Neural-network-based motor rolling bearing fault diagnosis

By Bo Li, Mo-Yuen Chow, Yodyium Tipsuwan, J. Hung. IEEE transactions on industrial electronics (1982. Print), 2000

806 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/09e5dcd265b0462f2e54eb38abf7d88b1f530bda

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In Control and Systems Engineering

In Mechanical Engineering

Citations in the corpus

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017, methodology): “With the useful features extracted and selected from raw signals, the last step is to train classiﬁers like k-nearest neighbor (KNN) [11], artiﬁcial neural networks(ANN), also known as Multi-layer Perceptron (MLP) [12,13], or support vector machine (SVM) [14] with these features.”
Information Fusion for Simulation Based Decision Support in manufacturing (2006, background): “Most condition monitoring applications in manufacturing are limited to sensor information fusion for applications such as tool-wear monitoring [8, 19] or spindle bearing monitoring [20, 21].”
Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks (2016, methodology): “For comparison with major competing signal processing techniques for current-based bearing fault detection, we implemented wavelet packet decomposition [12], [13] and FFT [8], [18] based feature extraction techniques with three commonly used classifiers from the literature: MLP [18], radial basis fu”
A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery (2016, methodology): “Li et al.(26) used the frequency domain of the vibration signal from rolling bearings to detect ball and inner race damage along with looseness using the fundamental bearing frequencies as features to train an ANN.”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “…connection, W ho is the connection weight matrix between the hidden layer and the output layer, and b h and b o are bias vectors of the hidden layer and output layer, respectively. observation that DBNs can be trained greedily, one at a time, led to one of the ﬁrst effective deep learning [139].”
Advances in Diagnostic Techniques for Induction Machines (2008, methodology): “In [86], a supervised MLP structure is used for the fault diagnosis of rolling bearing relying on time–frequency vibration analysis.”
Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring (2008, neutral): “A detailed calculation of these frequencies can be found in [16].”
Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis (2014, background): “They consume over 50% of the electrical energy in industrial applications in the United States [1].”