伴隨工業4.0的時代來臨，工業自動化使電動機在產業中扮演不可或缺的角色。電動機必須長期的持續運轉，若發生無預警的停機使工廠停擺，將造成龐大的經濟損失。維修工作若採用定期維修的方式，雖然能夠減少無預警停機的發生，但也消耗了大量人力與物力資源的浪費。因此，若能得知電動機即時的運轉狀態與故障可能原因，提早使維修人員進行機台維護與故障排除，有助於提升系統運轉可靠度。
本研究旨在研製一套基於模糊理論之具故障診斷功能的感應馬達狀態監測嵌入式系統。首先，基於先進Cortex-M4核心之微控制器，利用感測器，量測馬達三相電壓、三相電流及振動訊號，再由類比數位轉換器傳送至微控制器。接著，計算電氣與振動相關指標且參考國際規範，經由門檻值的設定及故障特徵擷取，進而設計基於模糊演算法之感應馬達運轉狀態監測及故障診斷嵌入式軟體系統。在運轉狀態監測系統上，運用變數篩選機制，由候選變數中篩選出當前嚴重性最高之指標進行模糊推理，減少了大量的模糊規則庫，降低微控制器的效能消耗；在故障診斷系統上，本文提出兩個架構，藉由電氣指標適切性評估計算出權重值並導入模糊推理系統。最後，透過模擬案例評估運轉狀態監測系統，初步測試結果確具可行性；此外，故障診斷系統透過瑕疵模型實驗所量測之數據分析比較，兩個架構皆有100 %故障辨識率。

The motor is an indispensable role in industrial automation with Industry 4.0. A regular maintenance can reduce the occurrence of non-warning downtime; however, it will consume a lot of resources. Therefore, it will upgrade system reliability if maintenance staff know the real-time operational condition and the possible causes of failure for the motor.
This study is mainly aimed to build a fuzzy-based embedded system for condition monitoring with fault diagnosis capability of induction motors. First of all, the motor three-phase voltage, three-phase current and vibration signals, which were transformed into digital signals by an analog-to-digital converter to send microcontrollers, were measured by sensors. Secondly, the electrical and vibration related indexes were calculated with the reference to international norms. Moreover, the induction motor operating status monitoring and fault diagnosis of embedded software system were designed based on the fuzzy algorithm, referring to setting the threshold and capturing the fault characteristics. In the condition monitoring system, the fuzzy reasoning was carried out with selecting the most severity indexes from the candidate variables by using the variable filtering mechanism, in order to reduce the large number of fuzzy rule bases and the performance consumption of the microcontrollers. In the fault diagnosis system, two architectures were proposed to calculate the weight values and evaluate them into the fuzzy reasoning system by means of the electrical index to assess adequately. Finally, the preliminary test results were feasible by evaluating the operating condition monitoring system. In addition, the fault diagnosis system had 100 % fault recognition rate through the data analysis and comparison measured by the defective model experiment.

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