System quality requirements are typically formed by consideration of reliability and safety performance. Failures caused by system weakness, degradation or fatigue may cause undesired, and potentially dangerous, consequences. For various reasons, not all processes of system degradation are easily monitored in the lifecycle of a system. Degradation evolution leads to changes in both performance and reliability characteristics.

In this article, we investigate a mining system consisting of dataset records on in-field operational characteristics of a drilling head. We work with these data in order to get a picture of system degradation and actual condition. For data assessment and modelling, we apply both improved and specific new mathematical models. We examine the data using extended and enhanced state space models, which are suitable for system state and condition investigation. Our time series approaches are based on a modified Kalman-type backpropagation recursion. The improved and modified state space models are accompanied by improved forms of selected stochastic diffusion processes. The diffusion processes are used both for degradation modelling and also for forecasting potential failure occurrence. All of these models are expected to help both with deterioration propagation assessment and with the indication of when the degradation of the system under investigation is predicted to reach the critical limit. Such a limit is represented by threshold performance characteristics that may lead to either soft or hard failure with related faults. The outcomes presented in this article may help with i) failure occurrence prediction, ii) residual useful life prognosis, iii) safer system operation, iv) system utilisation rationalisation and v) maintenance forecasting.

系统质量要求通常是通过考虑可靠性和安全性能而形成的。由系统弱点、退化或疲劳引起的故障可能会导致不希望的和潜在的危险后果。由于各种原因，在系统的生命周期中，并非所有系统退化过程都可以轻松监控。退化演变导致性能和可靠性特性的变化。

在本文中，我们研究了一个由钻头现场操作特性的数据集记录组成的采矿系统。我们使用这些数据来了解系统退化和实际情况。对于数据评估和建模，我们应用改进的和特定的新数学模型。我们使用适用于系统状态和条件调查的扩展和增强状态空间模型检查数据。我们的时间序列方法基于改进的卡尔曼型反向传播递归。改进和修改的状态空间模型伴随着选定随机扩散过程的改进形式。扩散过程既用于退化建模，也用于预测潜在的故障发生。预计所有这些模型都有助于进行退化传播评估，并有助于指示所调查系统的退化预计何时会达到临界极限。这种限制由阈值性能特征表示，可能导致软故障或硬故障以及相关故障。本文中提出的结果可能有助于 i) 故障发生预测，ii) 剩余使用寿命预测，iii) 更安全的系统运行，iv) 系统利用率合理化和 v) 维护预测。