带式输送机故障准确诊断方法

针对带式输送机运行过程中的典型故障,提出了一种基于特征级与决策级的双层融合故障准确诊断方法。建立了带式输送机故障诊断信息融合模型,提取带式输送机故障信息的基本特征和小波包特征,实现特征级融合,并使用量子粒子群优化的核极限学习机与支持向量机2种分类器进行特征级的故障诊断;采用D-S证据理论将2种分类器的特征级故障诊断结果再融合,实现决策级的故障诊断。利用2种分类器的概率输出构造基本概率赋值函数,有效解决了D-S证据理论中基本概率赋值函数的构造。搭建带式输送机实验台,使用MATLAB进行实验验证,结果表明该方法的故障识别准确率可达97%,提高了故障诊断的准确度。

Accurate Diagnosis Method for Belt Conveyor Fault

Aiming at the typical faults in the operation of belt conveyors, an accurate diagnosis method for double-layer fusion faults based on feature level and decision level is proposed. A fusion model for belt conveyor fault diagnosis information is established to extract the basic characteristics of the belt conveyor fault information and the wavelet packet feature and to realize the feature level fusion. Two classifiers including of the quantum particle swarm optimization kernel limit learning machine and the support vector machine are adopted to carry out the feature-level fault diagnosis. The D-S evidence theory is used to recombine the feature-level fault diagnosis results of the two classifiers to obtain decision-level fault diagnosis. The probability outputs of the two classifiers is used to construct the basic probability assignment function, which can effectively solve the construction of the basic probability assignment function in D-S evidence theory. The belt conveyor test bench was built and verified by MATLAB. The results show that the fault identification accuracy of the method can reach 97%, which improves the accuracy of fault diagnosis.