The research of fault diagnosis and prediction technology of construction machinery can not only improve the working efficiency of the staff, but also shorten the fault time. Therefore, the use of appropriate engineering machinery fault diagnosis and prediction methods, early prediction of the fault, failure, can quickly and accurately determine the nature and location of the fault, timely troubleshooting, to avoid economic losses caused by the fault. In this paper, a fault diagnosis model based on particle swarm optimization wavelet neural network is proposed. In order to effectively enhance the optimization ability of particle swarm optimization algorithm and solve the problem of slow convergence speed of particle swarm optimization, the optimization ability of improved particle swarm optimization algorithm is greatly improved through the improvement of inertia weight factor, learning factor and position iteration formula. Experimental results show that the proposed optimization algorithm can obtain higher classification accuracy, which verifies the effectiveness of PSO-WNN in identifying fault degree.

Jiwu Tang, Xu Liang and Ming Huang. Method for Troubleshooting Construction Machinery Based on Particle Swarm Optimization and Wavelet Theory. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 4: 46-53. https://doi.org/10.38007/KME.2022.030406.

[1] Regazzo E . Troubleshooting issues with solar hot water. ReNew, 2017(139):87-88. 

[2] Bordewieck M , Elson M . Withdrawal: Martin Bordewieck and Malte Elson, the impact of inducing troubleshooting strategies via visual aids on performance in a computerized digital network task. Applied cognitive psychology, 2021(3):35. https://doi.org/10.1002/acp.3809

[3] Raymond S . Troubleshooting Issues and Eliminating Headaches Related to Control Valves. Power: The Magazine of Power Generation and Plant Energy Systems, 2021(4):165.

[4] Putra A E, Rukun K, Irfan D, et al. Designing and developing artificial intelligence applications troubleshooting computers as learning aids. Asian Social Science and Humanities Research Journal (ASHREJ), 2020, 2(1): 38-44. https://doi.org/10.37698/ashrej.v2i1.22

[5] Zhong B, Li T. Can pair learning improve students' troubleshooting performance in robotics education?. Journal of Educational Computing Research, 2020, 58(1): 220-248. https://doi.org/10.1177/0735633119829191

[6] Varrà M O, Ghidini S, Husáková L, et al. Advances in troubleshooting fish and seafood authentication by inorganic elemental composition. Foods, 2021, 10(2): 270. https://doi.org/10.3390/foods10020270

[7] Lustgarten D L, Sharma P S, Vijayaraman P. Troubleshooting and programming considerations for His bundle pacing. Heart Rhythm, 2019, 16(5): 654-662. https://doi.org/10.1016/j.hrthm.2019.02.031

[8] Albert D R. Constructing, Troubleshooting, and Using Absorption Colorimeters to Integrate Chemistry and Engineering. Journal of Chemical Education, 2020, 97(4): 1048-1052. https://doi.org/10.1021/acs.jchemed.9b00548

[9] Caswell-Midwinter B, Whitmer W M. The perceptual limitations of troubleshooting hearing-aids based on patients' descriptions. International Journal of Audiology, 2021, 60(6): 427-437. https://doi.org/10.1080/14992027.2020.1839679

[10] Manner J, Kolb S, Wirtz G. Troubleshooting serverless functions: a combined monitoring and debugging approach. SICS Software-Intensive Cyber-Physical Systems, 2019, 34(2): 99-104. https://doi.org/10.1007/s00450-019-00398-6

[11] Sukumaran A, Thomas T, Thomas R, et al. Development and troubleshooting in lateral flow immunochromatography assays. Indian Journal of Clinical Biochemistry, 2021, 36(2): 208-212. https://doi.org/10.1007/s12291-020-00887-5

[12] Zhong B, Si Q. Troubleshooting to learn via scaffolds: Effect on students' ability and cognitive load in a robotics course. Journal of Educational Computing Research, 2021, 59(1): 95-118. https://doi.org/10.1177/0735633120951871

[13] Sengupta S, Basak S, Peters R A. Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction, 2018, 1(1): 157-191. https://doi.org/10.3390/make1010010

[14] Jahandideh-Tehrani M, Bozorg-Haddad O, Loáiciga H A. Application of particle swarm optimization to water management: an introduction and overview. Environmental Monitoring and Assessment, 2020, 192(5): 1-18. https://doi.org/10.1007/s10661-020-8228-z

[15] Xia X, Gui L, Yu F, et al. Triple archives particle swarm optimization. IEEE transactions on cybernetics, 2019, 50(12): 4862-4875. https://doi.org/10.1109/TCYB.2019.2943928

[16] Ibrahim R A, Ewees A A, Oliva D, et al. Improved salp swarm algorithm based on particle swarm optimization for feature selection. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(8): 3155-3169. https://doi.org/10.1007/s12652-018-1031-9

[17] Elbes M, Alzubi S, Kanan T, et al. A survey on particle swarm optimization with emphasis on engineering and network applications. Evolutionary Intelligence, 2019, 12(2): 113-129. https://doi.org/10.1007/s12065-019-00210-z

[18] Jain N K, Nangia U, Jain J. A review of particle swarm optimization. Journal of the Institution of Engineers (India): Series B, 2018, 99(4): 407-411. https://doi.org/10.1007/s40031-018-0323-y