Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment

Shaojiang Dong,Juan Liu,Xiangyang Xu,Baoping Tang,Lili Chen,Zhengyuan Gao

doi:10.1155/2015/893504

Abstract

In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm (PSO) and the nonlinear manifold learning algorithm local tangent space alignment (LTSA) is proposed. Firstly, the signal is purified by the morphological filter; the filter’s structure element (SE) is selected by PSO method. Then the filtered signals are decomposed by the empirical mode decomposition (EMD) method, and the extract features are mapped into the LTSA to extract the character features; then the support vector machine (SVM) model is used to achieve the rotating machine fault diagnosis. The proposed method is evaluated by vibration signals measured from bearings with faults. Results show that the method can effectively remove the noise and extract the fault features, so the rotating machine fault diagnosis can be achieved effectively.

Highlights

Rotating machinery is widely used in industry
We should take three main steps: (1) the collection of the fault signals of the machine, (2) the extraction of the fault features though signal processing methods, and (3) condition identification and fault diagnosis
The vibration signals, collected by a sensor, are often severely polluted by various noises, for example, the background noise presented in the measurement device, and the interfering vibrations generated by other mechanical components which are of no significance for condition monitoring [2]

Summary

Introduction

Rotating machinery is widely used in industry. Unexpected machine faults could cause unscheduled downtime and loss. This paper uses the morphological filter to remove the noise [4]. It can decompose the original signal into several physical parts according to certain geometric characteristics. The people who first introduced morphological filter into fault diagnosis of rotating machinery were Nishida et al [5]. The EMD method is used to decompose the signal and the Shannon entropy of the intrinsic mode function (IMF) is used to extract the features; the extracted features remained high-dimensional and has the characteristic of nonlinear, so we need to reduce the dimensionality in order to extract the feature and diagnose the fault of the rotating machine [6].

Optimized Morphological Filter for Noise Removing

Basic Concepts of Local Tangent Space Alignment

Experimental Validation

Methods