A comparison of artificial neural networks and other statistical methods for rotating machine condition classification-Reference-Cited by-同舟云学术

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A comparison of artificial neural networks and other statistical methods for rotating machine condition classification

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Container-title:IEE Colloquium on Modelling and Signal Processing for Fault Diagnosis
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Author:

McCormick A.C.

Publisher

IEE

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review;Machines;2022-11-23

2. Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals;Entropy;2022-04-05

3. Deep Neural Network based Classification of Rolling Element Bearings and Health Degradation Through Comprehensive Vibration Signal Analysis;Journal of Systems Engineering and Electronics;2022-02

4. Time domain vibration analysis techniques for condition monitoring of rolling element bearing: A review;Materials Today: Proceedings;2022

5. Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks;Chinese Journal of Mechanical Engineering;2021-04-12

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