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In the present study, an algorithm has been developed using the adjoint method to optimise the position and cross-section of an internal cooling channel for a 3D printed tool steel insert for use in the Aluminium die-casting process. The algorithm enables the development of an optimised complex industrial mould with relatively low computational cost. A transient model is validated against multiple experimental trials, providing an adapted interface heat transfer coefficient. A steady state thermal model, based on the casting cycle and thermal behaviour at the mould surface, is developed to evaluate the spatial distribution of temperature and to serve as the initial solution for the subsequent optimisation stage. The adjoint model is then applied to optimise the cooling channel emphasising the minimisation of the temperature standard deviation for the mould surface. The original transient model is applied to the optimised mould configuration via calibration using experimental data obtained from a dedicated aluminium furnace. The optimised cooling channel geometry, which uses a non-uniform cross-section across the entire pipe surface region, improves the pressure drop and cooling uniformity across the mould/cast interface by 24.2% and 31.6%, respectively. The model has been used to optimise cooling channels for a range of industrial high-pressure aluminium die-casting (HPADC) inserts. This has yielded a significant improvement in the mould operational lifetime, rising to almost 130,000 shots compared to 40,000 shots for prior designs.

Strain gauge type pressure sensors are widely used in different branches of industry to measure pressure from very low to very high (1400 Mpa) values. This article investigates a strain gauge type pressure sensor that uses silicon oil within its housing to transmit working pressure from the external environment to a sensing plate. An important failure mode arises from loss/leakage of the silicon oil, whereby a portion of the internal volume is replaced by gas, usually air. Coupled nonlinear governing equations have been derived and solved in both static and dynamical states to describe the behavior of the external membrane, the interface oil including pockets of gas, and the sensing plate. Nonlinear behavior arises from the plate and membrane midplane stretching, and of course the behavior of the gas. The resulting model describes how oil loss affects the sensor performance and changes the sensor output and pressure measurable range.

Validating real-time measurements from a sensor/instrument is a multifaceted challenge. One goal is the provision of a dynamic uncertainty analysis, ideally incorporating the influence of instrument faults and the local environment. Such an analysis requires a broader model of instrument behaviour, accommodating non-ideal operational conditions. It may also require the analysis of a wider range of transducer data than when ideal, fault-free operation is assumed. A companion presentation describes an ultra-precise Fast Fourier Transform (FFT) technique. In this presentation, the technique is applied to a Coriolis mass flow meter, a resonant sensor with rich spectral characteristics. Typically, the meter is driven in a single natural mode of mechanical vibration; the frequency of this mode varies with fluid density, while the flow rate and density calibration coefficients assume a fixed frequency ratio to an adjacent vibration mode. A prototype Coriolis meter can operate in two modes of vibration simultaneously. The new FFT technique exhibits high precision (mode frequencies from the two independent vibration sensors agree by up to 10−8Hz), allowing precise tracking of the true frequency ratio for on-line calibration and measurement validation. Additional transducer signal components, such as mains noise and external vibration, may be identified and monitored.

The Fast Fourier Transform (FFT) is a cornerstone of digital signal processing, generating a computationally efficient estimate of the frequency content of a time series. Its limitations include: (1) information is only provided at discrete frequency steps, so further calculation, for example interpolation, is often used to obtain improved estimates of peak frequencies and amplitudes; (2) ‘energy’ from spectral peaks may ‘leak’ into adjacent frequencies, potentially causing lower amplitude peaks to be distorted or hidden; (3) the FFT, like many other DSP algorithms, is a discrete time approximation of continuous time mathematics. This paper describes a new FFT calculation which uses two windowing functions, derived from Prism Signal Processing. Separate FFT results are obtained from each windowing function applied to the data set. Calculations based on the two FFT results yields high precision estimates of spectral peak location (frequency), amplitude and phase. This technique addresses FFT limitations as follows: (1) spectral peak parameters are calculated directly, unrestricted by FFT frequency step discretization; (2) the windowing functions have narrowband characteristics which attenuate and localize spectral leakage; (3) the windowing functions incorporate a Romberg Integration mechanism to overcome the discrete/continuous time approximation.

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects., 48 pages, https://iopscience.iop.org/article/10.1088/1361-6501/ac2dbd

The Industrial Internet of Things (IIoT) and Industry 4.0 require new intelligent sensor designs with enhanced functionality, including local diagnostics. In previous work, we have experimentally investigated an important fault mode of a commercial pressure sensor, working in partnership with the sensor manufacturer who has provided modified sensors with calibrated levels of the fault condition. We have further developed simple signal processing techniques to detect the fault condition, based on a low cost noise analysis. In the current paper, we describe the development of a prototype wireless pressure transmitter. This transmitter monitors the analogue output of the pressure sensor, and applies the diagnostic procedures in real time. The resulting pressure measurement in engineering units, together with diagnostic information, are both communicated wirelessly to a receiving system.

The Prism is a new signal processing module which implements fully recursive, linear phase FIR filtering, so its computational cost is fixed irrespective of filter length. The Prism also has negligible design cost. Recent work has demonstrated how, using simple design rules, a chain of six Prisms can create a narrowband filter with arbitrary central frequency and bandwidth. In this paper, the technique is applied to spectral analysis of data, whereby a sequence of filters is applied to a data set to provide narrow frequency analysis, yielding accurate estimates of the frequencies and amplitudes of spectral peaks in the data. Although this time domain technique remains computationally expensive compared to the FFT, it can identify and reject spectral leakage, offering an alternative analysis for low amplitude and/or adjacent spectral peaks, including hidden tones, where FFT discrimination may be limited.

Prism signal processing is a new FIR signal processing technique offering a fully recursive calculation and minimal design cost, which is well-suited to the requirements of IoT sensing. This paper describes a family of low pass filter designs using one or more layers of Prisms, where the frequency response has minimal stopband gain. Combined with heterodyning and Prism-based tracking, these filters provide flexible and powerful real-time tracking of signal component properties for use in measurement applications.

This paper presents new complex signal processing methods, Complex Bandpass Filtering (CBF), Complex Notch Filtering (CNF) and their combination (CBF-CNF), applied to the Coriolis Mass Flowmeter (CMFM). CBF, CNF and CBF-CNF can be utilized to suppress the negative frequency component of each sensor signal in order to produce the corresponding analytic form with reduced tracking delay. Further processing of the analytic form yields the amplitude, frequency, phase and phase difference of the sensor signals. In comparison with previously published methods, the new techniques offer short delay, high noise suppression, high accuracy and low computational cost. A reduced delay is useful in CMFM signal processing especially for maintaining flowtube oscillation in two/multi-phase flow conditions. The central frequency and the frequency range for the CBF, CNF and CBF-CNF methods are selectable so that they can be customized for different flowtube designs.

This paper introduces the Prism, a new type of signal processing block, as a contribution to the challenges of 21st Century metrology. The Prism is a fully recursive, dual output, FIR filter: the computational burden is low and independent of data window length. Prism design is trivial, so that networks of Prisms can be created, whether at design time or autonomously in real time, to carry out a range of metrological tasks. Prism-based trackers can generate sample-by-sample estimates of frequency, phase, and/or amplitude of a sinusoid. A simulation example of sensor validation demonstrates how Prism signal processing can be used to autonomously detect, track, and compensate for an undesired frequency component in a frequency-based sensor.

An earlier paper introduced a dataset of Coriolis meter mass flow and density errors, induced by the effects of two-phase (gas/liquid) flow, as a benchmark for which various error correction strategies might be developed. That paper further presented a series of error correction models based on neural nets. The current paper presents an alternative analysis of the same data set, using a support vector machine (SVM) approach. The analysis demonstrates that, for the benchmark data set, more accurate models are generated than those developed using neural nets. More specifically, it is found that a linear SVM model provides better performance than non-linear SVM. This improved performance may arise from over-fitting by both non-linear SVM and neural nets on this relatively small data set.

A new, networked approach to signal processing, efficient for both design and real-time computation, is proposed to match the requirements of the Internet of Things (IoT). The Prism is a novel finite impulse response (FIR) filter, which is fully recursive: the computational cost per sample is low and independent of the filter window length. The design cost is also low; any device capable of running a Prism-based signal processing scheme may also adapt the design to match changing requirements. Various signal processing tasks can be implemented using networks of Prisms, thereby providing a useful tool set for a wide range of sensing and monitoring applications, including the IoT.

Prism signal processing is a new recursive FIR technique that facilitates the rapid tracking of sinusoidal signals, such as those used in a Coriolis Mass Flow Meter (CMFM). A Prism-based CMFM prototype has been developed using a commercial flowtube and a dual ARM processor-based transmitter, which is capable of generating flow measurement updates at 48 kHz. This has been applied in a feasibility study to the tracking of fast (e.g. 1.5 ms) injections of diesel fuel on a laboratory rig at engine speeds of up to 4000 rpm equivalent and at fuel pressures of up to 100 MPa. Due to the high level of vibration in the system, Prism-based notch filtering is used to suppress undesired modes of flowtube vibration in the sensor signal. Individual flow pulses can be detected by the system, but the relatively long period of oscillation of the flowtube compared to the fuel injection duration results in a spreading out over time of each flow pulse measurement. More precise measurement results may be obtained using a higher frequency resonant flowtube.

Prism Signal Processing is a new recursive FIR technique offering rapid filter design and calculation. It has previously been applied to Coriolis mass flow metering to generate fast (48 kHz) flow measurement updates, facilitating for the first time the direct mass flow measurement of individual fuel pulses injected into a laboratory diesel fuel injection test bench. In this paper we describe an augmented sensor signal filtering scheme which enables rapid tracking of the desired mode of flow tube vibration while notching out undesired modes. The new scheme is applied to a gasoline injection test bench where the vibrational interference is greater than for the previously described diesel system due to increased hydraulic shock. The paper presents experimental findings which illustrate the further challenges to be overcome in order to achieve the goal of traceable direct mass flow measurement of individual fuel injection pulses. For example, when a fuel pulse is shorter than the resonant period of the flow tube, the observed phase difference appears to show dependence on the instantaneous phase of the flow tube vibration.

— Two-phase (gas/liquid) flow is common in many industrial applications but its measurement remains challenging for Coriolis mass flow meters (CMFM), especially for high Gas Void Fraction (GVF). In this paper, we present experimental results applying previously developed complex signal processing techniques for tracking the rapidly changing sensor signals generated by two-phase flow. The techniques are implemented in a new System-on-Chip (SOC) prototype transmitter connected to a commercial Coriolis flow tube. Experiments have been carried out over a range of single phase and two-phase (water/air) flows. The signal tracking as well as mass flow rate and density measurement performance is compared with that of a laboratory version of a commercial Coriolis transmitter with two-phase flow capability. The results show that the complex bandpass algorithms, coupled with flowtube control algorithms, reduce the standard deviation of the mass flow measurement by a factor of 3 or more in 50% of the experiments undertaken. For the density measurement, the corresponding reduction in standard deviation is by a factor of 6.

The extended Kalman filter has been used to estimate a harmonic signal from noisy measurements. Most algorithms are based on the Cartesian model, which is a discretization in time of the continuous state space model associated with the differential equation that is satisfied by a sinusoidal signal with constant amplitude and frequency. In order to handle the more realistic case where both amplitude and frequency are changing, this basic model is modified by including ad hoc extensions. This paper starts by deriving a differential equation that explicitly includes time varying amplitude and frequency, and it is shown that this can be reduced to a Bessel's equation of order 1/2 that has a closed form solution. This is used to derive an explicit expression for a discrete-time model, which forms the basis of an extended Kalman filter. Simulation results show that this algorithm outperforms other approaches, particularly for harmonic signals where the frequency is changing rapidly.

A previously described Net Oil & Gas Skid (Henry et al., 2013) [1] combines a multiphase Coriolis mass flow meter with a water cut meter to generate on-line estimates of oil, water and gas flow rates, with an update rate of one second. This system has received approval from GOST, the Russian standards agency, for use in upstream oil and gas applications. The NOG system has been applied to well testing, where the output of an individual well is measured, typically over several hours. The conventional equipment used for this task is a test separator; however, a separator only provides the totalised flows of oil, water and gas over the test period. By contrast, the NOG system is able to provide time series of each flow rate, thus allowing more detailed characterisations of well behaviour. This paper draws on the results of several hundred well tests carried out in Russia to illustrate the different types of well behaviour observable using the Net Oil & Gas Skid.

For more than a decade there has been growing interest in the use of Coriolis mass flow metering applied to two-phase (gas/liquid) and multiphase (oil/water/gas) conditions. It is well-established that the mass flow and density measurements generated from multiphase flows are subject to large errors, and a variety of physical models and correction techniques have been proposed to explain and/or to compensate for these errors. One difficulty is the absence of a common basis for comparing correction techniques, because different flowtube designs and configurations, as well as liquid and gas properties, may result in quite different error curves. Furthermore, some researchers with interests in the modelling aspects of the field may not have suitable multiphase laboratory facilities to generate their own data sets. This paper offers a small data set that may be used by researchers as a benchmark i.e. a common data set for comparing correction techniques. The data set was collected at the UK National Flow Laboratory TUV-NEL, using air and a viscous oil, and provides experimental points over a wide flow range (8:1 turndown) and with Gas Volume Fraction (GVF) values up to 60%. As a first investigation using the benchmark data set, we consider how data sparsity (i.e. the flow rate and GVF spacing in the experimental grid) affects the accuracy of a correction model. A range of neural network models are evaluated, based on different subsets of the benchmark data set. The data set and some exemplary code are provided with the paper. Additional data sets are available on a web site created to support this initiative.

Knowledge of fuel mass injected in an individual cycle is important for engine performance and modeling. At the moment, such measurements are not possible on engine or in real time. In this article, a new method using Coriolis flow meters (CFMs) and a new, patented, signal processing technique, known as the Prism, are introduced. CFMs are extensively used for flow measurement both in the automotive industry and further afield and, when coupled with the Prism, have the potential to make these challenging high-speed measurements. A rig-based feasibility study was conducted injecting very small quantities of diesel (3 mg) at pressures of up to 1000 bar at simulated engine speeds of up to 4000 rpm. The results show that these small quantities can in principle be measured. The results also reveal a previously unknown behavior of CFMs when measuring very low flow rates at high speed. The study concludes that by combining high-resonant frequency flow tubes with the Prism technology in a new instrument—the fast next-generation Coriolis (fast NGC) flow meter—it will be possible to measure individual injector flow rates on engine in real time.

Knowledge of fuel mass injected in an individual cycle is important for engine performance and modelling. Currently direct measurements of fuel flow to individual cylinders of an engine are not possible on-engine or in real-time due to a lack of available appropriate measurement techniques. The objective of this work was to undertake real-time Coriolis fuel flow measurement using GDI injectors on a rig observing fuel mass flow rate within individual fuel injections. This paper evaluates the potential of this technology – combining Coriolis Flow Meters (CFMs) with Prism signal processing together known as Fast Next Generation Coriolis (Fast NGC), and serves as a basis for future transitions on-engine applications. A rig-based feasibility study has been undertaken injecting gasoline through a GDI injector at 150 bar in both single shot mode and at a simulated engine speeds of 1788 and 2978 rpm. The results show that these injections can, in principle, be observed. In addition a number of features of the Fast NGC system unique to gasoline are discussed, and the repeatability of the technique is preliminarily assessed. The study concludes that the Fast NGC system has the potential to measure individual injector flow rates on-engine in real-time.

The verification of the proper operation of individual sensors is increasingly important as a means of validating the measurements that they generate. One technique for verifying the structural integrity of a transducer and its housing is to use ultrasonic probing, whereby an ultrasonic pulse is generated within the sensor housing. The response signal, which contains multiple resonances, indicative of structural health, is monitored and analyzed. Alternative techniques have been applied to analyze the ultrasonic response signals, obtained from a pressure sensor housing. These include Prism Signal Processing and the Matrix Pencil Method. Both of these techniques are able to track the frequency and amplitude of six or more modes of vibration. Experimental results demonstrate that large shifts in calculated parameter values occur when a fault condition is introduced. In addition, the time varying amplitudes of the modes do not always exhibit the expected exponential decay: this characteristic behaviour may provide an important additional diagnostic information.

The paper considers one of the modern parametric methods of signal processing - the matrix pencil method (MPM). The method makes it possible to effectively estimate signal parameters from its samples which is the sum of complex exponentials. The number of exponentials is not assumed to be known in advance and can also be estimated using a singular value decomposition of a matrix constructed from the signal samples. The object of this work is a vector process - a set of signals having the same frequencies and damping factors (i.e., the same poles of the signal), but different complex amplitudes. Such signals occur, for example, when considering a phased array antenna, when it is necessary to evaluate the parameters of a signal generated by the same sources but coming from many antenna elements with their amplitudes and phases. A similar problem arises in the evaluation of signals parameters from two spatially-distributed motion sensors of a Coriolis flowmeter. When processing several signals with classical MPM, we obtain several different sets of poles of these signals, which then, for example, need to be averaged to obtain the desired values of the poles assumed to be the same for all signals. The proposed modification of the MPM works with the entire set of signals at once, giving one set of signal poles, and at the same time it proves to be more effective both in terms of speed and in the accuracy of determining the signal parameters. The algorithms of the classical MPM and its modifications for the vector process are presented, as well as numerical experiments with model and real signals taken from one of the commercially available Coriolis flowmeters Dul5. Experiments show that the proposed algorithm yields more accurate results in a shorter (approximately 1,5 times) time than the classical MPM.

The Xilinx Artix-7 family of FPGAs has a peak DSP performance of approximately 1 TMAC/s. This paper describes a floating point FIR bandpass filter instantiation on an Artix-7 with up to 192 million taps, updated at up to 2 MHz, delivering an equivalent performance of up to 384 TMAC/s, while using only one third of the DSP resources on the FPGA. This has been achieved using Prism signal processing, a new recursive FIR filtering technique. Filter design is simple; a PC host HMI facilitates the immediate creation of new filter implementations. Examples of experimental results are provided.

Operating frequency is one of the most important parameters in power system. This paper proposes a new algorithm for power system frequency estimation. The algorithm is based on the Hilbert-transform phase-locked loops (HTPLL) and the complex least mean square (CLMS) method. The three-phase voltage data is input to the HTPLLs to obtain an approximate frequency estimation, and a complex voltage vector is formed using an a s -transform. By then, combining the complex voltage vector and the approximate estimate, the CLMS algorithm provides a refined frequency estimation. Simulation results of the proposed algorithm are presented and the performance of the algorithm is compared to other frequency tracking techniques.

An earlier study described a wireless acceleration sensor used to perform condition monitoring of rotating machinery, via off-line spectral analysis. In this paper, a new technique called Prism signal processing is applied to the same problem to provide sample-by-sample analysis of the machine rotation. The Prism has low design cost and low computational cost, and so is particularly suited to the flexible signal processing requirements for the Internet of Things. This paper provides an overview of the sensor and its experimental rig as well as the Prism-based signal processing scheme used to track the key components for diagnostic monitoring. The validity of the signal processing scheme is demonstrate via a simulation.

The Internet of Things (IoT) concept, alongside wireless technologies, supports the mounting of sensors in inaccessible positions and thus provides new opportunities for machine condition monitoring. This paper outlines experimental results using Prism signal processing to track rotor angular acceleration via a Wireless Acceleration Sensor (WAS) mounted on a rotating shaft. The instantaneous frequency and amplitude of each component of the angular acceleration is tracked, with a view to providing diagnostic information. The experimental results illustrate how amplitude data can provide indications of gear faults, via further Prism signal processing.

Our companion conference paper describes the requirement for flexible, self-validating instrumentation to support the goals of Industrial Cyber-Physical Systems. For high-value instruments such as Coriolis Mass Flow (CMF) meters, this entails more sophisticated signal processing techniques to provide additional sources of diagnostic and measurement data, particularly in dealing with common, but difficult real-world conditions such as two-phase (gas/liquid) flow. A novel Matrix Pencil Method (MPM) was described, adapted to monitor two modes of vibration for the CMF flowtube, and its performance evaluated in simulation. In this paper, we present results from applying the MPM technique to experimental data.

A key requirement for Industrial Cyber-Physical System (ICPS) instrumentation is measurement validation i.e. assessing measurement quality, including detecting and correcting for fault conditions. Coriolis Mass flow meters are used widely throughout industry, but commonly only for single-phase fluids, i.e. either liquids or gases, since accuracy is degraded by gas/liquid flows. The provision of robust measurement in non-ideal conditions is an increasingly important requirement. Here we propose that a modified Matrix Pencil Method is able to track the modes of flowtube vibration excited by gas/liquid flow, and show the results of applying the technique to numerically simulated data.

This paper provides tutorial introductions to Field-Programmable Gate Arrays (FPGAs) and the concept of hardware compilation - the translation of a high-level programming language directly into a hardware design. As an illustration, a simple stepper motor control program is presented. The research aims of the sensor validation programme are described, and the benefits of using hardware compilation techniques are presented. This leads on in the conclusion of the paper to a more general discussion of the interaction between research and technology, and in particular the influence of information technology upon control engineering.

A theoretical analysis and experimental results are presented of the time-frequency characteristics of hydrodynamic cavitation signals. Because of the limitation of the frequency bandwidth of the data acquisition system and the random nature of cavity collapses, the signal consists of transient burst waveforms. The acoustic pulses from collapses in close time proximity mix together and form transient oscillatory bursts. The local oscillation frequency of a burst is related to either the time duration between burst collapses or the cut-off frequency of the system. These results enable the identification and detection of individual cavitation events by the time-frequency characteristics in the vibroacoustic signal.

Trials have taken place to determine the response of a prototype Coriolis mass flowmeter to step changes in flow rate. The meter typically exhibits a delay of 16 ms, and tracks step changes well. Comparison with previously published results suggests that this performance represents an improvement over several types of current commercial flowmeters. The effect of a correction technique to reduce noise is demonstrated. This is considered valuable, even though it is responsible for 6 ms of the overall delay. A good dynamic response is important in process industry applications with short batch times. © 2003 Elsevier Science Ltd. All rights reserved.

Previous work has described the use of Coriolis mass flow metering for multiphase flows, with applications in the oil and gas industry. This paper describes the development and testing of a Coriolis two-phase flow metering solution for nitrogen/synthetic oil mixtures with viscosities ranging from 50 cSt to 500 cSt, and with Gas Volume Fractions (GVFs) ranging from 0% to 90%. Formal trials took place at the UK National Flow Laboratory. A single synthetic oil was used; its viscosity was adjusted by controlling temperature. The results show that Coriolis metering is well suited to high viscosity oil/gas mixtures: in the formalized trials, mass flow errors for gas were mostly within 5% and for liquid were mostly within 2.5%. Achieving this accuracy up to 90% GVF represents an advance on previous work, which has been limited to around 50% GVF for low viscosity oils in two-phase and three-phase mixtures.

The Internet of Things (IoT) [1] and Industrie 4.0 [2] propose substantial increases in the deployment of sensors into a diverse range of environments. The challenges are considerable: local computational power must be efficiently and flexibly deployed both to perform current metrological tasks and to reconfigure/redesign the signal processing flow as monitoring requirements evolve over time. This paper introduces the Prism (precise, repeat integral, signal monitor), a new type of signal processing block, as a contribution towards to the challenges of 21st Century metrology. The Prism acts as a fully recursive, dual output, FIR filter: the computational burden is low and independent of data window length. Prism design is trivial, so that networks of Prisms can be assembled, whether at design time or autonomously in real time, to carry out a wide range of metrological tasks. Prism-based trackers for the frequency, phase and/or amplitude of a sinusoid perform close to the Cramer-Rao Lower Bound (CRLB) for SNRs down to 0 dB.

This paper introduces a new technique, Prism signal processing, which may be used for the tracking of one or more noisy sinusoids in a signal. A simulation study is presented demonstrating the potential of Prism signal processing as an alternative to Prony’s method for analyzing exponentially decaying sinusoids. One application is to sensor condition monitoring of an industrial pressure sensor, using ultrasonic excitation to evaluate the sensor’s structural integrity. Initial experimental results suggest the Prism technique can reveal details in the resulting frequency/amplitude time series of each component, which is not available through Prony’s method.

A well test system for testing fluids produced from one or more petroleum wells has a separator and a plurality of multiphase flow metering systems, each of which has the capability, over at least a portion of its operating envelope, of separately measuring flow rates of oil, water, and gas. The well test system has a fluidic system, including gas leg conduits coupling the separator to the multiphase flow metering systems, liquid leg conduits coupling separator to the multiphase flow metering systems, and bypass conduits for directing multiphase fluid to the multiphase flow metering systems while bypassing the separator. Valves are configured to selectively route fluid flow though the fluidic system to selectively bypass the separator when the multiphase flow metering systems can be used to provide separate flow rates of oil, water, and gas in the unseparated multiphase fluids from the well.

The Prism is a new signal processing object which provides fully recursive FIR filtering i.e. the computational load is small and fixed irrespective of filter length. The Prism also has negligible design cost. These features are attractive to the next generation of Industry 4.0 sensors requiring low cost and flexible signal processing. Recent work has demonstrated how, using a few simple design rules, a chain of six Prisms can create narrowband filters with arbitrary central frequency and bandwidth, also with low design and computational cost. In this paper this technique is extended to provide spectral analysis of data, whereby a sequence of filters is applied to a data set to provide selective frequency analysis.

This paper explores the possible application of the complex bandpass filter in Coriolis mass flow meter signal processing. The method shows high robustness for frequency, amplitude and phase tracking under noisy conditions, such as those generated by two-phase (gas/liquid) flow. Simulations of the new technique are compared with existing academic and industrial signal processing solutions to evaluate their relative performance.

Self-validation is a valuable tool for extending the operating range of sensing systems and making them more robust. Wireless sensor networks suffer many limitations meaning that their efficacy could be greatly improved by self-validation techniques. We present two independently developed data analysis techniques and demonstrate that they can be applied to a wireless sensor network. Using an acoustic ranging application we demonstrate an improvement of more than ten-fold in the uncertainty of a single measurement where multiple sensor readings are appropriately combined. We also demonstrate that of the two methods for determining a largest consistent subset one is more rigorous in dealing with correlation, and the other more suited to time-series data. © 2008 IOP Publishing Ltd.

Two similar approaches are presented for performing consistency checking of measured data in cases where associated uncertainty values are provided. The key differences between the approaches concern the determination of a consistent subset, and the treatment of measured data deemed to fall outside that subset. The different approaches reflect the priorities of their respective domains of application, namely on-line sensor monitoring, and interlaboratory comparisons.

The sensor validation or SEVA project (Henry and Clarke 1991; Henry and Clarke 1993) promotes the use of intelligence in ‘smart’ sensors and the use of standard metrics to efficiently communicate self-diagnostics to the outside world. The standard metrics describe the status of the sensor including on-line uncertainty and a status flag to describe how the current validated measurement value has been derived. The end result is to provide a compact generic description of the quality of a measurement to the controller, with which decisions as to how to use the measurement can be made. This paper proposes the use of SEVA principles in the interpretation of data from biomedical instrumentation, in order to aid the decision-making process, particularly in critical care. For these purposes the pulse oximeter and polarographic oxygen tension meter will be used as working examples of typical ‘intelligent sensors’ because they make use of a microprocessor to perform self-diagnostics, as well as implementing measurement algorithms.

Remote condition monitoring and validation of railway points were discussed. Recent developments in microelectronics and information technology (IT) made possible low-cost, high-quality condition monitoring for trackside equipment. These equipments reduced the likelihood to breakdown and to enhance railway service quality. The results showed that there was value in continuous monitoring of the overall infrastructure which provided fundamentally new data not available under existing maintenance practice.

Previous work has described the use of Coriolis mass flow metering for two-phase (gas/liquid) flow. As the Coriolis meter provides both mass flow and density measurements, it is possible to resolve the mass flows of the gas and liquid in a two-phase mixture if their respective densities are known. To apply Coriolis metering to a three-phase (oil/water/gas) mixture, an additional measurement is required. In the work described in this paper, a water cut meter is used to indicate what proportion of the liquid flow is water. This provides sufficient information to calculate the mass flows of the water, oil and gas components. This paper is believed to be the first to detail an implementation of three-phase flow metering using Coriolis technology where phase separation is not applied. Trials have taken place at the UK National Flow Standards Laboratory three-phase facility, on a commercial three-phase meter based on the Coriolis meter/water cut measurement principle. For the 50. mm metering system, the total liquid flow rate ranged from 2.4. kg/s up to 11. kg/s, the water cut ranged from 0% to 100%, and the gas volume fraction (GVF) from 0 to 50%. In a formally observed trial, 75 test points were taken at a temperature of approximately 40. °C and with a skid inlet pressure of approximately 350. kPa. Over 95% of the test results fell within the desired specification, defined as follows: the total (oil+water) liquid mass flow error should fall within ±2.5%, and the gas mass flow error within ±5.0%. The oil mass flow error limit is ±6.0% for water cuts less than 70%, while for water cuts between 70% and 95% the oil mass flow error limit is ±15.0%. These results demonstrate the potential for using Coriolis mass flow metering combined with water cut metering for three-phase (oil/water/gas) measurement. © 2013 Elsevier Ltd.