Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Authors

  • Zhiwen Wang Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Bo Yang Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Qian Ma Department of Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Hu Wang Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Rupp Carriveau Turbulence and Energy Laboratory, Ed Lumley Centre for Engineering Innovation, University of Windsor, Windsor N9B 3P4, Ontario, Canada
  • David S-K. Ting Turbulence and Energy Laboratory, Ed Lumley Centre for Engineering Innovation, University of Windsor, Windsor N9B 3P4, Ontario, Canada
  • Wei Xiong Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

DOI:

https://doi.org/10.13052/ijfp1439-9776.2442

Keywords:

Pneumatic System, pneumatic cylinder, energy monitoring, fault diagnosis, exergy, machine learning

Abstract

Pneumatic systems are widely used in industrial production sectors. Increasing penetrations of Intelligent Manufacturing and Green Manufacturing are highlighting the drawbacks of pneumatic technology in terms of particularly low energy efficiency and low-level fault diagnosis intelligence. Here we propose that a combined energy-based maintenance and fault diagnostic approach for pneumatic systems could be a game-changer for pneumatics. In this study, a pneumatic cylinder with internal and external leakages is examined and a typical pneumatic experimental system is built. Exergy is adopted for evaluating the available energy of compressed air. Data-driven machine learning models, SAE + SoftMax neural network model and SAE + SVM model, are developed for fault detection and diagnosis. By comparing different machine learning methods with various pressure, flowrate, and exergy data, it is found that the diagnostic accuracy when using pressure and flowrate data is highly dependent on operating conditions, while the diagnostic accuracy when using exergy data is always high regardless of operating conditions. This indicates the promise of developing an exergy-based maintenance paradigm in pneumatic systems. Besides, with exergy and machine learning, more downstream faults can be detected and diagnosed with fewer upstream sensors. This study is the first attempt to develop an exergy-based maintenance paradigm in pneumatic systems. We hope it could inspire the following investigations in other energy domains.

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Author Biographies

Zhiwen Wang, Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

Zhiwen Wang received the bachelor’s degree in Marine Engineering from Dalian Maritime University in 2012 and the philosophy of doctorate degree in Marine Engineering from Dalian Maritime University in 2018, respectively. He is currently working as an Associate Professor at the Department of Mechanical Engineering, Dalian Maritime University. His research areas include energy saving and FDD of pneumatics, thermodynamics, and energy storage.

Bo Yang, Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

Bo Yang received the bachelor’s degree in Mechanical Engineering from Zaozhuang University in 2020. He is now pursing the master’s degree in Mechanical Engineering in Dalian Maritime University. His research areas include energy saving and FDD of pneumatics, and machine learning.

Qian Ma, Department of Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China

Qian Ma received her B.D., M.D. and Ph.D. degrees in computer software and theory from Northeastern University in 2011, 2013, and 2019, respectively. She is currently an associate professor with the College of Information Science and Technology, Dalian Maritime University, Dalian, China. Her research interests include missing data imputation, data management and artificial intelligence.

Hu Wang, Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

Hu Wang received the bachelor’s degree in Mechanical Engineering from Ludong University in 2019 and the master’s degree in Mechanical Engineering from Dalian Maritime University in 2022, respectively. He is currently pursuing the doctorate degree in Dalian Maritime University. His research areas include energy storage, and fluid power transmission and control.

Rupp Carriveau, Turbulence and Energy Laboratory, Ed Lumley Centre for Engineering Innovation, University of Windsor, Windsor N9B 3P4, Ontario, Canada

Rupp Carriveau is a Professor of Civil and Environmental Engineering, University of Windsor, Canada. He is the director of Turbulence & Energy Lab. He received his BSc in Civil Structural Engineering from University of Windsor. He obtained his MSc and PhD in Fluids Engineering from Western University, Canada. His research interests cover energy storage, renewable energy, and systems optimization etc.

David S-K. Ting, Turbulence and Energy Laboratory, Ed Lumley Centre for Engineering Innovation, University of Windsor, Windsor N9B 3P4, Ontario, Canada

David S-K. Ting is a Professor of Mechanical, Automotive & Materials Engineering, University of Windsor, Canada. He received his BSc in 1989 from University of Manitoba. He obtained his MSc and PhD in 1992 and 1995 from University of Alberta, Canada. His major research interests are turbulence, heat transfer, energy & thermal systems, renewable energy, and aerodynamics.

Wei Xiong, Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

Wei Xiong is a Professor of Mechanical Engineering, Dalian Maritime University. He is the director of Ship Electromechanical Equipment Institute. He received his PhD in the Faculty of Mechatronic Engineering from Harbin Institute of Technology, China. His major research interests are fluid power and control, marine rescue, and compressed air energy storage.

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Published

2023-11-07

How to Cite

Wang, Z. ., Yang, B. ., Ma, Q. ., Wang, H. ., Carriveau, R. ., Ting, D. S.-K. ., & Xiong, W. . (2023). Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning. International Journal of Fluid Power, 24(04), 643–682. https://doi.org/10.13052/ijfp1439-9776.2442

Issue

2023: Vol 24 Iss 4

Section

Original Article