Transducers and Transducer Calibration

ed from: Figliola, R.S. and Beasley, D. S., 1991, “Theory and Design for Mechanical Measurements” GENERAL MEASUREMENT SYSTEM Assigning a specific value to a physical variable is done with the help of a measurement system. Once a value has been assigned to a physical variable it becomes known as a measured value. There are four stages to the process of assigning a value to a physical variable. Generally these stages are: 1. A Sensor-Transducer Stage 2. A Signal Conditioning Stage 3. An Output Stage 4. A Feedback Stage These stages form a bridge between the input to and the output from a measurement system. Each stage leads to the assignment of a quantity to a physical variable, which infers its value. The relationship between the input information and the output information is established by a calibration of the measurement system. Calibration of a system is the act of applying a known input to a system to observe the systems reaction or output. The goal of the sensor-transducer stage is to convert the sensed information into a form that can be easily quantified. The sensor in a measurement system is typically a physical element that employs some natural phenomenon by which it senses the variable being measured. The transducer in a measurement system is a device that converts the sensed information into a detectable signal. This signal can be visual, mechanical, or electrical. The signal conditioning stage is the point that a signal from the transducer stage may be modified. This stage is optional and sometimes includes increasing a signal magnitude though amplification and applying filtering techniques. This stage provides a mechanical or optical linkage between the transducer and the output stage, for example, converting a transitional displacement of a sensor to a rotational a displacement of a pointer. The output stage provides the identification of the value of the variable being measured. For examples, a digital read out or a disk drive. For those measurement systems involved in process control the feed back control stage would contain a controller that would interpret the measured variable and make a decision regarding the control of the process. An example of a simple measurement system with a fee back control stage is a household thermostat. EXPERIMENTAL TEST PLAN Engineering measurements are not as simple as turning on the equipment and reading the numbers. Relevant information can only be extracted from test data obtained from a well though out measurement test plan. A test plan should be drawn from the following three steps: 1. Parameter Design Plan. An identification of process variables and parameters and a means for their control. 2. System and Tolerance Design Plan. The selection of a measurement technique, equipment, and test procedure based on some preconceived tolerance limits for error. 3. Data Reduction Design Plan. A methodology for analyzing, presenting and using the acquired data. Experimental design includes the development of a measurement test plan. Such a plan assists the engineering in achieving an optimization between time, accuracy, and cost. Expenditure regarding acquiring and analyzing test data versus information obtained. The identification of relevant process parameters and variables is the first step in developing a measurement strategy. All known variables should be listed and evaluated for any possible cause and effect relationships. Independent variables are variables that can be changed independently and without affecting any other variables. A dependent variable is a variable that is affected by changes in one or more variables. The control of a variable is important. A variable is controlled if it can be held at a relatively constant value during a measurement. The cause and effect relationship between an independent and dependent variable is found by applying a controlled value of the independent variable while measuring the dependent variable, also known as calibration. Variables can also be described as being discrete and continuous in nature. A discrete variable is a variable whose value can be enumerated. For example, variables that describe test specimens, testing devices, or operators are discrete variables. A continuous variable is a variable that is not discrete, such as displacement, pressure, or strain. In most instances variables that cannot be controlled but have an affect on the value of the variable being measured are present. These variables are known as extraneous variables. Extraneous variables can take form of signals superimposed over the measured signal such as noise or drift. Parameters are functional relationships between variable. A parameter that has an effect on the behavior of the measured variable is called a control parameter. Available methods for establishing control parameters based on known process variables include similarity and dimensional analysis techniques and physical laws. A control parameter is completely controlled if it can be held at a constant value during a set of measurements. The effects of extraneous variables can be delineated into noise and interference. Noise can be described as a random variation of the value of the measured signal resulting from variations in extraneous variables. Some examples would be thermal or Johnson noise due to random temperature-induced excitation of electrons within current carrying conductors and electronic shot noise common in transistors because of random fluctuations in the rate at which carriers diffuse across transistor junctions. Interference, on the other hand, would produce undesirable deterministic trends on the measured values because of extraneous variables. Examples would include well-defined deterministic variations in environmental conditions, local ac power lenience (60 or 50 Hz,) or fluorescent lighting arc noise (120 or 100 Hz). Interference is most undesirable if the period of the interference is longer than the period over which the measurement is made, because the interference will superimpose a false trend in the behavior of the measured variable. The influence of the extraneous variables can be reduced with the use of proper test strategies. A random test is one such strategy. A random test is defined by a measurement plan that sets a random order in the value of the independent variable applied. This can break up any trend caused by the coupling of uncontrolled extraneous variables with sequential application of independent variables. This type of plan is effective for the control of extraneous variables that change in a continuous manner. Discrete extraneous variables present a different sort of problem. The use of different operators and equipment are examples of discrete extraneous variables. Randomizing a test plan to minimize the influence of discrete variables can be done with the use of random test blocks. A block would consist of a data set of the measured variable where the controlled variable is varied but the extraneous variables are fixed. The extraneous variable would be held constant between blocks. This enables some amount of local control over the discrete extraneous variable. There are many strategies for randomizing blocks as well as advanced statistical methods for data analysis. In general, the estimated value of the measured variable improves with the number of measurements. Repeated measurements made during a test run or a batch of tests are called repetitions. Repetition allows for quantifying the variation in a measured variable as it occurs during any one test while the operating conditions are held under nominal control. However, repetition will not permit an assessment of how exact the operating conditions can be set. An independent duplication of a set of measurements is knows as a replication. This allows for quantifying the variation in a measured variable, as it occurs between different tests, each having the same nominal values operating conditions. All measurement plans should incorporate the use of concomitant methods. The goal is to obtain two or more estimates for the result, each based on a different method, which can be compared as a check for agreement. This may affect the experimental design in that additional variable may need to be measured.