Fault diagnosis of underground water pump based on fuzzy Petri net and condition monitoring
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摘要: 为了快速找到井下水泵故障的原因，建立了一种基于模糊Petri网和状态监测的井下水泵故障诊断模型。首先通过井下排水设备状态监测系统测得水泵故障的振动信号，经过振动分析后，对获得的水泵故障样本进行学习训练；然后在水泵故障诊断的模糊Petri网模型结构上，引入神经网络中的BP算法对权值、阈值和置信度等参数进行网络优化训练。实例分析结果表明,该模型能较准确地找到水泵故障原因，具有较好的准确性、快速性和适应性。Abstract: In order to rapidly find out causes of failure of underground water pump, a fault diagnosis model of underground water pump based on fuzzy Petri net and condition monitoring was established. Firstly, vibration signal of the water pump was measured by the condition monitoring system of underground drainage equipment, training was carried out on the water pump fault samples after vibration analysis. Then, on the structure of fuzzy Petri net model of water pump fault diagnosis, BP algorithm of neural network was introduced to train parameters such as weight values, threshold values and credibility. The results of instances analysis show that the model can be used to find out the causes of pump failure accurately, and has good accuracy, rapidity and adaptability.
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Keywords:
- water pump /
- condition monitoring /
- fault diagnosis /
- fuzzy Petri net /
- BP algorithm
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