感應馬達具有廉價、堅固耐用的特性，因此被廣泛應用在各工業領域中，在任何工廠中更是不可或缺的動力來源。目前，大部分工廠採取基於時間性定期保養的運維策略，若將運維策略提升到狀態性保養或主動性保養，即可得知電動機即時的運轉狀態，一旦有問題，馬上進行故障診斷，提早使維修人員進行馬達維護與故障排除，即可避免突發事故的發生，提升運轉可靠度，達到工廠穩定生產之目的。
本文旨在提出一系列適用於不同場域、需求的感應馬達之狀態監測系統，包含標準參照監測系統、同機參照監測系統及故障預診斷監測系統。將馬達運轉狀態分為良好、注意、警戒及危險四區域，當狀態達到注意或以上時啟動故障診斷分析模組來辨別馬達故障部位，達到能預知報警，快速維修之效。本研究利用感應馬達運轉時產生之振動訊號及電氣訊號推算出振動參數及電氣參數，統計挑選出適合應用於狀態監測系統與故障診斷分析模組的參數，建立基於規範ISO 10816之狀態監測方法。再分別利用模糊演算法及類神經網路建立故障診斷分析模組，最後經過馬達故障模型之數值比對驗證其有效性。

Induction motors are cheap, rugged and durable, therefore widely used in various industrial fields and are an indispensable power source in any factory. At present, most factories adopt an O&M(Operation and Maintenance) strategy based on “Time-Based Maintenance”. If the O&M strategy is upgraded to “Condition-Based Maintenance” or “Proactive Maintenance”, the motors’ condition will be real-time monitored, and if there is any problem, the fault diagnosis will be carried out immediately.
The purpose of this thesis is to propose a series of condition monitoring systems for induction motors with different fields and requirements, including standard reference monitoring system, same-machine reference monitoring system and fault pre-diagnosis monitoring system. The motor operating condition is divided into four areas: normal, caution, warning and danger. When the condition reaches caution or above, the fault diagnosis module is activated to diagnose motor faults, to achieve predictable alarms and fast maintenance. In this study, vibrational and electrical parameters are calculated from the vibrational signals and electrical signals which are generated during the operation of the induction motor. Parameters suitable for the condition monitoring system and the fault diagnosis module are statistically selected and the condition monitoring method based on the ISO 10816 is established. Then the fault diagnosis module was developed using fuzzy algorithm and neural network respectively. Finally, the numerical comparison of the motor fault model was used to verify its effectiveness.

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