Rolling bearing fault diagnosis using an optimization deep belief network

By Haidong Shao, Hongkai Jiang, Xunan Zhang, Maogui Niu. 2015

495 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/f9f67c572c1550b8ba547ccadbbbde49f69ec38f

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. Shen Zhang
5. Bingnan Wang
6. Shibo Zhang
7. Thomas G. Habetler
8. Chao Liu

In Mechanical Engineering

1. Liang Gao
2. Xinyu Li
3. Long Wen
4. Zhuyun Chen
5. Weihua Li
6. Haidong Shao
7. Qiuhua Miao
8. Yudong Cao

Citations in the corpus

• A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings (2021, methodology): “[3] proposed a rolling bearing fault diagnosis method based on a deep belief network (DBN).”
• An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, methodology): “proposed optimized DBN for rolling bearing fault diagnosis [18].”
• Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network (2017, methodology): “And then, these features are input to a classifier, such as support vector machines (SVM) [1], random forest [2], artificial neural networks [3], and deep belief network (DBN) [4] for fault classification.”
• A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (2019, methodology): “The configuration of the experiments is according to [24].”
• Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, background): “Shao et al. [106] propose an optimization DBN for rolling bearing fault diagnosis.”
• A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (2018, methodology): “Machine learning is also one of the dominant tools for data-driven fault diagnosis, such as SVM [6], FL [7], ANN [8], etc.”
• Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components (2018, background): “promising results for machine fault diagnostics on extraction of raw vibration signals [23] as well as time-domain features [24].”
• Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, background): “In the past few years, the deep learning models, including stacked auto-encoder (SAE) [15], deep belief networks (DBN) [16] and, in particular, convolutional neural networks (CNN) [17]-[23], have gained much popularity and success in mechanical fault diagnosis issues, showing an extraordinary…”