Flow Meter Diagnostics The Future of Measurement Craig Marshall, NEL 1 INTRODUCTION Flow meters have been in use for thousands of years with the first documented case being in Egypt some 3000 years ago [1]. However, it was not until around 1500 AD that the theory behind most of today s technologies actually started to be studied and fully understood. Work by da Vinci, Torricelli, Euler and Bernoulli advanced the field of fluid dynamics tremendously and many of the scientific phenomena relied upon today were first studied in their laboratories. Older style meters, often referred to as traditional meters, generally involve the removal of energy from the system [2]. Differential pressure meters such as orifice plates and Venturi tubes fall into this category as they are based on measuring the differential pressure caused by a restriction in the flow. Positive displacement, turbine and vortex meters can be described by this definition too they all extract energy from the system in order to infer or measure flowrate. Electromagnetic, ultrasonic and Coriolis meters are newer technologies and these generally add energy to the system to infer flowrate from the property added. These meters tend to involve the use of modern electronics. Over the past few decades, the electronics industry has substantially increased in levels of technology and some meters are now taking advantage having increased processing abilities. Regardless of the design or operating principle of flowmeters, their sole purpose has been to estimate the mass or volume flow of fluids flowing in a pipe or an open channel. However, with a push for more efficient and cost effective processes there is a need for more information from these devices. No longer are they only used to estimate flow but now they are being asked to operate in non-ideal conditions or to estimate operating performance. Operators of these devices would like secondary information in conjunction with the flowrate measurement in order to gain more insight into their fluids. Alongside the operators, manufactures are trying to improve the performance of their devices in order to stand out from their counterparts. 1
Flow measurement technology has advanced to a point where it is within an order of magnitude of national standards with respect to measurement uncertainty. The next key milestone will not be improving on this figure but instead making the equipment more cost effective for the end user. This process will be enabled by the correct use of flow measurement diagnostics and secondary measurements to create smart flowmeters. Modern meters can now record and store a vast amount of flow measurement-related data. The advances in electronics have not only enhanced data acquisition, but also digital signal processing techniques which enable more information to be obtained. This has allowed the detailed monitoring of all the recorded data to be used as diagnostic tools to identify any problems within the metering system and to complete a health-check of the meter in operation. If diagnostics exceed accepted levels, then the system alerts an operator. These alarms can be time-dependent, which means any erroneous measurement will not be recorded as a fault until the software has confidence that the problem is real and not due to one instantaneous fault or error with the system. This diagnostic information is of particular interest to industry worldwide as it gives confidence that the measurement systems are functioning correctly. Additionally, trending of the data over time can then be used to provide regulators and auditors with information on the present state of meters, with the aim of reducing the need for recalibration. It is widely known that to calibrate an offshore fiscal measurement device, the cost including shut-down, packaging, transport, calibration, witnessing etc could be in the region of $50,000 [3]. This cost is typically an annual expense and does not include the planning and preparation. Depending on meter size there may be issues in finding accredited laboratories available to complete the calibration. Recalibrations are both costly and labour intensive, particularly when multiple meters are involved. Taking a fingerprint of the diagnostic parameters during calibration can provide a traceable link to meter performance. Once the meter is installed for use in a process stream, comparing the fingerprint with calibration values can ensure no change or shift from the calibration, providing confidence that the calibration is successfully transferred to the operating location and conditions. Using qualitative information about the flowing fluid and embedded technical knowledge, the resultant flowrate information can be reassessed and a confidence level applied. If there is no shift in meter diagnostics over a period of time, then this indicates that the meter has not shifted in service and therefore does not need a recalibration. Examples exist in 2
industry where this evidence has been used to extend recalibration intervals for ultrasonic meters [4]. This will lead to a condition based monitoring recalibration timescale rather than a calendar based one. However, most end users are either unaware or don t understand the technologies or huge potential savings on offer through the use of diagnostics. Potentially, the benefits do not stop at extending recalibration intervals and diagnostics could take industry a step closer to the realisation of a recalibration-free utopia. If shifts in diagnostics can be detected and attributed to a specific source then models could be used to predict and correct the meter performance. If confidence can be built in such systems it will inevitably lead to the removal of uneccessary calibrations altogether. Meters would then truly be recalibration free for the entirity of their operating lives. 2 SCOPE OF STUDY The scope of this study will review the state of the art with respect to flow measurement diagnostics. A review will be conducted into how diagnostics are generated and how they are utilised in some of the most common flow meters in use today. The flow meters being investigated are: Ultrasonic meters Coriolis meters Differential pressure meters Turbine meters Recommendations will then be put forward with the aim of highlighting the areas for further research. 3 ULTRASONIC METERS 3.1 Theory of Operation Most ultrasonic meters operate using the transit-time principle where the difference in time to complete a traverse between two axially offset transducers in both the forward and reverse direction is measured. Figure 1 shows this principle: 3
Figure 1: Principle of operation of a USM The difference in transit-time is generated by the motion of the fluid within the pipe and is often said to be analogous to a boat crossing a river; it takes longer when battling against the current than it does when travelling with the current. The same theory applies to an ultrasonic beam as it traverses across a pipe between two transducers. This transit-time difference is directly related to the velocity of the fluid in which the beams have propagated through. More information, including detail of the calculations involved in the measurement of fluid volumetric flowrate using ultrasonic meters can be found in the material detailed in the references [5, 6]. 3.2 Diagnostic Generation Ultrasonic meters can generate very large amounts of diagnostic data to be used in assessing its performance. Consider the principle of operation in greater detail. When an ultrasonic beam is transmitted from the transmitting transducer, it is sent with a characteristic waveform and frequency. When it travels through the fluid across the pipe, various interactions occur with vortices, eddies etc within the flow. These act to modulate and attenuate the ultrasonic signal to some degree. It is not only turbulence in the flow that can cause modulation to the ultrasonic signal. The physical properties of the fluid itself may naturally attenuate the signal more than others. There may be gas or solids that could act to reflect the signals altogether or even noise from valves or pumps that simply drown the meters signal in similar frequency sound waves. When the ultrasonic beam is received at the reception transducer, the signal is not identical to the one transmitted. By measuring these small changes several diagnostics parameters can be found that relate to the signal and hence measurement quality. For instance, changes in signal amplitude, shape, modulation, noise levels can all be monitored. Figure 2 4
shows a typical difference in transmitted and received signals for an ultrasonic meter in both the upstream and downstream transits. Figure 2: Difference in transmitted (left) and received (right) signals for an ultrasonic meter Now consider the speed of sound in common fluids like water (1485 m/s) or air (340 m/s) and it is apparent that hundreds if not thousands of measurements can be made per second. Couple this with the fact that most ultrasonic meters now have multiple paths and the sheer volume of data available in diagnostic form is staggering. Moreover, some diagnostic parameters can be combined to deliver some very useful additional diagnostics. For instance, each path in a multipath meter will give a velocity. Combining this velocity with path position on the diameter of the pipe allows a calculation of various factors relating to any asymmetry and swirl in the flow. Some common diagnostic parameters can be found in Table 1 but these are not a complete list. It is also important to point out that some manufacturers may use different names for similar diagnostic parameters. 5
TABLE 1 Common Diagnostics Parameters for Ultrasonic Meters Parameter Profile flatness or profile factor Profile symmetry Swirl Cross flow Turbulence Speed of sound (SoS) Automatic Gain Performance or signals percentage Signal to noise ratio (SNR) Description This parameter describes the amount of flow on the outer paths compared to the centre paths. It quantifies how parabolic the flow profile is. Profile symmetry indicates the amount of flow on the top planes compared to the bottom planes. Swirl describes the amount of transversal flow that is rotating in the pipe. Typically, this describes flow profile after a double elbow out of plane. A positive number means that the swirl flow is clockwise if one looks downstream. This parameter describes the amount of transversal flow that is generating a double swirl pattern with individual vortices in the top and bottom of the pipe. Typically, this describes flow profile after a single bend. The sign of the number indicates the direction of the cross flow. The cross flow compares velocities in the chords in one plane with those in the other plane at right angles and in good flow condition the ratio should be close to unity. The turbulence level describes the stability of the flow measurements on each path. Speed of sound is calculated from the transit time measurements. Once calculated it is compared to a theoretical value or a value calculated using AGA-10 [7]. The SoS should be the approximately the same for each path. The gain is a measure of how much amplification is being applied by the electronics to effectively detect the transmitted ultrasonic signal. This is controlled by the automatic gain control (AGC) function built into the software. The AGC tries to keep the received signal level constant. The amplification needed to achieve this is represented by the gain value. This value describes how many of the ultrasonic signals are acceptable to be used for custody transfer flow measurement. The value is displayed as a percentage indicating how many of the transmitted signals are being used. The SNR is a measure of the amplitude of the received ultrasonic signal compared to the background noise amplitude. The signal amplitude should be significantly greater to ensure good measurement. 6
3.3 Diagnostics Uses The diagnostics capabilities of USM can be classified into three main groups [6]: Functional diagnostics - This is used to check that the USM is operating correctly and there are no signs of degradation. The following parameters are normally checked: 1. Gain (db) 2. Signal to Noise Ratio (SNR) 3. Performance (%) Process condition diagnostics These are used to check that the conditions of the stream are stable and suitable for custody transfer measurement. The following parameters are normally checked: 1. Turbulence (%) or velocity fluctuations for each chord 2. Profile factor 3. Symmetry 4. Cross flow 5. Swirl angle Measurement integrity diagnostics check that the measurement system is operating within the design specifications: 1. AGA-10 comparison for SoS 2. Comparison of measured & calculated density 3. Independent gas chromatography, temperature and pressure checks. The diagnostics parameters described above are normally monitored in order to ensure optimum performance and combinations of these may serve as the basis of an expert system. An example of common problems often associated with USMs is shown in Table 2 alongside the diagnostic parameters that would indicate their presence. 7
Relational diagnostics diagram International Flow Measurement Conference 2015: TABLE 2 Relation Diagnostic Diagram for Ultrasonic Meters Performanc e Automati c Gain Control (AGC) (per path) Signal to Noise Ratio (SNR) (per path) Speed of Sound (SoS) (per path) Flow velocity (per path) Transducer X X X X X failure Detection X X X X problems Ultrasonic X X X noise Process X conditions- Pressure Process X conditions- Temperature Fouling X X X X Changes in X the flow profile High velocity X X X Table 2 is by no means complete and is simply given to demonstrate some common problems. As can be seen in Table 2 changes in some diagnostic parameters can have multiple causes and this underlines the difficulty in diagnosing a problem especially when detailed knowledge of fluid dynamics and the meters software are often required. 4 CORIOLIS METERS 4.1 Theory of Operation Coriolis meters operate on the principle that a mass of fluid flowing in a rotating plane will generate a force at right angles to the direction of the flow [8]. Typically, these meters consist of two tubes that are vibrated at their natural frequency. When the mass of fluid is stationary, no force is generated and both tubes are in phase. However, if the mass is not stationary the Coriolis forces generated cause twisting in the tubes placing them out of phase with each other. Figure 3 shows the mechanical sensor of a typical Coriolis meter. 8
Figure 3: Mechanical sensor of a Coriolis meter The phase shift is measured by two sensors located at a position away from the centre of rotation of the tubes. The phase shift is directly proportional to the mass flowrate of the fluid being measured. More information, including detail of the calculations involved in the measurement of fluid mass flowrate using Coriolis meters can be found in the material detailed in the references [6, 8]. 4.2 Diagnostic Generation Coriolis maters can generate a vast amount of diagnostics data that can be used to determine the meters performance. Consider the principle of operation in more detail. Flow tubes are oscillated at their resonant frequency which is dependent on the tube material, stiffness and also the media that is within the tubes. By monitoring this frequency over time it can give an indication of issues that affect the combined mass of the tubes and fluid. For instance, erosion, deposition, gas entrainment can all cause this. Sensors measure the phase the oscillations to calculate the mass flowrate. However, the sensors themselves can pick up a larger amount of information. For instance, the magnitude of the oscillations, voltage generated, noise level can all be used to give information on the quality of the signal received. Figure 4 shows typical results from Coriolis sensors that show tubes in and out of phase with other. 9
1.5 1.5 1 1 0.5 0.5 0 Tube A Tube B 0 Tube A Tube B -0.5-0.5-1 -1-1.5 Figure 4: Coriolis sensors with tubes in and out of phase with each other -1.5 The typical frequency of the oscillations of the flow tubes suggests that hundreds of measurements are attained every second. This in turn means that hundreds of diagnostic parameters are recorded every second. The volume of information available is enormous. Some common diagnostic parameters can be found in Table 3 but these are not a complete list. It is also important to point out that some manufacturers may use different names for similar diagnostics parameters. 10
TABLE 3 Common Diagnostics Parameters for Coriolis Meters Parameter Density Drive Gain or Tube Damping Oscillation Frequency Sensor Symmetry or Phase Shift Sensor Signal Amplitude Sensor Signal Voltage Temperature Zero Value Description Coriolis meters calculate density of the fluid it is measuring as well as the mass flowrate. It is possible to convert to volume flowrate if required through this value This parameter describes the amount of energy required to drive the flow tubes at their resonant frequency. Impurities tend to increase the energy required for oscillation The oscillation frequency is the actual frequency the flow tubes are being oscillated at. It changes dependent on tube mass, tube stiffness and fluid mass including presence of 2nd phases This parameter gives an idea of how symmetrical each sensors signals are This parameter describes the strength of the measured signal This parameter describes the strength of the measured signal in terms of voltage An internal temperature sensor is used for temperature corrections of the mass flow rates. It corrects for changes in tube stiffness etc. The zero value is concerned with an offset introduced through the manufacturing process. When no flow is present both flow tubes are never identically in phase. This phase shift is the zero offset that must be removed in order to improve the uncertainty of the device 4.3 Diagnostics Uses The diagnostic capabilities of Coriolis meters cannot be easily placed into groups as with ultrasonic meters. In general, each parameter is trended over time and compared with fingerprint values normally taken at calibration and initial installation. Advanced diagnostics data provide detailed information on both process and system conditions that can be employed to trigger warning messages under one or more of the following conditions:- 11
Process conditions - Presence of air (entrained gas) - Presence of liquid in gas (wet gas) - Partially filled tube - Empty tube - Fluid is not homogeneous - High flow velocity - Flow limits are exceeded - Excess noise - Zero point failure System condition or health - Fouling or coating of inner tube wall - Abrasion of inner tube wall - Corrosion of inner tube wall - Software errors - Output of range error - Excitation current limits exceeded - Tube oscillation problems - Simulation mode is left on instead of measurement mode - Hardware failure In order to detect changes in the measurement system, various process and meter parameters can be recorded on a regular basis. By monitoring the trend of these values, a deviation of the measuring system from a "reference status" can be detected at an early stage. The variables provided for trend analysis are most of those listed above under process and system parameters [9]. An example of common problems often associated with Coriolis meters is shown in Table 4 alongside the diagnostic parameters that would indicate their presence. 12
TABLE 4 Relation Diagnostic Diagram for Coriolis Meters Relational diagnostics diagram Second Phase Deposition or Coating Erosion or Corrosion Meter Stress Temperatu re Change Densit y Drive Gain or Tube Damping Oscillatio n Frequenc y Sensor Symmet ry or Phase Shift Sensor Signal Amplitud e X X X X X Zero Valu e X X X X X X X X X X X X X X X X Table 4 is by no means complete and is simply given to demonstrate some common problems. Also, at time of writing many of these parameters are under investigation to further understand their influence on measurement. As such, it is always recommended to contact manufacturers for meter specific information. As can be seen in Table 4 changes in some diagnostic parameters can have multiple causes and this underlines the difficulty in diagnosing a problem especially when detailed knowledge of fluid dynamics and the meters software are often required. 5 DIFFERENTIAL PRESSURE METERS 5.1 Theory of Operation Differential pressure meters operate on the principle that the flowrate within a pipe is proportional to pressure drop caused by a restrictive primary element. The calculations are derived from Bernouilli s principle for inviscid flow. The primary element can have several designs with the most common being Venturi s (left), orifice plates (centre) and cones (right) as shown in Figure 5. 13
Figure 5: Common differential pressure primary elements As a fluid flows past a restriction it causes a reduction in the cross section area available for flow. From continuity, to achieve the same mass flowrate through the smaller area, the velocity of the fluid must increase. The energy to accomplish this is found by reducing the static pressure of the fluid. It is this pressure drop that is proportional to the flowrate (either mass or volumetric). More information, including detail of the calculations involved in the measurement of fluid flowrate using differential pressure meters can be found in the material detailed in the references [10-13, 15]. 5.2 Diagnostic Generation Traditionally, differential pressure meters do not generate their own diagnostics as a result of their primary measurement. Unlike ultrasonic meters, where the waveform used to calculate transit time can be analysed for signal quality, the pressure measurement over the constriction does not, at present, deliver any qualitative data. Instead, diagnostics for differential pressure meters tends to rely more towards redundancy and comparison of simultaneous measurements. However, in the future, the pressure transmitter could be used for diagnostics. By monitoring the scatter or a similar parameter from a pressure transmitter qualitative information about the flow may be found. 5.3 Diagnostics Uses In the 60 s and 70 s, differential pressure diagnostics of a form were being used in the UK s National Gas Transmission System (NTS) [14]. This consisted of orifice plate metering stations at the NTS outlet that required indications of the stations health. This was completed by the use of three differential pressure transmitter devices. As standard, two of these transmitters were for the high and low ranges respectively. The third transmitter was a check device that compared the values of either of the high or low transmitters with the check device 14
values. If they differed by a set limit then an alarm would warn of possible issues with the station. Strangely, with improvements in transmitter technology, the system fell away due to the increase in false alarms generated. Newer technology had an increased sensitivity to process fluctuations and peaks in pressure and eventually limit values were increased in an effort to reduce the number of alarms being generated. This ultimately resulted in poor diagnostic information being generated. Diagnostics for differential pressure meters hasn t kept up to date with newer technologies such as Coriolis and ultrasonic meters for the vast majority of the last few decades. Only in the last five years has this began to change with the introduction of the Prognosis [15] measurement system. Utilising a single additional pressure tapping downstream of the meter allows for measurement of another two differential pressures across the meter. These additional differential pressures are used in separate flowrate equations. This in essence makes three meters in one body, each with a flowrate and associated uncertainty value. Each meter should give the same exact result but in practice this is not the case. For diagnostics purposes, comparing two measurement results should be within the uncertainty levels stated by each. If they are not then this indicates a metering problem exists. The system is not limited to calculating and comparing three flowrates. There is also a diagnostic method that looks at the intercomparison of the differential pressure measurements themselves as a parameter. In true turbulent flow the pressure loss ratios (a differential pressure divided by another differential pressure) should be independent of Reynolds number which means additional diagnostic information can be gleamed by reviewing the measured differential pressures as ratios of each other. By normalising and comparing the uncertainties and differential pressure ratios it allows monitoring of the meters performance in real time with alarms provided in the event of error. Below lists, but is not limited to, possible sources of error that the system has been shown to diagnose and raise alarms for: Two-phase flow Buckled orifice plates Drifting transmitter Pressure tapping partially blocked Work meter edges Meter installed incorrectly Incorrect calibration report data 15
For differential pressure meters, this system remains, at present, one of the only true diagnostic methods for determining meter health during operation. 6 TURBINE METERS 6.1 Theory of Operation Turbine meters operate on the principle of a flowing fluid spinning a bladed wheel within a pipe. The number of rotations of the wheel around a central point per second is directly proportional the volumetric flowrate of the fluid i.e. the faster the fluid, the faster the turbine spins. The blades are typically angled between 30 and 45. Figure 6 shows the internals of a typical turbine meter with curved blades. Figure 6: Internals of a turbine meter with curved blades A sensor picks up how often the blades spin and essentially counts the number of pulses generated as a result of the blade motion. This number is multiplied by a calibrated correction factor, often called a K-Factor, which allows the calculation of flowrate. Turbine meters remove energy from the fluid in order to spin the rotor. This in turn causes a pressure drop across the meter body. In general, turbines are very repeatable and linear over their operating range. More information, including detail of the calculations involved in the measurement of fluid volumetric flowrate using turbine meters can be found in the material detailed in the references [16]. 16
6.2 Diagnostic Generation Several diagnostic systems are available for turbine meters and all operate on similar principles. It is essentially the analysis of the sensor signal generated by the spinning blades [17]. Figure 7 shows a typical signal in operation: Figure 7: Typical sensor signal for a turbine meter in operation When each blade passes the sensor it generates a peak as shown in Figure 7 (8 blade peaks). As each blade is identical, the signals should be exactly the same. The period, amplitude, noise level of the signal can be recorded and analysed. Some of the parameters measured can be found in Table 5. 17
TABLE 5 Common Diagnostics Parameters for Coriolis Meters Parameter Footprint Balance Bearing Friction Signal Amplitude Noise Description This parameter looks at the signal generated from all the turbine blades allowing an analysis of how stable the blades and supports are. Blade damage or eccentric spin can be observed This parameter looks at how balanced the rotation of the turbine is around the support Can only be completed during sudden flow shutdown. Analysis of continued blade rotation and momentum in zero flow conditions This parameter looks at the amplitude of each peak generated in the signal This parameter gives an impression of signal noise generated from external meter sources. This could be electrical or mechanical It is important to note that as with other meter technologies, some manufacturers may have different nomenclature for diagnostic parameters. It is always recommended to discuss any issues with manufacturers. 6.3 Diagnostics Uses By having fingerprint values of the measurement signal generated from the blades at calibration, it allows for performance checking of meter. Continued analysis of the blade signature in operation can be compared with these patterns and the results trended over time. Even if this information shows a small change that does not affect the measurement performance, at the very least it allows for a more accurate prediction of the future maintenance requirements. Table 6 shows some typical turbine meter issues and how they can be diagnosed using the measurement signal from the spinning blades [18]. 18
TABLE 6 Diagnostic response of a turbine meter to typical meter issues Issue Response Bent Blade Perfect signature with a single distorted repetitive wave form Bad Bearing Perfect signature with a single distorted non-repetitive wave form Bearing Wear Distorted non-repetitive multiple wave signature Debris on Good signature with repetitive frequency modulation Rotor Cavitation Good signature with non-repetitive frequency modulation It is clear ultrasonic and Coriolis meters have more diagnostic parameters available than turbine meters and that rather than assessing the quality of the measurement signal, they are more geared towards highlighting mechanical issues. This fits well with the general theory of operation with these meter types. 7 DISCUSSION It is clear from the above descriptions that some technologies lend themselves more to the use of diagnostics than others do. For instance, any instrument that requires at least some degree of signal processing generally offers more individual diagnostic parameters than technologies that do not. Due to the vast amount of information available in these types of meters, it has led to a wider industry perception and more research and development into those particular technologies. Diagnostics that are produced as a consequence of the primary flow measurement fall nicely into this category as no major additional work is required to reap the benefits of using the information generated. Ultrasonic and Coriolis meters are prime examples of this and much research can be found on the qualitative use of their diagnostics. The meters that do not offer up secondary information as a result of the primary measurement have been left behind somewhat in the field of diagnostics. However, in the past few years, there has been a shift in this trend and diagnostics are becoming available for these technologies. With differential pressure meters being a prime example, diagnostics can be obtained from these meters but only through the use of a secondary measurement system i.e. additional differential pressure measurement. 19
Diagnostics generated as a consequence of the primary measurement nearly always gives information about the quality of the measurement. Diagnostics generated through the use of additional equipment has a tendency to provide information about the fluid and process conditions. For instance, an ultrasonic signal calculates velocity through transit-time measurements. By analysing the received signal, information about the quality of the measurement can be found. By comparing the measurement of one path with three additional paths, information about the measurement conditions can be found i.e. flow profile. In order to maximise the use of diagnostics in industry both principles should be further developed for all types of meter technologies. Where no primary measurement diagnostics are available, research should be undertaken to fully explore the potential for their generation. The use of additional measurement technologies has by far more potential for the generation of diagnostics. Not only can it give information about the flow conditions but potentially physical properties can be calculated too. In its simplest form, by using a mass flow meter alongside a volumetric flow meter, it is possible to calculate density. Other combinations of meters or other measurement technologies can be used in similar vein. At present, as detailed in the preceding sections, diagnostics are at a level of qualitatively indicating when measurement issues occur. Whatever the method of generation, they alert the user to a potential problem only. The user then has to investigate the alarm and deal with the issue. While this is still a major step forward from the original fault finding process i.e. waiting for a recalibration, it does not use diagnostics to their full potential. However, it does allow for quick and decisive corrections of measurement issues. The next stage in making diagnostics more user friendly and cost effective is by combining the data with embedded expert technical knowledge. This will result in taking the somewhat daunting data produced from some meters and will turn it into usable information. Again taking ultrasonic meters as an example, all the diagnostics generated can be overwhelming especially if they are not fully understood. Instead, the data can be processed further to tell the user exactly what to do. If the diagnostics have an instantaneous change in asymmetry and swirl then it is likely that a flow conditioner is partially blocked. Therefore, instead of software raising alarms for several diagnostics parameters, it will also suggest possible causes for the alarms. This information is much more valuable to the end user as rarely will they have access to this level of detailed analysis on-site. This sort of information is available for some 20
technologies and manufacturers but not all. More work is required to further develop the technologies lacking in this detail. Diagnostics can be developed further still; the information remains qualitative and does not act to correct the measurement result. If correlations can be created between diagnostic parameters and measurement performance shift, then potentially models can be created that can predict and correct measurements using diagnostics. Diagnostics can then become qualitative tools for measurement. If confidence can be given to diagnostics and predictive models then potentially diagnostics can be used to provide evidence to the uncertainty of flow measurement. It would also negate the need for recalibrations as any shift in measurement performance would be monitored and corrected for. This situation can only benefit the end user, as it will result in more measurement confidence and less effort in maintaining the system. Anything that can reduce the input effort and cost for the end user will always be of interest. Diagnostics have been used for many years now and have been through much iteration. Technology is now advancing to a point where much more computer processing can be completed in real time allowing for the opportunity to further develop the field of diagnostics. Knowing the history of the industry and the technology available today, it can be said with confidence that the use of diagnostics is the future of flow measurement. 8 RECOMMENDATIONS From the above discussion, the following recommendations are being put forward in order to further develop the field of diagnostics in flow measurement. 1. Further work to develop diagnostics to assess measurement signal quality 2. Further work to develop diagnostics to derive information about the flowing fluid. This can be related to the process fluid properties (physical properties, cavitation etc) or the velocity profile 3. Develop a centralised nomenclature for diagnostics. So far, manufacturers have developed their own diagnostic parameter and names. This has resulted in similar parameters having multiple names which can be confusing 21
4. Wherever possible, try to utilise diagnostics in some way. Even a small change in procedures to record an extra piece of information can be valuable and if nothing else will change mind sets for the future. 5. Manufacturers should always try to embed their technical knowledge into software or transmitters. Anything to make the use of diagnostics easier for the end user will be at an advantage 6. Further work to develop direct correlations between diagnostic parameters and meter shift. NEL are currently undertaking a research and development project into this particular area. These recommendations are based on experience and overall opinion of technologies available. It is likely there are other avenues to explore in advancing the role of diagnostics in industry and the reader is encouraged to find these and report on their effectiveness. 9 REFERENCES [1] Endress + Hauser, Flow Handbook, 3 rd Edition, 2006 [2] BS 7405:1991, Guide to the Selection and Application of Flowmeters for the Measurement of Fluid Flow in Closed Conduits. [3] Marshall, C. and Kenbar, A., Exploring The Diagnostic Capabilities Of Ultrasonic Flow Meters, NEL Report No. 2011/301, 2011 [4] Peterson, S. Lightbody, C. Trail, J., Online Condition based Monitoring of Gas USM s, NSFMW, 2008. [5] ISO 17089-1:2010, Measurement of Fluid Flow in Closed Conduits Ultrasonic Meters for gas Part 1: Meters for Custody Transfer and Allocation Measurement [6] Marshall, C. and Kenbar, A. Diagnostic Capabilities Of Ultrasonic And Coriolis Flow Metering Technologies, NEL Report No. 2010/262, 2010 [7] AGA report No. 10, Speed of sound in natural gas and other related hydrocarbon gases, Jan. 2003. 22
[8] ISO 10790:1999, Measurement of Fluid Flow in Closed Conduits Guidance to the Selection, Installation and Use of Coriolis Meters (Mass Flow, Density and Volume Flow Measurement). [9] Endress + Hauser, Coriolis Flowmeter Advanced Diagnostics Training Handbook, Module No. FC170ABEA. [10] ISO 5167-1:2003, Measurement of Fluid Flow by Means of Pressure Deifferential Devices Inserted in Circular Cross-Section Conduits Running Full Part 1: General principles and Requirements. [11] ISO 5167-2:2003, Measurement of Fluid Flow by Means of Pressure Deifferential Devices Inserted in Circular Cross-Section Conduits Running Full Part 2: Orifice Plates. [12] ISO 5167-3:2003, Measurement of Fluid Flow by Means of Pressure Deifferential Devices Inserted in Circular Cross-Section Conduits Running Full Part 3: Nozzles and Venturi Nozzles. [13] ISO 5167-4:2003, Measurement of Fluid Flow by Means of Pressure Deifferential Devices Inserted in Circular Cross-Section Conduits Running Full Part 4: Venturi Tubes. [14] Information provided by National Grid, UK [15] Steven, R., Diagnostic Methodologies for Generic Differential Pressure Flow Meters, NSFMW, 2008 [16] ISO 2715:1981, Methods for Volumetric Measurement of Liquid Hydrocarbons Part 2: Turbine Meter Systems. [17] Elster Instromet, Turbinscope: The Turbine Meter Inside and Out, 2011 [18] FMC Technologies, A Smart Preamplifier for Real-Time Turbine Meter Diagnostics, White Paper Issue (2/95), Bulletin TP02004. 23