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During actual operation, a steel wire rope is deformed due to stress interaction between its internal steel wires. In this article a finite element approach is employed to simulate the interaction of steel wire under practical working conditions. In addition, according to the amount of fatigue broken wire under experimental conditions, corresponding parameters are put forward to estimate the service life of steel rope. In this article, the effect of various conditions on service life of steel wire rope is analyzed, and approaches are put forward to improve its service life. The results show that the total stress in straight strand is larger than side strand stress, while the stress increases from inside to outside while increasing from inside to outside. The bigger the pulley diameter, the more steel wire breaks inside the wire rope. [ABSTRACT FROM AUTHOR]

Degradation processes of practical chemical engineering systems are difficult to model accurately because of complicated nonlinearities, non‐stationarities, and non‐Markovian properties. Traditional prognostics techniques tend to neglect the multi‐mode switching issue, and therefore cannot be used for predicting the remaining useful life (RUL) of a piece of long‐life equipment, especially when it is continuously operated under varying modes. Specifically, the stationarity of differential data also plays an important role in depicting the evolutionary trend, and further affects the extrapolation procedure for RUL prediction. In this paper, we construct a multi‐mode degradation model with regard to fractional diffusion processes by considering both stationary and non‐stationary increments. The drift term is represented as a time‐related nonlinear function, while the diffusion term is driven by fractional Brownian motion (FBM) or sub‐fractional Brownian motion (sub‐FBM), according to the results of the stationary test. Based on the monitored data, the model is identified by combining change‐point detection and parameter estimation. The closed‐form distribution of RUL is then derived through a weak convergence transformation. A numerical example and a case study of a large blast furnace are provided to evaluate the performance of the proposed prognostics framework. [ABSTRACT FROM AUTHOR]