Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

By M. Prieto, G. Cirrincione, A. Espinosa, J. A. Redondo, Humberto Henao. IEEE transactions on industrial electronics (1982. Print), 2013

418 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/e3ccd78eae121ffed6a6d9294901db03704df2e5

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. Dong Yang
6. Weiting Zhang
7. Hongchao Wang
8. Ruqiang Yan

In Mechanical Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler
5. Haidong Shao
6. Enrico Zio
7. Dong Yang
8. Thomas Walter Rauber

Citations in the corpus

• 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”
• Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, methodology): “After feature extraction and selection by a nonlinear manifold learning technique, Prieto et al. [101] use a hierarchical neural network to perform classification for bearing fault detection.”
• Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis (2015, methodology): “In [21], statistical features describe the bearing fault; linear discriminant analysis is used to rank the features in a preprocessing step in a univariate, bivariate, and trivariate combination; a curvilinear component analysis is used for feature extraction; and a multilayer perceptron (MLP) provi”
• Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks (2018, methodology): “To verify the above statement empirically, we tested our proposed LFGRU under five additional different numbers of segments: T = [5, 10, 50, 100, 200] and our previous setting T = 20 over one tool prediction task C1.”
• An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016, neutral): “Traditionally, the framework of intelligent fault diagnosis includes three main steps: 1) signal acquisition; 2) feature extraction and selection; and 3) fault classification [9]–[11], as shown in Fig.”
• A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017, methodology): “Common intelligent fault diagnosis methods can be divided into two steps, namely, feature extraction and classification [4,5].”
• 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”
• Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018, methodology): “[9] calculated 15 time-domain statistical parameters to characterize the bearing health conditions, then discriminant analysis was used for feature selection and ANN was adopted for fault detection.”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “A hierarchical NN structure was used in [93] to perform the fault classifica-”