Prediction method of dedicated power supply for tank commander panoramic based on grey Markov model optimized by PSO

Yingshun Li, Yu Xiao, X. Yi
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Abstract

Electrical equipment failure is a monotonous process with a sudden change in performance due to its own life cycle and a sudden change in state deterioration. Taking the dedicated power supply for tank commander panoramic of a certain type tank as the research object. The concepts of grey prediction model and Markov model are introduced. The Markov model is used to classify the residuals of the grey GM(1,1) prediction model, and the state transition matrix is determined. The particle swarm optimization algorithm is used to find the whitening coefficient of the Markov model residual state, and the grey Markov model of particle swarm optimization is established.The dedicated power supply for tank commander panoramic measurement values (highest value, lowest value, median value) are predicted. The results show that the optimized prediction results are better than the original gray GM (1,1) and gray Markov prediction methods. With the increase of the working time of the dedicated power supply of the dedicated power supply for tank commander panoramic, the trend of relative error occurrence state transition is more stable, the accuracy of prediction will be further improved, and the equipment can predict and maintain a certain value for maintenance work.
基于PSO优化的灰色马尔可夫模型的坦克指挥员全景专用电源预测方法
电气设备故障是一个单调的过程,由于其自身的寿命周期,性能突然发生变化,状态突然恶化。以某型坦克指挥员全景专用电源为研究对象。介绍了灰色预测模型和马尔可夫模型的概念。利用马尔可夫模型对灰色GM(1,1)预测模型残差进行分类,确定状态转移矩阵。利用粒子群优化算法求解马尔可夫模型残差状态的白化系数,建立粒子群优化的灰色马尔可夫模型。对坦克指挥员专用电源全景测量值(最高值、最低值、中位数)进行预测。结果表明,优化后的预测结果优于原有的灰色GM(1,1)和灰色马尔可夫预测方法。随着坦克指挥员全景专用电源工作时间的增加,相对误差发生状态转换的趋势更加稳定,预测的精度将进一步提高,设备能够预测并保持一定的维修工作值。
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