Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network

By Zhuyun Chen, Weihua Li. IEEE Transactions on Instrumentation and Measurement, 2017

754 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/89f400ec784d496810f62ab065a4f9e672614704

How researchers see this paper

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

In Analytical Chemistry

1. Dong Yang
2. Weiting Zhang
3. Zhu Mao
4. Robert X. Gao
5. Meng Ma
6. Shen Zhang
7. Hee-Jun Kang
8. Bingnan Wang

In Control and Systems Engineering

1. Xuefeng Chen
2. Shen Zhang
3. Bingnan Wang
4. Shibo Zhang
5. Thomas G. Habetler
6. Chuang Sun
7. Dong Yang
8. Weiting Zhang

Citations in the corpus

• Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction (2021, methodology): “…shipping schedule, and poor customer satisfaction [2], it is necessary to adopt the PHM technique, which provides users with an integrated view of the health state of a machine or system [3] and is expected to reduce unnecessary maintenance and improve their reliability and safety as well [4].”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “A multisensor data fusion model was developed in [111], which used a two-layer sparse AE and combined it with a DBN to assess health conditions of rotating machinery.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, neutral): “bearing fault diagnosis [1], [2], [3], [4], [5], [6], [7], [8].”
• An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network (2020, methodology): “Tran et al. [15] presented a new diagnosis method using Teager–Kaiser energy operator and DBN. Chen and Li [16] presented an approach using sparse autoencoder and DBN for multisensor feature fusion.”
• WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019, background): “The rise of PHM has benefited from the development of advanced sensing technology, wireless communication and computing systems in recent years, which have generated a large amount of data for manufacturing systems [4], [5].”
• A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion (2020, background): “In a system with multiple sensors installed, the signals measured by these sensors are disordered and correlated with multiple sources [18].”