If you work with temperature sensors in control engineering, you know how important it is to analyze and visualize the data they produce. But what are the most effective ways to do that? In this article, you will learn about some of the common methods and tools for temperature sensor data visualization, and how to choose the best one for your project.
The simplest way to visualize temperature sensor data is to use basic plots, such as line charts, scatter plots, or bar charts. These plots can show the trends, patterns, and outliers in the data over time or across different locations. You can use software like Excel, MATLAB, or Python to create basic plots from your data. However, basic plots may not be enough to capture the complexity and dynamics of temperature sensor data, especially if you have multiple sensors or variables.
Techniques such as heatmaps, time-series decomposition, box plots, histograms, correlation plots, spatial visualization, interactive visualization, and machine learning-based visualization offer deeper insights into temperature variations across time and space
A heat map is a type of plot that uses colors to represent the values of temperature sensor data on a grid or a map. A heat map can show the spatial distribution and variation of temperature in a region or a system. You can use heat maps to compare the temperature profiles of different scenarios, such as day and night, seasons, or control settings. You can also use heat maps to identify hot spots, cold spots, or anomalies in the data. You can use software like R, Tableau, or QGIS to create heat maps from your data.
Time series analysis is a method that applies statistical and mathematical techniques to analyze and model the behavior of temperature sensor data over time. Time series analysis can help you understand the characteristics, patterns, and trends of the data, such as seasonality, cycles, trends, or noise. You can also use time series analysis to forecast the future values of the data, based on the past and present data. You can use software like SAS, SPSS, or Python to perform time series analysis on your data.
Frequency domain analysis is a method that transforms the temperature sensor data from the time domain to the frequency domain, using techniques such as Fourier transform or wavelet transform. Frequency domain analysis can help you identify the dominant frequencies, harmonics, or modes of the data, and how they change over time or across different conditions. You can also use frequency domain analysis to filter out the noise or unwanted components of the data, and enhance the signal quality. You can use software like MATLAB, LabVIEW, or Python to perform frequency domain analysis on your data.
Machine learning is a method that uses algorithms and models to learn from the temperature sensor data and make predictions or classifications. Machine learning can help you discover the hidden patterns, relationships, or features of the data, and generate insights or recommendations. You can also use machine learning to detect anomalies, outliers, or faults in the data, and improve the reliability and performance of your system. You can use software like TensorFlow, Scikit-learn, or PyTorch to apply machine learning to your data.
An interactive dashboard is a tool that allows you to visualize and explore the temperature sensor data in an interactive and intuitive way. An interactive dashboard can help you customize the plots, filters, and parameters of the data, and see the results in real-time. You can also use an interactive dashboard to communicate and share your findings or reports with others, and get feedback or input. You can use software like Power BI, Dash, or Plotly to create interactive dashboards from your data.
I have commented on multiple DCS and HMI display discussions. I advocate a minimalist approach with a great deal of feedback from the operators over the average course of five revisions. A PLC intends to act like a conductor in an orchestra, where the HMI represents the bridge to the audience. It is also how the system is generally judged by those unfamiliar with PLC and control systems. Temperature is simply one analog value to display and must be exhibited in the most useful way based on the application and audience.
If you are using data visualization tools like Qliksense , Tableau you can use the Geo analytics visualization stack to present the temperature data with multilayer drill through analysis If the data is complex which requires more analysis you can choose to present through visualization tools like Smartmet, WpDatatables and Unidata, MeteoInfo which comes with various essential GIS function support that can view or read widely-used meteorological data formats, like GRIB and NetCD and allows access to a variety of real-time and archive datasets