Study of pre-processing model of coal-mine hoist wire-rope fatigue damage signal

• 作 者: Tian Jie Wang Hongyao Zhou Junying Meng Guoying
• 机 构: School of Mechanical, Electronic, and Information Engineering, China University of Mining and Techno
• 出 处: International Journal of Mining Science and Technology
• 发表日期: 2015年06期

In this paper, we propose a pre-processing method for the detection of wire-rope signals. This is necessary because of the lack of processing methods that are currently employed. First, we investigated the one-dimensional discrete morphological and wavelet transform. Then, we developed a pre-processing model that is based on the morphological wavelet-filtering algorithm. We then proposed a modified morphology filtering algorithm. We also designed an experiment platform for wire-rope detection.Eight levels of localized flaws(LFs) and damage were formed in the wire-rope specimen. We performed a series of experimental studies, and the results show that the proposed method can effectively filter the drift signal. The signal-to-noise ratio of the new filtering algorithm was over 26 d B. The signal-to-noise ratio of the existing method is less than 15 d B, and the noise-signal ratio of the new filtering algorithm has improved by 73%. Based on our results, the filtering effect of the proposed method is better than that of the present method. This study has great significance and practical value in engineering applications.

In this paper, we propose a pre-processing method for the detection of wire-rope signals. This is necessary because of lack of processing methods that are currently employed. First, we investigated the one-dimensional discrete morphological and wavelet transform. Then , we developed a pre-processing model that is based on the morphological wavelet-filtering algorithm. We then designed an experiment platform for wire-rope detection. Light levels of localized flaws (LFs) and damage were formed in the wire-rope specimen. We performed a series of experimental studies, and the results show that the proposed method could effectively filter the drift signal. The signal-to-noise ratio of the new filtering algorithm was over 26 d B. The signal-to-noise ratio of the existing method is less than 15 d B, and the noise-signal ratio of the new filtering algorithm has improved by 73%. Based on our results, the filtering effect of the proposed method is better than that of the present method. This study has great significance and practical value in engineering applications.