Thermocouples and Raspberry Pi for IIoT Machine Monitoring

Thermocouples and Raspberry Pi for IIoT Machine Monitoring
Thermocouples and Raspberry Pi for IIoT Machine Monitoring

Leading-edge Internet of Things (IoT) technology and advanced analytics are increasingly being used for process optimization and improved efficiency of industrial machinery because they enable predictive maintenance. The data being analyzed for this form of asset management often includes temperature measurements. And the compute power to perform those analyses is increasingly being provided by IoT devices based on Raspberry Pi

Raspberry Pi is a series of small single-board computers developed in the U.K. by the Raspberry Pi Foundation in association with Broadcom. The Raspberry Pi project originally focused on teaching basic computer science in schools and in developing countries, but the growing base of Raspberry Pi means the computer boards are increasingly finding their way into industrial automation applications—particularly as IIoT devices. The use of open-source C/C++ and Python lets users develop applications on Linux.

Although thermocouples are a popular way to measure temperature, designing and building data acquisition (DAQ) devices that accurately measure thermocouples in a Raspberry Pi environment is challenging. This article explains the difficulties in making accurate thermocouple measurements, how the MCC 134 DAQ HAT accomplishes it, and how MCC 134 is being used in IIoT devices for machine health monitoring.


How thermocouples work

A thermocouple is a sensor used to measure temperature. It works by converting thermal gradients into electrical potential difference—a phenomenon known as the Seebeck effect. A thermocouple is made of two wires with dissimilar metals joined together at one end, creating a junction. Because two dissimilar metal wires create different electric potentials over a temperature gradient, a voltage that can be measured is induced in the circuit.

Different thermocouple types have different combinations of metal in the wires and are used to measure different temperature ranges. For example, J type thermocouples are made with iron and constantan (copper-nickel alloy) and are suited for measurements in the –210°C to 1200°C range, while T type thermocouples are made with copper and constantan and are suited for measurements in the –270°C to 400°C range.

The thermal gradient mentioned above is referred to as the temperature difference between the two junctions: the measurement, or hot junction, at the point of interest and the reference, or cold junction, at the measurement device connector block (figure 1). Note that the hot junction refers to the measurement junction and not its temperature; this junction might be hotter or colder than the reference or cold junction temperature.

Thermocouples produce a voltage relative to the temperature gradient—the difference between the hot and cold junction. The only way to determine the absolute temperature of the hot junction is to know the absolute temperature of the cold junction.

While older systems relied on ice baths to implement a known cold junction reference, modern thermocouple measurement devices use a sensor or multiple sensors to measure the terminal block (cold junction) where thermocouples connect to the measurement device.


Sources of thermocouple errors

Thermocouple measurement error comes from many sources, including noise, linearity, and offset error; the thermocouple itself; and measurement of the reference or cold junction temperature. In modern 24-bit measurement devices, high-accuracy ADCs are used, and design practices are implemented to minimize noise, linearity, and offset errors.

Thermocouple error cannot be avoided, but it can be minimized. This error is due to the imperfections in alloys used, because they vary slightly from batch to batch. Certain thermocouples inherently have less error. Standard type K and J thermocouples have up to ±2.2°C error, while type T thermocouples have up to a ±1°C error. More expensive thermocouples (special limits of error [SLE]) are made with higher-grade wire and can be used to reduce errors by a factor of two.

Accurately measuring the cold junction, where the thermocouples connect to the device, can be a challenge. In more expensive instruments like the DT MEASURpoint products, an isothermal metal plate is employed to keep the cold junction consistent and easy to measure with good accuracy

In lower-cost devices, isothermal metal blocks are cost prohibitive, and without an isothermal block it is not possible to measure the temperature at the exact point of contact between the thermocouple and the copper connector. This fact makes the cold junction temperature measurement vulnerable to temporary error driven by quickly changing temperatures or power conditions near the cold junction.


Design challenges

To better understand the design challenges of the MCC 134, we can compare it to the design of MCC’s popular E-TC—a high-accuracy, Ethernet-connected thermocouple measurement device. The cold junction temperature of the E-TC is measured by Analog Devices’ ADT7310 IC temperature sensor.

The IC sensor design works well in the MCC E-TC because the measurement environment is controlled and consistent. The outer plastic case controls the airflow, and the electronic components and processors operate at a constant load. In the controlled environment of the E-TC, the IC sensor does an excellent job of measuring the cold junction temperature accurately.

However, when the MCC 134 was first designed with an IC sensor to measure the cold junction temperature, the accuracy was insufficient. Because the IC sensor could not be placed close enough to the connector block, large and uncontrolled temperature gradients caused by the Raspberry Pi and the external environment led to poor measurement repeatability.

So the MCC 134 was redesigned with an improved scheme that has far better accuracy and repeatability while keeping the cost low. Instead of using an IC sensor and one terminal block, MCC redesigned the board with two terminal blocks and three thermistors—one placed on either side and in between the terminal blocks, as shown in Figure 2.


Although this added complexity to the design, the thermistors more accurately tracked the temperature changes of the cold junction, even during changes in processor load and environmental temperature. This design yields excellent results that are far less susceptible to the uncontrolled Raspberry Pi environment

The MCC 134 should achieve results within the maximum thermocouple accuracy specifications when operating within the documented environmental conditions. Because certain factors still affect accuracy, users can improve measurement results by reducing quick changes in temperature gradients across the MCC 134 and following other best practices.


MCC 134 in action: Thinaer health usage monitoring system

The Thinaer Health Usage Monitoring System (HUMS) collects data from machining centers, CNC machinery, milling machines, and engines and uses this data to provide an “always-on” solution for monitoring, utilization reports, and predictive maintenance. Thinaer’s IoT platform integrates machine data with human feedback and uses a mix of MCC and Thinaer hardware and software to capture real-time machine data like temperature, location, vibration, voltage, pressure, and electrical current.

Thinaer systems use Raspberry Pi nodes that communicate with smart sensors via Bluetooth Low Energy. These smart sensors, however, do not have the high-accuracy temperature or high-speed vibration data needed for better analysis.

The solution for Thinaer was to use the MCC 134 thermocouple measurement HAT (see box) to measure temperature (as well as the MCC 172 IEPE measurement HAT to measure vibration) and to collect the data needed to create accurate measurements, analyses, and strategy.

The stackable DAQ HATs also allow Thinaer to scale without having to change its platform or do any internal hardware development or assembly. The system was programmed using provided C and Python libraries for continuous, multi-HAT acquisition of data.

Using MCC technology saved Thinaer both time and labor. The MCC DAQ HATs easily fit into the existing system enclosure and the off-the-shelf design saved Thinaer from having to develop a custom, in-house solution.


Accurately measuring

Thermocouples provide a low-cost and flexible way to measure temperature, but measuring thermocouples accurately is difficult. Through innovative design and extensive testing, MCC overcame the challenge of measuring thermocouples accurately in the uncontrolled Raspberry Pi environment. The MCC 134 DAQ HAT provides the ability to use standard thermocouples with the fast growing, low-cost computing platform.


Raspberry Pi Thermocouple Measurement HAT

The MCC 134 thermocouple measurement HAT for Raspberry Pi brings high-quality, temperature measurement capability to the popular low-cost computer. The device has four thermocouple (TC) inputs capable of measuring the most popular TC types, including J, K, R, S, T, N, E, and B. Each channel type is selectable on a per-channel basis. The MCC 134 has 24-bit resolution and professional-grade accuracy. Open thermocouple detection lets users monitor for broken or disconnected thermocouples. Up to eight MCC HATs can be stacked onto one Raspberry Pi. With the already available MCC 118, eight channel voltage measurement HAT, and the MCC 152 voltage output and digital I/O HAT, users can configure multifunction, Pibased solutions with analog input, output, and digital I/O.

This article comes from the March 2021 InTech Focus: Temperature and Pressure.

About The Author


Steve Radecky is marketing engineer for Measurement Computing. Measurement Computing designs and manufactures data acquisition devices that are easy to use, easy to integrate, and easy to support. Included software options are extensive and provided for both programmers and nonprogrammers. Please contact Measurement Computing Corporation if you have any questions or if you would like any further information: (508) 946-5100 or info@mccdaq.com

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