As one of the most important components within a vehicle, diesel engines have high requirements for reliability due to the harsh operating environments. However, previous studies have mainly focused on the reliability assessment of diesel engines, while less research has been conducted on the modeling of the diesel engine reliability analysis and its management. For this reason, this paper proposes a comprehensive method for reliability analysis and its management based on the use of 4F integration technology in the early stages of diesel engine design. First of all, an expert group used FEMCA (failure mode, effects and criticality analysis) and FHA (functional hazard analysis) to find the most harmful level of fault mode. At the same time, a new method for the repair of dynamic fault trees to find the weak links at the component level was developed. Finally, a FRACAS (fracture report analysis and corrective action system) was used during the above analysis process. By applying this method to the reliability assessment of a diesel engine in the design stage, the problems of failure information feedback and the reuse of failure information in the actual reliability assessment can be solved.

In order to enhance the wear resistance of 304 stainless steel, a FeCrAlNbNi alloyed zone (AZ) was deposited on its surface using laser surface alloying technology, and the wear resistance of the AZ was investigated. The results found that the AZ had a dense and fine structure and no obvious defects, and the microstructure was mainly composed of equiaxed dendrites. A large amount of iron compounds and iron-based solid solutions in the AZ made the average microhardness of the AZ about 2.6 times higher than of the substrate. The friction and wear performance of the AZ at 25 °C, 200 °C, 400 °C and 600 °C better than that of the substrate. As far as the AZ was concerned, the abrasion resistance was the best under normal temperature environment. At 200 °C and 400 °C, due to the repeated extrusion and grinding of the friction pair, the oxide layer formed on the AZ surface was prone to microcracks and peeling off, which reduces the wear resistance. Especially at 400 °C, the formation and peeling speed of the oxide layer is accelerated, and the wear resistance is the lowest. However, when the temperature reached 600 °C, an Al₂O₃ layer was formed. And the Al₂O₃ has greater wear resistance to protect the AZ. At this time, the wear resistance was greatly improved compared to 200 °C and 400 °C. Therefore, as the temperature increased, the wear resistance of the AZ first decreased and then increased.

Due to the time property of natural phenomena and human activities, time series are very common in our lives. The analysis and study of time series can help us to better understand the world, predict the future and make scientific decisions. Focusing on time series prediction, in this paper we propose a method of constructing non-periodic time series into periodic time series and design a framework for time series prediction based on the constructed periodic time series. The proposed construction method and prediction framework for the periodic time series are then applied to predict the state of health (SOH) of lithium-ion (Li-ion) batteries. The effectiveness of the proposed approach is verified and evaluated on publicly available datasets from the National Aeronautics and Space Administration (NASA), Ames Prognostics Center of Excellence (PCoE), and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland. The experimental results show that the early SOH prediction of Li-ion batteries can be improved by at least one order of magnitude on both the NASA and CALCE battery datasets when using the method proposed in this paper.

The accurate prediction of remaining useful life (RUL) is crucial in order to reasonably and efficiently utilize lithium-ion batteries (LiBs). In this paper, a construction method of periodic time series is applied to the degradation data of LiBs to address the issues of insufficient training data and smooth degradation in the RUL interval prediction method based on trend filtering segmentation and fuzzy information granulation. The construction method for periodic time series is used to form a new dataset from the original data, based on which the fusion model, by combining the variational mode decomposition (VMD) and gated recurrent unit (GRU), is used as the RUL interval prediction model of LiBs. Moreover, the effectiveness and advantage of the RUL interval prediction method proposed in this paper was verified and analyzed by utilizing the CALCE battery dataset and NCA battery dataset.