A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit
Although a number of fault diagnosis algorithms for inertial sensors have been proposed in previous decades, the performance of these algorithms needs to be improved with regard to small faults. In this paper, we introduce a data driven-based algorithm, namely, SaPD, for the anomaly detection and ou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9025030/ |
| _version_ | 1818616676085661696 |
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| author | Xiaoqiang Hu Xiaoli Zhang Xiafu Peng Delong Yang |
| author_facet | Xiaoqiang Hu Xiaoli Zhang Xiafu Peng Delong Yang |
| author_sort | Xiaoqiang Hu |
| collection | DOAJ |
| description | Although a number of fault diagnosis algorithms for inertial sensors have been proposed in previous decades, the performance of these algorithms needs to be improved with regard to small faults. In this paper, we introduce a data driven-based algorithm, namely, SaPD, for the anomaly detection and output reconstruction of a redundant inertial measurement unit (RIMU). SaPD implements the fault identification of an inertial apparatus by combining an artificial neural network with the Q contribution plots method in parity space. To improve the performance of the fault detection part, in particular for small faults, we introduce a novel hyperplane that measures the distances between inputs and the primary-neuron set obtained from a self-organizing incremental neural network (SOINN). We also employ the Q contribution plots of sensors in the fault isolation part by analyzing historical data with principal component analysis (PCA). We perform quantitative evaluations in a realistic simulation environment, which demonstrates that the proposed SaPD algorithm outperforms other related algorithms in terms of the fault identification accuracy of tiny faults with an acceptable computational complexity. |
| first_indexed | 2024-12-16T16:53:35Z |
| format | Article |
| id | doaj.art-5141fdb836b94263ab3d949a1d7107d5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:53:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5141fdb836b94263ab3d949a1d7107d52022-12-21T22:23:57ZengIEEEIEEE Access2169-35362020-01-018460814609110.1109/ACCESS.2020.29785219025030A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement UnitXiaoqiang Hu0https://orcid.org/0000-0003-3538-3761Xiaoli Zhang1https://orcid.org/0000-0002-0918-2483Xiafu Peng2https://orcid.org/0000-0003-2633-7712Delong Yang3https://orcid.org/0000-0001-8913-3886Department of Automation, School of Aeronautics and Astronautics, Xiamen University, Xiamen, ChinaDepartment of Automation, School of Aeronautics and Astronautics, Xiamen University, Xiamen, ChinaDepartment of Automation, School of Aeronautics and Astronautics, Xiamen University, Xiamen, ChinaDepartment of Automation, School of Aeronautics and Astronautics, Xiamen University, Xiamen, ChinaAlthough a number of fault diagnosis algorithms for inertial sensors have been proposed in previous decades, the performance of these algorithms needs to be improved with regard to small faults. In this paper, we introduce a data driven-based algorithm, namely, SaPD, for the anomaly detection and output reconstruction of a redundant inertial measurement unit (RIMU). SaPD implements the fault identification of an inertial apparatus by combining an artificial neural network with the Q contribution plots method in parity space. To improve the performance of the fault detection part, in particular for small faults, we introduce a novel hyperplane that measures the distances between inputs and the primary-neuron set obtained from a self-organizing incremental neural network (SOINN). We also employ the Q contribution plots of sensors in the fault isolation part by analyzing historical data with principal component analysis (PCA). We perform quantitative evaluations in a realistic simulation environment, which demonstrates that the proposed SaPD algorithm outperforms other related algorithms in terms of the fault identification accuracy of tiny faults with an acceptable computational complexity.https://ieeexplore.ieee.org/document/9025030/Inertial navigationanomaly detectionfault diagnosisartificial neural networksprincipal component analysis |
| spellingShingle | Xiaoqiang Hu Xiaoli Zhang Xiafu Peng Delong Yang A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit IEEE Access Inertial navigation anomaly detection fault diagnosis artificial neural networks principal component analysis |
| title | A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit |
| title_full | A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit |
| title_fullStr | A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit |
| title_full_unstemmed | A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit |
| title_short | A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit |
| title_sort | novel algorithm for the fault diagnosis of a redundant inertial measurement unit |
| topic | Inertial navigation anomaly detection fault diagnosis artificial neural networks principal component analysis |
| url | https://ieeexplore.ieee.org/document/9025030/ |
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