Adaptive dynamic mode decomposition and GA-SVM with application to fault classification of planetary bearing
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摘要: 行星齒輪箱在運行過程中由于齒輪間的相互作用會產生強噪聲，導致行星軸承的故障特征被完全淹沒在背景噪聲中并難以提取，從而使得行星軸承故障分類的準確率較低。本文提出一種自適應動模式分解(ADMD)和遺傳算法優化支持向量機(GA-SVM)的行星軸承故障分類方法。首先，針對傳統動模式分解(DMD)中截斷秩無法準確選取的問題，定義了一種新的適應度函數，并采用改進的蚱蜢優化算法(IGOA)自適應選取最優截斷秩，進而實現對原始振動信號的降噪處理。然后對處理后的信號計算其歸一化后的復合精細多尺度離散熵(IRCMDE)并構成特征矩陣。最后采用遺傳算法優化支持向量機，構建GA-SVM分類模型，并將其應用到行星軸承故障診斷中。利用行星齒輪箱中行星軸承故障數據驗證了此方法的有效性和實用性，最終分類結果為96.43%，表明了該方法可以準確識別出行星軸承的故障類型。Abstract: Recently, planetary gearboxes have been widely used in helicopters, heavy trucks, ships, and other large and complex mechanical equipment because of their smooth transmission characteristics, small volume, and large reduction ratio. The planetary bearing, which plays a supporting role in the planetary gearbox, usually works in a worse environment but suffers from low speed and heavy load for a long time. Additionally, because of the strong noise generated by the interaction between gears during the operation of the planetary gearbox, the fault characteristics of planetary bearings are completely submerged in the background noise and are difficult to extract, which complicates classifying planetary-bearing faults accurately. Therefore, to effectively remove noise information from planetary-bearing signals, accurately extract fault information, and classify the fault types of planetary bearings, an adaptive dynamic mode decomposition (ADMD) and genetic algorithm and support vector machine (GA-SVM) with application to the fault classification of planetary bearing is proposed in this paper. The hard threshold selection of the traditional truncated rank cannot effectively process the time-domain vibration signals using the dynamic mode decomposition (DMD) method. Hence, this paper proposes improved grasshopper optimization algorithm (IGOA) to optimize the grasshopper optimization algorithm (GOA) by using dynamic weight and avoid the linear gradient mechanism, which cannot fully use the entire iterative process. Furthermore, IGOA can perform a global search to achieve the adaptive optimal parameter selection of the truncated rank. Besides, a new fitness function is defined that can effectively process the original time-domain signals. The traditional refined composite multiscale discrete entropy (RCMDE) is relatively dispersed, and it cannot characterize the features hidden in the signal better. Therefore, we normalize the RCMDE, forming the improved refined composite multiscale discrete entropy (IRCMDE). Then, the IRCMDE is calculated for the denoised signal, and a feature matrix is constructed to better mine the hidden features in the signal. Finally, GA is used to optimize the key parameters C and g of the SVM. The GA-SVM classification model is also constructed and applied to the bearing fault classification of the planetary gearbox, which can avoid the overfitting phenomenon in the training process and provide better generalization performance. Taking the planetary-bearing fault data in the planetary gearbox of Nanchang Hangkong University as the research object, the validity and practicability of the proposed method are verified, and the final classification result of the inner ring fault, outer ring fault, rolling body fault, and normal condition is 96.43%. In addition, this method can more accurately identify the fault types of planetary bearings and has better generalization ability than the empirical mode decomposition (EMD) signal processing method and the convolutional neural network (CNN) classification method.
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圖 4 行星齒輪箱傳感器布置圖. (a) 行星齒輪箱; (b) 傳感器位置
Figure 4. Planetary gear box sensor layout: (a) planetary gear box; (b) sensor location
圖 5 不同故障模式下振動信號時域圖. (a) 內圈信號; (b) 外圈信號; (c) 滾動體信號; (d) 正常信號
Figure 5. Time-domain diagrams of vibration signals under different fault modes: (a) inner ring signal; (b) outer ring signal; (c) rolling body signal; (d) normal signal
圖 7 不同方法分類結果. (a) ADMD+GA-SVM法; (b) ADMD+CNN法; (c) EMD+GA-SVM法
Figure 7. Classification results by different methods: (a) ADMD+GA-SVM method; (b) ADMD+CNN method; (c) EMD+GA-SVM method
表 1 行星齒輪箱的主要參數
Table 1. Main parameters of planetary gear box
Number of teeth
of sun wheelNumber of planetary
gear teethRing gear 28 36 (3) 100 
下載: 導出CSV
表 2 不同方法對行星齒輪箱行星軸承分類結果
Table 2. Classification results of planetary bearings in planetary gearboxes by different methods
Methods Accuracy /% ADMD+GA-SVM 96.43 ADMD+CNN 78.57 EMD+GA-SVM 91.07
下載: 導出CSV
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