Abstract
The safe and stable operation of roadheader is of great significance to the efficient and rapid production of a coal mine. Health diagnosis based on vibration signals has been studied in bearings and motors. Complex geological conditions and bad working environment lead to the characteristics of nonlinear and time-varying vibration signals of a roadheader. In this paper, a health state analysis method based on reference manifold (RM) learning and improved K-means clustering analysis was proposed; the method was verified by using the real-time collected roadheader cutting reducer fault signal. Firstly, the comparison signal and analysis signal were extracted from the actual collected vibration data of the roadheader, and the referential analysis samples were constructed through time domain and wavelet packet energy analysis. Then, the characteristic structure of the low-dimensional space of the referential analysis samples is obtained by Locally Linear Embedding (LLE), which is a method of manifold learning. Through the improved K-means clustering analysis method, the low-dimensional structure parameters were analyzed and the clustering effect index was obtained, which was used as the health evaluation index (HEI). Finally, the normal distribution model of the health evaluation index is established, and the confidence interval of the health evaluation index is determined, so as to realize the health state analysis of the roadheader and realize the fault warning function. Through the analysis of data of three sensors, the results show that the roadheader failed on the 15th day, which is consistent with the actual working condition. Through practical analysis, the effectiveness of the method was verified and provided a kind of fault analysis idea and method for equipment working under complex working conditions and the theoretical basis for fault type analysis.
Highlights
In the past 20 years of coal mining in China, the development of roadheader has experienced the introduction of foreign technology, the self-development of small roadheader, the application of high-power multiauxiliary function roadheader, and the research and development of intelligent roadheader [1, 2]
Piotr Cheluszka designed a method for the vibration identification of tunneling machine cutting head using a high-speed camera. e effectiveness of this method is verified by the data collection [5]
Qiang Liu used four machine learning tools to analyze the vibration data of the tunneling machine and complete the incipient fault detection and identification [9]
Summary
In the past 20 years of coal mining in China, the development of roadheader has experienced the introduction of foreign technology, the self-development of small roadheader, the application of high-power multiauxiliary function roadheader, and the research and development of intelligent roadheader [1, 2]. E analysis of the vibration signal of the roadheader is carried out on the basis of theoretical analysis and simulation test It provides some theoretical support for the fault analysis of the roadheader, it lacks the actual data of tunneling operation as support. In order to deeply understand the relationship between the vibration and running state of the roadheader, the problem of resource waste caused by excessive maintenance in the actual production of the roadheader was solved, the lowproduction efficiency caused by faults was reduced, and a feasible fault state evaluation method of the roadheader was explored; in this paper, health vibration data and analysis data are fused, and the relationship between the health data and the analysis data is compared through reference manifold learning, and the structural characteristic parameters of the samples in the low-dimensional space are mapped out. The normal distribution model of the evaluation index was established by using the Gaussian model, and the confidence interval of the evaluation index is determined
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