Sensors dynamic characteristics

Reliable measurements of the periodically changing outputs are not always available, as they are highly dependent on the sensor dynamic characteristics. 

The balancing process must be in accord with the rotor dynamics, as specified by the operating environment. Unfortunately, the dynamic characteristics are often not properly recognized when the balancing procedure is specified. As a result, the unbalance distribution problem may not be identified not enough planes may be provided sensors may be located at nonoptimum positions, or critical speeds may be overlooked entirely. It is the responsibility of the machinery end user to satisfy himself that the manufacturer has considered ... 

The sample system is responsible for collecting a representative sample of the process and delivering it to the analyzer for analysis. Obviously, the reliability of the sample system directly affects the reliability of the overall composition analysis system. The transport delay associated with the sample system contributes directly to the overall deadtime associated with an on-line composition measurement. This difference in sampling deadtime can have a drastic effect on the performance of a control loop. Table 15.2 summarizes the dynamic characteristics and repeatability of typical control valve systems and several different types of sensors. 

The dependence of the concentration of ionized groups (and consequently the water uptake) on the duration tgx of th immersion in the solution leads to an additional dynamical hysteresis in the sensor output characteristic which can be minimized by setting constant tgx for all pH measurements. 

Cho S.-S. and Kim H.-G., Dynamic characteristics of SrTiOj thick film as an oxygen sensor, 4th International Conference on Electronic Ceramics and Applications, Proceedings 2 Electroceramics IV, Aachen, 1994, 749. 

Since the limiting faw tors of the coarse alignment were the sensor biases, not only the states describing the unforced dynamical behaviour of the process, i.e. the Schuler loop and earth loop dynamics, but also parameters describing the dynamical characteristics of the main error sources are introduced as state-variables. The key problem of extending the underlying model to additional bias-terms is the observability of the states, i.e. if enough information is contained in the measurements and in the structure of the filter to determine all states. 

In addition to the static characteristics, there are dynamic ones. These are related with the sensor s response to a time-related input. As sensors perform the transduction between a domain under analysis and a processable domain, the understanding of the dynamic characteristic of a sensor becomes fundamental. 

In the dynamic characteristics context, it is crucial to provide the transfer function for the sensor s response. Moreover, in dynamic systems, the responses may have different behaviors, which can be related to the mathematical orders. Fig. 11.3 illustrates the differences of orders for the same type of system. To understand how the components change the systems, it is advisable to explore the specific hterature in the field (Nise, 2011). 

Dynamic explosion detectors use a piezoresistive pressure sensor installed behind the large-area, gas-tight, welded membrane. To ensure optimum pressure transference from the membrane to the active sensor element, the space between the membrane and the sensor is filled with a special, highly elastic oil. The construc tion is such that the dynamic explosion detec tor can withstand overpressures of 10 bar without any damage or effect on its setup characteristic. The operational range is adjustable between 0 and 5 bar abs. Dynamic explo-... 

The third block in Fig. 2.1 shows the various possible sensing modes. The basic operation mode of a micromachined metal-oxide sensor is the measurement of the resistance or impedance [69] of the sensitive layer at constant temperature. A well-known problem of metal-oxide-based sensors is their lack of selectivity. Additional information on the interaction of analyte and sensitive layer may lead to better gas discrimination. Micromachined sensors exhibit a low thermal time constant, which can be used to advantage by applying temperature-modulation techniques. The gas/oxide interaction characteristics and dynamics are observable in the measured sensor resistance. Various temperature modulation methods have been explored. The first method relies on a train of rectangular temperature pulses at variable temperature step heights [70-72]. This method was further developed to find optimized modulation curves [73]. Sinusoidal temperature modulation also has been applied, and the data were evaluated by Fourier transformation [75]. Another idea included the simultaneous measurement of the resistive and calorimetric microhotplate response by additionally monitoring the change in the heater resistance upon gas exposure [74-76]. 

Sensors should always be evaluated in vitro. While this is clearly not a substitute for in vivo testing, it is easier to diagnose fundamental problems without the complications that the biological milieu introduces. Furthermore, if they do not work reliably in vitro, they will not work in vivo. In addition to the linear dynamic range mentioned above, stability and reproducibility of characteristics in sensor production are very important. Linearity can be characterized by comparing the sensitivities (slope of the dose/response curve) at 5 and 15 mM glucose, assuming that they should not deviate by more than 10%. Stability can be measured in several different ways. Sensors can be stored dry and at room temperature between periodic sensitivity checks. This tends to... 

Sensor type and location, as well transmitter characteristics, noise, and sampled data issues, also can affect loop performance. Most continuous measurement sensors and transmitters have relatively fast dynamics and a noise filter, which can be approximated by a... 

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