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At present, machine learning is widely used for classification, such as automatic speech recognition, image identification, text classification and numbers of researches for fault diagnosis besides. Generally, most of the models used for fault diagnosis are based on the same data distribution, while the applications of the equipment in actual production and operation are mostly under unstable conditions,...
Feature extraction plays an important role in machinery fault diagnosis and prognosis. The features extracted from time, frequency and time-frequency domains are widely investigated to describe the properties of overall signal from different perspectives, seldom considering the sequential characteristic of time-series signal in which the fault information may be embedded. This paper investigates a...
Feature extraction plays an important role in machinery fault diagnosis and prognosis. The features extracted from time, frequency and time-frequency domains are widely investigated to describe the properties of overall signal from different perspectives, seldom considering the sequential pattern of time-series signal in which the fault information may be embedded. This paper investigates a novel...
Association rule mining provides the feasibility by taking an inverse approach for bearing defect signature analysis to directly mine associations between labeled defects and defect features instead of traditional forward fault diagnosis steps. Different from the common uniform partitioning approach used in association rule mining, a novel association rule mining approach has been proposed, based...
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