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#News(General) [ via IoTForIndiaGroup ]
Prior to implementing a comprehensive, IoT-enabled data management solution, Jaguar Land Rover (JLR), a subsidiary of Tata Motors, analyzed only 10 percent of its vehicle test data. JLR Powertrain Manager Simon Foster said, “We estimate that we now analyze up to 95 percent of our data and have reduced our test cost and number of annual tests because we do not have to rerun tests.”
Applying IoT capabilities to automated test data begins with ready-to-use software adapters for ingesting standard data formats. These adapters must be built with an open, documented architecture to enable ingestion of new and unique data, including non-test data from design and production. Test systems must be able to share their data with standard IoT and IIoT platforms to unlock value from data at the enterprise level.
Visualizing and Analyzing Data
As stated previously, test data tends to be extremely complex and multidimensional. Visualizing this data using general business analytics software can be limiting as it does not include common visualizations in test and measurement, like combined graphs of analog and digital signals, eye diagrams, Smith charts, and constellation plots.
By creating test-oriented schemas with appropriate metadata, optimal visualization and analysis for test data as well as its correlation to design and production data can be made. Engineers are thus able to use well-organized test data for the analysis of elements from basic statistics to artificial intelligence and machine learning. This enables workflows that integrate and leverage common tools, like Python, R, and The MathWorks, Inc. MATLAB software, and generates greater insights from data.
Developing, Deploying, and Managing Test Software
The move towards a digital economy has now enabled organizations to move from exclusively using desktop applications toward integrating web and mobile apps into every day functioning. Test, however, faces a certain level of challenge in replicating this endeavor. Computing at the device under test (DUT) is necessary to process large amounts of data and make real-time pass/fail decisions, and local operators need to interact with the tester and the DUT. Along with this, companies also wish to have a high level of oversight in the testing process with remote access to testers to evaluate results and operating status. Some companies have looked to address this by building one-off architectures for the centralized management of software which can later be downloaded to testers based on the DUT. This process, however, implies that custom architecture must be maintained, causing a demand for additional resources that could otherwise be used for activities with higher business value.
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