Fault identification and isolation in pneumatic valve using image processing

Santhosh, K. V.; Shenoy, Vignesh; Navada, Bhagya R.

doi:10.1109/discover.2016.7806219

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The DAMADICS model is used to generate faults like apositioner supply pressure drop, a partly opened bypass valve and a flow-sensor fault in [15], and an eccentricity data analysis algorithm is used for fault detection. A non-contact process for pneumatic control valve fault detection using a camera and image processing is discussed in [16]. A model predictive controller is designed to stabilize the flow process dynamics even with a stiction fault in [17].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Venkata

Rao

2019

Electronics

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A control valve plays a very significant role in the stable and efficient working of a control loop for any process. In a fluid flow process, the probability of failure of a control valve may increase for many reasons pertaining to a flow process such as high pressures at the inlet, different properties of the liquid flowing through the pipe, mechanical issue related to a control valve, ageing, etc. A method to detect faults in the valve can lead to better stability of the control loop. In the proposed work, a technique is developed to determine the fault in a pneumatic control valve by analyzing the vibration data at the outlet of the valve. The fault diagnosis of the valve is carried out by analyzing the change in vibration of the pipe due to the change in flow pattern induced by the control valve. The faults being considered are inflow and insufficient supply pressure faults. Vibration data obtained is processed using a signal processing technique like amplification, Fourier transform, etc. The support vector machine (SVM) algorithm is used to classify the vibration data into two classes, one normal and the other faulty. The designed algorithm is trained to identify faults and subjected to test with a practical setup; test results show an accuracy of 97%.

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Section: Introductionmentioning

confidence: 99%

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Venkata

Rao

2019

Electronics

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Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review

2023

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The industrial sector is a crucial economic pillar, seeing annual increases in the production output. In the last few years, a greater emphasis has been placed on the efficient and sustainable use of resources within industry. The use of compressed air in this field is hence gaining interest. These systems have numerous benefits, such as relative low investment costs and reliability; however, they suffer from low-energy efficiency and are highly susceptible to faults. Conventional detection systems, such as ultrasonic leak detection, can be used to identify faults. However, these methods are time consuming, meaning that leakages are often left unattended, contributing to additional energy wastage. Studies published in this area often focus on the supply side rather than the demand side of pneumatic systems. This paper offers a novel review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology of fault detection methods on the demand side of compressed air systems, leading towards a comprehensive understanding of smart and sustainable pneumatic systems. Fifty-three studies were classified and reviewed under the following three areas: (a) demand parameters which help in identifying fault sources; (b) approaches taken to analyse the parametric data; and (c) the role of Artificial Intelligence (AI) in pneumatic fault monitoring systems. This review shows that fault detection on the demand side has received greater importance in the last five years and that data analysis is crucial for AI to be implemented correctly. Nevertheless, it is clear that further research in this sector is essential, in order to investigate more complex systems. It is envisaged that this study can promote the adoption of such systems, contributing to an energy-efficient and cost-effective industry.

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Fault identification and isolation in pneumatic valve using image processing

Cited by 2 publications

References 15 publications

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review

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