轉動機械之DSP線上故障監測診斷系統研發

Abstract

本研究探討轉動機械之線上故障診斷(FDI)技術。整個故障診斷 系統包括兩個部份，分別是產生特徵值及故障推論，產生特徽值部份 有以信號為基礎的方法及以模型為基礎的方法－連續時域轉子模型及 離散時域轉子模型，而推論部份則利用類神經模糊推論綱路達成。以 信號為基礎的方法中，側向及軸向振動信號都需量測用來計算信號的 特徵值，如平均值、標準差、峰值及各轉速倍頻值。以模型為基礎的 方法中，僅需要量測側向振動信號用來辦識系統模型參數並加以計算 系統的特徵值。整個FDI系統用數位信號處理器(DSP)實現。在實驗方 面，我們選用轉子測試台及離心風扇顯示機械預防保養的重要性。

On-line fault detection and isolation (FDI) techniques are proposed for the diagnosis of rotating machinery. The architecture of the systems mainly comprises of feature generation and fault inference. A signal-based method and two model-based methods are used for generating the features required by the subsequent neural fuzzy inference. In the signal-based approach, both lateral and axial vibration data are used for calculating signal features such as the average, the standard deviation, the maximum, and the harmonic multiples. In the model-based approaches, only lateral vibration data are used for calculating the system features. Both the continuous-time and the discrete-time parameter estimation algorithms are employed to generate the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed FDI systems are implemented on the platform of a digital signal processor (DSP). Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance.