最小熵解卷積法輪對(duì)軸承故障診斷
中國測試王 晗1， 何 劉2
摘 要：針對(duì)強(qiáng)噪聲下輪對(duì)軸承弱故障特征難以提取，以及在實(shí)際信號(hào)檢測中檢測信號(hào)在故障點(diǎn)到檢測點(diǎn)的傳播路徑中有變形和失真導(dǎo)致實(shí)際采集信號(hào)成分復(fù)雜難以判別的問題，提出基于最小熵解卷積的軸承故障診斷方法。該方法的核心是利用熵最小原理設(shè)計(jì)最優(yōu)濾波器，突出信號(hào)中的脈沖沖擊，使濾波后信號(hào)近似于原始沖擊信號(hào)，消除檢測中傳遞路徑對(duì)信號(hào)的干擾，對(duì)解卷積后的信號(hào)做包絡(luò)譜分析達(dá)到輪對(duì)軸承故障診斷的目的。通過實(shí)驗(yàn)分析，基于最小熵解卷積的軸承故障診斷方法能很好突出沖擊脈沖，在包絡(luò)譜中能夠準(zhǔn)確檢測到故障的基頻和高次諧波。
關(guān)鍵詞：輪對(duì)軸承；最小熵解卷積；包絡(luò)譜；故障診斷
文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1674-5124（2016）01-0114-07
Wheel bearing fault diagnosis based on minimum entropy deconvolution method
WANG Han1， HE Liu2
（1. Central Academy of CSR Corporation Limited，Beijing 100036，China；
2. State Key Laboratory of Traction Power，Southwest Jiaotong University，Chengdu 610031，China）
Abstract: A new approach to diagnose wheel bearing failure has been proposed with minimum entropy deconvolution（MED） to extract weak fault features of wheel bearings in strong background noise and ensure in actual signal detections that the detection signals are undistorted when passing from fault points to detection points. The core of this new approach was to design an optimal filter via MED， which was used to filter the vibration signals of wheel bearing axle boxes and make them close to the original impact signals， that is， to eliminate the interfering signals of propagation paths. The signals， after filtering， were analyzed with envelope spectrum to diagnose wheel bearing failure. Experiments have indicated that the MED method can accurately detect the fundamental frequency and harmonic components of wheel bearing faults.
Keywords: wheel bearings； MED； envelope spectrum； fault diagnosis
