Reliability Assessment of Mining System Based on Time Mining Data The degradation of mechanical systems is a typical phenomenon accompanying most systems. When considering dependability, safety and costeffectiveness, the degradation may result in serious consequences. Direct advancing degradation in all technical systems is not easy to observe. In order to do so, related information is used, e.g. the study of diagnostic and operation measuressignals. This article presents a study of the deterioration of a mining head with multitool knives. There is a dataset containing the records of the drilling head behaviour in standard operation. The records contain typical operation characteristics such as moment and power for both sharp and blunt knives. Degradation modelling of the studied mining head knives is performed with stochastic continuous diffusion processes. They are Pearson type, GaussMarkov type and Levy type processes. The achieved results are expected to be used for the observation of i) the first passage time of degradation critical value), ii) the prediction of residual useful life, and iii) the rationalization of in field operation and maintenance.

Adaptive Remaining Useful Lifetime Prediction of Magnetic Head under Varying Stress Conditions Current Remaining useful life studies mainly focus on the linear degradation case. A few nonlinear degradation models often rely on parameters learning process from a batch of samples obtained from the same population. This result in large estimation biases and uncertainties in term of RUL prediction under varying stress conditions. To address this problem, this paper proposed an adaptive RUL prediction method only using the observed head wear data. First, an exponential item is incorporated into a power law wear model to build a nonlinear wear model under varying stress conditions. Second, PF method was used to update the model parameters dynamically. Third, an adaptive strategy was developed based on expectationmaximization algorithm to update these initial values of parameters of degradation model recursively. Finally, a reallife data set was used to verify the effectiveness of our proposed approach, and the results show that the proposed approach can improve the prediction accuracy significantly.