An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks

By V. Tran, Faisal AlThobiani, Andrew D. Ball. Expert systems with applications, 2014

377 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/f60031792228bac93080d5f0e03e6f279bee9f3b

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. Dong Yang
2. Weiting Zhang
3. Hongchao Wang
4. Haidong Shao
5. Hongkai Jiang
6. Réne-Vinicio Sánchez
7. Yu Cheng
8. Zhiqiang Chen

In Mechanical Engineering

1. Haidong Shao
2. Réne-Vinicio Sánchez
3. Dong Yang
4. Weiting Zhang
5. Zhiqiang Chen
6. Fuan Wang
7. Huiwei Zhao
8. Chuan Li

In Ocean Engineering

1. Zhang Tao
2. Weining Lu
3. Haidong Shao
4. Hongkai Jiang

Citations in the corpus

• Deep belief network based electricity load forecasting: An analysis of Macedonian case (2016, methodology): “Nonetheless, it is also used in mechanical engineering classi-fi cation of defects in compressor valves [45] and in fi nance forecasting of the exchange rate [46].”
• Deep Model Based Domain Adaptation for Fault Diagnosis (2017, background): “For instance, state-of-the-art performance in various fault diagnosis applications is mentioned in [15]–[20].”
• An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, methodology): “combined DBN and Teager-Kaiser energy operator for reciprocating compressor valves fault diagnosis [20].”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, background): “It can be seen that such a system can properly implement a production and maintenance strategy, and it will lead to a longer running time and the sufficient use for key components [34], such as bearings [35], blades [36], [37], and driving motors [38], [39].”
• Gearbox Fault Identification and Classification with Convolutional Neural Networks (2015, background): “Tran et al. [26] introduced the application of the deep belief networks to diagnose reciprocating compressor valves.”
• Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018, background): “DBN is a kind of probabilistic generative model which can acquire the joint distribution of the observed data and labels [22].”
• 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.”