Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis

By Thomas Walter Rauber, Francisco de Assis Boldt, Flávio Miguel Varejão. IEEE transactions on industrial electronics (1982. Print), 2015

320 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/6bb35ca695e816214f78c26a9a3d34b350c10c92

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler
5. Liang Gao
6. Xinyu Li
7. Long Wen
8. Yihao Xue

In Mechanical Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler
5. Liang Gao
6. Xinyu Li
7. Long Wen
8. Haidong Shao

Citations in the corpus

• A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, methodology): “Cross-validation (CV) is a popular technique to obtain the reliable performance evaluation of fault classifier [43].”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, background): “Similarly, other k-NN based papers [45]–[47] employ k-NN to perform a distance analysis on each new data sample and determine whether it belongs to a specific fault class.”
• Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019, unknown): “[5] used an”
• Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018, methodology): “[12] designed an original feature vector based on 26 statistical parameters, 72 envelope features, and 32 wavelet packet features, and then utilized SVM for identifying bearing faults.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, methodology): “In the feature extraction step, the data attributes are analyzed and the features are manually selected and extracted based on predefined formulas designed from expert knowledge [23], [24], [25], [26], [27],”
• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, background): “Similarly, other k-NN based papers [44]–[46] employ k-NN to perform a distance analysis on each new data sample and determine whether it belongs to a specific fault class.”