Quantum neural network fault diagnosis technology for coal mine rotating electromechanical equipment

In view of problem of mutual interference of failure mode for rotating electromechanical equipment in coal mine, a quantum neural network fault diagnosis algorithm was proposed based on quantum neural network theory, a quantum neural network fault diagnosis model with three-layer was constructed by using the phase-shift gate and controlled-not gate of quantum theory. A gradient descent algorithm was taken as learning algorithm for the model which was applied to recognize the fault diagnosis of scraper conveyor reducer. The preliminary research results show that the algorithm is feasible and helpful to improve fault diagnosis rate of rotating electromechanical equipment used in coal mine.