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It is not so easy to obtain the complete fault data for mechanical equipment toward certain variable working conditions. Traditional deep learning algorithms based on limited training data are difficult to accurately identify unknown faults, which seriously restricts the development of intelligent fault diagnosis. Open-set recognition can identify the faults that do not exist in the training data and label them as "unknown," so it is becoming a powerful tool for solving the above problems. A novel l2,1 regularized sparse filtering and multi-sample distances fusion-based open-set fault diagnosis method (RSDOS) is developed in this article. Firstly, the l2,1 regularized term is induced in a traditional sparse filtering model to learn a more representative feature representation. Then, multi-sample distances fusion is applied to calculate the distances of intra-class and inter-class samples. Thirdly, the generalized Pareto distributions (GPD) concerning the tail information of those two distance distributions are modeled by the statistical extreme value theory (EVT). Finally, hypothesis testing models and the inference threshold are established to identify unknown faults. The diagnosis results of two rolling bearing datasets verify the progressiveness and robustness of RSDOS. [ABSTRACT FROM AUTHOR]

The scale of fluctuation is one of the vital parameters for the application of random field theory to the reliability analysis of geotechnical engineering. In the present study, the fluctuation function method and weighted curve fitting method were presented to make the calculation more simple and accurate. The vertical scales of fluctuation of typical layers of Tianjin Port were calculated based on a number of engineering geotechnical investigation data, which can be guidance to other projects in this area. Meanwhile, the influences of sample interval and type of soil index on the scale of fluctuation were analyzed, according to which, the principle of determining the scale of fluctuation when the sample interval changes was defined. It can be obtained that the scale of fluctuation is the basic attribute reflecting spatial variability of soil, therefore, the scales of fluctuation calculated according to different soil indexes should be basically the same. The non-correlation distance method was improved, and the principle of determining the variance reduction function was also discussed. [ABSTRACT FROM AUTHOR]