基於機器學習模型解釋之取得振動訊號特徵方法

專利類型

發明

專利國別 (專利申請國家)

中華民國

專利申請案號

109138838

專利證號

I 749852

專利獲證名稱

基於機器學習模型解釋之取得振動訊號特徵方法

專利所屬機關 (申請機關)

國立陽明交通大學

獲證日期

2021/12/11

技術說明

本發明係提供一種基於機器學習模型解釋之取得振動訊號特徵方法，其步驟包含：接收振動訊號；將振動訊號經轉換取得時頻圖；訓練以時頻圖作為輸入之一分類模型；透過梯度加權類別活化映射法(Gradient Class Activation Mapping, Grad-CAM)以尋找關注區域；統計分析所述時頻圖中所述關注區域中特定頻段之頻率分佈，以取得所述特定頻段之統計特徵；藉此，本發明可實現振動訊號分析的模型解釋，並可確實得到訊號的關鍵頻段，藉可用以分析機器之運作情形，如：軸承缺陷之分類、刀具磨耗分類，以精確且快速的查找機器振動之因素，此外，於分析未知訊號時，亦可縮小分析的範圍，提供初步的分析方向者。 The present invention provides a method of obtaining vibration signal features based on machine learning model explanation, including the following steps: receiving vibration signal, converting the vibration signal to obtain Spectrogram, training the said Spectrogram to be a classification model of input, looking for area of interest through Gradient Class Activation Mapping (Grad-CAM), and statistical analyzing the frequency distribution of the specific frequency group from the said area of interest of the Spectrogram to obtain the statistical features of the said specific frequency group; thereby the present invention can realize the model explanation of vibration signal analysis and indeed obtain the key frequency group of the signals which can be used to analyze the operational status of a machine, such as bearing failure classification and cutter wear classification, to find out the features of machine vibration accurately and quickly. Besides, it can also narrow down the scope of analysis and provide a preliminary direction of analysis while analyzing unknown signals.