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Fault diagnosis of diesel engine information fusion based on adaptive dynamic weighted hybrid distance-taguchi method (ADWHD-T)

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Abstract

In the case of the fuzzy correlation between variables of faults due to the complex monitoring information of diesel engines, neither Mahalanobis distance (MD) nor Euclidean distance (ED) can effectively diagnose by features, in this paper, a novel Adaptive Dynamic Weighted Hybrid Distance-Taguchi method (ADWHD-T) is presented to diagnose diesel engine faults. The method used Adaptive Dynamic Weighted Hybrid Distance (ADWHD) to fuse data of many sensors into the single system-level performance index. The ADWHD is an adaptive and dynamic weighting of MD and Standardized Euclidean distance (SED). The adaptive weights are adjusted according to the distance scale of MD and SED. The dynamic weight coefficients are calculated by the correlation coefficient of characteristic variables to consider the correlation and independence of characteristic variables. The diagnosis results are derived according to the optimized and adjusted fault threshold of ADWHD defined by 3σ method. In view of the dimension reduction optimization of characteristic variables, combining Taguchi method (T), ADWHD-T provides one systematic method for determining the key parameters of characteristic variables to solve the cost problem of multi-sensor analysis. Aiming at the real-time diagnosis, offline-online modeling and real-time fault diagnosis program based on ADWHD-T are designed. Quoting real-time data from diesel engine benches verifies the effectiveness of the scheme. Compared with MD and MD-T methods, ADWHD-T could promote diagnosis efficiency, enhance classification accuracy and expand its application range in fault diagnosis.

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Acknowledgments

Thanks are due to Xingcheng Wang and Zhanhua Wu for assisitance with the experiments. This paper is partly supported by the National Natural Science Foundation of China (61627810), National Key R&D Program of China (2017YFE0128500), Key R&D Program of Guangdong (2020B1111010002), Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (NJYT-17-B34), Inner Mongolia Autonomous Region Natural Science Foundation (2017BS0605), National Science Fund cultivation project of Inner Mongolia University for Nationalities (NMDGP17101), and Inner Mongolia University for Nationalities doctoral research initiation Fund Project (BS416).

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

    Gang Liu, Xiaolong Zhou & Weidong Zhang

  2. College of Engineering, Inner Mongolia University for Nationalities, Tongliao, 028000, China

    Gang Liu

  3. Mechanical Engineering College, Beihua University, Jilin, 132021, China

    Xiaolong Zhou

  4. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Xinli Xu

  5. School of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China

    Longda Wang

  6. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Weidong Zhang

  7. Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai, 200240, China

    Weidong Zhang

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  1. Gang Liu
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  2. Xiaolong Zhou
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  3. Xinli Xu
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  4. Longda Wang
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Correspondence to Weidong Zhang.

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Liu, G., Zhou, X., Xu, X. et al. Fault diagnosis of diesel engine information fusion based on adaptive dynamic weighted hybrid distance-taguchi method (ADWHD-T). Appl Intell 52, 10307–10329 (2022). https://doi.org/10.1007/s10489-021-02962-7

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