Abstract: In view of the shortcomings such as poor prediction accuracy and lowcomputational efficiency of the existing prediction methodsbased on artificial intelligence in state degradation trend prediction of rotating machinery,a novel state degradation trend prediction method is proposed based on double hidden layer quantum circuit recurrent unit neural network (DHL-QCRUNN). In this method, a normalized permutation entropy error feature set is firstly constructed, and then this feature set is input into DHL-QCRUNN to accomplish state degradation trend prediction of rotating machinery. In the proposed DHL-QCRUNN, a double hidden layer structure is designed to raise the network nonlinear mapping ability; quantum phase-shift gates and multi-qubits controlled NOT gates are introduced to this network for information transfer; the overall memory of input sequences can be obtained by the quantum feedback mechanism in double hidden layers; moreover, the final output is described by the probability amplitudes of excited states in output layer. Therefore, the nonlinear approximation capability and generalization property of network are improved, and then the higher prediction accuracy of the proposed state degradation trend prediction method based on DHL-QCRUNN is obtainedforrotating machinery. Besides, the network parameters can be renewed by the quantum Levenberg-Marquardt (LM) algorithm to improve convergence speed of DHL-QCRUNN,accordingly, higher computational efficiency can be obtained forthe proposed trend prediction method.The example of state degradation trend prediction for rolling bearing demonstrates the effectiveness of the proposed method. A novel state degradation trend prediction method is proposed based on DHL-QCRUNN for rotating machinery, whichownshigher prediction accuracy and higher computational efficiency.

Key words: double hidden layer quantum circuit recurrent unit neural network, permutation entropy error, quantum computation, rotating machinery, trend prediction