Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System

Hongfeng Tao; Dapeng Chen; Huizhong Yang

doi:10.1109/JAS.2016.7510052

Volume 4 Issue 3

Jul. 2017

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(3): 534-542

Hongfeng Tao, Dapeng Chen and Huizhong Yang, "Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 534-542, July 2017. doi: 10.1109/JAS.2016.7510052

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Hongfeng Tao, Dapeng Chen and Huizhong Yang, "Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 534-542, July 2017. doi: 10.1109/JAS.2016.7510052

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Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System

doi: 10.1109/JAS.2016.7510052

Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China

Funds:

the National Natural Science Foundation of China 61273070

the National Natural Science Foundation of China 61203092

the Enterprise-college-institute Cooperative Project of Jiangsu Province BY2015019-21

111 Project B12018

the Fundamental Research Funds for the Central Universities JUSRP51733B

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Abstract

Abstract

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances, an iterative learning fault diagnosis algorithm is proposed. Firstly, in order to measure the impact of fault on system between every consecutive output sampling instants, the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem, then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay method. Afterwards, an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault, and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials, so the algorithm can detect and estimate the system faults adaptively. Simulation results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.
- Equivalent fault model,
- fault diagnosis,
- iterative learning algorithm,
- non-uniform sampling hybrid system,
- virtual fault

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References(36)

References

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Iterative Learning Fault Diagnosis Algorithm for Non-uniform Sampling Hybrid System

doi: 10.1109/JAS.2016.7510052

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