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Analysis in the age of Big Analogue Data

Author : Simon Foster and Pablo Abad are with Jaguar Land Rover.

03 August 2016

The challenge: Implementing a comprehensive data management solution to manage and analyse up to 500GB of time-series data per day, generated by over 200 data loggers continuously collecting data and ad-hoc measurements performed by over 400 engineers.

The solution: Building a system based on DIAdem software and DataFinder Server Edition to index the metadata of any file, regardless of its origin, and create a workflow to search, inspect, analyse, and report on data 20 times faster than any previous manual method.

Jaguar Land Rover (JLR) is the flagship for two iconic British car brands known for rugged design and luxury. To ensure the quality and reliability customers demand, the company place a strong emphasis on advanced design, engineering, and technology - an emphasis that has driven it to invest more in research and development than any other manufacturing company in the United Kingdom.

For the more than 400 engineers who work in powertrain calibration and controls department, this investment includes implementing new strategies and solutions from NI to better capture and manage huge volumes of raw test data to make smarter decisions before a vehicle ever goes to market.

With better access to better data, JLR can make informed decisions when it comes to building superior automobiles, enhancing its reputation, and most importantly, providing customers with a product that lives up to high standards.

Faced with managing up to 500GB of time-series data collected daily, JLR found it often repeated tests because it could not properly locate specific results. It based its original analysis routine on a manual process. The team estimated it took 20 times longer than its current solution, which is based on a fully automated analysis routine. It traditionally used multiple analysis tools, all of which required special scripting to implement algorithms, so nothing was standardised, including metadata and channel names.

This process led JLR to analyse only ten percent of the data captured when testing the vehicles. Only understanding a fraction of one’s test data means running the risk of missing out on key information, which can lead to inefficiency in the design process and costly delays.

Technology

To address this Big Analogue Data challenge, JLR benchmarked nine tools to determine which platform would be the best for the application. System requirements included the ability to automate uploads, combine metadata from multiple sources, add metadata that was not originally saved and search files from metadata. JLR also needed an interactive tool that empowered engineers to perform their own pre-defined routines or custom ad-hoc analysis. It had to be able to run the analysis on select files or as a batch process, and reporting templates needed to produce consistent, reliable reports to aid in decision-making. Finally, the platform had to integrate into the existing data acquisition processes with the ability expand to other JLR departments in the future.

After reviewing these nine tools, JLR chose to build its solution on DIAdem software and DataFinder Server Edition for a variety of reasons, in addition to the above criteria. One key reason is that DataFinder Server Edition can index metadata to which the team can send queries to find specific test results. With DIAdem, JLR have peace of mind knowing that it has the option to load over 1,000 file formats, so if its data acquisition processes changes, it doesn’t have to worry about compatibility with the data analysis program. JLR can also interactively create analytics dashboards without programming and selectively load data from multiple files.

When testing the vehicles, JLR collect data in a variety of ways from a variety of data acquisition devices. Whether it’s through data loggers capturing information from sensors during a test drive or by directly connecting laptops to the vehicle to collect network data–CAN, MOST, FlexRay, or ECU protocols–CCP, XCP, ETK, all data that’s captured is automatically transferred to a data analysis process where it is checked for proper metadata. JLR cross-reference metadata from several sources and calculate any missing parameters (such as average temperature, speed, MPG, and more). It then saves these in a server based on DataFinder Server Edition.

At this point, any JLR engineer can query the data and perform an analysis routine on all test results that meet the specified parameters. Different types of analysis can be applied to the data. An engineer can choose to run a batch process, sometimes on thousands of files, using a predefined analysis routine or can create an ad-hoc analysis routine on data that requires closer inspection. The result of any analysis routine is a templated report to help JLR make data-driven decisions faster.

Simon Foster, Jaguar Land Rover, said within one year of developing and implementing this solution, he estimates that the company now analyse up to 95 percent of its data and have reduced its test cost and number of annual tests because it doesn’t have to rerun them.

Thanks to these benefits and more, JLR now find and address more issues before passing final products to the customer. As a result, customer satisfaction ratings are higher because the products are more robust.


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