早期的旋轉電機設備維修保養是採用修復性維修保養，此維修策略容易造成設備維修費用過高、停機所造成的鉅額經濟損失、人員的安全等等問題的浮現，因此，現階段實務上大都採用預防性維修保養的定期性維修保養的方式，此維修策略未充分考慮旋轉電機實際的運轉狀態，導致超量的維修與保養，造成人力及物力的大量浪費，也可能造成臨時的故障情況發生，為此逐漸往基於狀態性維修保養的維修保養方式發展。基於此，本研究主要研製一套具智慧故障診斷之旋轉電機混合式運轉狀態監測系統，混合式運轉狀態監測系統採用電氣與振動檢測法，藉由不同訊號的資訊，提供更為全面的運轉狀態，搭配相應的國際規範做為規則庫，運用模糊理論將電氣與振動運轉狀態進行整合，並提供正常、警戒、危險運轉狀態以及運轉狀態間的餘裕。智慧型故障診斷模組採用振動的時域訊號為來源，透過小波轉換將時域訊號轉為時頻域訊號，藉由不同訊號域的資訊，提供更為有效的故障診斷，再透過深度學習的卷積神經網路架構進行特徵擷取與故障分類。混合式運轉狀態監測系統，透過實際裝設與實驗量測兩套系統的量測數據，進行完整的功能展示，包含正常、警戒、危險的運轉狀態以及運轉狀態間的數值。智慧型故障診斷模組透過混淆矩陣的四項評估指標：分類精度、精度、召回率與F分數的分析評估之下，在五種類型的綜合分類精準度達到99.7 %，在精度、召回率與F分數最低皆為99 %，只有極少數的定子故障、轉子故障、軸承故障與對心故障會造成誤判，但是在健康情形之下的四項評估指標判別率皆為100 %。

The common practice in motor maintenance had been corrective maintenance, which exposes personnel to safety hazards and incurs excessive costs in equipment maintenance and from downtime. Thus, time-based preventive maintenance has supplanted corrective maintenance in most motor maintenance practices. However, time-based preventive maintenance overlooks the actual operational statuses of rotating electrical machines—causing excessive demands on repairs and maintenance, large costs in human and material resources, and frequent temporary failures. Condition-based maintenance has thus been proposed as an approach that ought to gradually replace its time-based counterpart. This study incorporated a hybrid condition monitoring system with intelligent fault diagnosis for rotating electrical machines. This system adopts the method of testing electrical and vibrational signals, and it also implements the relevant international standards in its rule base. Furthermore, in the system, fuzzy theory is applied to the integration of electrical and vibration operation statuses, and the differences between the current operation signal value and the threshold values of normal, cautionary, and dangerous statuses are calculated. The intelligent fault diagnosis module converts time-domain vibration signals to frequency-domain signals through wavelet transform. Through the use of information from different signal domains, the module provides fault diagnosis with enhanced effectiveness; it also extracts features and classifies faults using a convolutional neural network in deep learning. The hybrid condition monitoring system displays comprehensive functional status information using the data measured through the actual installed system and the experimental measurement system, including the differences between the current operation signal values and the threshold values of normal, cautionary, and dangerous statuses. The intelligent fault diagnosis module analyzes and evaluates the four indices of the confusion matrix, namely classification accuracy, precision, recall rate, and F-score. The module exhibited 99.7% comprehensive classification accuracy on all the five motor types, and it displayed a minimal percentage of 99% in precision, recall rate, and F-score. The module had made an extremely small number of erroneous assessments regrading stator, rotor, bearing, and centering failures, but it exhibited 100% rates in all the four evaluation indices to a health motor.

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