Tampering with a test station thatshouldalready be working fine is nerve-racking as wellbecausenoble intentions here could cause new problems, especially when insertion countsarelimited.This holiday season, we’re thankful there’s a better way
The race to 5G commercialization has many forms – faster broadband, mobility, and IoT – and it’s compelling RF, microwave, and millimeter-wave engineers to take a smarter approach to microwave design and test. Here’s what we expect to see and hear a lot about at European Microwave Week (EuMW) 2018.
With so much emphasis being placed on how things WILL change, and to a certain extent rightly so, the market is losing sight of the existing challenges that stand in the way of producing safe and reliable vehicles today
In my last blog post, I discussed the type of data test engineers are collecting. The key takeaway: we’re dealing with a lot of data.
All this data leads to my next topic: the three biggest problems test engineers face. At
NI, we refer to these collectively as the Big Analog Data™ problem.
Finding the Valuable Data Points in a Mountain of Data
All the data from the increasingly complex tests run every day eventually needs to be stored somewhere, but it's often stored in various ways—from a local test cell machine to an individual employee’s computer. Simply locating the data you need to analyze can be a giant pain, let alone trying to sift through pages of meaningless file names and types without metadata for context.
We see too many of our customers wasting time because they don't have an efficient way of searching files. Even if engineering groups are lucky enough to have a centralized database to store test data, they still run into difficulties accessing it because it’s not optimized for waveform data types and rarely shared between groups.
All of this leads to “silos” of data that then can’t be used efficiently, causing more wasted time trying to access it. In extreme cases, these problems even cause companies to rerun tests because they simply can’t find data they’ve already collected.
Validating Data Collected
An issue that most don’t think about until they experience it firsthand is the validation of collected data. Ideally, every test runs the way it’s intended to, but there’s no way to know, unless some validation steps are performed.
There are countless ways to get incorrect data, from improper test rig setup to data corruption. If invalid data goes on to be analyzed and used in decision-making, there could be disastrous results.
Big Analog Data validation presents extra headaches due to the sheer volume and variety of data types. A gut-wrenching example of this is NASA’s 1999 Mars Climate Orbiter that burned up in the Martian atmosphere because engineers failed to convert units from English to metric.
Manual processes work, but are extremely time-consuming. To save engineers from wasting valuable person-hours, an automated solution is usually required.
A great way to illustrate this in the engineering world is the Nyquist Theorem, which states that you must use a minimum number of data points in your analysis to get accurate results. For example, without analyzing more data points, you may just see an exponential signal (Figure 1) instead of the sine wave that’s actually there (Figure 2).
Figure 1Figure 2
There are two reasons why test engineers don’t analyze more data. The first, as I mentioned earlier, is being unable to find the right data in the mountains of Big Analog Data they’ve collected. But, they’re also using systems and processes that aren’t optimized for large data sets. Manual calculations with inadequate tools are typically the roadblock when it comes to analyzing large quantities of data.
Even when the right tools are used, processing Big Analog Data can be troublesome and usually requires an automated solution where processing can be offloaded to a dedicated system.
In my next post, I’ll give you some options for tackling your Big Analog Data problem, so you can be sure you’re making the best data-driven decisions possible.
Test engineers of “in-the-loop” systems such as model in the loop (MIL), software in the loop (SIL), and hardware in the loop (HIL) can choose the best tool for their application thanks to the ASAM XIL standard governed by the Association for Standardization of Automation and Measuring Systems (ASAM).
Since the first release of HIL-API 1.0 in 2011 this standard has gained wide adoption in automotive testing. It offers the assurance that test cases can run independently of the executing hardware. With this approach, users can create test cases that can be reused between projects, protecting the investment and reducing test costs.
To ensure a smooth user experience the XIL working group meets to test automation tools (clients) and XIL tools (servers). In 2016 the focus was to test the model access (MA) port extensively, hosted at dSPACE in Paderborn, Germany. Since now the electrical error simulation (EES) port has been implemented by the majority of the working group members, another cross-test event took place in October 2017, hosted by National Instruments in Munich, Germany.
Cross test participants included representatives from carts GmbH/MicroNova AG, dSPACE GmbH, embeddeers GmbH, NI, TraceTronic GmbH, and Volkswagen AG
The 2017 cross test focused on validating the interoperability of electrical error simulation (EESPort) in XIL systems. Typical use cases of this center on testing wiring errors such as loose contacts, short circuits, and broken wires.
The cross test began by testing the XIL servers with an ASAM-provided test suite. Subsequently, all combinations of client test tools and servers were tested. In general, there was great interoperability between vendors, and EESPort implementations proved ready to use.
The event was a success with a solution-oriented atmosphere and good relations among participants. Results are very promising with all 4 server implementations passing 100% of the ASAM NUnit tests for EESPort. For the cross-test the group decided that it would make more sense to test use cases specific to each test automation tool’s workflow. Every tool provided defined workflows and usage scenarios and tested these against each XIL server implementation. Overall 1291 tests were executed with a rate of 94% passing. Some minor interoperability issues were identified and will be fixed.
Recently ASAM AE XIL 2.1 was released, boasting a ton of new features like pause simulation, a common interface to real-time scripts, simultaneous read/write, DiagPort redesign, and more.
The group agreed to continue work on XIL 2.1.1 in 2018 and 2.1.2 in 2019 to incorporate feedback from the growing user base. The ASAM XIL NUnit test suite, a vendor independent, neutral test catalog for both MAPort and EESPort will be provided together with a future version of the XIL standard by ASAM. The benefit is that vendors can already check in-house the standard conformity of their port implementations. Additionally, XIL working group members are encouraging all tool vendors to be consumers of ASAM XIL as well as actively contribute their ideas and experiences to further improve the standard.
The emergence of smarter cars means automotive companies are looking for smarter ways to work. And they know getting there requires new efficiencies in their existing design and development processes.
One way companies want to do this is by extracting more knowledge from corporate data. As a common goal across the industry, we see the same issues arise when tackling this challenge. We also see a solution: Viviota’s Time-to-Insight Software Suite™ (TTI) combined with NI’s Data Management Software Suite.
Address in a model-based design process
In a model-based design (MBD) process, data from physical testing is combined with data from model-based simulations, and then analyzed. The main point is to build products faster. So, when one of our international automotive customers sought ways to address inefficiencies, we looked at test data preparation, analysis, and reporting processes.
Their engineers were spending precious time not only finding, managing, and standardizing test data but also analyzing and reporting it—upward of 10 hours in one case! This was happening for three reasons:
Decentralized data management
Inconsistent file formats
Limited access to reliable historical data
Using TTI, which harnesses the power of the Data Management Software Suite, we proposed a solution and piloted it with a single work cell. We targeted three areas for efficiency improvement:
Cleanse and standardize data to provide a consistent format and labeling system for all data
Index cleansed data by a powerful search engine
Provide a server-level analysis engine and interface to centralize and speed analysis
The power of managing data saves valuable time
Our solution moved the processing of more than 1,000 data files from individual engineering systems to a server-class machine (32 cores). Before the Viviota implementation, engineers in a single test cell typically spent five hours per week locating data and five hours processing and analyzing the data.
With TTI and the Data Management Software Suite, the time to locate and analyze data went from 10 hours to seven minutes. Although this initial project was aimed at one automotive work cell (powertrain), we’re eager to replicate this success in other work cells such as engines, brakes, and transmissions.
This blog is a part of a series with our Alliance Partner, Viviota. Find out how NI Alliance Partners are uniquely equipped and skilled to help solve your toughest engineering challenges. Alliance Partners are a business entity independent from and has no agency, partnership, or joint-venture relationship with NI.
Defining a test strategy that reduces your costs and maximizes the efficiency of your production is tough, but building your test system can be even more challenging. We’ve put together four on-demand webinars to help you, whether you are building from scratch or augmenting your current system.
Learn from NI test engineers and guest presenters from Bloomy Controls Inc. and Bose Corporation as they share their experiences.These sessions are on demand, so tune in when it’s best for you!
What I Wish I Had Known Before I Started Deploying Test Systems
Gain effective approaches to developing a successful deployment process for test systems with this 15-minute webinar presented by James Kostinden, lead software test engineer at Bose Corporation. Watch now.
Switching, Mass Interconnect, and Fixturing Considerations
Our own David Roohy, systems engineer at NI, helps you learn about different switching architectures and ways to determine the best switching strategy for your test system needs in this 19-minute presentation. Watch now.
Improving ATE Test Sequence Adaptability Using HALs and MALs
Explore common options for implementing hardware abstraction layers and measurement abstraction layers, from out-of-the-box drivers to object-oriented solutions, in this 25-minute webinar by Grant Gothing, ATE business unit manager at Bloomy. Watch now.
Thermal and Power Planning of Automated Test Systems
Patrick Robinson, principal test engineer at NI, shows you best practices for planning cooling and power systems in your automated test systems in this 21-minute session. Watch now.
Wireless engineers face a big challenge today. They must prototype next-generation wireless communications systems and increasingly connected devices in a more competitive and fast-changing communications industry.
European Microwave Week (EuMW) 2017, a six-day event in Nuremberg, will focus on the future of microwave technology globally and give us a better look at how this challenge is impacting the industry today and changing the way engineers work.
Look for the following topics at EuMW 2017:
5G prototyping: the progress we have made
5G continues to capture headlines as wireless companies everywhere take on the challenge of building a 5G wireless network. NI’s engagement with industry leaders in 5G prototyping has resulted in MIMO systems with world record-breaking spectrum efficiency, including one of the world’s fastest mmWave channel sounders.
NI at EuMW:We’ll demonstrate a real-time, 28 GHz, over-the-air prototype aligned with the Verizon 5G specification. We’ll also showcase an academic partnership enabling research on ultra-reliable, low-latency wireless communications for mobile video recording and broadcasting. Follow @NIglobal for updates during the show.
Sensor fusion test: a key part of the race towards autonomous vehicles
As automakers race to produce autonomous vehicles, sensors like cameras, lidar, GNSS, and radar are making automotive test much more complex. Due to the speed at which this industry trend is evolving, shows like EuMW help us keep up with the progress towards making sensor fusion test faster and safer. This is critical for automotive suppliers to remain competitive as we move toward more connected autonomous cars.
NI at EuMW:We’ll demonstrate an advanced driver assistance system (ADAS) test solution, developed in collaboration with Germany’s ADAS IIT consortium, for short- and long-range radar at 76–81 GHz. The solution is based on the industry-standard PXI modular instrumentation platform. Using PXI’s timing, triggering, and synchronisation capability, along with instruments from DC to RF and bus interfaces like CAN, this system provides an ideal solution for testing sensor fusion. Follow @NIglobal for updates during the show.
Also, in the MicroApps theatre, NI distinguished engineer Paul Khanna will discuss high-performance test techniques for automotive radar sensors at 12:30 p.m. on Wednesday, 11 October.
Software: the solution for faster and smarter microwave design and test innovation
As wireless capability is integrated into a dramatically growing number of devices, manufacturers increasingly need to test larger volumes of connected devices. This makes it even more important to efficiently design, deploy, and maintain automated wireless test systems. Productive development software is key to achieving the goal of efficiently creating test systems.
NI at EuMW:At NIWeek 2017, we announced LabVIEW NXG 1.0, the next generation of LabVIEW systems engineering software. LabVIEW NXG accelerates automated test system development and deployment with these essential features: guided, instrument-specific examples; test and function reuse; engineering data exploration; ability to build scalable libraries; and remote result viewing.
The automotive industry is facing its biggest challenge yet: adapting to rapidly-changing technology and customer expectations. The CEOs of each ADAS iiT company (read more about the ADAS iiT collaboration, a single-platform solution for advanced driver assistance systems (ADAS) from NI Alliance Partners, here) sat down with us to share their perspective on the automotive industry’s future and the challenges automotive engineers face right now.
“There’s a lot of real driving to be done to get autonomous vehicles onto the streets, and there are many prerequisites for that,”measX General Manager Dr. Joachim Hilsmann said. “Structured data handling in these tests avoids double or triple tests, which reduces the time you need to drive with this technology. And structured data is what all automakers will need as they work with other automakers and governments to standardize vehicle data.”