Research on shearer performance degradation evaluation and applicatio
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摘要: 准确判别采煤机各部件的磨损和失效情况,可为实现采煤机故障及相关事故的预防、预警提供必要支撑,采煤机性能退化评估是判别采煤机磨损和失效情况的一种有效途径。针对采煤机性能退化过程的非线性,提出了一种基于人工智能的采煤机性能退化评估方法。选取出采煤机的工况监测参数和性能监测参数,通过极限学习机方法进行采煤机工况识别;通过主成分分析方法对性能监测参数进行降维,并建立各工况下的基准高斯混合模型;选取相对熵来度量某时刻高斯混合模型与基准高斯混合模型的差异,从而度量采煤机各部件性能退化趋势。提出可从地质条件、环境因素、振动及载荷、机身倾斜等方面来选取工况监测参数,并根据实际应用中数据的可获得性和变动情况等来确定。提出了采煤机性能监测参数选取原则,可在常见的机电设备监测参数分类基础上,结合实际采煤机传感器的装配情况选定性能监测参数。以采煤机故障发生率最高的截割部为例进行案例分析,将采煤机截割部的工况分为高速直切、高速斜切、低速直切和低速斜切4种,选取牵引速度等作为工况监测参数,选取左截割电动机电流等作为性能监测参数,并通过相关性分析验证参数的合理性。分析结果表明,通过对比高斯混合模型能够判断采煤机性能退化状况,通过相对熵实现了对每个监测点采煤机截割部性能退化趋势的度量。Abstract: Accurate identification of the wear and failure of shearer components can provide the necessary support for the prevention and early warning of shearer failures and related accidents, and shearer performance degradation evaluation is an effective way to identify the wear and failure of shearer. For the non-linearity of shearer performance degradation process, an artificial intelligence-based shearer performance degradation evaluation method is proposed. The working condition monitoring parameters and performance monitoring parameters of the shearer are achieved, and the working condition of the shearer is identified by applying the extreme learning machine method. The performance monitoring parameters are dimension reduced by using the principal component analysis method, and the benchmark Gaussian mixture model under each working condition is established. The relative entropy is used to measure the difference between the Gaussian mixture model and the benchmark Gaussian mixture model at a certain moment, so as to measure the performance degradation trend of each component of the shearer. It is proposed that the performance monitoring parameters can be obtained from geological conditions, environmental factors, vibration and load, shearer tilt, etc. The parameters can be obtained according to the availability of data and changes in practical applications. The principles of selecting shearer performance monitoring parameters are proposed, and the performance monitoring parameters can be chosen based on the classification of common electromechanical equipment monitoring parameters and the actual assembly condition of the shearer sensor. A case study is carried out by analyzing the performance of shearer cutting part, which is the part with the highest fault rate. The working conditions of the cutting part of the shearer are divided into four types: high-speed straight cutting, high-speed oblique cutting, low-speed straight cutting and low-speed oblique cutting. The traction speed is used as the working condition monitoring parameter, and the left cutting motor current is used as the performance monitoring parameter. The rationality of the parameters is verified by correlation analysis. The analysis results show that the performance degradation status of the shearer can be obtained by comparing Gaussian mixture model, and the performance degradation trend of the shearer cutting part at each monitoring point can be measured through the relative entropy.
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