Russian Federation
Russian Federation
Purpose: Efficiency increase of operational monitoring of technical state of gas-air tract (GAT) of a locomotive diesel, assessment of possibility of using locomotive regular measuring tools to monitor diesel GAT technical state; feasibility justification for expanding the list of GAT parameters that are monitored by locomotive board diagnostics. Methods: An intelligent classifier based on Support Vector Machine (SVM) algorithm is used to determine the current technical condition (TC) class of GAT. Training sample of 96 item volume for eight classes of GAT TC was formed using “Diesel-RK” software complex. Classifier structure three variants were studied, differing in the set of input parameter vector components: regular, with two inputs (pressure increase degree in a supercharger and exhaust gas temperature before a turbine), expanded, with additional control of turbocharger rotor rotation speed, and proposed, with control of air instantaneous consumption by a diesel instead. Model quality was assessed by cross-validation on five fragments and by classification results for test sample which wasn’t used while training. Results: Classification accuracy of testing sample by classifier with input parameter regular set doesn’t exceed 41%. The expansion of being controlled list of parameter on account of inclusion therein of turbocharger rotor rotation speed does not noticeably affect diagnostic system efficiency, increasing diagnostic accuracy only till 43%. At the same time, rotor rotation speed replacement with air instantaneous consumption rises testing sample classification accuracy till 91%. Practical significance: The effectiveness of the use of machine learning methods for operative monitoring of diesel GAT technical state at condition of optimization of the list of diesel parameters, controlled by locomotive board diagnostics tools, is confirmed. With the purpose to increase control efficiency it is recommended to include diesel instantaneous air consumption into the list with parallel exclusion from their of a turbocharger rotor rotation frequency.
Gas-air tract, turbocharger, baffle, supercharger, working process, diesel, diagnosis, technical state, classifier, learning sample, transducer, air consumption
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