Research on Adaptive Fault Diagnosis Control System of Audio Management Component Environment of Airborne Electronic Equipment

Authors

  • Xiaomin Xie Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China
  • Shuguo Gui Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China
  • Renwei Dou Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China
  • Xuanfu Du Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China

DOI:

https://doi.org/10.13052/jicts2245-800X.1124

Keywords:

Adaptive control, intelligent control, adaptive diagnostic control system

Abstract

The system adopts adaptive control, and the controller of audio management component is directly applied to the controlled object. Through online calculation, the model is identified online by using the dynamic characteristics of the object, and the relationship between input and output variables is expressed. It can be corrected by entering and leaving data, which is actually to correct the controller. The initial rules of the controller are composed of default models. Through continuous self-reasoning learning, the controller is optimized to achieve data tracking, fast convergence, strong anti-interference ability and excellent performance. Combining intelligent control with adaptive technology not only expands the scope of adaptive system, but also provides an effective way for intelligent control. The environmental adaptive diagnostic control system uses adaptive technology to adjust the parameters, data and knowledge base of the controller. The system detects IO signal, AD signal and the status data sent by the sub-unit through the internal CAN bus according to the power-on self-check of the unit, and at the same time, it monitors all the status data in real time during the operation, judges the operation of each sub-component, gives an alarm in time and carries out protection control. Through the design of integrated detection module, the overall installation space of detection sensors is reduced, signal interfaces and connecting cables are reduced, and the overall adaptive diagnosis effect can be improved. Through the data recording and storage function, the system stores the operation information of each subunit, compares it with the built-in health data table, and prompts the maintenance information in time.

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Author Biographies

Xiaomin Xie, Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China

Xiaomin Xie received the bachelor’s degree in Electronic Information Engineering from Anhui Jianzhu University in 2008, the master’s degree in detection technology and automatic equipments from China civil aviation university in 2011, respectively. He is currently working as an associate professor at the Department of Intelligent Manufacturing College, Anhui Vocational and Technical College. His research areas include circuit fault diagnosis and intelligent detection, deep learning, and neural network analysis.

Shuguo Gui, Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China

Shuguo Gui is currently working as an professor at the Department of Intelligent Manufacturing College, Anhui Vocational and Technical College. His research areas include mechatronic engineering.

Renwei Dou, Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China

Renwei Dou received the bachelor’s degree in Electronic Information Engineering from Anhui university of finance and economics in 2006, the master’s degree in power electronic technology from University Of Anhui in 2020, respectively. He is currently working as an lecturer at the Department of Intelligent Manufacturing College, Anhui Vocational and Technical College. His research areas include circuit fault diagnosis and intelligent detection, deep learning.

Xuanfu Du, Intelligent Manufacturing College, Anhui Vocational and Technical College, Hefei 230011, China

Xuanfu Du received the bachelor’s degree in Mechanical Manufacture and Automation from Hefei University of Technology in 2014, the master’s degree in Mechanical Manufacture and Automation from Hefei University of Technology in 2017, respectively. He is currently working as an Teaching assistants and R&D engineers at the Department of Intelligent Manufacturing College, Anhui Vocational and Technical College. His research areas include Electric power technology research, and neural network analysis.

References

Hang J, Wu H, Ding S, Hua W, Wang Q (2020) A DC-flux-injection method for fault diagnosis of high-resistance connection in direct-torque-controlled PMSM drive system. IEEE Trans Power Electron 35(3):3029–3042.

Md Nor N, Hussain MA, Che Hassan CR (2017) Fault diagnosis and classification framework using multi-scale classification based on kernel Fisher discriminant analysis for chemical process system. Appl Soft Comput 61(2017):959–972.

Md Nor N, Hussain MA, Che Hassan CR (2017) Fault diagnosis based on multi-scale classification using kernel Fisher discriminant analysis and Gaussian mixture model and K-nearest neighbor method. J Teknol 79:89–96.

Swain RR, Dash T, Khilar PM (2019) Investigation of RBF kernelized ANFIS for fault diagnosis in wireless sensor networks. In: Verma N, Ghosh A (eds) Computational intelligence: theories, applications and future directions – volume II. Advances in intelligent systems and computing, vol 799. Springer, Singapore.

Singh G, Jain VK, Singh A (2018) Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system. Energy Environ 29(7):1073–1097.

Misra G, Kumar V, Agarwal A, Agarwal K (2016) Internet of things (iot) – a technological analysis and survey on vision, concepts, challenges, innovation directions, technologies, and applications (an upcoming or future generation computer communication system technology). Am J Electr Electron Eng 4(1):23–32.

Yadav A, Swetapadma A (2015) Enhancing the performance of transmission line directional relaying, fault classification and fault location schemes using fuzzy inference system. IET Gener Transm Distrib 9(6):580–591.

Ding J, Zhao W, Miao B et al. (2018) Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault detection. Mech Syst Signal Process 111: 399–422.

Chen M, Shao H, Dou H et al. (2022) Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples. IEEE Trans Reliab 1–9.

Wang D, Guo Q, Song Y et al. (2019) Application of multiscale learning neural network based on CNN in bearing fault diagnosis. J Signal Process Syst 91(10):1205–1217.

Balaga H, Gupta N, Vishwakarma DN (2015) GA trained parallel hidden layered ANN based differential protection of three phase power transformer. Electr Power Energy Syst 67(3):286–297.

Du ZM, Jin XQ, Yang YY (2009) Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network. Appl Energy 86(9):1624–1631.

Han H, Cao ZK, Gu B, Ren N (2010) PCA-SVM-Based automated fault detection and diagnosis (AFDD) for vapor-compression refrigeration systems. HVAC&R Res 16(3):295–313.

Chouaib Chakour, Abdelhafid Benyounes, Mahmoud Boudiaf (2018) Diagnosis of uncertain nonlinear systems using interval kernel principal components analysis: application to a weather station. ISA Trans 83:126–141.

Elshenawy Lamiaa M, Mahmoud Tarek A, Chouaib Chakour (2020) Simultaneous fault detection and diagnosis using adaptive principal component analysis and multivariate contribution analysis. Ind Eng Chem Res 59(47):20798–20815.

Hamed Badihi, Youmin Zhang, Henry Hong (2014) Wind turbine fault diagnosis and fault-tolerant torque load control against actuator faults. IEEE Trans Control Syst Technol 23(4):1351–1372.

Wang X, Cai Y, Li A, et al. (2021) Intelligent fault diagnosis of diesel engine via adaptive VMD-rihaczek distribution and graph regularized bi-directional NMF[j]. Measurement 172:108823.

Muralidharan V, Sugumaran V. Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump. Measurement, 2013, 46: 353–359.

Islam M M M, Kim J M. Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network. Comput Industry, 2019, 106: 142–153.

Aljemely AH, Xuan J, Jawad FKJ et al. (2020) A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder [J]. J Mech Sci Technol 34:4367–4381.

Li S, Wang H, Song L et al. (2020) An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network. Measurement 165:108122.

G. Tao, X. D. Tang, S. H. Chen, J. T. Fei, S. M. Joshi. Adaptive failure compensation of two-state aircraft morphing actuators. IEEE Transactions on Control Systems Technology, vol. 14, no. 1, pp. 157–164, 2006.

Al Younes Y, et al. (2016) Sensor fault diagnosis and fault tolerant control using intelligent-output-estimator applied on quadrotor UAV. In 2016 international conference on unmanned aircraft systems (ICUAS). IEEE.

Mohammadi A, Ramezani A (2019) An active actuator fault-tolerant control of a quadrotor based on analytical redundancy relations. Iran J Sci Technol Trans Electr Eng 44(3):1069–1079.

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Published

2023-05-15

How to Cite

Xie, X. ., Gui, S. ., Dou, R. ., & Du, X. . (2023). Research on Adaptive Fault Diagnosis Control System of Audio Management Component Environment of Airborne Electronic Equipment. Journal of ICT Standardization, 11(02), 175–196. https://doi.org/10.13052/jicts2245-800X.1124

Issue

Section

Intelligent System Concepts, architecture, standards, tools and applications