Прогнозування технічного стану тягових редукторів електропоїздів дискретними стохастичними моделями

Abstract

UA: У статті здійснено прогнозування технічного стану тягових редукторів електропоїздів дискретними стохастичними моделями. На кожному ТО-3 реєструвалися вібраційні реалізації попередньо відібраних чотирнадцяти тягових редукторів і розраховувалася фрактальна розмірність покриття. Для прогнозування технічного стану обиралася ARIMA-модель. Незважаючи на високу складність, відсутність автоматичного процесу підрахунку та необхідність здійснення декількох ітераційних процедур, підраховане найменше значення точності прогнозування ARIMA- моделлю становить 91,4 %.

EN: The article deals with the forecasting of the technical state of the traction gearboxes of electric trains with discrete stochastic models. During every 3rd level maintenance, the vibration signals of the previously selected fourteen traction gearboxes were recorded, and the box counting dimension was estimated. For the forecasting of the technical state, among the broadcast deterministic methods of exponential smoothing and trend analysis, the most effective ARIMA model was selected. This model in certain applications has higher precision than GRNN and BPNN neural networks. The implementation of the ARIMA model requires accomplishment of the identification, evaluation, fitting and practical application stages for the forecasting model. The identification was performed due to the one-step differentiation with a further stationarity check according to the autocorrelation function and partial autocorrelation function. The evaluation of the ARIMA model with the different orders of the autoregression component and moving average component was done. For the model fitting, the Akaike’s information criterion and Bayes information criterion were calculated, and the autoregression component of the ARIMA model having the minimum values of these criteria was selected. The forecasting of the fitted ARIMA models with an 80 % confidence interval was done for the period since the 71st 3rd level maintenance up to the next 2nd level current repair. After disassembling 14 traction gearboxes during the 2nd level current repair, the insufficient amount of a lubricant in the first gearbox, wear of a roller bearing of the front cover in the second gearbox, a crack of a bearing ring of the front cover in the third gearbox, a tooth break of a gear in the fourth traction gearbox, destruction of a bearing in the fifth gearbox and a tooth crack of its gear were detected.