Ocean Instrumentation, Course 13.998 Lecture on Instrumentation Specifications Jim Irish, WHOI, 10 Feb 2005 I. Sensor/Instrument Specification Definitions: Range – is the maximum and minimum value range over which a sensor works well. Often sensors will work well outside this range, but require special or additional calibration. e.g. salinity sensors deployed in salinity of a few PPT in an estuary which is below where the PSU scale is defined (2 PSU). However, generally if you try to operate a sensor outside its range, it will not work (give a constant output at the max, significantly change sensitivity or give erratic results) or be damaged e.g. a 130 m pressure sensor deployed at 200 m depth. Accuracy – how well the sensor measures the environment in an absolute sense. That is how good the data is when compared with a recognized standard. e.g. a temperature sensor accurate to 0.001º C is expected to agree within 0.001º C with a temperature standard such as a triple-point-of-water cell or the temperature measured by a PRT standardized in recognized calibration standards or by another sensor with the same accuracy calibrated properly. This is what you want to compare results with other observations. Resolution – the ability of a sensor to see small differences in readings. e.g. a temperature sensor may have a resolution of 0.000,01º C, but only be accurate to 0.001º C. That is you can believe the size of relative small changes in temperature, which are smaller than the accuracy of the sensor. Resolution in often controlled by the quantization in digitizing the signal – e.g. one bit is equal to 0.0005º C. This is not a function of the sensor, but the sampling process. Repeatability – This is the ability of a sensor to repeat a measurement when put back in the same environment. It is often directly related to accuracy, but a sensor can be inaccurate, yet be repeatable in making observations. Drift – This is the low frequency change in a sensor with time. It is often associated with electronic aging of components or reference standards in the sensor. Drift generally decreases with the age of a sensor as the component parts mature. A smoothly drifting sensor can be corrected for drift. e.g. Sea Bird temperature sensors that are drifting about 1 mºC/yr (and have been smoothly changing for several years) allow one to correct for the drift and get more accurate readings. Drift is also caused by biofouling that can’t be properly corrected for, but we often try. Hysteresis – A linear up and down input to a sensor, results in an output that lags the input e.g. you get one curve on increasing pressure and another on decreasing. Many pressure sensors have this problem, for better ones it can be ignored. It is often seen in a CTD when the pressure reading on deck after recovery is different from the reading before it is deployed. It is not a problem with the response time of the sensor, but is an inherent property of some sensors that is undesirable. In a CTD it also may be a temperature sensitivity problem. Stability – is another way of stating drift. That is, with a given input you always get the same output. Drift, short and long term stability are really ways of expressing a sensor’s noise as a function of frequency. Sometimes this is expressed as guaranteed accuracy over a certain time period. Drift is often a problem with pressure sensors under high pressure. All sensors drift with time – hence the standardization of PRTs in triple-point-of-water and gallium melt cells.Response time – a simple estimate of the frequency response of a sensor assuming an exponential behavior. We will discuss in more detail below. Refer to Sea Bird temperature specification sheets: Self heating – to measure the resistance in the thermistor to measure temperature, we need to put current through it. Current flowing through a resistor causes dissipation of heat in the thermistor, which causes it to warm up, or self-heating. This is especially important in temperature measurement. If the water velocity changes, the amount of advective cooling will change, and the temperature sensed will change as a function of velocity – anemometer effect. Settling time – the time for the sensor to reach a stable output once it is turned on. Therefore, if you are conserving power by turning off the sensors between measurements, you need to turn on the power and wait a certain time for the sensor to reach a stable output. Voltage required – the voltage supply range over which the sensor has a stable output. Too low a voltage and the sensor doesn’t work properly, too high and something burns out. Current drawn – what the sensor draws at each voltage. Generally this varies inversely with voltage (the sensor draws a nearly constant power - current times voltage). Therefore, you need to supply a known voltage and current. If you do this from a battery, beware that the voltage decreases as the battery discharges, and the current rises. Also, the amount of power you can get out of a battery changes with the amount of current drawn. Therefore, when you calculate power requirements, you need to know the current over the full voltage range to get power and battery requirements. We will discuss this in more detail later in a lab. Output – a voltage range e.g. 0 to 5 volts for an input range of 0 to 30º C, or a frequency modulated sine wave, or a square wave of frequency range 6 to 12 kHz, etc. Fit of calibrations to equation – thermistors are log devices, so fit as an inverse polynomials of log of sensor output (frequency), which change from K to Celsius by the 273.15 Sensor Noise Estimation: - Besides digitizing noise, there is another limitation in a measurement due to the inherent noise in a sensor itself. Today we have the technology to reduce the digitizing interval or digitizing noise to below the sensor noise, so the limiting factor in a measurement is generally the physics of the sensor itself. Ideally, one would put the sensor in a noise free environment and measure the spectra of the sensor’s output to get the sensor noise. However, this would only work if the sensor’s noise was not related to signal level, and if you could find a noise free environment. Consider the case where several sensors are measuring the same geophysical signal, s(t), and that in addition to this signal each