Fault Diagnosis Method Based on Multi-sensor Signal Analysis
A signal analysis and fault diagnosis technology, which is applied in the testing of machines/structural components, instruments, and measuring devices, can solve problems such as incomplete diagnostic information, artificial determination of fuzzy membership functions, and lack of fault sample training for neural networks. The effect of improving sensitivity and accuracy, reducing misdiagnosis rate and missed diagnosis rate
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[0040] First, the operating parameters of the unit at a certain time, including temperature, pressure and other data, are fused into a feature vector at this time, and multiple sensor data at multiple times or multiple mechanical states are obtained to obtain the relationship between faults and symptoms. The pattern mapping process is done according to different failure symptoms. Use this as the training set of BP neural network to determine the number of input layer nodes and output layer nodes of BP neural network, and according to the empirical formula Determine the number of hidden layer nodes. Common soft faults of air source heat pump units are: refrigerant leakage, compressor discharge valve leakage, liquid pipeline obstruction, condenser fouling and evaporator fouling. First determine the input sample data and target output of the network. For the heat pump unit, there are 8 characteristic quantities: high pressure P1, low pressure P2, condensation temperature P3, e...
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