Abstract This article reports on a study aimed to assess the extent to which engine operation time can be prolonged without oil change in systems working in a discontinuous mode. This applies particularly to elements of critical infrastructure or vehicles performing occasional work. The analysis of longitudinal oil sample data has enabled us to study long-term changes in the levels of particles and substances in the oil. This new database will support the validation and verification of the team's former research and determine the practical implications emerging from the revised results. The data were investigated by means of a synergy-based method combining the computational powers of reinforced decision trees and artificial neural networks. The complex selection of signals from laboratory tests of oil samples obtained from over a decade of vehicle testing is the factor that facilitated establishing optimal system condition parameters. The proposed reinforced learning tree (RLT)-based model is an implementation of stochastic gradient boosted decision trees – a method that is particularly useful for predictive data mining. By combining RLT with artificial neural network modelling, we have produced and validated an effective tool for predicting the investigated technical system's operation time. A distinct advantage of using neural networks on a new set of variables derived from an authentic tribological system is that it enables a genuine evaluation of the model's operation. The advantages of the proposed method are: i) high-accuracy technical system condition and reliability assessment; ii) improved equipment usage planning; iii) optimising the safe use of the equipment and its lifetime cost.

中文翻译：

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使用混合强化学习树-人工神经网络系统验证技术设备退化方法

摘要 本文报告了一项研究，旨在评估在不连续模式下工作的系统中无需换油可以延长发动机运行时间的程度。这尤其适用于关键基础设施的元素或偶尔执行工作的车辆。纵向油样数据的分析使我们能够研究油中颗粒和物质水平的长期变化。这个新数据库将支持团队先前研究的验证和验证，并确定修订结果产生的实际影响。通过结合增强决策树和人工神经网络的计算能力的基于协同作用的方法对数据进行了调查。从十多年的车辆测试中获得的油样的实验室测试信号的复杂选择是促进建立最佳系统条件参数的因素。所提出的基于强化学习树 (RLT) 的模型是随机梯度提升决策树的实现——一种对预测数据挖掘特别有用的方法。通过将 RLT 与人工神经网络建模相结合，我们已经生成并验证了一种有效的工具来预测所研究的技术系统的运行时间。在源自真实摩擦学系统的一组新变量上使用神经网络的一个明显优势是，它能够对模型的运行进行真正的评估。所提出的方法的优点是：i) 高精度技术系统状态和可靠性评估；ii) 改进设备使用计划；iii) 优化设备的安全使用及其寿命成本。