“Using Chameleon turnkey software, the NI PXI hardware platform, and source control based on NI LabVIEW and DAQ software, we can measure more than 32 channels of pressure data from our sensors with expansion capabilities while maintaining time-synchronized data from transient shot events on the firing range.”
– Jeff Mazurek, Raytheon BBN
Jeff Mazurek – Raytheon BBN
Raytheon BBN is a high-technology company that provides research and development services. With expertise spanning information security, speech and language processing, networking, distributed systems, and sensing and control systems, BBN scientists and engineers have amassed a substantial collection of innovations and patented solutions. The extensive calculations required for acoustics work lead to business opportunities like the Boomerang shooter detection system.
Using an array of seven microphones, Boomerang detects the direction of incoming small-arms fire from the shock wave and muzzle blast, indicating the azimuth, range, and elevation of the shooter in less than one second. A clock face display shows the direction of fire and a recorded voice announces the direction and range. Azimuth, range and elevation are displayed on an LED screen display.
Algorithms in the Boomerang system suppress return fire events by users. Boomerang does not generate false alerts commonly caused by non-ballistic events, such as road bumps, door slams, and wind noise, or other extraneous noise events such as vehicle traffic or firecrackers. The system requires extensive signal processing and testing of algorithms to provide reliable and robust performance over a wide range of operational conditions as well as eliminate false positives.
System Setup and Testing
We recently tested the system successfully in a football stadium with simulated crowd noise and reverberation. It was also successfully tested in a US Military Operations in Urban Terrain training facility. Over 10,000 systems are currently fielded by the military.
As part of the ongoing improvements to Boomerang, we decided to replace an older DAQ system. While considering other solutions, we liked the NI PXI platform, but we wanted an out-of-the-box solution like the old system. Working with our local NI field sales engineer, we learned about and ultimately chose the Chameleon DAQ system from PVI Systems.
The Chameleon system is a flexible, scalable, turnkey, PXI-based DAQ system that works with the NI PXI-449x dynamic signal acquisition modules used to record data from the microphones as well as NI PXI-668x timing and synchronization modules. We plan to incorporate other sensors in the future. The DAQ rates, up to 204.8 kS/s for 24-bit dynamic signal acquisition, are simply amazing. When testing to improve systems, we often collect many channels of data at high sample rates. With this new system, we can easily adjust our sampling rate
to optimize overall system performance.
Diverse Flexibility and Functionality
The Chameleon system provides built-in functionality for diverse DAQ applications for I/O channel synchronization, time and frequency acquisition, live monitoring of all channels, and post-acquisition data display and processing. It supports reconfigurable hardware for multiple test programs, which is important for the variety of tests that we need to run. Using Chameleon turnkey software, the NI PXI hardware platform, and source control based on NI LabVIEW and DAQ software, we can measure more than 32 channels of pressure data from our sensors with expansion capabilities while maintaining time-synchronized data from transient shot events on the firing range.
The flexibility of the system works perfectly for our staff. We can use the system to export data to popular file formats such as .mat, CSV, .uff, and TDM streaming for post-processing analysis. It’s easy to configure the system quickly and save the files in a time series format using conventions such as GPS time tags or simple names. Conducting live fire tests results in many events, and this makes handling bookkeeping features easy. The built-in tools for spectral analysis are a handy feature when you want to observe signals in real time. We also use the pre-trigger functionality of the system by setting thresholds for events and have had no issues with data quality.
The Chameleon DAQ system has proven to be rugged and reliable after more than a year of use in harsh conditions. Mounted in a field case, the system has external connections for easy connection. Operators can communicate with the system via remote desktop. The data then can be quickly put on our network. We plan to use the system for years to come and share it with other groups at our company.
New Technologies Pair the Physical with the Digital
Digital twinning is one part of the technology road map for Industry 4.0 and the Industrial Internet of Things. A gamut of new technologies must be integrated to work seamlessly together to pair the physical domain with the digital information domain.
Digital twinning seeks to improve the design and maintenance of physical systems by offering datadriven ways to discretely map these physical systems into digital and computerized replicas of themselves. With the arrival of automation and data exchange, digital twinning could be useful in a myriad of industrial applications.
This new industrial context, where the physical and the digital worlds meet, is known as the fourth industrial revolution—or Industry 4.0. Brought on by the intersection of a host of high-technology electronic and computer systems, the “new way” of Industry 4.0 promises increasing gains, efficiencies, and flexibility. A gamut of new technologies must be integrated to work seamlessly together to pair the physical domain with the digital information domain. Digital twinning is only one part of the technology roadmap for Industry 4.0, as these additional technologies are helping to enable digital twinning for Industry 4.0 to manifest its potential:
• Pairing technologies
• Cyber-physical systems
• Augmented, virtual, and mixed reality
• Artificial intelligence
• Additive manufacturing
• 3D printing
• Digital thread
Pairing technologies are critical to digital twinning and the world of Industry 4.0, as these technologies empower a device or system to find, connect, and communicate with other devices and systems. For example, sensors and the Industrial Internet of Things (IIoT) products require the ability to find and connect with other devices successfully. Technologies such as Bluetooth®, among others, are employed to make these connections. To accomplish this, connected devices must be able to interrogate other potentially connectable devices successfully. When inquiring other devices, units must be able to ascertain whether they are communicating with a unit that they should be corresponding and exchanging data with. When properly enabled and successful, this accomplishment is called pairing.
Security issues are paramount. Every device should pair only after proper identification has been confirmed to avoid crosstalk or misinformation. Shortcuts may be achieved through programming algorithms that allow the devices to quickly and easily identify other units that they should pair with. Pairing gets accomplished through authentication keys employing cryptography. Pairing works to ensure that the connections stay bonded in a data exchanging relationship between devices and works to prevent an external source from prying into their data exchanges.
Being that flexibility is paramount, units must be able to make and break their pairing quickly and without external, human involvement. Successful pairing may require ongoing communication to keep the pairing active. If one of the units determines that the pairing bond is no longer relevant to its successful operational objectives, it will remove its pairing relationship and present itself available for a different pairing opportunity.
The National Science Foundation (NSF) defines cyber-physical systems (CPS) as, “The tight conjoining of and coordination between computational and physical resources.” The critical element in this definition is that it focuses on a system approach— where a set of connected things or parts form a complex whole.
A current example of a CPS is the automated airline flight-control systems. Industry 4.0 requires traffic control, not for airplanes, but for the machines, computers, robots, sensors, and processes that comprehensively work together for its realization. It represents a system of higher order than IIoT, because it sits one level higher in the complexity chain. Where IIoT is concerned with collecting, handling, and sharing of large amounts of data, CPS is focused on ensuring that this large amount of data, collected from multiple systems, gets properly utilized across multiple disciplines that are relevant to the industry involved. The unique dilemmas of any given industry will require engineering expertise to address these specific challenges.
Augmented, Virtual, and Mixed Reality
New technologies are augmenting our reality. They are providing us with the ability to overlay digital content in front of us physically, merging the real with the virtual, creating a mixed reality that should be considered augmented. This gain is allowing engineers to view things in new ways. For example, rather than viewing a DT on a computer monitor, we could view a DT using an augmented reality (AR) headset that enables the users to engage with digital content or interact with holograms.
The use of such AR empowers viewers to have an immersive experience whereby they engage their bodily senses.
Reality-enhancing headsets can create real-time experiences of actual conditions happening in the physical world, by way of experiencing them through a digitized environment. AR could lead to new insights and understandings. Additionally, a DT display could appear in the user’s field of view, making real-time feedback that much more accessible and easy to use.
Artificial Intelligence Technologies
IIoT offers the promise to provide connected data; therefore, useful data must be stored and analyzed. Artificial intelligence (AI) is a solution to how to analyze and successfully handle large amounts of digital data. It helps in allowing digital twinning to become more realized because it promotes value by enabling rapid integration, hybrid integration, investment leverage, and system management and compliance.
Through machine learning, it offers the opportunity to use digital data to model, analyze, train, apply, and infer how best to make decisions. AI is helping to change the traditional perspective of computing, moving it beyond what primarily has been an automating- and scaling-process perspective towards a knowledgebased perspective, via actionable insights. Soon, it will help engineers gather new insights and ways to create value. By using a data-science approach, rapidly powered decisions will enable the generation of further opportunities.
Additive manufacturing (AM) is a method of production in which 3D objects are built by adding layer-upon-layer of material. AM holds promise because it leads to industries that can address variable demand and produce products that are distributable and flexible. Two areas of AM – 3D printing and digital thread – are advancing to make digital twinning possible.
3D printing is perhaps the most well-known example of AM. In 3D printing, a printer is programmed to print an object using plastics, metals, or other custom materials with virtually zero lead-time. 3D printing is extremely flexible and eliminates the issues involved in producing objects with large economies of scale. What this means for the future is that you will be able to get what you want quickly—as if walking up to the fast food counter.
With complex systems, however, AM has been confined primarily to the laboratory because all the systems involved do not operate under a unified system and, thus, are hard to scale. Digital thread promises to change that.
A digital thread is a single, seamless strand of data that acts as the constant behind a data-driven digital system. It activates the potential of AM by allowing a unification of disparate applications by way of their adherence to the thread, which is their source of shared information. A digital thread creates an easier process for collecting, managing, and analyzing information from every location involved in the redesigned Industry 4.0. It enables better and more efficient design, production, and utilization throughout the entire process.
Digital twinning will be a hallmark of Industry 4.0, helping to increase gains, efficiencies, and flexibility for existing products and processes. But digital twinning is just one part of the Industry 4.0 road map. Pairing technologies, CPS, AI, and AM are key to seamlessly bringing together the physical realm and the realm of its DT information and insights. While these technologies are bringing their complexities into the digital twinning equation, ultimately, they promise to enable Industry 4.0 to manifest its potential.
by Paul Golata for Mouser Electronics
Industrial manufacturers turn AI to “turbocharge” products and services, says Accenture
The vast majority of manufacturers are turning to artificial intelligence (AI) to “turbocharge” their products and services, finds a new research report from Accenture.
Based on a survey of 500 manufacturing companies in six industries across Europe, North America and Asia, the report notes the ability of AI – particularly when combined with mobile computing and big data analytics – to transform not only core operations, but also worker and customer experiences, and ultimately even business models, and to enable “Industry X.0” strategies.
Yet the research found that only a small group is already leveraging AI in a way that Accenture refers to as Applied Intelligence – intelligent technology and human ingenuity, combined with analytics and industry expertise, applied at the core of business – at scale. For instance, while 98 percent of the surveyed organizations have begun to enhance their offerings with AI, only 16 percent of them have established a holistic AI vision for their business, only 5 percent are committing resources to AI-driven product initiatives, and only 2 percent report that they have begun to leverage AI solutions at scale.
The research also highlights the challenges companies face when trying to use the technology: The concerns cited most often were data quality (identified by 51 percent of respondents); data- and cyber security (45 percent); deciding between ‘buying vs. making’ AI-embedded solutions (45 percent); and data sharing and protecting intellectual property (40 percent).
“The re-invention of industrial products with AI is still in its early stages, and getting it right is anything but easy,” said Eric Schaeffer, a senior managing director at Accenture and global lead of its Industrial practice. “However, the successes of the AI leaders in our sample clearly show that it can be done and that the business case for AI in industrial is very strong.”
The report mentions how companies that re-invent their products by combining AI with other digital technologies can reap huge rewards. For instance, it cites other Accenture research showing that mastering AI can enable industrial-equipment manufacturers to boost their market capitalization by as much as 25 percent.
How AI leaders succeed
To get to these kinds of results, companies must go through a journey which, according to the Accenture report, comprises four stages: (1) exuding belief in AI and its ability to digitally reinvent products; (2) building a vision for leveraging existing offerings with AI; (3) committing resources to AI-driven product reinvention; and (4) executing on their vision and planned initiatives to digitally reinvent the product at scale.
Through clustering the surveyed companies by both industry and “AI-journey stage,” the report shows that AI maturity seems to vary by industry: Automotive companies seem to be more likely to commit to and execute AI initiatives, with 9 percent reaching the third stage and 5 percent reaching the fourth stage. However, only 7 percent and 3 percent of consumer durables companies and industrial and heavy equipment makers, respectively, reach the third stage, with only 1 percent of companies in each of those two sectors reaching the fourth stage.
Other results indicate what sets apart the 16 percent of companies that are at least envisioning AI-enabled ways to reinvent their products: Companies that reach the “vision” stage develop the investment and ecosystem strategies to acquire, process and secure the data needed to drive maximum value from AI. Moreover, they carefully analyze what they need to focus on: 82 percent of these “visionaries” cited enhanced “customer loyalty” and “deeper insights from product and service usage” as the key value drivers for themselves. The same proportion also said that “greater safety” and “smarter solutions and services” would be critical benefits of AI use for their customers.
Most of the 5 percent of surveyed companies that commit significant resources to AI initiatives concentrate on building both the IT capabilities and the skills necessary for large-scale AI implementation: 91 percent of those companies cited analytics and systems integration skills as imperative, and 64 percent said they would change elements of their business model as a result of embedding AI.
The 2 percent that reach the fourth stage – execution at scale – set themselves apart by closely working with ecosystem partners to identify, in granular detail, the AI components they want to combine with other digital technologies, now and in the future, as part of their customer value propositions. Among the key AI solutions these companies are planning to use are computer vision (73 percent), deep learning (64 percent) and robotics process automation (64 percent).
“Our findings suggest a strong correlation between a holistic, well-planned strategy and AI success,” said Raghav Narsalay, a managing director and Industry X.0 research lead at Accenture. “However, three-quarters of the companies we surveyed are still experimenting, using what might be called a ‘scattershot’ approach. But this is likely to change in the near future — and that’s when we’ll really see a rise in product reinvention with AI.”
Results from the research also indicate that the rate of product reinvention with AI will likely vary by industry. Changing sources of profitable revenue is a priority for 65 percent of those in the automotive sector, for example, while the sub-groups of the reports’ “industrial equipment manufacturing” cluster are split: Most heavy equipment makers (57 percent) state that product-lifecycle sales and marketing strategy is their key priority, while 42 percent of those in the industrial and electrical equipment sector expect that embedding AI will lead to changes in their innovation architecture.
hordon kim / email@example.com
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