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Composite-Graph-Based Sparse Subspace Clustering for Machine Fault Diagnosis

https://doi.org/10.1109/tim.2019.2923829

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

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Matching Demodulation Transform and SynchroSqueezing in Time-Frequency Analysis

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Induction Motor Fault Diagnosis and Classification Through Sparse Representation

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Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy C -Means Clustering

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Collaborative Double Sparse Period-Group Lasso for Bearing Fault Diagnosis

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A New Adaptive Mixture Distance-Based Improved Density Peaks Clustering for Gearbox Fault Diagnosis

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