What is Intelligent Instrumentation? An instrumentation system is a physical system which is a collection of physical objects connected in such a way to get the desired output for a given input condition Such instrumentation is termed as intelligent instrumentation when the system is made capable of automating the process by just simply feeding in a software program in it and secondly capable of storing the processed data in its memory for its further use. Thus a system is said to be an intelligent instrumentation system when it is included with a microprocessor and memory thus acting as a micro computer About the intelligent instruments in use The intelligent instrument behaves as a black box as far as the user is concerned, and no knowledge of its internal mode of operation is required in normal measurement situations. Intelligent instruments offer many advantages over their non-intelligent counterparts, principally because of the improvement in accuracy achieved by processing the output of transducers to correct for errors inherent in the measurement process. Proper procedures must always be followed in their use to avoid the possibility of introducing extra sources of measurement error. One such example of the benefit that intelligence can bring to instruments is in volume flow rate measurement, where the flow rate is inferred by measuring the differential pressure across an orifice plate placed in a fluid-carrying pipe. The flow rate is proportional to the square root of the difference in pressure across the orifice plate. For a given flow rate, this relationship is affected both by the temperature and by the mean pressure in the pipe, and changes in the ambient value of either of these cause measurement errors. A typical intelligent flow rate measuring instrument contains three transducers: a primary one measuring the pressure difference across an orifice plate and secondary ones measuring absolute pressure and temperature. The instrument is programmed to correct the output of the primary differential pressure transducer according to the values measured by the secondary transducers, using appropriate physical laws which quantify the effect of ambient temperature and pressure changes on the fundamental relationship between flow and differential pressure. The instrument is also normally programmed to convert the square root relationship between flow and signal output
into a direct one, making the output much easier to interpret. Typical accuracy levels of such intelligent flow measuring instruments are ±0.1%, compared with ±0.5% for their non-intelligent equivalents, showing an improvement by a factor of five. Intelligent instruments usually provide many other facilities in addition to those mentioned above, such as the following: 1. Signal damping with selectable time constants. 2. Switchable ranges (using several primary transducers within the instrument which each measure over a different range). 3. Switchable output units (e.g. display in Imperial or SI units). 4. Diagnostic facilities. 5. Remote adjustment and control of instrument options from up to 1500 meters away via four-way, 20 mA signal lines.
the term intelligent when applied to measurement ,or control, systems , means that a microprocessor or computer is used for signal processing the term dumb is applied to conventional measurement systems when no such microprocessor is used.
signal processing with a microprocessor requires the signals to be digital rather than analogue. with a digital signal information is transmitted in the form of pulses, i.e. on-off or high -low signals the off or low signal is represented by the binary number 0 and the on or high signal by the binary number 1 each such number is called a bit . a set of such binary no’s is called a word.
Many transducers are analogue devices. i.e. their output is some replica oor scaling of the input.
an obvious example of an analogue device is a watch where the time is represented by the position of the watch hands, while the digital watch represents time by a sequence of numbers.
because of this analogue nature of many transducers, intelligent instrumentation require a signal conditioning element which converts the analogue transducer output to digital form before it can be fed to the microprocessor.
Intelligent Instrumentation: Principles and Applications
With the advent of microprocessors and digital-processing technologies as catalyst, classical sensors capable of simple signal conditioning operations have evolved rapidly to take on higher and more specialized functions including validation, compensation, and classification. This new category of sensor expands the scope of incorporating intelligence into instrumentation systems, yet with such rapid changes, there has developed no universal standard for design, definition, or requirement with which to unify intelligent instrumentation. Explaining the underlying design methodologies of intelligent instrumentation, Intelligent Instrumentation: Principles and Applications provides a comprehensive and authoritative resource on the scientific foundations from which to coordinate and advance the field. Employing a textbook-like language, this book translates methodologies to more than 80 numerical examples, and provides applications in 14 case studies for a complete and working understanding of the material. Beginning with a brief introduction to the basic concepts of process, process parameters, sensors and transducers, and classification of transducers, the book describes the performance characteristics of instrumentation and measurement systems and discusses
static and dynamic characteristics, various types of sensor signals, and the concepts of signal representations, various transforms, and their operations in both static and dynamic conditions. It describes smart sensors, cogent sensors, soft sensors, selfvalidating sensors, VLSI sensors, temperature-compensating sensors, microcontrollers and ANN-based sensors, and indirect measurement sensors. The author examines intelligent sensor signal conditioning such as calibration, linearization, and compensation, along with a wide variety of calibration and linearization techniques using circuits, analog-to-digital converters (ADCs), microcontrollers, ANNs, and software. The final chapters highlight ANN techniques for pattern classification, recognition, prognostic diagnosis, fault detection, linearization, and calibration as well as important interfacing protocols in the wireless networking platform. Read more at http://ebookee.org/Intelligent-Instrumentation-Principles-andApplications_1336593.html#tMl6MpebyYGsyzdx.99
