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In this paper, a reliability sensitivity analysis methodology for the kinematic accuracy of rack-and-pinion steering linkages (RPSLs) is developed. The direct linearization method is applied to obtain the kinematic accuracy errors of planar linkages due to link-length variations. The RPSL widely used in many types of vehicles is chosen as an example to propose an analytical model for reliability analysis of the kinematic accuracy of planar linkages. Furthermore, reliability sensitivity analysis of planar linkages, which is the main focus of this paper, is used to compute the reliability sensitivity of the kinematic accuracy of steering linkages with respect to the statistical parameters (e.g., mean, standard deviation, or higher moments) of the basic errors of random variables. Finally, the practicality and efficiency of the proposed method are demonstrated by a numerical example.

It is often difficult for a phased mission system (PMS) to be highly reliable, because this entails achieving high reliability in every phase of operation. Consequently, reliability analysis of such systems is of critical importance. However, efficient and interpretable analysis of PMSs enabling general component lifetime distributions, arbitrary structures, and the possibility that components skip phases has been an open problem.In this paper, we show that the survival signature can be used for reliability analysis of PMSs with similar types of component in each phase, providing an alternative to the existing limited approaches in the literature. We then develop new methodology addressing the full range of challenges above. The new method retains the attractive survival signature property of separating the system structure from the component lifetime distributions, simplifying computation, insight into, and inference for system reliability.

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In recent years, prognostic and health management (PHM) has played an important role in industrial engineering. Efficient remaining useful life (RUL) prediction can ensure the development of maintenance strategies and reduce industrial losses. Recently, data-driven based deep learning RUL prediction methods have attracted more attention. The convolution neural network (CNN) is a kind of deep neural network widely used in RUL prediction. It shows great potential for application in RUL prediction. A CNN is used to extract the features of time-series data according to the spatial feature method. This way of processing features without considering the time dimension will affect the prediction accuracy of the model. On the contrary, the commonly used long short-term memory (LSTM) network considers the timing of the data. However, compared with CNN, it lacks spatial data extraction capabilities. This paper proposes a double-channel hybrid prediction model based on the CNN and a bidirectional LSTM network to avoid those drawbacks. The sliding time window is used for data preprocessing, and an improved piece-wise linear function is used for model validating. The prediction model is evaluated using the C-MAPSS dataset provided by NASA. The predicted results show the proposed prediction model to have a better prediction performance compared with other state-of-the-art models.

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