Fault prediction method based on wavelet neural network and hidden Markov model

The invention discloses a fault prediction method based on a wavelet neural network and a hidden Markov model. The fault prediction method comprises the following steps: 1, inputting a sample; 2, carrying out data dimension reduction on the sample data by utilizing a wavelet neural network, updating the weights and bias values of the input layer to the hidden layer and from the hidden layer to the output layer, returning to 1 if the difference value between the value of the output layer and the value of the input layer exceeds a threshold value, otherwise, turning to 3; 3, outputting a wavelet neural network model; 4, initializing a hidden Markov model; 5, adopting different samples and replacing sample data with hidden layer neuron values of the wavelet neural network; 6, establishing a hidden Markov model; 7 updating a hidden Markov model parameters using a forward-backward algorithm to calculate the conditional probability 8, if the calculated conditional probability converges, turning to 9, otherwise, ret