Self-diagnosis techniques and their applications to error reduction for ultrasonic flow measurement
Abstract (Summary)Flow metering plays a major role in modern life. In the process industry, flow metering is critical in industries ranging from food processing to cosmetics. It is also essential in custody transfer or billing, as flow meters are present in gas pumps and district heating substations. In the district heating industry, the ultrasonic flow meter has become the desired meter in many of its applications because it has a low cost while being accurate. This accuracy is however sensitive to installation effects and other sources of errors. This thesis stems from research that addresses the recognition of these installation effects, informs when they are unacceptable and considers reducing the measurement errors. To present these concepts, the thesis details the estimation of the mean flow velocity, the calibration of the meter and the measurement noise properties. Once installed, any kind of meter provides larger errors than in the facility where it has been calibrated and compensated. It is particularly true for ultrasonic flow meters as they are very sensitive to installation effects. Installation effects can either be static or dynamic. Special attention is paid to errors generated by temperature and velocity profile variations. Velocity profile variations can be due to pipe bends or flow pulsations. Such disturbances often induce a bias error and change the properties of the measurement noise. It is therefore with help of the change in noise that velocity profile disturbances can be detected. The detection of such abnormal behaviour of the measurement process constitutes a diagnosis. A diagnosis of the sensitivity of the meter to installations effects would allow for compensations for the errors. Signal analysis allows detection of specific noise properties, characteristic of installation effects. An example of self-diagnosis showing the detection of real pulsations in a flow is described in details. The detection of the flow pulsations and the estimation of their frequency allow to reduce the error of estimation on the flow rate. This technique is confirmed by the simulations of a pulsating flow. To empower one with the decision whether a flowmeter performance is normal or abnormal, a study of the relative error as a function of flow rate and temperature has been conducted.
School:Luleå tekniska universitet
Source Type:Doctoral Dissertation
Date of Publication:01/01/2004