CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2022-05-11
Cited: 0
Clicked: 2660
Jin-chuan SHI, Yan REN, He-sheng TANG, Jia-wei XIANG. Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor[J]. Journal of Zhejiang University Science A, 2022, 23(4): 257-271.
@article{title="Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor",
author="Jin-chuan SHI, Yan REN, He-sheng TANG, Jia-wei XIANG",
journal="Journal of Zhejiang University Science A",
volume="23",
number="4",
pages="257-271",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2100394"
}
%0 Journal Article
%T Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor
%A Jin-chuan SHI
%A Yan REN
%A He-sheng TANG
%A Jia-wei XIANG
%J Journal of Zhejiang University SCIENCE A
%V 23
%N 4
%P 257-271
%@ 1673-565X
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2100394
TY - JOUR
T1 - Hydraulic directional valve fault diagnosis using a weighted adaptive fusion of multi-dimensional features of a multi-sensor
A1 - Jin-chuan SHI
A1 - Yan REN
A1 - He-sheng TANG
A1 - Jia-wei XIANG
J0 - Journal of Zhejiang University Science A
VL - 23
IS - 4
SP - 257
EP - 271
%@ 1673-565X
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2100394
Abstract: Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise, the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it. Therefore, a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments. Firstly, the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network (CNN) to form a complete and stable multi-dimensional feature set. Secondly, to obtain a weighted multi-dimensional feature set, the multi-dimensional feature sets of similar sensors are combined, and the entropy weight method is used to weight these features to reduce the interference of insensitive features. Finally, the attention mechanism is introduced to improve the dual-channel CNN, which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors, to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis. Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy. It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods. This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.
[1]AzamfarM, SinghJ, Bravo-ImazI, et al., 2020. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis. Mechanical Systems and Signal Processing, 144:106861.
[2]BaiRX, XuQS, MengZ, et al., 2021. Rolling bearing fault diagnosis based on multi-channel convolution neural network and multi-scale clipping fusion data augmentation. Measurement, 184:109885.
[3]CaccavaleF, PierriF, VillaniL, 2008. Adaptive observer for fault diagnosis in nonlinear discrete-time systems. Journal of Dynamic Systems, Measurement, and Control, 30(2):021005.
[4]ChenLR, CaoJF, WuK, et al., 2022. Application of generalized frequency response functions and improved convolutional neural network to fault diagnosis of heavy-duty industrial robot. Robotics and Computer-Integrated Manufacturing, 73:102228.
[5]DongHH, ChenFZ, WangZP, et al., 2021. An adaptive multisensor fault diagnosis method for high-speed train traction converters. IEEE Transactions on Power Electronics, 36(6):6288-6302.
[6]DragomiretskiyK, ZossoD, 2014. Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3):531-544.
[7]GaoXE, JiangPL, XieWX, et al., 2021. Decision fusion method for fault diagnosis based on closeness and Dempster-Shafer theory. Journal of Intelligent & Fuzzy Systems, 40(6):12185-12194.
[8]GaoYD, KimCH, KimJM, 2021. A novel hybrid deep learning method for fault diagnosis of rotating machinery based on extended WDCNN and long short-term memory. Sensors, 21(19):6614.
[9]HoangDT, TranXT, VanM, et al., 2021. A deep neural network-based feature fusion for bearing fault diagnosis. Sensors, 21(1):244.
[10]IshamMF, LeongMS, LimMH, et al., 2018. Variational mode decomposition: mode determination method for rotating machinery diagnosis. Journal of Vibroengineering, 20(7):2604-2621.
[11]JiXC, RenY, TangHS, et al., 2020. An intelligent fault diagnosis approach based on Dempster-Shafer theory for hydraulic valves. Measurement, 165:108129.
[12]JiXC, RenY, TangHS, et al., 2021. DSmT-based three-layer method using multi-classifier to detect faults in hydraulic systems. Mechanical Systems and Signal Processing, 153:107513.
[13]JiangXX, WangJ, ShenCQ, et al., 2020. An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis. Structural Health Monitoring, in press.
[14]KordestaniM, SamadiMF, SaifM, 2018. A distributed fault detection and isolation method for multifunctional spoiler system. Proceedings of the 61st IEEE International Midwest Symposium on Circuits and Systems, p.380-383.
[15]KordestaniM, RezamandM, OrchardM, et al., 2020. Planetary gear faults detection in wind turbine gearbox based on a ten years historical data from three wind farms. IFAC-PapersOnLine, 53(2):10318-10323.
[16]KordestaniM, SaifM, OrchardME, et al., 2021. Failure prognosis and applications—a survey of recent literature. IEEE Transactions on Reliability, 70(2):728-748.
[17]LeTT, WattonJ, PhamDT, 1997. An artificial neural network based approach to fault diagnosis and classification of fluid power systems. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 211(4):307-317.
[18]LefebvreD, 2014. Fault diagnosis and prognosis with partially observed Petri nets. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(10):1413-1424.
[19]LiangMX, CaoP, TangJ, 2021. Rolling bearing fault diagnosis based on feature fusion with parallel convolutional neural network. The International Journal of Advanced Manufacturing Technology, 112(3):819-831.
[20]LiuP, SunZY, WangZP, et al., 2018. Entropy-based voltage fault diagnosis of battery systems for electric vehicles. Energies, 11(1):136.
[21]LiuQJ, MaGJ, ChengC, 2020. Data fusion generative adversarial network for multi-class imbalanced fault diagnosis of rotating machinery. IEEE Access, 8:70111-70124.
[22]LiuSQ, JiZS, WangY, et al., 2021. Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network. Computer Communications, 173:160-169.
[23]LiuZ, ZhangM, LiuF, et al., 2020. Multidimensional feature fusion and ensemble learning-based fault diagnosis for the braking system of heavy-haul train. IEEE Transactions on Industrial Informatics, 17(1):41-51.
[24]MousaviM, MoradiM, ChaibakhshA, et al., 2020. Ensemble-based fault detection and isolation of an industrial Gas turbine. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, p.2351-2358.
[25]PanLZ, ZhaoL, SongAG, et al., 2021. Research on gear fault diagnosis based on feature fusion optimization and improved two hidden layer extreme learning machine. Measurement, 177:109317.
[26]PatelSP, UpadhyaySH, 2020. Euclidean distance based feature ranking and subset selection for bearing fault diagnosis. Expert Systems with Applications, 154:113400.
[27]PengT, ZhaoS, DanHB, et al., 2017. Open-circuit fault diagnosis and fault tolerance for shunt active power filter. Journal of Central South University (Science & Technology of Mining and Metallurgy), 24(11):2582-2595.
[28]RefaeilzadehP, TangL, LiuH, 2009. Cross-validation. In: Liu L, Özsu M (Eds.), Encyclopedia of Database Systems. Springer, New York, USA, p.532-538.
[29]RezamandM, KordestaniM, CarriveauR, et al., 2020. Critical wind turbine components prognostics: a comprehensive review. IEEE Transactions on Instrumentation and Measurement, 69(12):9306-9328.
[30]ShanPF, LvH, YuLM, et al., 2020. A multisensor data fusion method for ball screw fault diagnosis based on convolutional neural network with selected channels. IEEE Sensors Journal, 20(14):7896-7905.
[31]ShaoHD, LinJ, ZhangLW, et al., 2021. A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance. Information Fusion, 74:65-76.
[32]ShiJC, YiJY, RenY, et al., 2021. Fault diagnosis in a hydraulic directional valve using a two-stage multi-sensor information fusion. Measurement, 179:109460.
[33]SongH, HanPQ, ZhangJX, et al., 2018. Fault diagnosis method for closed-loop satellite attitude control systems based on a fuzzy parity equation. International Journal of Distributed Sensor Networks, 14(10).
[34]SouzaRM, NascimentoEGS, MirandaUA, et al., 2021. Deep learning for diagnosis and classification of faults in industrial rotating machinery. Computers & Industrial Engineering, 153:107060.
[35]SreekumarKT, GeorgeKK, KumarCS, et al., 2019. Performance enhancement of the machine-fault diagnosis system using feature mapping, normalisation and decision fusion. IET Science, Measurement & Technology, 13(9):1287-1298.
[36]TanYH, ZhangJD, TianH, et al., 2021. Multi-label classification for simultaneous fault diagnosis of marine machinery: a comparative study. Ocean Engineering, 239:109723.
[37]TangSN, YuanSQ, ZhuY, 2020. Convolutional neural network in intelligent fault diagnosis toward rotatory machinery. IEEE Access, 8:86510-86519.
[38]TangXH, GuX, WangJC, et al., 2020. A bearing fault diagnosis method based on feature selection feedback network and improved D-S evidence fusion. IEEE Access, 8:20523-20536.
[39]TidririK, TiplicaT, ChattiN, et al., 2018. A generic framework for decision fusion in fault detection and diagnosis. Engineering Applications of Artificial Intelligence, 71:73-86.
[40]ToscanoR, LyonnetP, 2003. Diagnosis of the industrial systems by fuzzy classification. ISA Transactions, 42(2):327-335.
[41]WanST, ChenL, DouLJ, et al., 2018. Mechanical fault diagnosis of HVCBs based on multi-feature entropy fusion and hybrid classifier. Entropy, 20(11):847.
[42]XiaoYC, XueJY, ZhangL, et al., 2021. Misalignment fault diagnosis for wind turbines based on information fusion. Entropy, 23(2):243.
[43]XuWX, JingLY, TanJW, et al., 2020. A multimodel decision fusion method based on DCNN-IDST for fault diagnosis of rolling bearing. Shock and Vibration, 2020:8856818.
[44]XueYJ, CaoJX, WangDX, et al., 2016. Application of the variational-mode decomposition for seismic time–frequency analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8):3821-3831.
[45]YanXA, JiaMP, 2019. Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection. Knowledge-Based Systems, 163:450-471.
[46]YeQ, LiuSH, LiuCH, 2020. A deep learning model for fault diagnosis with a deep neural network and feature fusion on multi-channel sensory signals. Sensors, 20(15):4300.
[47]YuanZ, ZhouTT, LiuJ, et al., 2021. Fault diagnosis approach for rotating machinery based on feature importance ranking and selection. Shock and Vibration, 2021:8899188.
[48]YuanZW, ZhangJ, 2016. Feature extraction and image retrieval based on AlexNet. Proceedings of the 8th SPIE International Conference on Digital Image Processing, article 100330E.
[49]ZhangHR, SunJX, HouKN, et al., 2021. Improved information entropy weighted vague support vector machine method for transformer fault diagnosis. High Voltage, in press.
[50]ZhangWB, ZhouJZ, 2019. A comprehensive fault diagnosis method for rolling bearings based on refined composite multiscale dispersion entropy and fast ensemble empirical mode decomposition. Entropy, 21(7):680.
[51]ZhangY, ChenHC, DuYP, et al., 2021. Power transformer fault diagnosis considering data imbalance and data set fusion. High Voltage, 6(3):543-554.
[52]ZhuHB, HeZM, WeiJH, et al., 2021. Bearing fault feature extraction and fault diagnosis method based on feature fusion. Sensors, 21(7):2524.
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