A Practical Approach to Build Predict Disk Failure System Based on Deep Neural Network
碩士 === 國立高雄科技大學 === 資訊管理系 === 107 === PdM (Predictive Maintenance) has been adopting in production line in recent years. However, PdM has not adopting on Industrial Computer in production line for maintenance tasks. So it will cause production cost increasing as the Industrial Computer is faulted. T...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/yez6kt |
| Summary: | 碩士 === 國立高雄科技大學 === 資訊管理系 === 107 === PdM (Predictive Maintenance) has been adopting in production line in recent years. However, PdM has not adopting on Industrial Computer in production line for maintenance tasks. So it will cause production cost increasing as the Industrial Computer is faulted. The hard drive failure is one of main reasons of computer failure. In this study, we perform a hard drive failure prediction model via analyzing S.M.A.R.T. (Self-Monitoring Analysis and Reporting Technology) attributes. By using Elastic Stack architecture to build a system that can collect S.M.A.R.T. information. We introduce a well method based on LSTM (Long Short-Term Memory) networks to estimate hard drive RUL (Remaining Useful Life). Our experiments show the proposed hard drive failure prediction model constructed by real-world datasets could achieve a high FDR (Failure Detection Rate) of 0.9632% with low FAR (False Alarm Rate) of 0%.
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