In order to simplify the structure of model and enhance the performance of pattern recognition, the net model of restricted Boltzmann machine based on quantum computation (QRBM) is proposed. In QRBM network, based on the net structure of RBM and quantum computation, firstly, the data is coded with quantum states. Then, by quantum operation, weight matrix is created for simplifying computation step and enhancing computation efficiency. After that, the number of net layer is confirmed to improve accuracy and shorten execution time. Finally, the parameters in the model are updated. The method is applied in gear fault diagnosis. The original feature is comprised of data which are extracted form vibration signal of gear box with normal states, wearing, crack and broken. QRBM is used for diagnosis with feature set. The results indicated that, compared with neural network, SVM and RBM network, QRBM has better performance in classification accuracy and execution time, which has proved the efficient and feasibility.
Key words
Quantum computation /
Restricted Boltzmann Machine (RBM) /
neural network /
gear /
pattern recognition
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