Reduced kernel recursive least squares algorithm for aero-engine degradation prediction
•The novel method considers all the constraints generated by the whole training set.•The redundant data is used to modify the coefficients related to each kernel unit.•The structure update and coefficient adjustment are carried out independently.•A more compact network without accuracy deterioration...
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| Published in: | Mechanical systems and signal processing Vol. 95; pp. 446 - 467 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.10.2017
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| ISSN: | 0888-3270, 1096-1216 |
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| Abstract | •The novel method considers all the constraints generated by the whole training set.•The redundant data is used to modify the coefficients related to each kernel unit.•The structure update and coefficient adjustment are carried out independently.•A more compact network without accuracy deterioration is obtained.•A novel prognostic model based on KAF and HMM is developed.
Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach. |
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| AbstractList | •The novel method considers all the constraints generated by the whole training set.•The redundant data is used to modify the coefficients related to each kernel unit.•The structure update and coefficient adjustment are carried out independently.•A more compact network without accuracy deterioration is obtained.•A novel prognostic model based on KAF and HMM is developed.
Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach. Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach. |
| Author | Zhou, Haowen Huang, Jinquan Lu, Feng |
| Author_xml | – sequence: 1 givenname: Haowen surname: Zhou fullname: Zhou, Haowen email: zhouhaowen@nuaa.edu.cn organization: Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China – sequence: 2 givenname: Jinquan surname: Huang fullname: Huang, Jinquan organization: Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China – sequence: 3 givenname: Feng surname: Lu fullname: Lu, Feng organization: Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Keywords | Reduced technique Aero-engine Remaining useful life Kernel recursive least squares Prognostics Sparse kernel method |
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| Snippet | •The novel method considers all the constraints generated by the whole training set.•The redundant data is used to modify the coefficients related to each... Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness.... |
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| SubjectTerms | Accuracy Adaptive algorithms Adaptive filters Aero-engine Basis functions Degradation Kernel recursive least squares Least squares method Markov chains Mathematical models Prognostics Radial basis function Recursive algorithms Reduced technique Redundancy Remaining useful life Signal processing Sparse kernel method Time series Training |
| Title | Reduced kernel recursive least squares algorithm for aero-engine degradation prediction |
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