最小熵解卷積法輪對軸承故障診斷
中國測試王 晗1， 何 劉2
摘 要：針對強噪聲下輪對軸承弱故障特征難以提取，以及在實際信號檢測中檢測信號在故障點到檢測點的傳播路徑中有變形和失真導致實際采集信號成分復雜難以判別的問題，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)計最優(yōu)濾波器，突出信號中的脈沖沖擊，使濾波后信號近似于原始沖擊信號，消除檢測中傳遞路徑對信號的干擾，對解卷積后的信號做包絡(luò)譜分析達到輪對軸承故障診斷的目的。通過實驗分析，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)譜中能夠準確檢測到故障的基頻和高次諧波。
關(guān)鍵詞：輪對軸承；最小熵解卷積；包絡(luò)譜；故障診斷
文獻標志碼：A 文章編號：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
