基于改进型ANFIS的矿用空压机故障诊断系统

Fault diagnosis system of mine air compressor based on improved ANFIS

考虑到矿用空压机在长期运行过程中容易由多种因素复合共同作用而出现各种故障,且产生故障的原因和故障之间表现出非线性关系难以用数学模型表达等问题,建立基于改进型自适应神经模糊推理系统的故障诊断系统。该系统采用附加动量算法不断修正自适应神经模糊推理系统中的前题参数以避免采用梯度下降算法时易陷入局部极小,训练速度较慢等缺点,提高系统的忽略网络中微小变化的能力。为了验证该故障诊断系统的性能,将其与基于BP神经网络的故障诊断系统相比较。分析与实验结果表明,改进型ANFIS模型的诊断输出与实际情况完全相符,最大误差为13.7%,最小误差为0.17%,其诊断准确度达到95.85%,在训练速度、误差精度以及收敛性等方面,其性能优于BP神经网络。

Considering the problem that various faults have happened frequently by a variety of factors combined ac-tion in the long term operation of mine air compressor and it is difficult to use mathematical model to express the non-linear relation between reasons and faults, a fault diagnosis system based on improved ANFIS is established.This sys-tem uses the additional momentum method to constantly modify premise parameter in ANFIS in order to avoid the shortcoming that it easy to fall into local minimum point and slow training speed when using the gradient descent algo-rithm, and improve the capacity of ignoring tiny changes in the network.In order to verify the performance of the fault diagnosis system, it is compared with the fault diagnosis system based on BP neural network.The analysis and experimental results show that the improved ANFIS model diagnostic output is fully consistent with the actual situa-tion.Its maximum error and minimum error is 13.7% and 0.17%, and the diagnostic accuracy has reached 95.85%.Its performance is better than BP neural network in the terms of training speed, accuracy, convergence and so on.