Extrapolation of calibration curve of hot-wire spirometer using a novel neural network based approach

Abstract

Hot-wire spirometer is a kind of constant temperature anemometer (CTA). The working principle of CTA, used for the measurement of fluid velocity and flow turbulence, is based on convective heat transfer from a hot-wire sensor to a fluid being measured. The calibration curve of a CTA is nonlinear and cannot be easily extrapolated beyond its calibration range. Therefore, a method for extrapolation of CTA calibration curve will be of great practical application. In this paper, a novel approach based on the conventional neural network and self-organizing map (SOM) method has been proposed to extrapolate CTA calibration curve for measurement of velocity in the range 0.7-30 m/seconds. Results show that, using this approach for the extrapolation of the CTA calibration curve beyond its upper limit, the standard deviation is about –0.5%, which is acceptable in most cases. Moreover, this approach for the extrapolation of the CTA calibration curve below its lower limit produces standard deviation of about 4.5%, which is acceptable in spirometry applications. Finally, the standard deviation on the whole measurement range (0.7-30 m/s) is about 1.5%.

Conceptual Design of a Device for Online Calibration of Spirometer Based on Neural Network

“…In the previous study [ 10 ], we used A Radial Basis Function (RBF) neural network to estimate the calibration curve of a hot-wire spirometer. Based on RBF neural network, one of the features of the estimator is the capability to retrain and change its internal weights, i.e.…”

“…Figure 3 shows the air flow when the calibration syringe piston was moved from start to end at different speeds. Previous experience shows that the highest measurement errors occur in a low fluid velocity range of less than 3 m/s [ 10 ]. According to the mouthpiece size (diameter of about 1.5 cm), the measured flows in Figure 3 are within the same speed range.…”

Calibration and data reduction for X-hotwires using cross validation

“…Artificial neural network models have been implemented for data reduction in the case of multiple hole pressure probes and hot-wire probes. [22][23][24] The majority of implementations of ANN models in literature concern data reduction and optimization for single hot-wire probes in situations where an appreciable change in the ambient conditions (e.g., temperature or relative humidity 23 ) is present, causing drifts in calibration or when some sort of extrapolation outside the calibration domain is necessary. 24 An implementation of a NN model using multi-layer perceptrons (MLPs) is discussed briefly and compared to the proposed LUT method (the Results and Some Measurements in a Single Round Turbulent Free Jet sections).…”

A Smart Phone Based Handheld Wireless Spirometer with Functions and Precision Comparable to Laboratory Spirometers

“…The Lilly type sensing unit uses a flow restrictor to create a linear flow/differential pressure relationship at both sides of the restrictor immediately when the air flow travels through it (see Figure 2) according to the Poiseuille’s law:Δ P = Q R . Here, Q is the flow rate, R is the flow resistance, Δ P is the pressure difference. Its performance is well understood, and in comparison with other types of flowmeters such as turbine [10], Hot-wire [14], ultrasonic [13], Pitot tube [20] and Venturi tube types [12], it is simpler to make and cheaper, containing no moving parts but with a satisfactory accuracy.…”

“…Furthermore, they did not have adequate functions and lacked capabilities to display the graphical information such as volume-time and flow-volume graphs [3,4,6,11]. Though later on, some of them had become digitalized and some other kinds of spirometers based on the methods of differential pressure [12], ultrasound [13], and hot-wire [14] flowmeters had also developed. The improved spirometers still could not perform systematic spirometry tests and give graphical information without connecting to a PC or a console.…”