Predictive Models for Temporal Parameter Drift of Automotive Equipment during its Operation under Special Conditions
Abstract
Introduction: Impact of external environmental factors affects automotive engineering operations. Failures in forecasting and diagnostics significantly reduce the reliability of individual units and the vehicle as a whole, which in turn limits its operational life. Purpose: Depending on the mathematical tools, we analyze the forecasting methods which determine the vehicle condition parameters, and develop predictive models of temporal drift of these parameters for functioning under various conditions. Results: Predictive models have been developed for the parameter drift of a vehicle during its operation under special conditions. The special operation conditions are a combination of several adverse natural factors: temperature, dustiness of the air, etc. The novelty of our approach is that the functions describing the temporal changes of the controlled parameters of the vehicle units should be chosen with due regard to the above-mentioned destabilizing factors which influence the technical condition. Practical relevance: Predictive models allow you to calculate the moment when the critical parameters of a vehicle achieve their maximum permissible values. The obtained analytical expressions can be used to find the probability of failure-free operation of units and assemblies at a specified interval of time. In the future, these dependencies can be used to form vehicle operating efficiency indicators and calculate the optimal duration for technical inspection period during which these indicators will achieve their extreme values.Published
2017-06-21
How to Cite
Savin, L., Korolev, M., & Nosov, M. (2017). Predictive Models for Temporal Parameter Drift of Automotive Equipment during its Operation under Special Conditions. Information and Control Systems, (3), 58-66. https://doi.org/10.15217/issn1684-8853.2017.3.58
Issue
Section
System and process modeling