The industry is witnessing the implementation of increasingly complex Internet of Things (IoT) devices and Industrial IoT (IIoT) systems – from semiconductors and electronic subsystems to the smart machines at the heart of Industry 4.0.
By Mike Santori
The industry today is witnessing the implementation of increasingly complex Internet of Things (IoT) devices and Industrial IoT (IIoT) systems – from semiconductors and electronic subsystems to the smart machines at the heart of Industry 4.0. In fact, reports state that the number of IoT devices in India is set to reach 2.7 billion by 2020. Any changes in devices, however, also engenders a change in testing. Device complexity is causing a parallel increase in test complexity as organizations look to integrated IoT into their automated testing processes. Applying IoT capabilities such as systems management, data management, visualization and analytics, and application enablement to the automated test workflow can better equip test engineers to overcome the challenges of the IoT.
Managing Test Systems
While connected, managed devices are fundamental to the IoT and IIoT, their wide distribution has neither guaranteed their connected nature nor their effective management. Test engineers today struggle to simply trace the software being run on specific hardware or the location of the system let alone tracking performance, utilization, and health. Most modern test systems have moved towards resolving these issues by being based on a PC or PXI that can connect directly to the enterprise. By leveraging this connection, multiple other capabilities are gained such as managing software and hardware components, tracking usage, and performing predictive maintenance to maximize the value of test investments.
Ingesting and Managing Data
Connected systems tend to generate massive amounts of data that must be analysed, a space where IoT truly showcases its business value. In India, this potential for this business value is best exhibited by the energy and manufacturing sectors. The data to be analysed, however, comes in many formats from different sources, often at significantly higher rates and volumes than from consumer or industrial devices, making its consumption difficult. Additionally, data tends to be stored in siloes with little standardization, making it ‘invisible’ and hampering the process of deriving valuable insights at other phases of the product life cycle. 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.
A viable solution in this predicament is the use of higher level test management to move from the local tester to a cloud deployment. By using web-based tools, tester status can be easily viewed along with the scheduling of tests and the movement of test data under evaluation to a cloud or server. A modular test software architecture (test management, test code, measurement IP, instrument drivers, hardware abstraction layers) provides companies with the ability to objectively evaluate the movement of different software capabilities from local to server or cloud-based execution. As more of the test software stack moves to cloud deployments, companies will realize the benefits of cloud computing for data storage, scalable computing, and easy access to software and data from anywhere.
Taking Advantage of the IoT for Test
Leveraging the IoT for test is not a futuristic idea; it can be done today. According to industry reports, 81% of Indian organizations already agree that IoT is key to digital transformation, a fact that validates the feasibility of IoT implementation. The country has also demonstrated adequate potential to be a leader in the IoT space. The determining factors that will enable the realization of this potential will be organizations’ current automated test infrastructure and their most pressing business needs. Some common areas to consider are improving test system management, increasing test equipment utilization, gaining better insight from test data, and remotely accessing shared test systems. A software-defined approach with a high degree of modularity will allow businesses to focus on the areas of greatest value without having to make an all-or-nothing decision.
Mike Santori is NI Business and Technology Fellow
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