A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

Abstract

Mathematical morphology (MM) is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE). Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.

Wavelet and Mathematical Morphology as the de-noising methods for PD analysis of high voltage transformer windings

“…Although the WT is considered as a very good noise reduction method, it is still accompanied with some interference and distortion [7]. In this paper, MM has also been implemented to compare the performance of noise reduction of both approaches.…”

Notice of Removal: Morphology based radon processed neural network for transmission line fault detection

“…In [27][28][29] the authors have used mathematical morphology for fault diagnosis in mechanical applications, by extracting noise components in time domain fault signals. Morphology is used in power system fault detection in [30][31].This works especially [31] are recent attempts in employing morphological operations in different types of protection in power system.…”

Fault Diagnosis of Rolling Element Bearing Using an Adaptive Multiscale Enhanced Combination Gradient Morphological Filter

“…For SE, it consists mainly of three parts: length, height, and shape. Some scholars have already researched [11,12] the height and shape of SE have less effect on the filtering results. erefore, this paper will further study the performance of morphological filtering from the two aspects of the length of SE and the morphology operator.…”