本研究提出基於啟發式演算法之模型參數識別及故障偵測方法。本研究論文中發現混沌動態粒子最佳化方法優於其他一般的基因演算法和差分進化演算法。因此，可藉由此方法估測系統模型參數瞭解系統狀態，並透過T檢定和Bonett’s檢定判定損壞與健康馬達參數平均值與標準差的變異，瞭解系統異常屬於機械損壞或電機損壞。當機械損壞發生時，其參數平均值和標準差並沒有顯著差別，然而當電機損壞發生時，參數平均值和標準差有顯著差別。此外，本研究也透過殘差分析了解整體馬達狀況。隨著較高的殘差出現，代表著損壞越嚴重。同時也比較殘差訊號分析與量測訊號分析，從中發現，殘差訊號相較量測訊號對於損壞現象較為敏感且較不受雜訊影響。所以殘差分析相較量測訊號分析能放大損壞特徵，幫助發現初期異常。

This research investigates system identification and fault detection on induction motor using heuristic methods. In this paper, we find out that the chaotic dynamical particle swarm optimization (CDPSO) method is better than other heuristic methods such as genetic algorithm and differential evolution algorithm. Therefore, the CDPSO method is applied on induction motor system parameter identification to understand the system’s condition. Moreover, T test and Bonett’s test are also applied to the identified parameters to determine the difference between healthy motor and failure motor. According to the changing pattern of estimated average and variance from resistance and inductance of induction motor, electrical anomaly can be isolated. On the other hand, mechanical fault appeared in the induction motor cannot cause readable parameter difference in the estimated values. As a result, induction motor mechanical fault and electrical fault can be detected and isolated by examining the variation of estimated parameter average value and variance. Furthermore, residual analysis is also applied to detect overall anomaly of induction motor. With increased residual value, it generally implies developing fault of some failures in the motor. In addition, by comparing the result of measurement signal analysis and residual signal analysis, the analysis using residual signal is not only more sensitive, but also more robust to the noise. Thus, residual signal analysis can provide more sensitive features clearly to observe the incipient fault.

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