2000
DOI: 10.1016/s0967-0661(99)00177-x
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A self-validating digital Coriolis mass-flow meter: an overview

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Cited by 76 publications
(56 citation statements)
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“…Measurement aberration detection permits to reveal faults, taking into account how they change the behaviour of the signal (e.g., bias, noise, etc.). A self-validating Coriolis flow meter developed on the basis of the above sources of additional information provides the self-diagnostics and diagnostics of corresponding actuators, the result of measurements being accompanied by a value of uncertainty (Henry et al, 2000). In (Feng et al, 2007), sources of information intended for diagnostics of sensor device faults are classified in the following way:  hardware redundancy (e.g., combination of a thermocouple and resistance thermometer);  analytical redundancy taking into account a known relationships between the signals of several sensors or the signals of sensors and parameters of a technological process model;  information redundancy of a sequence of sensor device signals which is revealed with the help of mathematical methods.…”
Section: Metrological Self-check Methodsmentioning
confidence: 99%
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“…Measurement aberration detection permits to reveal faults, taking into account how they change the behaviour of the signal (e.g., bias, noise, etc.). A self-validating Coriolis flow meter developed on the basis of the above sources of additional information provides the self-diagnostics and diagnostics of corresponding actuators, the result of measurements being accompanied by a value of uncertainty (Henry et al, 2000). In (Feng et al, 2007), sources of information intended for diagnostics of sensor device faults are classified in the following way:  hardware redundancy (e.g., combination of a thermocouple and resistance thermometer);  analytical redundancy taking into account a known relationships between the signals of several sensors or the signals of sensors and parameters of a technological process model;  information redundancy of a sequence of sensor device signals which is revealed with the help of mathematical methods.…”
Section: Metrological Self-check Methodsmentioning
confidence: 99%
“…Development of sensor devices with a structure that enables, to some extent, to control their metrological serviceability within the process of operation has been started in Russia since 1980s (Druzhinin & Kochugurov, 1988;Sapozhnikova, 1991;Sapozhnikova et al, 1988;Tarbeyev et al, 2007). Later on, such activity was also expanded in the UK and USA as well as in Germany, China and other countries (Barberree, 2003;Hans & Ricken, 2007;Henry & Clarke, 1993;Henry et al, 2000;Feng et al, 2007Feng et al, , 2009Reed, 2003;Werthschutzky & Muller, 2007;Werthschutzky & Werner, 2009). In general, the above works are of an heuristic character.…”
Section: Metrological Self-checkmentioning
confidence: 99%
“…In the Coriolis prototype, a significant proportion of transmitter functionality has been implemented within the FPGA, and in particular the entire amplitude control subsystem (Clarke, 1998;Henry et al, 2000). This reads the sensor data and uses it to synthesise the drive waveform, a function carried out in analogue hardware within the original commercial transmitter.…”
Section: Example 1: Digital Coriolis Mass Flow Metermentioning
confidence: 99%
“…Ironically, much of the technology used to implement the prototype described in Henry et al (2000) is now obsolete. As well as using eight 3000-series Xilinx FPGAs, it was based on transputers, due to the legacy of prior research within the group.…”
Section: Example 1: Digital Coriolis Mass Flow Metermentioning
confidence: 99%
“…Flow diagnostic parameters are derived from mean flow velocity distribution to verify the flow conditions as to whether the velocity profile is fully-developed turbulent or not [8][9][10][11]. While most of the previous works have focused on the selfvalidation of flow metering in a metering station in applications with wireless sensor networks, some other works have suggested flow diagnostic parameters to investigate flow conditions by examining the mean flow velocity profile.…”
Section: Introductionmentioning
confidence: 99%