Intelligent Fault Diagnosis for BLDC With Incorporating Accuracy and False Negative Rate in Feature Selection Optimization

Early fault diagnosis is essential for the proper operation of rotating machines. This article proposes a fitness function in differential evolution (DE) that considers accuracy rate and false negative rate for optimization in brushless DC (BLDC) motor fault diagnosis. Feature selection based on a distance discriminant (FSDD) calculates the feature factors which base on the category separability of features after the Hilbert–Huang transform (HHT) which extracts the features of four different type signals from BLDC motor Hall sensor. The feature rank through DE to optimize before the features into the backpropagation neural network (BPNN) in order. By reducing the feature number of Hall signal and decreasing the complexity of neural network input, the combined method was proposed in this article can significantly reduce the calculation cost. Finally, the identification model obtained an accuracy rate of 98.98% and false negative rate of 13.66% when there were 18 features; besides, receiver operating characteristic (ROC) curve and probability curve have been evidenced the number of false negative is decreased. Moreover, the experiments have verified that the proposed method is effective in UCI data set.