Abstract
Monitoring the operational state of sensors promptly and the accurate diagnosis of faults are essential. This paper proposes an improved fault diagnosis scheme for sensors, which includes both fault detection and fault identification. Firstly, trend analysis combined with least squares support vector machine (TA-LSSVM) method is proposed and implemented to detect faults. Secondly, an improved error correcting output coding-support vector machine (ECOC-SVM) based fault identification method is proposed to distinguish different sensor failure modes. To demonstrate the effectiveness of the proposed scheme, experiments are conducted with an MTi-series sensor, and some comparisons are made with other fault identification methods. The experimental results demonstrate that the proposed fault diagnosis scheme offers an essential improvement with detection real-time property and better identification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ding S X, Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, Springer, Berlin, 2013.
Yin S, Zhu X, Qiu J, et al., State estimation in nonlinear system using sequential evolutionary filter. IEEE Transactions on Industrial Electronics, 2016, 63: 3786–3794.
Yin S, Gao H, Qiu J, et al., Fault detection for nonlinear process with deterministic disturbances: A just-in-time learning based data driven method. IEEE Transactions on Cybernetics, 2016, DOI: 10.1109/TCYB.2016.2574754.
Wang H, Chai T, Ding J, et al., Data driven fault diagnosis and fault tolerant control: Some advances and possible new directions. Acta Automatica Sinica, 2009, 35: 739–747.
Yin S, Gao H, Qiu J, et al., Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults. Automatica, 2016, 76: 282–292.
Maurya M R, Paritosh P K, Rengaswamy R, et al., A framework for on-line trend extraction and fault diagnosis. Engineering Applications of Artificial Intelligence, 2010, 23: 950–960.
Moura M D C, Zio E, Lins I D, et al., Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety, 2011, 96: 1527–1534.
Vong C M and Wong P K, Engine ignition signal diagnosis with wavelet packet transform and multi-class least squares support vector machines. Expert Systems with Applications, 2011, 38: 8563–8570.
Li J and Wang J, Research of temperature predictive control based on LSSVM optimized by improved PSO for thick steel plate Roller hearth normalizing furnace. Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA), Jinan, 2010, 3717–3721.
Jolliffe I T, Principal Component Analysis. Springer, Berlin, 1986, 87: 41–64.
Chung S, Park T S, Park S H, et al., Colorimetric sensor array for white wine tasting. Sensors, 2015, 15: 18197–18208.
Acquah G E, Via B K, Billor N, et al., Identifying plant part composition of forest logging residue using infrared spectral data and linear discriminant analysis. Sensors, 2016, 16(9): 1375.
Jin J and Cui H, Discriminant analysis based on statistical depth. Jounal of Systems Science and Complexity, 2010, 23(2): 362–371.
Schölkopf B, Smola A, and Müller K, Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1998, 10: 1299–1319.
Li J, Pan L, Chen M, et al., Parametric and non-parametric combination model to enhance overall performance on default prediction. Jounal of Systems Science and Complexity, 2014, 27(5): 950–969.
Baudat G and Anouar F, Generalized discriminant analysis using a kernel approach. Neural Computation, 1990, 12: 2385–2404.
Mercorelli P, Denoising and harmonic detection using nonorthogonal wavelet packets in industrial applications. Jounal of Systems Science and Complexity, 2007, 20(2): 325–343.
Feng Z, Wang Q, Xu T, et al., Sensor fault diagnosis based on wavelet packet and support vector machines. Journal of Nanjing University of Science & Technology, 2008, 32: 609–614.
Deng F, Guo S, Zhou R, et al., Sensor multifault diagnosis with improved support vector machines. IEEE Transactions on Automation Science & Engineering, 2017, 14(2): 1053–1063.
Cortes C and Vapnik V, Support-vector networks. Machine Learning, 1995, 20: 273–297.
Jankowski N and Grabczewski K, Learning machines. Studies in Fuzziness & Soft Computing, 2008, 207: 29–64.
Allwein E L, Schapire R E, and Singer Y, Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 2011, 1: 125–126.
Escalera S, Pujol O, and Radeva P, On the decoding process in ternary error-correcting output codes. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2010, 32: 120–134.
Zhou R, Chen J, and Deng F, Sensor fault identification based on error-correcting output codes method. Proceedings of 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and Robotics, Automation and Mechatronics (RAM), Angkor Wat, 2015, 131–136.
Author information
Authors and Affiliations
Corresponding author
Additional information
This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 61304254, 61321002 and 61120106010, and in part by the Key Exploration Project under Grant No. 7131253.
This paper was recommended for publication by Editor SUN Jian.
Rights and permissions
About this article
Cite this article
Gu, X., Deng, F., Gao, X. et al. An Improved Sensor Fault Diagnosis Scheme Based on TA-LSSVM and ECOC-SVM. J Syst Sci Complex 31, 372–384 (2018). https://doi.org/10.1007/s11424-017-6232-3
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11424-017-6232-3