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According to the characteristics of high-dimensional imbalance distribution of motor bearing fault data, a design scheme of classification model is proposed for the high-dimensional data reduction problem in the classification algorithm. For details: Combining standard particle swarm optimization algorithm and random forest algorithm, a new high-dimensional data reduction algorithm is proposed. Aiming at the imbalance problem of data categories in the classification algorithm, we proposes to use machine learning under the sum of squares of dynamic deviations criterion to divide the minority sample data set into mixed regions, high-purity minority sample regions and outlier regions, and then use smote algorithm to complete the data equalization processing, so as to make the sample data equalization processing more reasonable, Focusing on the task of motor bearing fault classification, a design scheme of using standard particle swarm optimization algorithm to improve the least squares support vector machine model is proposed.

Undertaking varying working conditions is common for a machine tool. One single reliability model for determining a single effect is not suitable for the reliability evaluation of machine tools due to the multi-effect of working conditions on the reliability of machine tools. This paper proposes a generalized model using hazard functions and Weibull distribution to assess the reliability of machine tools under different effects of working conditions. This model considers three effects, including the proportional hazard, time, and additive hazard effects. The distribution of time effect is discussed. Furthermore, a secure parameter estimation method is presented to calculate the proposed reliability model. A case study based on field data is presented. The proposed model is compared with classical reliability models. The result shows that the proposed model has better goodness of fit for the realistic cumulative distribution of machine tools.