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%.
This paper presents the design of an instrument for measuring the liquid level. The objective of this proposed work is to measure the level of liquid accurately even with variations in liquid concentration. The designed instrument should also be able to compute the concentration of additives in the liquid. For this purpose, a multisensor model comprising a capacitive level sensor (CLS), ultrasonic level sensor (ULS), and capacitance pressure sensor is used to acquire information of the liquid. The data acquired from all these sensors are processed using Pau’s multisensor data fusion framework to compute the level of liquid along with the concentration of additives added to the solution. Pau’s framework consists of alignment, association function, analysis, and representation functions. The designed multisensor technique is tested with real-life data for varying liquid levels and additives. The results obtained show that the successful implementation of the proposed objective producing a root mean square of percentage error is 1.1% over the full scale is possible.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.