Quantum recurrent encoder–decoder neural network for performance trend prediction of rotating machinery

Traditional neural networks generally neglect the primary and secondary relationships of input information and process the information indiscriminately, which leads to their bad nonlinear approximation capacity and low generalization ability. As a result, traditional neural networks always show poor...

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Vydáno v:Knowledge-based systems Ročník 197; s. 105863
Hlavní autoři: Chen, Yong, Li, Feng, Wang, Jiaxu, Tang, Baoping, Zhou, Xueming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 07.06.2020
Elsevier Science Ltd
Témata:
Acceleration
Algorithms
Approximation
Artificial intelligence
Attention
Attention mechanism
Coders
Degradation
Entropy
Information
Machinery
Mathematical analysis
Matrices
Networks
Neural networks
Noise reduction
Performance degradation
Performance prediction
Performance trend prediction
Predictions
Quantum neuron
Quantum recurrent encoder–decoder neural network (QREDNN)
Recurrent
Roller bearings
Rotating machinery
Rotation
Solution space
Trends
Attention mechanism
Performance trend prediction
Rotating machinery
Quantum neuron
Artificial intelligence
Quantum recurrent encoder–decoder neural network (QREDNN)
ISSN:0950-7051, 1872-7409
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Shrnutí:Traditional neural networks generally neglect the primary and secondary relationships of input information and process the information indiscriminately, which leads to their bad nonlinear approximation capacity and low generalization ability. As a result, traditional neural networks always show poor prediction accuracy in the performance degradation trend prediction of rotating machinery (RM). In view of this, a novel neural network called quantum recurrent encoder–decoder neural network (QREDNN) is proposed in this paper. In QREDNN, the attention mechanism is used to simultaneously reconstruct encoder and decoder of QREDNN, so that QREDNN can fully excavate and pay attention to important information but suppress the interference of redundant information to obtain better nonlinear approximation capacity. On the other hand, the quantum neuron is used to construct a new quantum gated recurrent unit (QGRU) in which activation values and weights are represented by quantum rotation matrices. The QGRU can traverse the solution space more finely and has a lot of multiple attractors, so it can replace the traditional recurrent unit of the encoder and decoder and enhance the generalization ability and response speed of QREDNN. Moreover, the Levenberg–Marquardt (LM) algorithm is introduced to improve the update speeds of the rotation angles of quantum rotation matrices and the attention parameters of QREDNN. Based on the superiorities of QREDNN, a new performance trend prediction method for RM is proposed, in which the denoised fuzzy entropy (DFE) of vibration acceleration signal of RM is input into QREDNN as the performance degradation feature for predicting the performance degradation trend of RM. The examples of predicting the performance trend of rolling bearings demonstrate the effectiveness of our proposed method.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.105863