風機軸承故障診斷中的振動信號特征提取方法研究

隨著風電產業的快速發展以及對風機系統高可靠性、易維護性等的各方面要求，風機狀態監測與故障診斷技術引起了學術界和工業界的廣泛關注。軸承作為風機機械傳動系統和發電機系統的核心部件，其運行狀態的實時監測和準確分析，對整個風機的故障診斷和運行維護均具有重要的意義。本文針對風機軸承故障診斷中的振動信號特征提取問題展開研究，運用局部均值分解(Local Mean Decomposition, LMD)瞬態信號分解技術、信息熵和非線性動力學參數分析，分別從瞬態特征描述和非線性特征分析兩個角度，對風機軸承振動信號特征提取方法進行理論研究和實驗驗證，為軸承狀態監測和故障診斷提供了有效的理論方法。論文主要工作如下：
(1)  在分析風機軸承運行特點、故障機理及其振動故障特征的基礎上，針對復雜工況下風機軸承振動信號非平穩、非線性特征難以提取及量化問題，研究基于 LMD的瞬態信號分解技術和基于信息熵的信號特征定量描述方法，實現風機軸承振動信號特征的有效提取和準確描述。
(2)  研究基于 LMD 和信息熵的 Wigner-Ville 譜熵的振動信號瞬態能量特征提取方法，用于定量刻畫軸承不同狀態下振動信號的時頻能量分布的規律，并設計基于LS-SVM 的智能故障診斷模型，實現軸承狀態和故障類型的自動分類與識別。仿真分析和風機軸承診斷實驗驗證了該方法和模型較好的特征提取與故障診斷效果。
(3)  從非線性動力學角度出發，提出基于 LMD 的多尺度排序熵的軸承振動信號非線性特征提取方法，有效刻畫軸承振動信號的非線性復雜度特征，實現了軸承內不同故障程度的有效識別。仿真分析和風機軸承故障診斷實驗驗證了該方法的有效性。 
關鍵詞：振動信號；特征提取；風機軸承；局部均值分解；排序熵；故障診斷

石家莊風機廠石家莊風機石家莊風機銷售石家莊風機維修

Abstract
With  the  rapid  development  of  wind  power  industry,  the  reliability  and maintainability of the wind turbine system are very urgent. Condition monitoring and fault diagnosis technology of wind turbine system cause the extensive concern of the academia and  industry.  Bearings  are  as  the  core  components  of  wind  turbine  mechanical transmission  system  and  generator  system,  there  has  a  realistic  significance  to  make  a condition monitoring and fault diagnosis to them. In this paper, aimed at feature extraction methods  of  wind  turbine  bearing  diagnosis, applied  Local  Mean  Decomposition  (LMD), Shannon  entropy  and  nonlinear  dynamic  parameters,  in  view  of  the  transient characteristics description and nonlinear feature analysis research, the proposed methods are verified by simulation experiment and experimental platform. The proposed methods provide a solution for wind turbine bearing condition monitoring and fault diagnosis. The specific research ways are as follows: Firstly, the wind turbine bearing operation characteristics and the failure mechanism are  discussed,  besides,  aiming  at  nonstationary  and  nonlinear  characteristics  of  bearings, the transient signal decomposition technique based on LMD are studied; the quantitative description method based on information entropy are analyzed. Both of that is in order toeffective extraction and accurate description of wind turbine bearing vibratory signals.Secondly,  a  transient  characteristic  extraction  method  based  on  LMD  and Wigner-Ville  spectral  entropy  is  proposed,  in  order  to  quantitatively  describe  thetime-frequency energy distribution of bearing vibratory signals under different condition. After  that,  a  intelligent  fault  diagnosis  model  based  on  LS-SVM  is  used  for  automaticclassification  and  recognition  of  bearing  faults.  Simulation  experiment  and  experimental platform verified the proposed method and diagnosis model. Finally, in view of nonlinear dynamics, a nonlinear feature extraction method named a multi-scale permutation entropy based on LMD is proposed. The proposed method can effectively represent nonlinear complexity characteristics of bearing vibratory signals and identify  different  fault  degree  of  bearing.  Simulation  experiment  and  experimental  III platform verified the proposed method.
Keywords:  vibratory  signals;  feature  extraction;  wind  turbine  bearings;  local  mean decomposition (LMD); permutation entropy; fault diagnosis