Peter Hung

Difference equation approach to two-thermocouple sensor characterization in constant velocity flow environments

Thermocouples are one of the most popular devices for temperature measurement due to their robustness, ease of manufacture and installation, and low cost. However, when used in certain harsh environments, for example, in combustion systems and engine exhausts, large wire diameters are required, and consequently the measurement bandwidth is reduced. This article discusses a software compensation technique to address the loss of high frequency fluctuations based on measurements from two thermocouples. In particular, a difference equation (DE) approach is proposed and compared with existing methods both in simulation and on experimental test rig data with constant flow velocity. It is found that the DE algorithm, combined with the use of generalized total least squares for parameter identification, provides better performance in terms of time constant estimation without any a priori assumption on the time constant ratios of the thermocouples.

A Total Least Squares Approach to Sensor Characterisation

Abstract The use of robust, low-bandwidth sensors makes exhaust gas temperature variations difficult to measure in internal combustion engines. One common solution involves measuring gas temperature using two thermocouples with different time-constants and estimating the time-constants from the resulting signals. This assumes that the ratio of the thermocouple time-constants is invariant and known a priori. In addition they are generally subject to singularities and sensitive to noise. This paper presents a novel total least squares (TLS) difference equation based characterisation method. It makes no such assumption and is potentially superior to existing methods in terms of time-constant estimation accuracy and noise tolerance.

Exploiting A Priori Time Constant Ratio Information in Difference Equation Two-Thermocouple Sensor Characterization

The characterization of thermocouple sensors for temperature measurement in varying-flow environments is a challenging problem. Recently, the authors introduced novel difference-equation-based algorithms that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. In particular, a linear least squares (LS) lambda formulation of the characterization problem, which yields unbiased estimates when identified using generalized total LS, was introduced. These algorithms assume that time constants do not change during operation and are, therefore, appropriate for temperature measurement in homogenous constant-velocity liquid or gas flows. This paper develops an alternative beta formulation of the characterization problem that has the major advantage of allowing exploitation of a priori knowledge of the ratio of the sensor time constants, thereby facilitating the implementation of computationally efficient algorithms that are less sensitive to measurement noise. A number of variants of the beta formulation are developed, and appropriate unbiased estimators are identified. Monte Carlo simulation results are used to support the analysis

Blind Deconvolution for Two-Thermocouple Sensor Characterization

Thermocouples are one of the most popular devices for temperature measurement due to their robustness, ease of manufacture and installation, and low cost. However, when used in the harsh environment found in combustion systems and automotive engine exhausts, large wire diameters are required and consequently the measurement bandwidth is reduced. This paper describes two new algorithmic compensation techniques based on blind deconvolution to address this loss of high-frequency signal components using the measurements from two thermocouples. In particular, a continuous-time approach is proposed, combined with a cross-relation blind deconvolution for parameter estimation. A feature of this approach is that no a priori assumption is made about the time constant ratio of the two thermocouples. The advantages, including small estimation variance and limitations of the method, are highlighted using results from simulation and test rig studies.

Population Mobility Dynamics Estimated from Mobile Telephony Data

Abstract In the last decade, mobile phones and mobile devices using mobile cellular telecommunication network connections have become ubiquitous. In several developed countries, the penetration of such devices has surpassed 100 percent. They facilitate communication and access to large quantities of data without the requirement of a fixed location or connection. Assuming mobile phones usually are in close proximity with the user, their cellular activities and locations are indicative of the users activities and movements. As such, those cellular devices may be considered as a large scale distributed human activity sensing platform. This paper uses mobile operator telephony data to visualize the regional flows of people across the Republic of Ireland. In addition, the use of modified Markov chains for the ranking of significant regions of interest to mobile subscribers is investigated. Methodology is then presented which demonstrates how the ranking of significant regions of interest may be used to estimate national population, results of which are found to have strong correlation with census data.

Unbiased Thermocouple Sensor Characterisation in Variable Flow Environments

Abstract A novel two-thermocouple sensor characterisation method for use in variable velocity flow environments is described. A difference equation method, recently developed by the authors for constant velocity flow applications, is extended to accommodate variable velocity flows using polynomial parameter fitting on a sliding data window. In particular, by using a novel difference equation formulation the invariance of time-constant ratio with respect to flow velocity is exploited to produce an efficient unbiased and consistent time-constant estimator. Monte-Carlo simulation studies show that the new algorithm outperforms alternatives in the literature without the restrictive requirement of a priori knowledge of thermocouple time constant ratios.

Difference equation sensor characterization algorithms for two-thermocouple probes

The characterization of thermocouple sensors for temperature measurement in variable flow environments is a challenging problem. In this paper, novel difference equation-based algorithms are presented that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. Linear and nonlinear least squares formulations of the characterization problem are introduced and compared in terms of their computational complexity, robustness to noise and statistical properties. With the aid of this analysis, least squares optimization procedures that yield unbiased estimates are identified. The main contribution of the paper is the development of a linear two-parameter generalized total least squares formulation of the sensor characterization problem. Monte-Carlo simulation results are used to support the analysis.

On the stability and biasedness of the cross-relation blind thermocouple characterisation method

The in situ characterisation of t hermocouple sensors is a challenging problem. Recently the authors introduced a novel blind characterisation t echnique based on the cr oss-relation m ethod of blind identification that allo ws in situ characterisation of tem perature measurement probes consisting of two- thermocouple sensors with differing time constants. While the technique has a number of advantages over competing methods, in cluding low estimation variance and no need for a p riori estimation of the time constant ratio, it was found to be positively biased and becomes unstable at high noise levels. In this paper the orig in of th e stab ility issues an d bias are analysed. It is shown that an alternative n ormalised cost function formulation, which eliminates the stab ility problem, resu lts in negatively biased time constant estimates at high noise levels. Further, it is demonstrated that this bias is less significant when temperature variations are broadband. All results are verified using Monte-Carlo simulations. �

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models.

In Situ Two-Thermocouple Sensor Characterisation using Cross-Relation Blind Deconvolution with Signal Conditioning for Improved Robustness

Thermocouples are one of the most widely used temperature measurement devices due to their low cost, ease of manufacture and robustness. However, their robustness is obtained at the expense of limited sensor bandwidth. Consequently, in many applications signal compensation techniques are needed to recover the true temperature from the attenuated measurements. This, is turn, necessitates in situ thermocouple characterisation. Recently the authors proposed a novel characterisation technique based on the cross-relation method of blind deconvolution applied to the output of two thermocouples simultaneously measuring the same temperature. This offers a number of advantages over competing methods including low estimation variance and no need for a prioriknowledge of the time constant ratio. A weakness of the proposed method is that it yields biased estimates in the presence of measurement noise. In this paper we propose the inclusion of a signal conditioning step in the characterisation algorithm to improve the robustness to noise. The enhanced performance of the resulting algorithm is demonstrated using both simulated and experimental data.