Axial Piston Pump Fault Diagnosis Method Based on Symmetrical Polar Coordinate Image and Fuzzy C‐Means Clustering Algorithm

Abstract

In this paper, a fault diagnosis method based on symmetric polar coordinate image and Fuzzy C-Means clustering algorithm is proposed to solve the problem that the fault signal of axial piston pump is not intuitive under the time-domain waveform diagram. In this paper, the sampled vibration signals of axial piston pump were denoised firstly by the combination of ensemble empirical mode decomposition and Pearson correlation coefficient. Secondly, the data, after noise reduction, was converted into images, called snowflake images, according to symmetric polar coordinate method. Different fault types of axial piston pump can be identified by observing the snowflake images. After that, in order to evaluate the research results objectively, the obtained images were converted into Gray-Level Cooccurrence Matrixes. Their multiple eigenvalues were extracted, and the eigenvectors consisting of multiple eigenvalues were classified by Fuzzy C-Means clustering algorithm. Finally, according to the accuracy of classification results, the feasibility of applying the symmetric polar coordinate method to axial piston pump fault diagnosis has been validated.

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

“…The amplitude of signal x(t) at time i is x i , at time i + L it is x i + L. Figure 2 shows the basic schematic symmetrical polar coordinate method [26,27].…”

“…The gray level co-occurrence matrix with texture information in the spatial distribution relationship between pixels, can accurately describe the roughness, vertical direction and complexity of the image. Therefore, it is often used as a feature parameter in image analysis [27]. The gray level co-occurrence matrix is generated as follows: take one pixel A (x, y) and another pixel B (x + Dx, y + Dy) in the gray image, then make statistics on the probability of their simultaneous occurrence P(i, j, d, θ).…”

Current Status and Applications for Hydraulic Pump Fault Diagnosis: A Review

“…Yu et al [35] proposed an EWT-VCR fusion method based on EWT and VCR to deal with the nonlinear, multi-frequency, and noise data of vibration signals. Jiang et al [36] used the method of combining EEMD and PCC to denoise the collected hydraulic pump vibration signals, converted the denoised data into snowflake images by using the symmetric polar coordinate method, and converted the obtained images into gray level co-occurrence matrix, and used the fuzzy c-means algorithm for fault diagnosis. In view of the problem that the vibration signal of the hydraulic pump will be polluted by stronger Gaussian and non-Gaussian noise, Zheng et al [37] proposed using PSE to extract fault information, effectively highlighting fault features and suppressing noise pollution.…”

A Novel Fault Diagnosis Strategy for Diaphragm Pumps Based on Signal Demodulation and PCA-ResNet

“…The fuzzy information of different pump faults and operating states was analyzed, and the fuzzy relationships between different fault symptoms and events were revealed, achieving pump fault diagnosis and state recognition. Jiang et al [ 14 ] proposed a fault diagnosis method based on symmetric polar coordinate images and clustering algorithms to address the issue of nonintuitive time-domain spectral features of pump fault signals (artificial intelligence method). The effectiveness of the diagnosis method was verified through fault classification results.…”