A test procedure optimization method was proposed in this paper to improve the test efficiency of the industrial robot servo system (IRSS) to be tested on the IRSS integrated testing platform (IITP). First, an ordered sequence was used to define the IRSS test project when tested on the IITP. The ordered sequence consisted of execution subelements, which were a combination of control variable parameters of the IITP. Second, the optimization relationships among the IRSS test projects were dug out according to the IRSS test project sequences. Finally, by using the traveling salesmen problem (TSP), an optimization model was established based on the optimization relationships of the IRSS test projects, and the optimal test order of the IRSS test projects to be tested on the IITP was obtained by solving the model. A case analysis showed that the proposed method optimized the test procedure on the IITP effectively, and the test time when implementing the test on the IITP according to the optimized order was nearly 50% shorter than before optimization. The optimization effect was thus found to be significant.

Due to the lack of fault data in the daily work of rotating machinery components, existing data-driven fault diagnosis procedures cannot accurately diagnose fault classes and are difficult to apply to most components. At the same time, the complex and variable working conditions of components pose a challenge to the feature extraction capability of the models. Therefore, a transferable pipeline is constructed to solve the fault diagnosis of multiple components in the presence of imbalanced data. Firstly, synchrosqueezed wavelet transforms (SWT) are improved to highlight the time-frequency feature of the signal and reduce the time-frequency differences between different signals. Secondly, we proposed a novel hierarchical window transformer model that obeys a dynamic seesaw (HWT-SS), which compensates for imbalanced samples while fully extracting key features of the samples. Finally, a transfer diagnosis between components provides a new approach to solving fault diagnosis with imbalanced data among multiple components. The comparison with the benchmark models in four datasets proves that the proposed model has the advantages of strong feature extraction capability and low influence from imbalanced data. The transfer tests between datasets and the visual interpretation of the model prove that the transfer diagnosis between components can further improve the diagnostic capability of the model for extremely imbalanced data.

Pan, G.; Li, X.; Luo, Q.; Li, D., and Zhang, Z., 2020. A reliability evaluation of marine power electronic devices based on performance degradation data and a competitive failure model. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 67–70. Coconut Creek (Florida), ISSN 0749-0208.This paper proposes a reliability evaluation method for marine power electronic (MPE) devices that considers the characteristics of high reliability, long life, and multi–failure-mode competition. Performance degradation data are used to construct performance degradation and lifetime distribution models of MPE devices, and methods of parameter estimation, goodness-of-fit testing, and optimization are proposed to solve them. By considering the coupled competitive relationships of multiple performance parameters, a competitive failure model is proposed to rapidly evaluate the reliability of MPE devices. The Copula function and Monte Carlo method are used to solve the model. As an example, reliability evaluation is carried out for a marine insulated-gate bipolar transistor. The error between the evaluation result of the proposed method and an actual statistical case is demonstrated to be small, which verifies that the proposed method is accurate and effective.

According to the characteristics of rice planting and rice operation machinery in South China, the paper discusses how to use the satellite of the operation machinery to position trajectory information, to extract operation task by field based on the clustering algorithm of OPTICS, and to calculate the operation area by broken line buffer algorithm. The paper also discusses about parameter setting of OPTICS. The accuracy of extracting tasks by field can be over 84%. When the area of each task is over 3 mu (0.2 hectares), the calculation accuracy can reach over 95%. On the surface of practical application in Guangdong Province in recent three years, the algorithm can meet the requirements of operation charging and government subsidy giving, and realize accurate task differentiation and area calculation.