以多尺度熵為特徵之風扇品質診斷系統

Abstract

為因應智慧化、自動化製造的趨勢，以及全球製造業普遍缺工的困境，產線及檢測自動化為首要的目標之一。有別於一般風扇製造廠以異音做為檢測之指標，本研究針對冷卻風扇的振動訊號進行分析，除傳統的方均根值、傅立葉轉換外，也使用評估訊號之複雜度的多尺度熵做為訊號處理方式，以找出初期損壞之風扇於振動訊號的特徵。 由於風扇在運轉時會受到流場的影響，將造成其振動型態為循環穩態(Cyclostationary)之訊號，若使用傳統的傅立葉轉換則有其限制，本研究利用多尺度熵將風扇振動訊號進行不同尺度之粗粒化，使得訊號受到紊流影響之程度降低，再計算其亂度值，發現結果之多尺度熵曲線無論在合格或不合格樣本間的重複性皆相當高，因此將其做為樣本之特徵。 最後利用特徵建立類神經網路之模型，以經專業聽音員判斷是否符合出廠標準為樣本的分類。第一個模型使用36 個樣本進行訓練，並針對新的9 個樣本進行模型之測試，準確率達100 %；第二個模型針對重複性實驗的結果建立，確認模型是否可預測出同樣的結果，使用兩次的結果建立模型，以第三次的實驗結果進行預測，驗證之準確率達88.9 %，達成以振動訊號對風扇之品質進行辨識的目的。

Aiming at long term smart manufacturing goal, in solving the global problem of skilled labor shortage, production line and quality testing automation is one of the primary high agenda issues. In this research vibrational signal is used for fan QC as compared with conventional manufacturing factories which apply abnormal sound detection as index. Apart from traditional analytical tools RMS value and FFT, multiscale entropy, which estimates the complexity of signals is also adopted in the present study. Cooling fans will be affected by the turbulence air flow while it is operating, making its vibrational signal in a type of ‘Cyclostationary’. Traditional FFT has its limitations on analyzing cyclostationary signals. In this research, multiscale entropy is adapted to make the signal coarse-grained, decreasing the effect of turbulence. The multiscale entropy curves of the sample are found to have good repeatability, and also it gives the characteristic of the sample quality. A neural network model was developed in this research. The labeled samples that had been classified by the professional fan quality controllers were used to train the model. The first model obtained using 36 fans as the training samples, and the validation has been made with 9 new samples, with the accuracy 100 %. Repeated experiments were also carried out for further observation. The second model obtained was used to validate the result of the third repeated experiment, with the accuracy of 88.9 %. The approach has found to be succeessful in classifying fan quality by its vibrational signal.