Abstract
This paper proposes a method for diagnosing bolt looseness faults using the principle of PCA to extract time-domain features of monitoring data. First of all, five dimensionless factors of IMF are calculated after empirical mode decomposition (EMD) is performed on the original data. Then, principal component analysis (PCA) is applied to the data vectors, which are processed by dimensionality reduction and residual space projection, to calculate the prediction error of the data sample. At last, a fault judgment test of bolt loosening was carried out on the test bench of the intelligent water supply system. The test results show that the PCA model can effectively judge the bolt loosening fault.
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