Abstract
In order to accurately identify the pipeline leak fault of a mine air compressor, a novel intelligent diagnosis method is presented based on the integration of an adaptive wavelet threshold denoising (WTD) algorithm, improved firefly algorithm (IFA), Otsu-Grabcut image segmentation algorithm, histogram of oriented gradient (HOG), gray-level co-occurrence matrix (GLCM) and support vector machine (SVM). In the proposed method, the adaptive step strategy and local optimal firefly self-search strategy for the basic firefly algorithm (FA) are used to improve the optimization effect. The infrared thermal image is denoised by using wavelet threshold algorithm which is optimized by IFA (WTD-IFA). The Otsu-Grabcut algorithm is used to segment the image and extract the target. The HOG and GLCM are calculated to reveal the intrinsic characteristics of the infrared thermal image to extract feature vectors. Then the IFA is utilized to optimize the parameters of SVM so as to construct an optimal classifier for fault diagnosis. Finally, the proposed fault diagnosis method is fully evaluated by experimentation and the results verify its feasibility and superiority.
Highlights
Mine air compressors are important equipment for guaranteeing safety
In order to solve the problem of early convergence and local extremum, this paper proposes an improved firefly algorithm (IFA)
A novel intelligent diagnosis method based on the integration of IFA, the adaptive wavelet threshold denoising algorithm, Otsu-Grabcut image segmentation algorithm, histogram of oriented gradient (HOG)-gray-level co-occurrence matrix (GLCM)
Summary
Mine air compressors are important equipment for guaranteeing safety. On one hand, compressed air provides power for wind hammers, air pumps and other mine pneumatic equipment [1]. Due to the high-pressure characteristics of compressed air, compressed air often leaks from the gas delivery pipeline during operation. There is a serious loss of compressed air during production and utilization, which accounts for about 25% to 30% of the total demand for compressed air [3]. Leakage is the most obvious and important reason for the loss of compressed air, which can reach 20% to 40% of the total gas usage [5]. If the compressed air leaks in a very short time, it will cause huge economic losses, and cause equipment damage and endanger personal safety. The detection efficiency is very low and the leak fault cannot be found in time
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