Remaining useful life prediction of cylinder liner based on nonlinear degradation model

In order to effectively monitor the wear and predict the life of cylinder liner, a nonlinear degradation model with multi-source uncertainty based on Wiener process is established to evaluate the remaining useful life (RUL) of cylinder liner wear. Due to complex service performance of cylinder liner, the uncertainty of operational environment and working conditions of cylinder liner wear are considered into the model by a random function. The probability density function (PDF) formula of RUL is derived, and the maximum likelihood estimation method is adopted to estimate the unknown parameters of PDF. Considering the evaluated parameters as the initial values, the model parameters are updated adaptively, and an adaptive PDF is obtained. Furthermore, the proposed model is compared with two classical degradation models. The results show that the proposed model has a good performance for predicting the life, and the error is within 5%. The method can provide a reference for condition monitoring of cylinder liner wear.

The Correlation Between the Statistical Pass-By Index Values and the Total Number of Vehicle Passes

In this study, the traffic noise degradation in asphalt pavements was analysed using the ‘Statistical Pass-By method’. The sound levels of two surfaces were monitored during 9 and 12 years of service, respectively. By comparing the dependencies of the maximum A-weighted sound pressure level on logarithm of vehicle velocity, an increase in the sound level was found at all recorded speeds. Following an analysis of sound levels, as combined with the statistical pass-by index (SPBI) calculated versus age (expressed in vehicles), it was determined that the noise is an increasing power function of SPBI values on vehicle passes, based on an approximation of noise level adjustment to a reference temperature of 20 °C (using a coefficient of 0.06 for asphalt concrete surface AC11 and - 0.03 for mastic asphalt SMA11). The adjusted traffic noise degradation model showed that the SMA11 surface has a higher resistance to acoustic degradation than AC11 surface.

Unscented Kalman Filtering for Prognostics Under Varying Operational and Environmental Conditions

Prognostics gained a lot of research attention over the last decade, not the least due to the rise of data-driven prediction models. Also hybrid approaches are being developed that combine physics-based and data-driven models for better performance. However, limited attention is given to prognostics for varying operational and environmental conditions. In fact, varying operational and environmental conditions can significantly influence the remaining useful life of assets. A powerful hybrid tool for prognostics is Bayesian filtering, where a physical degradation model is updated based on realtime data. Although these types of filters are widely studied for prognostics, application for assets in varying conditions is rarely considered in literature. In this paper, it is proposed to apply an unscented Kalman filter for prognostics under varying operational conditions. Four scenarios are described in which a distinction is made between the level in which real-time and future loads are known and between short-term and long-term prognostics. The method is demonstrated on an artificial crack growth case study with frequently changing stress ranges in two different stress profiles. After this specific case, the generic application of the method is discussed. A positioning diagram is presented, indicating in which situations the proposed filter is useful and feasible. It is demonstrated that incorporation of physical knowledge can lead to highly accurate prognostics due to a degradation model in which uncertainty in model parameters is reduced. It is also demonstrated that in case of limited physical knowledge, data can compensate for missing physics to yield reasonable predictions.