Quantum Gene Chain Coding Bidirectional Neural Network for Residual Useful Life Prediction of Rotating Machinery

Abstract

In classical recurrent neural networks, the pre- and post-relationships of time series tend to be neglected so that long-term overall memory is generally inaccessible; meanwhile, the weights are transferred and updated mainly by the gradient descent method, which leads to their low prediction accuracy and high computation cost in the application of residual useful life (RUL) prediction of rotating machinery (RM). In view of this, a quantum gene chain coding bidirectional neural network (QGCCBNN) is proposed to predict RUL of RM in this paper. In our proposed QGCCBNN, the quantum bidirectional transmission mechanism is designed to establish the pre- and post-relationships of time series for readjusting the weight parameters according to the feedback from the output layer, so that higher consistency between the input information and the overall memory of the network can be realized, thus endowing QGCCBNN with better nonlinear approximation ability. Moreover, in order to improve the global optimization ability and convergence speed, the quantum gene chain coding instead of gradient descent method is constructed to transmit and update data, in which the qubit probability amplitude real number coding is adopted and the cosine and sinusoidal qubit probability amplitudes corresponding to the minimum loss function are compared with those of the current time by the phase selection matrix for the directional parallel updating of the weight parameters. On this basis, a new RUL prediction method for RM is proposed, and higher prediction accuracy as well as desirable efficiency can be obtained due to the advantages of QGCCBNN in nonlinear approximation ability and convergence speed. The experimental example for RUL prediction of a double-row roller bearing demonstrates the effectiveness of our proposed method.