The earliest form of data recording was manual measurement, where data was recorded in written logbooks and plotted on graph paper. By the late 19th century, the advent of the strip chart recorder automated the data recording process, mechanically transferring measurement results onto paper. While still having its limitations, the strip chart recorder was undoubtedly a significant leap forward compared to manual recording.
Today, data loggers (or paperless chart loggers) have become the most popular method of data recording. A data logger is a standalone instrument that measures signals, converts them into digital data, and stores the data internally. Many data loggers have built-in displays and can transfer data to a PC for offline analysis, permanent storage, or report generation.
PCs were used for data logging shortly after their invention. However, early PC-based data loggers required specialized hardware and software to perform the logging function. The standard PC interface of that era was too slow for high-speed data acquisition. Hewlett-Packard, a manufacturer of test and data acquisition equipment at the time, developed the HPIB interface bus to bypass the slow I/O interfaces of early PCs. As most equipment manufacturers adopted HPIB, it quickly became the General Purpose Interface Bus (GPIB), and was later adopted as an IEEE standard, IEEE-488. The limitations of PC processing power meant that PCs were merely data loggers in name only. Only after the raw data was acquired could subsequent processing create useful information.
As the processing power of PC-based measurement equipment has increased, so too has its applications. PCs are now an integral part of PC-based data logger systems. Data logging software utilizes high-performance PC processors, hard drives, displays, and I/O buses to add features beyond simply recording data. Today's PC-based data loggers offer five key advantages: real-time visualization, online analysis, user-defined functions, terabyte data storage, and networking and collaboration capabilities.
Real-time visualization
Traditional standalone data loggers are purely acquisition devices, only capable of recording data to memory. Before the data is usable, technicians need to transfer it from the logger to a PC. In early systems, this meant manually entering the data into the computer. Later systems allowed technicians to download data using communication cables. Once the data is stored on the PC, spreadsheet software or other software provides charts and visualization tools for analysis.
Figure 1: NI hardware and software help laptops easily capture automated measurements. A four-channel analog input module from NI is used, connected to a PC via a USB port.
Because PC-based data acquisition systems are always connected to a PC, online measurement results can be displayed on the PC monitor in near real-time, making the results immediately visible. Some programming environments, such as NI LabVIEW, allow users to create custom interfaces to control the measurement equipment and display the results. Creating a user interface using LabVIEW is very simple; just drag and drop controls and graphs in the programming window.
Online analysis
Standalone data loggers typically acquire raw, unfiltered data. However, in most cases, the data must be processed or conditioned before it can be used. Data conditioning can take many forms, ranging from simple filtering to applications of Fast Fourier Transform. Due to the slow speed of early systems, there was a time delay between data acquisition and its availability for analysis.
LabVIEW 3D sensor mapping maps 3D models to analytical data, visually displaying the data.
Modern PCs have eliminated this bottleneck. Multi-core processors and large amounts of memory enable today's PCs to perform signal conditioning and data analysis online. Analysis software includes many commonly used mathematical and signal processing functions, such as differential equations, curve fitting, integration and differentiation, interpolation and extrapolation, and many other operations. Most of these tools use configuration wizards, making measurement analysis simple.
User-defined functions
Traditional data loggers are typically limited by vendor-defined hardware and functionality. While they can perform general tasks, they may not be suitable for specific applications. For example, technicians may need to log data under certain conditions, or the data logger may not be able to create built-in custom alarms.
PC-based data loggers are software-defined devices, meaning the software can be easily customized to meet specific needs. Furthermore, alarms, logging conditions, report generation, data display, and other tools can be added to the basic logging functionality, expanding the software's capabilities. Flexible file formats allow data to be exported to or shared with other tools.
NI LabVIEW can be used to create custom alarm conditions with simple drag-and-drop functionality.
Terabytes of data storage
The highlight of PC-based data loggers is their data storage capacity. Standalone loggers typically have limited memory, often based on flash memory or other long-term storage devices. Once the memory is full, the data logger cannot perform further readings.
However, with PC-based data loggers, storage capacity virtually becomes unlimited. Most importantly, it's common to have PC hard drives with terabyte capacities, providing ample space for measurement and permanent storage. Furthermore, the PC can transfer its contents to other devices, such as CDs, DVDs, or Memory Sticks, or transfer data to another system on a network, freeing up space for further measurements and continued recording of new data.
Network connection
PCs' network communication capabilities offer significant advantages to PC-based data logging. First, as mentioned earlier, networks can virtually limit data storage capacity. More importantly, it also allows access to logged data from another machine on the network, providing the ability to remotely monitor data logging from any location. Custom alerts can send email notifications of anomalies or link to a webpage, allowing anyone to view the activity data via a web browser.
Applications such as Adobe Flex Web can extract information directly from sensors for display purposes.
Continuously evaluating the results of an application that requires long-term monitoring over days or weeks is extremely difficult. Remote monitoring via a network for observation from a distance solves this problem well. For example, NI LabVIEW can be used to create custom alarm conditions that can send emails when triggered. It can even be used to design network services that allow data to be observed directly through network-based applications.
By combining the data acquisition and storage functions of a standalone data logger with the archiving, analysis, reporting, and display functions of a modern PC, PC-based recording systems ultimately automate the data recording process completely.
