Research on fault diagnosis of airborne fuel pump based on EMD and probabilistic neural networks |
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Affiliation: | 1. Department of Electronics Technology, Budapest University of Technology and Economics, Budapest, Hungary;2. Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Electrotechnology, Technická 2, Prague 6 166 27, Czech Republic;1. Department of Engineering Mechanics, School of Aerospace Engineering, Tsinghua University, Beijing 100084, PR China;2. China Longyuan Power Group Corporation Limited, China Goudian, Beijing 100034, PR China;3. Guangzhou CABR&SC Co.Ltd Architectural Design and Research Institute of Guangdong Province, Guangzhou 510010, PR China;1. Physics Department, Faculty of Science, Zagazig University, Zagazig, Egypt;2. Basic Sciences, College of Engineering, University of Business and Technology (UBT), Jeddah, Saudi Arabia;1. Airbus Group Innovations, Suresnes, France;2. Université de Lyon, Ampère, CNRS UMR 5005, INSA Lyon, Villeurbanne, France;3. Université de Technologie de Troyes, Institut Charles Delaunay, CNRS UMR 6279 STMR, Troyes, France |
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Abstract: | Airborne fuel pump is a key component of the airborne fuel system, which once fails will bring a huge negative impact on aircraft safety. Therefore, accurate, reliable and effective fault diagnosis must be performed. However, the current airborne fuel pump has several difficulties: fault samples shortage, high maintenance costs and low diagnostic efficiency. In this paper, after Failure Mode, Effects and Criticality Analysis (FMECA) of airborne fuel pump, an experimental platform of airborne fuel transfusion system is developed and then a fault diagnosis model based on empirical mode decomposition (EMD) and probabilistic neural networks (PNN) is established. Meanwhile, the diagnosis model is verified by practical experiments, and the sensor layout of the experimental platform is optimized. Firstly, the vibration signals and pressure signals under normal state and six types of typical fuel pump faults are acquired on the experimental platform. Then EMD method is applied to decompose the original vibration signals into a finite Intrinsic Mode Functions (IMFs) and a residual. Secondly, the energy of first four IMFs is extracted as vibration signals fault feature, combined with the mean outlet pressure to construct fault feature vectors. Then feature vectors are divided into training samples and testing samples. Training samples are used to train PNN fault diagnosis model and testing samples are used to verify the model. Finally, the experimental results show that only one pressure sensor and one y-axis vibration sensor are needed to achieve 100% fault diagnosis. Furthermore, compared with SVM and GA-BP, the PNN fault diagnosis model has fast convergence, high efficiency and a higher performance and recognition for the typical faults of airborne fuel pump. |
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