Department of Information and Electrical, Xuzhou Institute of Technology School of Information and Electrical
Engineering, China University of Mining and Technology

基于自适应神经模糊推理系统(ANFIS),以从提升机系统采集的电流信号、液压站压力信号、提升载荷、提升速度、加速度信号为输入变量,构造了矿井提升机自适应神经模糊故障诊断模型,该诊断模型以减法聚类算法为基础,通过将提升系统中机械、电气、液压等参数经过预处理后作为输入特征向量引入该诊断模型。采用从某矿主井提升机系统中采集的提升机运行数据对ANFIS进行训练,训练成功后,利用该模型成功地实现了对该提升机系统过载、重物下放以及液压站欠压等情况的故障诊断,验证了该诊断策略的有效性。

Based on adaptive neuro- fuzzy inference system, the adaptive neuro- fuzzy fault diagnosis model of mine hoist was constructed, taking current signal, p ressure signal from hydraulic station, hoist load, hoist speed and accelerated velocity signal collected from hoist system as input variable. This adaptive neuro- fuzzy fa ult diagnosis was based on subtraction clustering algorithm and parameters of machine, electric and hydraulic pressure in hoist system were inducted into adaptive neu ro- fuzzy fault diagnosis system after properly processed, as the input vectors of adaptive neuro- fuzzy fault diagnosis model. Via hoist operating data collected from ma in shaft hoist system, adaptive neuro- fuzzy fault diagnosis model was trained. After the successful training, this prototype system was adopted successfully to diagnose the situation of overloading, transferring heavy objects to a lower level and hydraulic station under pressure in hoist system, verifying the effectiveness of this diagnosis strategy.