Quantum weighted gated recurrent unit neural network and its application in performance degradation trend prediction of rotating machinery

Traditional gated recurrent unit neural network (GRUNN) generally faces the challenges of poor generalization ability and low training efficiency in performance degradation trend prediction of rotating machinery. In this paper, a novel quantum neural network called quantum weighted gated recurrent unit neural network (QWGRUNN) is proposed. Firstly, quantum bits are introduced into gated recurrent unit (GRU) to express network weights and activity values. Then, a new learning algorithm based on quantum phase-shift gate and quantum gradient descent is presented to quickly update the quantum parameters of weight-qubits and activity-qubits, thus endowing QWGRUNN with faster convergence speed, superior nonlinear approximation capability and higher computational efficiency than GRUNN. Performance degradation trend prediction for rolling bearings demonstrates that higher prediction accuracy and shorter computation time can be obtained due to the advantages of QWGRUNN in terms of convergence speed, nonlinear approximation capability and computational efficiency. It is believed that the proposed method using QWGRUNN is effective for performance degradation trend prediction of rotating machinery.