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Ni-P-β-SiC was prepared using different granularity β-SiC and rolling electroless plating technology. The test results show that β-SiC particles in composite coating are uniform, and the thickness and hardness of composite coating are 17~25μm and 520~620HV, respectively. At the same time, the friction and wear behavior of Ni-P-β-SiC composition coating have been studied. The results show that the wear weight loss decrease and the friction coefficient increase with the increase of β-SiC granularity.

With the rapid development of rail transit, the wear problem of track has become a serious challenge. Laser quenching is a new surface modification process, and it is expected to improve the service life of the track. Laser quenching was carried on the surface of rails by using 6[Formula: see text]kW fiber laser. The microstructure, hardness, wear resistance, and contact fatigue resistance (CFR) of the phase transformation hardening layer were studied by optical microscope (OM), scanning electron microscopy (SEM), X-ray diffraction (XRD), microhardness tester, and wear tester. The results show that the microstructure of the hardened layer is composed of fine martensite and a small fraction of retained austenite. The microstructure of substrate is pearlite and ferrite. The hardness of rails quenched by the laser increased nearly two times than that of the un-treated track materials. When the laser power is 1000[Formula: see text]W, the depth of hardened layer reaches 0.8[Formula: see text]mm, and the wear resistance of hardened layer increases 167% than that of the substrate. The number, length, and depth of fatigue cracks decreased on the cross-section of worn rails after laser quenching, which indicates that laser quenching can improve the CFR of rails.

The influence of boron concentration on corrosion resistance of high boron white cast iron dipped into a pure liquid zinc bath at 460°C was investigated. The results reveal that high boron white cast iron containing 3.5 wt.%B exhibits excellent corrosion resistance due to the dense continuous netlike or parallel Fe2B phase which hinders the Fe/Zn interface reaction. The corrosion rate decreases significantly when the boron concentration increases, but the corrosion rate declines slightly when the boron concentration exceeds 3.5wt.%. EDS results indicate the coarse and compact δ phase generated near the matrix and a large amount of massive and blocky  phase occurred close to the liquid zinc. The corrosion process includes Fe/Zn interface reaction and the spalling and fracture of Fe2B. The failure of Fe2B is mainly caused by the microcrack of phase transformation.

This study investigates the effect of rare earth and aluminium composite modification on the structural variations of as-cast and heat treated medium carbon Fe–B cast alloys. The as-cast microstructure of Fe–B cast alloy consists of the eutectic boride, pearlite, martensite and ferrite. Moreover, compared to a netlike distribution of the coarse eutectic borides in the unmodified alloy, the eutectic boride structures in the modified alloy are greatly refined and less interconnected. After heat treatment, the phases in Fe–B cast alloy consist of the boride and martensite. The addition of rare earth helps to increase the number of the rod-shaped and round borides in Fe-B cast alloy during austenitizing. Compared to the unmodified alloy, the boride volume fraction and Rockwell hardness of the modified alloy have no significant change, however, the average area of each boride in the modified alloy is lower and the impact toughness is higher.

This paper studies a real-time prediction method associated with the state and event of spacecraft in-orbit. The process includes the following steps: 1) Access the information about the parameter and state configuration related to the state and event of spacecraft, and construct the SERTS model (State-Event-Related-Time-Series model) of the changed parameters; 2) Select the parameters used to characterize the status of the system in the timing method to compose the vector, and extract linearly independent vectors; 3) Construct the distance discriminant functions, and identify the classification threshold value of the status according to the dynamic change of the system status; 4) Monitor the spacecraft in-orbit in real-time and predict the trend of the parameter according to the above distance discriminant function and the threshold. Besides, the adaptive threshold selection method during the process of SERTS modeling and predicting has also been debated. Finally, the experimental result shows that the SERTS model can be effectively applied to the predicting and alarming of the telemetry data related to the state and event of spacecraft. The proposed SERTS model and method can provide the efficient tool for the satellite operation and maintenance staff and provide the automated means to improve work efficiency.

As the number of spacecraft growing and the spacecraft playing a significant role in many aspects, it is necessary to research the prognostics in order to find the spacecraft fault in advance. This paper puts forward a method of particle swarm optimized neural network used to realize prognostics for spacecraft considering the importance of prognostics in the field of PHM study. Firstly based on the principle and operation traits of the spacecraft, the fault modes and fault characteristics are analyzed to obtain the feature that the prognostics can be achieved through the telemetry parameters of the spacecraft. Secondly the neural network is invoked to approximate and model the key telemetry parameters of the spacecraft due to the neural networks high nonlinearity. After fixed the input and hidden layers nodes, the neural network is optimized particle swarm optimization algorithm with considering the parameter slow convergence in the neural network and the specialty of particle swarm optimization algorithm. Thirdly times series method is incurred to predict the data of the telemetry parameters while there access the real-time downloading of telemetry data. Furthermore the predict data are contrasted to the outputs of the particle swarm optimized neural network to inspect whether the relations between the output layers and the input layers of the neural network is broken. Then the prognostics for spacecraft are achieved with the inspection information. Finally an experiment using there years telemetry data with fault data in the power system of a spacecraft is set up to verify the feasibility of the method proposed in this paper, and the experimental results prove the effectiveness of the particle swarm optimized neural network in prognostics for spacecraft.

Anomaly detection is to identify abnormal behaviors of a system. It plays an important role in Prognostics and System Health Management (PHM) which is applied to monitor spacecraft's performance, detect faults, identify the root cause of the fault, and predict the remaining useful life (RUL) in order to increase safety and reduce downtime of spacecraft in—orbit.