Multisensor thiết bị đo đạc thiết kế 6o (P1)

PROCESS, QUANTUM, AND ANALYTICAL SENSORS INTRODUCTION Automatic test systems, manufacturing process control, analytical instrumentation, and aerospace electronic systems all would have diminished capabilities without the availability of contemporary computer integrated data systems with multisensor information structures. This text develops supporting quantitative error models that enable a unified performance evaluation for the design and analysis of linear and digital instrumentation systems with the goal of compatibility of integration with other enterprise quality representations....
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Multisensor thiết bị đo đạc thiết kế 6o (P1) Multisensor Instrumentation 6 Design. By Patrick H. Garrett Copyright © 2002 by John Wiley & Sons, Inc. ISBNs: 0-471-20506-0 (Print); 0-471-22155-4 (Electronic)1PROCESS, QUANTUM, ANDANALYTICAL SENSORS1-0 INTRODUCTIONAutomatic test systems, manufacturing process control, analytical instrumentation,and aerospace electronic systems all would have diminished capabilities withoutthe availability of contemporary computer integrated data systems with multisensorinformation structures. This text develops supporting quantitative error models thatenable a unified performance evaluation for the design and analysis of linear anddigital instrumentation systems with the goal of compatibility of integration withother enterprise quality representations. This chapter specifically describes the front-end electrical sensor devices for abroad range of applications from industrial processes to scientific measurements.Examples include environmental sensors for temperature, pressure, level, and flow;in situ sensors for measurements beyond apparatus boundaries, including spectrom-eters for chemical analysis; and ex situ analytical sensors for manufactured materialand biomedical assays such as microwave microscopy. Hyperspectral sensing ofboth spatial and spectral data is also introduced for improved understandingthrough feature characterization. It is notable that owing to advancements in higherattribution sensors, they are increasingly being substituted for process models inmany applications.1-1 INSTRUMENTATION ERROR REPRESENTATIONIn this text, error models are derived employing electronic device, circuit, and sys-tem parameter values that are combined into a unified end-to-end performance rep-resentation for computer-based measurement and control instrumentation. Thismethodology enables system integration beneficial to contemporary technologiesranging from micromachines to distributed processes. Since the baseline perfor-mance of machines and processes can be described by their internal errors, it is ax-iomatic that their performance may also be optimized through design in pursuit of 12 PROCESS, QUANTUM, AND ANALYTICAL SENSORSerror minimization. Instrumentation system errors are interpreted graphically inFigure 1-1. Total error is shown as the composite of barred mean error contributionsplus the root-sum-square (RSS) of systematic and random uncertainties; the truevalue is ultimately traceable to a reference calibration standard harbored by NIST.Although total error may instantaneously be greater or less than mean error fromthe additivity of RSS uncertainty error, throughout this text total error is expressedas the sum of mean and RSS errors in providing accountability of system behavior. Total error is analytically expressed by equation (1-1) as 0–100% of full scale(%FS), where the RSS sum of variances represents a one-sigma confidence interval.Consequently, total error may be expressed over any confidence interval by addingone mean error value and a summation of RSS error values corresponding to the stan-dard deviation integer. Confidence to six sigma is therefore defined by mean errorplus six times the RSS error value. Mean error frequently arises in instrumentationsystems from transfer function nonlinearities that, unlike RSS uncertainty error,which may be reduced by averaging identical systems as shown in Chapter 4, Section4-4, instead increases with the addition of each mean error contribution, necessitat-ing remedy through minimal inclusion. Accuracy is defined as the complement of er-ror (100%FS – %FS), where 1%FS error corresponds to 99%FS accuracy. A six-sigma framework is accordingly offered in terms of models defining mul-tisensor instrumentation errors to provide a generic design and analysis methodolo-gy compatible with corollary enterprise-quality representations. Quantitative instru-mentation system performance expressed in terms of modeled errors assumes thatexternal calibration is maintained to known standards, as shown in Figure 1-21, ver-ifying zero and full-scale values for external instrumentation registration. Calibra-tion is essential and can be performed manually or by automated methods, but itcannot characterize instrumentation device, circuit, and system variabilities thatthese error budgets ably describe, including expression to 6 confidence. 2 2 1/2 total = mean %FS + [ systematic + random] %FS1 (1-1) FIGURE 1-1. Instrumentation error interpretation. 1-1 INSTRUMENTATION ERROR REP ...