Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis
By Xiaohang Jin, Mingbo Zhao, Tommy Wai-Shing Chow, Michael G. Pecht. IEEE transactions on industrial electronics (1982. Print), 2014
383 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/a60791316f5d749d9248c755112653bd527db2fe
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In Control and Systems Engineering
In Mechanical Engineering
Citations in the corpus
- Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks (2019, background): “In [42], [43], and [44], ten or more conditions were classified, and 88.”
- An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016, methodology): “In [42], nine time-domain statistical features combined with six features about the percentage of energy corresponding to wavelet coefficients were used to represent ten bearing health conditions under the load of 3 hp.”
- Deep Residual Networks With Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes (2018, methodology): “NN) using the statistical parameters in [26] were compared with the diagnosis methods with feature learning ability.”
- A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem (2016, background): “The failure of a bearing is not a sudden case, but generally a slow degradation, taking place in two stages: 1) the normal working stage; and 2) the irreversible degradation stage [35].”
- Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis (2016, background): “Signals that provide efficient monitoring include vibration signals [5], [6], motor current signals [7], [8], acoustic emission signals [9], [10], and more recently, stray flux [11].”
- An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, background): “Intelligent fault diagnosis of rotating machinery is a typical pattern recognition problem [2].”
- Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “Besides the commonly used ML methods listed above, many other algorithms have been applied to the identification of bearing faults, bringing in different characteristics and benefits, including neural fuzzy network [61]–[63], Bayesian networks [64]–[66], self-organizing maps [67], [68], extreme lear”
- Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine (2017, background): “As electric machines are widely used in modern society [3], numerous algorithms and techniques have been proposed for fault diagnosis of them, which vary from system to system [3]–[7].”
- Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, background): “Correspondingly, numerous studies have focused on how to extract effective features from measured time-domain signals based on various signal processing techniques, such as time-domain statistical analysis and spectral analysis [2], [3], time–frequency and other transform domain analysis [4]–[7], ad”
- Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “Besides the commonly used ML methods listed above, many other algorithms have been applied to the identification of bearing faults, bringing in different characteristics and benefits, including neural fuzzy network [62]–[64], Bayesian networks [65]–[67], self-organizing maps [68], [69], extreme lear”
- Multi-Layer domain adaptation method for rolling bearing fault diagnosis (2019, background): “In [65], [66] and [67], 10 or more bearing conditions were classified, and 88 .”