The measured uniaxial-head load spectrum in the road simulation test has a large number of useless small loads. When applying the measured load spectrum directly, it will take a lot of time. This paper designs a comprehensive road spectrum measurement system to collect data and proposes a method for editing the uniaxial-head acceleration load spectrum using short-time Fourier transform to speed up the reliability test process and reduce time costs. In this method, the time domain and frequency domain information of the signal is obtained by short-time Fourier transform. The concept of accumulated power spectral density is proposed to identify the reduced load data, and the relative fatigue damage is used as the pass criterion. The length of the edited spectrum is only 66% of the original spectrum through the above-mentioned editing method and retains the relative damage amount of 91%. Finally, through the analysis of time domain, frequency domain, and fatigue statistical parameters, it demonstrates that the short-time Fourier transform–based acceleration load spectrum edition method could achieve a similar fatigue damage to the original spectrum in a shorter time.

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Maintenance is inevitable for repairable components or systems in modern industries. Since the maintenance effectiveness has a great influence on the subsequent operations and in addition, different maintenance options are possible for the components of the system during the break between any two successive missions, the optimal selective maintenance strategy needs to be determined for a system performing successive missions. A number of selective maintenance models were set up on the basis that the durations of each mission are predetermined, the maintenance time is negligible, and the states of the components at the end of the previous mission can be accurately obtained. However, in the actual industrial and military missions, these premises may not always hold. In this paper, a novel selective maintenance model under uncertainties and limited maintenance time is proposed to improve these deficiencies. The genetic algorithm is selected to solve the optimization problem, and an illustrative example is presented to demonstrate the proposed method. The optimal selective maintenance decision without the constraint of maintenance time is used for comparison.

Due to hydraulic pump’s multiple fault parameters, imprecision of fault diagnosis and bad fuzzy properties, a novel method of data preprocess to remove the noise disturbance and extract the characteristics of parameters, in which the order analysis is applied, is put forward. Then the hydraulic pump’s fault is diagnosed with decision-level data fusion of multiple sensors. The practical results showed that the fault diagnosis method based on D-S proof theory and decision-level data fusion could promote the accuracy and efficiency of hydraulic pump’s fault diagnosis.

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