The multisensor data fusion method has been extensively utilized in many practical applications involving testability evaluation. Due to the flexibility and effectiveness of Dempster–Shafer evidence theory in modeling and processing uncertain information, this theory has been widely used in various fields of multisensor data fusion method. However, it may lead to wrong results when fusing conflicting multisensor data. In order to deal with this problem, a testability evaluation method of equipment based on multisensor data fusion method is proposed. First, a novel multisensor data fusion method, based on the improvement of Dempster–Shafer evidence theory via the Lance distance and the belief entropy, is proposed. Next, based on the analysis of testability multisensor data, such as testability virtual test data, testability test data of replaceable unit, and testability growth test data, the corresponding prior distribution conversion schemes of testability multisensor data are formulated according to their different characteristics. Finally, the testability evaluation method of equipment based on the multisensor data fusion method is proposed. The result of experiment illustrated that the proposed method is feasible and effective in handling the conflicting evidence; besides, the accuracy of fusion of the proposed method is higher and the result of evaluation is more reliable than other testability evaluation methods, which shows that the basic probability assignment of the true target is 94.71%.

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An increasing popularity of researches focuses on the vibration signal with the characteristics of nonstationary, nonlinear, and strong noise interference. A nonlinear dimension and feature reduction method called multiple empirical mode entropy decomposition-nonlocal orthogonal preserving embedding (MEMED-NLOPE) is proposed to implement condition monitoring in this paper. Different from multiple empirical mode decomposition (MEMD), MEMED adopts maximum entropy method, which can directly output the subsignal with the maximum correlation and realize nonlinear dimensionality reduction. Besides, multiscale feature extraction method is used during preprocessing nonlinear data process, which realizes feature reduction. Finally, nonlocal orthogonal preserving embedding algorithm-exponentially weighted moving average (NLOPE-EWMA) realizes the automatic detection of the fault. Taking the laboratory rolling bearing test and naval gun pendulum mechanism test as cases, the effectiveness of MEMED-NLOPE is verified.