【摘要】：提出一種基于局部均值分解(LMD)近似熵和概率神經(jīng)網(wǎng)絡(luò)(PNN)的滾動(dòng)軸承故障診斷方法。通過對(duì)信號(hào)LMD分解,非平穩(wěn)信號(hào)能夠轉(zhuǎn)換成若干個(gè)平穩(wěn)的乘積函數(shù)分量(PF)之和;軸承在發(fā)生不同故障時(shí),產(chǎn)生頻譜相異的信號(hào),其近似熵不同,因此可通過提取原始信號(hào)的近似熵,來判別軸承的運(yùn)行狀態(tài)。實(shí)驗(yàn)表明,信號(hào)經(jīng)過LMD分解得到若干PF分量,從中提取近似熵,組成N維特征向量,輸入PNN模型,能夠準(zhǔn)確地判斷故障類型;在小數(shù)據(jù)的情況下,相比于BP和RBF兩種傳統(tǒng)神經(jīng)網(wǎng)絡(luò),PNN具有更優(yōu)的故障分類能力。
[Abstract]:A fault diagnosis method for rolling bearings based on local mean decomposition (LMD) approximation entropy and probabilistic neural network (PNN) is proposed. By decomposing the signal LMD, the non-stationary signal can be converted into the sum of several stationary product function components (PF). When different faults occur in the bearing, the signal with different spectrum is produced, and its approximate entropy is different. Therefore, the operating state of the bearing can be judged by extracting the approximate entropy of the original signal. The experimental results show that the signal is decomposed by LMD to obtain some PF components, from which the approximate entropy is extracted, the N-dimensional eigenvector is formed and the PNN model is inputted, which can accurately judge the fault type. In the case of small data, compared with two traditional neural networks, BP and RBF, PNN has better fault classification ability.
【作者單位】： 中北大學(xué)機(jī)械與動(dòng)力工程學(xué)院;
【分類號(hào)】：TH133.3

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