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Detect errors in low-voltage measurements

06 Aug 2015  | Glenn Weinreb

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Many data-acquisition systems connect directly to sensors. As with all measurement systems, you must contend with errors and do your best to minimise them. Errors caused by thermal drift, EMI/RFI, internal noise, wiring, grounding, and shielding all contribute to total measurement error. Fortunately, you can minimise errors if you know what's causing them.

For this article, we used an instruNet i423 digitizer, one of several systems designed to attach directly to many different sensors such as voltage, current, resistance, load cell, strain gage, thermocouple, and RTD. You can apply the techniques covered here to other data-acquisition systems as well.

We ran experiments with a load-cell sensor that measures 0 to 2kg of force and internally contains four 600 Ω resistors that are bonded to a metal plate that flexes when pressed. Flexing of the plate changes the resistor values. You can think of this as a sensor with a 600 Ω source impedance that receives a 3.3Vdc excitation voltage and produces a ±10 mV signal with a 1.65 Vdc offset. The data acquisition differential amplifier sees ±10 mV and we will evaluate microvolt level errors. All pictures in this article are actual measurements from this setup. Figure 1 is a schematic of the sensor. Electrically, a load-cell sensor is the same as a strain gage and mV/V pressure sensor.


Figure 1: A strain gage is essentially a four-resistor bridge circuit where the voltage across it changes because of flexing.


We'll focus on these Error sources:

 • RFI Couples into Sensor Signal
 • 50/60Hz Power Couples into Sensor Signal
 • Data Acquisition System Internal Noise
 • Thermal Drift and Sensor Instability

Test setup
Normally, sensors attach to a data acquisition system through a shielded cable. For the purpose of demonstrating RFI (radio waves coupling into signal wires), however, we break out the IN+ wire and induce an offending signal with a function generator. The function generator 5 Vrms output is connected to a bare wire that wraps around the sensor IN+ wire ten times. We've placed 270 Ω in series with the function generator output to facilitate 18 mA through the offending coil (5 Vrms / 270 = 18 mArms).

We've also attached a dummy sensor, one that's electrically similar to the load cell, to a second measurement channel. It consists of four independent thin-film resistors floating in air at the end of a cable, where the function generator is attached in the same way as the load cell. RFI couples more with increased source impedance. Therefore, the dummy sensor has the same 600 Ω source impedance as the load cell. The second channel is used to identify slight instability from within the load cell itself.

A third channel is grounded with a 2-cm wire between data acquisition IN+ and IN- terminals, and between GND and IN+. This third channel is used to determine the internal system noise and thermal drift of the data-acquisition system itself. All experiments are conducted with an instruNet i423 card on its ±10 mV measurement range using instruNet World Oscilloscope/Strip chart software. This card provides software selectable 6Hz and 4000Hz two-pole analogue low-pass filters, software selectable digital filters, and software selectable integration (averaging).

Many load-cell manufacturers recommend an excitation voltage of 10 V, which applies 285 mW into the load cell (10^2/600 = 0.285). That much power creates heat and temperature drift. Therefore, we prefer to run at a lower 3.3 V, which corresponds to a gentler 31 mW.


RFI couples into sensor signal
RFI involves radio waves that travel through air and couple into wires. This is explained by Maxwell's equations, which state that a change in wire #1 current creates a magnetic field that flows through a loop of wire #2 and induces a current in that wire, which then converts to a voltage after traveling through resistance. The effect of RFI increases with increased source impedance (source is less strong to fight RFI); therefore, high source impedances and low level measurements are the most challenging. The experiments shown here explain how signal switching and sine waves can couple into your signals.

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