Features and nature of the infl uence of the crankshaft speed on the impulse of the support reactions evaluated when diagnosing an internal combustion engine

   The development of universal diagnostic parameters with increased information content and the possibility of prompt registration and processing will help assess the current state of individual elements and the diagnostic object as a whole. As a universal diagnostic parameter characterizing the technical condition of the internal combustion engine, the authors propose to use the reaction impulse of the engine supports, the value of which depends on the crankshaft speed. Using a laboratory installation that includes a D-243 diesel four-stroke four-cylinder engine and a set of measuring equipment, the authors studied the process of changing the impulse of the reactions of the internal combustion engine supports when changing the crankshaft speed in the idling mode. When conducting research, they used methods of regression, system and statistical analysis. It has been established that in the crankshaft speed range of 600 to 2200 min^–1, the maximum value of the bearing reactions for each cycle of engine operation changes from 345 to 122 N. In the range of 600 to 1000 min^–1, the minimum value of the bearing reactions for each engine cycle changes –272 to –305 N and increases to –109 N at a maximum frequency of 2200 min^–1. The maximum momentum of the support reactions is observed at a crankshaft speed of 1000 min^–1 and averages for positive and negative reactions of the supports, respectively, 17.34 and –17.35 kNs. At the maximum speed of the crankshaft, the momentum of the support reactions reaches 9.28 and –9.29 kNs for the positive and negative reactions of the supports, respectively. The results obtained can be used to improve the methods for diagnosing internal combustion engines, as they can help exclude the infl uence of the crankshaft speed on the measurement result.

1. Cai B., Sun X., Wang J., Yang C., Wang Z., Kong X., Liu Z., Liu Y. Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs / BPNNs. Journal of Manufacturing Systems. 2020; 57: 148-157. doi: 10.1016/j.jmsy.2020.09.001

2. Wen L., Li X., Gao L., Zhang Y. A new convolutional neural network-based data-driven fault diagnosis metho. Transactions on Industrial Electronics. 2018; 65 (7): 5990-5998. doi: 10.1109/TIE.2017.2774777

3. Wang J., Wang Z., Stetsyuk V., Ma X., Gu F., Li W. Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system. ISA transactions. 2019; 86: 276-286. doi: 10.1016/j.isatra.2018.10.044

4. Zhang M., Zi Y., Niu L., Xi S., Li Y. Intelligent diagnosis of V-type marine diesel engines based on multifeatures extracted from instantaneous crankshaft speed. Transactions on Instrumentation and Measurement. 2019; 68 (3): 722-740. doi: 10.1109/TIM.2018.2857018

5. Danilov I. K., Marusin A. V., Marusin V. A., Danilov S. I., Andryushchenko I. S. Diagnosis of the fuel equipment of diesel engines with multicylinder high pressure fuel injection pump for the movement of the injector valve for the diagnostic device. In Proceedings of the 4th International Conference on Frontiers of Educational Technologies. 2019; 157-160. doi: 10.1145/3233347.3233363

6. Zhang W., Li X., Huang L., Feng M. Experimental study on spray and evaporation characteristics of diesel-fueled marine engine conditions based on optical diagnostic technology. Fuel. 2019; 246: 454-465. doi: 10.1016/j.fuel.2019.02.065

7. Szymański G. M., Tomaszewski F. Diagnostics of automatic compensators of valve clearance in combustion engine with the use of vibration signal. Mechanical Systems and Signal Processing. 2016; 68-69: 479-490. doi: 10.1016/j.ymssp.2015.07.015

8. Gritsenko A. V., Shepelev V. D., Burtsev A. Yu. Diagnostics of the gas distribution mechanism based on the control of vibration of its elements. Bulletin of the South Ural State University. Series “Mechanical engineering industry”. 2022; 22 (1): 36-47. (In Rus.) doi: 10.14529/engin220103

9. Balyasnikov A. S., Gritsenko A. V., Glemba K. V., Lukomsky K. I. Improving the effi ciency of diagnosing expansion clearances of timing valves using a vibration sensor (accelerometer). APK Rossii. 2018; 25 (3): 377-387. (In Rus.)

10. Grebennikov A. S., Grebennikov S. A., Kuverin I. Yu. Dynamic method for diagnosing the elements of the vehicle. World of Transport and Technological Machines. 2016; 1 (52): 24-31. (In Rus.)

11. Panchenko M. N., Grachev V. V., Grishchenko A. V. Analyzing the instantaneous angular velocity of the crankshaft. Transport of the Russian Federation. 2018; 4 (77): 59-62. (In Rus.)

12. Krivtsov S. N. Dynamic method diagnosing for automobile diesel engines equipped with common rail fuel injection system. Avtomobilnaya promyshlennost. 2015; 9: 26-29. (In Rus.)

13. Kurnosov A. F., Guskov Yu. A., Kornienko V. N., Galynskiy A. A. Improving the method for assessing the technical condition of the cylinder-piston group based on the external impulse-power characteristic of the engine. APK Rossii. 2022; 29 (1): 36-41. (In Rus.)

14. Satskevich N. E., Kurnosov A. F., Galynsky A. A. Intelligent system for diagnosing of transport and technological machines based on identified impulse-power characteristics of the engine. AgroEkoInfo. 2020; 4 (42): 30. (In Rus.)