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The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using(More)
This paper proposed a novel fault diagnosis method by combining statistics filter (SF) and probability density functions (PDFs). First, the vibration signals are processed using SF to reduce the noise automatically. Second, PDFs is introduced to reflect the features of the vibration signals. The segment values of the PDFs (SVPDFs) are integrated into(More)
This letter puts forward a method for intelligent condition diagnosis of rotating machinery using the probability density analysis and the canonical discriminant analysis (CDA) comprising the following steps. First, the noise is cancelled by statistics filter (SF), and the probability density functions (PDFs) of the vibration signals measured in each state(More)
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