【摘要】：針對泵車液壓泵早期故障特征信號微弱、故障特征難以提取的問題,提出了一種基于符號動力學(xué)信息熵與支持向量機(jī)(support vector machine,簡稱SVM)的泵車液壓泵故障診斷方法。分別模擬了液壓泵9種故障狀態(tài),測取了各狀態(tài)下多測點(diǎn)的振動信號樣本值。利用時(shí)間序列的符號動力學(xué)信息熵,計(jì)算各振動信號的符號動力學(xué)信息熵Hk,確定了各狀態(tài)下相應(yīng)的信息熵特征向量。建立了不同狀態(tài)特征向量訓(xùn)練集,再結(jié)合支持向量機(jī)對液壓泵故障模式進(jìn)行診斷與識別,測試結(jié)果準(zhǔn)確率為98.71%。將該方法與改進(jìn)的BP(back propagation,簡稱BP)神經(jīng)網(wǎng)絡(luò)診斷結(jié)果進(jìn)行了對比,結(jié)果表明該方法的識別率更高,診斷時(shí)間更短,適用于現(xiàn)場液壓泵故障的在線診斷。
[Abstract]:In order to solve the problem that the early fault characteristic signal of hydraulic pump is weak and it is difficult to extract the fault feature, a fault diagnosis method based on symbolic dynamic information entropy and support vector machine (SVM) for hydraulic pump is proposed in this paper. Nine fault states of hydraulic pump were simulated, and the vibration signal samples of multiple measuring points were measured. The symbolic dynamics information entropy of each vibration signal is calculated by using the symbolic dynamics information entropy of the time series, and the corresponding information entropy eigenvector under each state is determined by using the symbol dynamics information entropy of the time series. The training sets of different state characteristic vectors are established, and the fault modes of hydraulic pump are diagnosed and identified with support vector machine. The accuracy of the test results is 98.71. The method is compared with the improved BP (back propagation, (BP) neural network diagnosis results. The results show that the method has higher recognition rate and shorter diagnosis time and is suitable for on-line diagnosis of field hydraulic pump faults.
【作者單位】： 武漢科技大學(xué)機(jī)械自動化學(xué)院;
【基金】：國家科技支撐計(jì)劃資助項(xiàng)目(2012BAF02B01,2011BAF11B01)
【分類號】：TU646

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