A Novel ANN-Based Adaptive Ultrasonic Measurement System for Accurate Water Level Monitoring

Low-cost ultrasonic sensors are widely used for non-contact distance measurement problems. Speed of ultrasonic waves is greatly affected by environmental conditions such as temperature and relative humidity among a few other parameters. Presence of acoustic and electronic noise also influences an ul...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 69; H. 6; S. 3359 - 3369
Hauptverfasser: Sahoo, Ajit Kumar, Udgata, Siba Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Acoustic noise
Acoustics
Adaptive systems
Algorithms
Artificial neural network (ANN)
Artificial neural networks
Back propagation
Computer architecture
Distance measurement
Error analysis
Error reduction
Humidity
Level measurement
Neural networks
Relative humidity
Sensors
Storage tanks
temperature
Temperature measurement
Temperature sensors
ultrasonic measurement system (UMS)
ultrasonic sensor
Ultrasonic variables measurement
Water levels
Water tanks
ISSN:0018-9456, 1557-9662
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Zusammenfassung:Low-cost ultrasonic sensors are widely used for non-contact distance measurement problems. Speed of ultrasonic waves is greatly affected by environmental conditions such as temperature and relative humidity among a few other parameters. Presence of acoustic and electronic noise also influences an ultrasonic sensor based distance measurement system. Existing standard techniques assume that the temperature and relative humidity levels remain constant throughout the measurement medium. In our proposed system, we measure water level in storage tanks of different depths, which exhibits a gradient of temperature and relative humidity across the measurement medium. Hence, the standard ultrasonic measurement system (UMS) is not able to estimate distance accurately. In this article, we propose an algorithm based on modified neural network architecture to increase the accuracy of UMS and also to extend the standard operating range of the ultrasonic sensor used. This article presents a novel approach to reduce measurement error using Levenberg-Marquardt backpropagation artificial neural network (LMBP-ANN) architecture. The proposed model is validated by comparing the actual water level at various depths under different environmental conditions with the output of the trained neural network. The measurement error in the proposed model is bounded by ±1 cm in the distance measurements ranging from 2 to 500 cm. This model is able to extend the maximum standard operating range of the ultrasonic sensor (HC-SR04 model) from 400 to 500 cm. The proposed model is evaluated using mean squared error (MSE) and <inline-formula> <tex-math notation="LaTeX">R </tex-math></inline-formula>-values to establish the effectiveness.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2019.2939932