Failure diagnosis using deep belief learning based health state classification

By Prasanna Tamilselvan, Pingfeng Wang. 2013

592 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Medical & Health AI

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. Liang Gao
2. Xinyu Li
3. Long Wen
4. Weihua Li
5. Zhuyun Chen
6. Haidong Shao
7. Shen Zhang
8. Bingnan Wang

In Mechanical Engineering

1. Liang Gao
2. Xinyu Li
3. Long Wen
4. Zhuyun Chen
5. Weihua Li
6. Haidong Shao
7. Réne-Vinicio Sánchez
8. Chen Lu

In Medical & Health AI

1. Réne-Vinicio Sánchez
2. Chen Lu
3. Weihua Li
4. Robert X. Gao
5. Xiaolong Liu
6. Yonghua Zheng
7. Zhiqiang Chen
8. Zhuyun Chen

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “CD algorithm is also used for the optimization of DBNs [55, 56, 72, 73].”
• Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification (2017, methodology): “presented a novel multisensor health diagnosis method using a deep belief network [29] demonstrating the feasibility and preponderance of deep learning in fault pattern recognition and diagnosis.”
• Gearbox Fault Identification and Classification with Convolutional Neural Networks (2015, methodology): “Tamilselvan and Wang [27] employed the deep belief learning based health state classification for iris dataset, wine dataset, Wisconsin breast cancer diagnosis dataset, and Escherichia coli dataset.”
• Rolling bearing fault diagnosis using an optimization deep belief network (2015, methodology): “In 2012, Tamilselvan [27] employed the DBN in an aircraft engine fault diagnosis based on health state classification, although it was still aimed at this fault diagnosis in a small sample with enough fault information.”
• The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, unknown): “A variety of technologies used in fault diagnosis like Support Vector Machines [36], Bayesian Networks [37], Deep Learning [38]–[40], andmany others [41]–[44] are capable of enhanced fault diagnosis.”
• A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, background): “With this advantage, DL could avoid the effect of handcrafted features designed by engineers, and it has shown a good prospect on fault diagnosis [11], [12].”