Data at the Heart of The Smart Test Bed
Intelligent, interconnected, and automated testing systems are becoming an integral part of the test ecosystem as we move into the age of industry 4.0. The need for better product quality, less resource employment, increased throughput, lower downtime, and better costs are emerging as organizational imperatives.
As such, the move towards smart testing is now non-negotiable.
It’s noteworthy that smart testing is not considered such just because it employs advanced automation and sensors to drive the testing process. It’s the “data” that lies at its heart, which is key to making smart testing smart.
When organizations look to build smart test labs, the enabling factors, like test equipment /test beds, occupy more headspace. These are, of course, crucial elements in the testing process. Selecting the right equipment from the right supplier will indeed impact testing outcomes.
But what separates the wheat from the chaff is the capacity of these test systems to generate and supply accurate data to the test teams. The absence of this ability will leave any smart test woefully unsmart.
Data does a great deal of heavy lifting for smart test beds and is the defining factor for excellent testing outcomes. Here’s a look at the related how(s) and the why(s).
Equipment Calibration
The equipment in use in smart testing for industry 4.0 applications is usually calibration equipment. Smart test beds for EV testing, for example, have to give accurate measurements till the last detail. For this, the equipment calibration must be accurate and on point as the calibration of the equipment determines the quality of the data that will be used as benchmarks for driving test results.
However, when looking at equipment calibration, organizations must not look at individual equipment alone. In other words, while the individual equipment needs to be accurately calibrated, it is critical to view the testing ecosystem calibration as a whole.
Organizations looking to leverage smart test beds must make the right interconnections while accurately calibrating individual test equipment. Equipment and sub-equipment must be calibrated and connected seamlessly to each other and the automation systems lying above them.
Along with this, it is important to develop the capacity to plan and schedule tests easily and manage the data at hand seamlessly. This ensures robust data quality, builds greater assurance into the testing process, and makes the testing ecosystem future-ready and scalable.
It’s noteworthy that automotive test beds are physical units and contain multiple components that must be tied together seamlessly. There are special considerations that some components demand. Since several components are sourced from suppliers, test facility managers must ensure that the component needs are clearly identified and documented for accurate calibration.
With so many moving parts and multiple stakeholders, there’s much to consider while implementing a smart test bed. Here is what goes into it.
First-Time Right
The objective of employing smart test beds is to improve decision-making while accelerating the pace of testing. Better quality products that evoke confidence are a direct outcome of using robust and reliable smart testing.
By getting the data right, organizations ensure the testing outcomes are accurate and first-time-right. Getting tests the first-time-right adds to development velocity and accelerates go-to-market speed. Product validations happen faster, and decision-making remains sound and rooted in confidence.
The smart testing process places data at the core of all decisions. This allows organizations to increase testing speed and confidence. Testing costs are also significantly reduced, and achieving cost efficiencies becomes easier as organizations do not have to spend time running the tests repeatedly.
To get the test first-time right, however, organizations must evaluate all the factors, connections, and dependencies at work. They need to ensure that the input data is clean and accurate and can easily flow into the test systems from the sensors and test equipment.
Centralized Repository for a Single Source of Truth
The data also must be stored and managed with clarity and transparency. This can happen only when data does not reside in silos in different systems. The test system in use, as such, has to maintain all the data in a format that becomes the single source of truth.
A centralized repository of data ensures data consistency and access and facilitates the seamless exchange of engineering data across the validation process.
But creating a single source of truth in the ecosystem can sometimes get challenging. This is primarily because the simulation data is available in a digital format. The physical test & validation data, on the other hand, can reside scattered across functional groups and departments in different formats.
To navigate this challenge, organizations need a scalable platform based on distributed and parallel computing architecture that employs concurrent computing resources to process data and complex algorithms in parallel.
To ensure data conformity, it is also essential to monitor all the testing data from one location with ease. A centralized platform that allows organizations to prepare all tests in a central location, capably synchronize it on all test beds, and function as a repository of all test beds helps in this process.
Organizations need to create a centralized data repository that acts as the basis of all decisions by employing such data management and test management platforms. This ensures that the data is organized and can be queried by both simulation engineers and test engineers and avoids expensive retests.
The Bottom Line
The smart test ecosystem is a complex web of interconnected systems and test beds. These systems must be calibrated and connected in the correct order for clean input data. Factors such as sensor data from utilities, test data from test beds, etc., and the capability to easily store these in a suitable format and seamlessly share it across engineering teams define testing accuracy and success.
As smart test beds become the mainstay in the modern-day testing ecosystem, organizations must pay closer attention to how the data is generated, stored, and shared. Creating an interconnected system that connects the test beds to each other and automation systems, simulation automation systems, and hosts is now critical.
Only such an interconnected system can do the heavy lifting that drives smart testing by ensuring that the quality, accuracy, and volume of data remain uncompromised, coherent, and available for all to use, test, and share.
