一种带式输送机故障诊断方法

针对传统浅层神经网络用于带式输送机故障诊断时存在故障状态样本数据不足、准确率不高等问题,提出了一种基于合成少数类过采样技术(SMOTE)和深度置信网络(DBN)的带式输送机故障诊断方法.该方法利用SMOTE生成带式输送机故障状态样本数据,克服样本数据分布不平衡现象;将样本数据输入DBN,利用无监督逐层训练方式提取数据中的故障特征,并通过有监督微调来优化故障诊断能力,实现带式输送机故障精确诊断.仿真结果表明,该方法提高了带式输送机故障诊断准确率.

A fault diagnosis method of belt conveyor

Aiming at problems of insufficient fault state sample data and low accuracy in fault diagnosis of belt conveyor by traditional shallow neural network, a fault diagnosis method of belt conveyor based on synthetic minority oversampling technique( SMOTE) and deep belief network( DBN) was proposed. Fault state sample data of belt conveyor is generated by SMOTE to overcome imbalance distribution of the sample data. The sample data is input into DBN, fault features in the data are extracted by means of unsupervised layer-by-layer training, and fault diagnosis ability is optimized by means of supervised fine-tuning to achieve accurate fault diagnosis of belt conveyor.The simulation results show that the method improves fault diagnosis accuracy of belt conveyor.