to be planned with confidence increasing production uptime and minimising maintenance.","PeriodicalId":123427,"journal":{"name":"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANNES.1995.499501","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
In industrial sawmills, bandsaws must work at a high production rate. Two major factors which limit cutting performance are cracking and instability of the saw blades. This paper describes the results from the development of a diagnostic system which monitors blade vibration and blade tension sensor data to estimate crack length using neurocomputing techniques, to help predict blade failure. It was found that a multi-layered feedforward artificial neural network with two hidden layers produces the most reliable results. The results indicate that this system should enable the detection of cracking in blades while in a running but unloaded (between cuts) state. This may help allow longer run times to be planned with confidence increasing production uptime and minimising maintenance.
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