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  • 學位論文

應用故障預知與產品健康管理技術於硬碟機的剩餘使用壽命估計

Estimate of Remaining Useful Life using Prognostics and Health Management Technique on Hard Disk Drive

指導教授 : 簡英哲

摘要


開發能預知產品故障的可靠度技術,可以協助設計者改善產品設計,並有助於產品使用者避免突發的致命故障。本研究以硬碟機為例,使用故障預知與產品健康管理 (PHM)技術,從故障預兆(Failure Precursor)的角度作為出發點,結合故障模式、機制與效應分析(FMMEA)、場域試驗(Field Test)和硬碟機的監測技術(Self-Monitoring Analysis and Reporting Technology, SMART),以確認故障前的早期異常徵兆,即所謂故障預兆,並建立其量化指標。經過母體分配的假設檢定,確認硬碟機的故障預兆發生時間服從韋伯分配,再以條件可靠度(Conditional Reliability)和殘餘平均故障發生時間(Residual MTTF)進行產品剩餘使用壽命(Remaining Useful Life, RUL)的預測。本研究所發展的故障預知流程,結合剩餘使用壽命的預測,可以達到運用產品健康管理技術以保障使用者之目的。

並列摘要


Development of reliability technique which can identify failure precursors in early product life time can help product designer to obtain early failure information for design improvement and also can help customers to prevent impact from unexpected catastrophic failure. In this study, hard disk drives (HDD) was selected for reliability analysis. The study applies Prognostics and Health Management (PHM) technique, using failure precursor as an early indication of true product failure. The techniques of Failure Modes, Mechanisms and Effects Analysis (FMMEA), field test and Self-Monitoring Analysis and Reporting Technology (SMART) for HDD, are used to confirm early abnormal signs before occurrence of true failure. Quantitative indicators are established for the identified failure precursors. The random variable of time-to-failure-precursor of HDD is validated having properties of Weibull distribution. Remaining Useful Life (RUL) is predicted based on conditional reliability of the TTF of precursor given the result of PHM monitoring. The Residual MTTF is also developed to represent mean time of remaining useful life. The prognostics process developed in this study combined with the methods developed for remaining useful life prediction will achieve the purpose of product health management for customer protection.

參考文獻


Agarwal, V., Bhattacharyya, C., Niranjan, T. and Susarla, S. (2009). Discovering Rules from Disk Events for Predicting Hard Drive Failures. International Conference on Machine Learning and Applications, 782-786.
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Cheng, S., Tom, K. and Pecht, M. (2010). Failure Precursors for Polymer Resettable Fuses. IEEE Transactions on Device and Materials Reliability, 10(3), 374-380.

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