Speakers Information- Controls, Measurement & Calibration Congress

Read more into what you read out - Developments in measure data analysis

Dr David Gary Hickman ETAS GmbH

ABSTRACT

Calibrators of automotive vehicles are facing increasing contradictory pressures. The time to calibrate and the number of vehicles available for calibration are being reduced. But the complexity of the calibration processes are increasing., and in addition there are increasing numbers of vehicle variants based on the same platform. Calibrators need to measure more data from each vehicle in order do multiple calibration tasks simultaneously and data needs to be measured from vehicles not previously used for calibration. The result is more measure data to be analysed, and the analysis itself needs extract more information out of the data. The traditional methods of analysing data by hand are no longer sufficient.

One approach is to use automation to analyse the measure data. Traditionally scripts have been programmed in tools like Matlab . Problem is not all calibrators are familiar with the programming languages In order to help calibrators create analysis scripts, ETAS have released a graphical scripting tool called INCA FLOW, which provides an intuitive environment for calibrators, to create, analysis scripts. The presentation will include a graphical scripting example that will analyse a measure file to determine vehicle performance.

However this method is slow, and also inflexible because a script can only analyse a specific function or feature, A new scalable data platform is being developed by ETAS, internally called Moogle,, which not only allows large number of measure files to be analysed quickly, but uses generic search routines that can be easily customized to anayse many different criteria. The result a report containing a reduced and indexed subset of measure data with only the points of interest is produced. These points of interest can then be further analysed using standard measure analysis tools.

INTRODUCTION

Analysing measure data is an essential part of the calibration process. By analyzing measure data the calibrator is able to determine calibration changes required to achieve the desired vehicle attributes, or to determine correct OBD responses.

The problem for the calibrator is there are contradictory pressures- Fewer vehicles are available for calibration, but the complexity of the functions and the number of vehicle variants is increasing.

The combination of these means the task of analyzing data is becoming crucial to calibrating model.

This paper gives describes a number of developments for analyzing measure data, including a new approach being developed by ETAS.. This includes the developments for a new MDA tool, and a method for creating routines for specific use cases that does not require the calibrator to have special programming skills. The new approach, Moogle, is a generic analysis method where uses can easily define results criteria.

DEVELOPMENTS IN CLASSIC MEASUREMENT ANALYSIS TOOLS The classical measurement analysis methods use tools like MDA (Measure Data Analayser) [1] or UNIPLOT [2] to view measured data. Calibration is an iterative process, that requires the calibrator to regularly check the effects of calibration changes to the system [3]. Despite other developments in measure data analysis, these classical tools are still required, and ETAS are currently developing a completely new MDA tool. The features of the tool have been customized to the needs of users.

Time is an important factor for calibrators, so reducing the waiting time when using tools is a high priority. So the time to open measure files has been minimized by carful optimization. In addition, time to navigate through the data in for example an oscilloscope has also been optimized. Initial tests have shown the time to open a measure file is about 10 times faster than classic MDA: Another important requirement is the ability to quickly switch been online (data direct from a hardware device like an ECU) and offline measure data, and to be able to mix online and offline data in order to compare responses.. The new MDA tool is designed to be the basis of the next generation online, the so called Experiment in INCA, the ETAS Measure and Calibration tool. The ability to switch online to offline will be inherent in the tool when it is released. The final development is to make the new MDA tool flexible and configurable. Calculation signals can be created using C++ programming, and the tool will interface with other analysis tools like MATLAB. This allows the users to customize the tool to their own needs. In addition the user interface is developed to the latest Windows standards. Although not available is the first releases, this will be development in the coming releases. A screenshot of the new tool is shown in Figure 1.

Figure 1 View of new MDA tool SIMPLIFYING THE AUTOMATION OF MEASUREMENTS Automating measurement analysis is nothing new. For many years tools such as Matlab, LabView or even Excel have been employed for such tasks.

Automation offers powerful analysis routines that can both speed up measure data analysis. In addition it allows accurate and reproducible determination of events that are difficult to achieve manually. However these methods have a number of disadvantages. The languages themselves must be learnt before the scripts can be written. Calibration engineers do not always have good coding skills, and rarely does a single person are good at calibration and coding. So writing the scripts can be a challenge. Once written, only engineers with the coding skills are able to understand the workings of the script. So the scripts become ‘box boxes’. It is difficult to understand how the script is working, and it is not straightforward modifying or updating the scripts.

To help the calibrator, it should be possible to write analysis scripts in an intuitive way preferably without learning the syntax of a programming language. This is the idea behind the ETAS product INCA-FLOW. The scripts are built using toolboxes, and are connected together to form flow charts, Scripts can be used for on-line data calibration task in the vehicle [3], or for offline data analysis.

An example of how INCA-FLOW can be used to analyse measure data is presented here, The task is to read as follows

Read all measure (.dat) files a folder (ignoring all other files types) The files contain a series of standing start accelerations Determine the acceleration time for 0-100kph, plus times for 20 to 80 and 40 – 100 kph Document the test results.

INCA-FLOW is a tool that allows scripts to be written in the form of a flow chart. No special programming skills are required. Toolboxes are provided for the functionality the calibrators need. The calibrator must just select the toolboxes, paramatise them and connect the toolboxes to each other to create a sequence. In order to use the tool for measure data analysis, the first thing to do is to be able to open all the measure files in a folder, and to filter out all other file types. This can be easily programmed in INCA_FLOW, Figure 2 shows the flow chart scripting that will only select .dat files, and filter out all other types

Figure 2 Reading all measure files from a folder

Once the data is read in, it can be analysed. The analysis consists of finding the time the vehicle starts moving plus the times for 20kph, 40 kph, 80kph and 100kph. Then a simple calculation finding the difference between these times can be done to determine all the acceleration times. The coding for this is shown in Figure 3

Figure 3 Determining the times of each speed point

The next step is to find the acceleration times. This is achieved with a simple calculation finding the difference between these times. The last thing to do is to document the data. In this example we will document the data in Excel files. The flow chart script is shown in Figure 4

There are several advantages the INCA_FLOW graphical programming systems. The coding can be easily written by calibration engineers with minimal programming skills, because the coding is in the form of a flow chart, This also makes the script easy to understand, and changes to be made “on the hoof”. One other advantage is such scripts can also be

included into calibration or test scripts, to do measure analysis at the same time as testing.

Figure 4 Calculating the acceleration times and writing the data to Excel files

LIMITATIONS OF TRADITIONAL TECHNIQUES Traditionally measure data is recorded by an individual users and analysed manually using data analysis tools like MDA[1] (Measure Data Analyser or UNIPLOT [2] or with a script like MATLAB. The results of the analysis are the calibration changes required to achieve the desired response. Changes were done and a new measurement taken to assess the effect of the changes. The calibration process was iterative, and the measure data discarded at the end of the calibration task, even though the files contain valuable measure data. If a way could be found to systematically re-analysis this data, it would provide a significant efficiency improvement,

Figure 5 Traditional measurement analysis

Although measure analysis automation methods like INCA-FLOW can increase the analysis possibilities, there are still circumstances where this analysis approach is not optimal. Each script is used to analyse a particular condition or event, and to analyse difference events, a complete new script is required. In addition this automation concept does not promote the reuse of measure data. One final point, this methodology is slow for measuring large amounts of data or many measure files.. Current developments in ECU interfaces are concentrating on increasing the amount of data that can be recorded from ECUs. This so called “Big Data” approach will allow the OEMs and Tier 1s to reduce the amount of testing time by being able to combine tests [5]. The challenge then becomes one of analyzing the data. So a systematic data analysis system is required that allows large amounts of measure data to be analysed both quickly and flexibly.

THE MOOGLE APPROACH Moogle is a different approach for analyzing data, The idea is for a scalable data platform to cover the whole measure process, from collecting data from the vehicle, storing the data, analyzing the measurements and reporting the results,. The whole idea is not limit measure data analysis to analyzing particular functions or attributes, but to create a general analyse methodology that can be used to analyse many more things in parallel.. The big picture is shown on figure 6

Figure 6 Moogle the big picture

The Moogle idea consists of several parts, see figure 7

Data Acquisition Data Transfer Data Management Data Analytics Result Reporting

The data acquisition, transfer and management are customizable for each customer. Data must be measured and send to a server to be further analysed, Two main business models exist, one where the server is located at the ETAS site, and the other the server is located at the customer location. When ETAS hosts the server, the customer sends the data to ETAS. ETAS then analyses the data and sends the results back to the customer. The conditions the customer wants to analyse are provided by the customer. So the data analysis is offered as a service by ETAS. Alternatively the server is hosted on the customer site, and is built by ETAS as an engineering project. The operation of the system is optimized for the customer operation.

DATA ANALYTICS AND REPORTING

In this paper we will concentrate on the Data Analytics and Results Reporting side. Considering the Data Analytics, the whole idea of the Moogle systems is to reduce the data in smaller pieces of just relevant data. A data crawler will trawl through the measure files, and look for predefined conditions described in xml format. The data crawler itself always analyses the data in the same way. The only thing that changes are the search criteria contained in the xml files. The output is the so called Moogle Index, which contains the reduced data of interest. The scheme is shown in figure 8.

Figure 8 Moogle Crawler concept

Typical criteria the Moogle crawler will look for is min max data, statistics information, and measurement events (plus combinations of these).The system itself is based on the Map Reduce programming schema. The output is a reduced set of data that maps each search criteria to file name and time the condition occurs, plus any additional measure data that was created.

The tool can be set up to automatically detect when new files are written to so called Watch folders, When Moogle recognizes the new files, they are automatically run through the Moogle crawler. Currently the search conditions are witten in the form of an xml file. However in the future, a configuration tool will be developed to make the condition editing easier. In Figure 9 the xml conditions to also calculate the same 0-100, 20-80 and 40-100 kph times are shown.

Figure 9 Example of xml conditions

The data is outputted as a series of reduced index files, which can be used in different ways, The user might want to see the data compared to previous data, to see trends over time, Of events can be plotted against engine operating conditions to see at what operating conditions events occur. The events defined in the reduced data can be further analysed. For example in MDA, by using the index to find where an event happens finding this point manually.Or the results can be documented using commercial tools like Reporting Portal from XMLA Consulting Inc. In addition Moogle has a web interface when the users can inspect the data output and set up different watch conditions to help find different events.

ADVANTAGES OF THE MOOGLE APPROACH

The method itself has many advantages over traditional methods because the actual data analysis method is always the same. The only thing that needs to be changed are the xml scripts. This makes the system much more flexible than traditional analysis with scripting tools, and easier to program In addition the analysis methods are much faster than traditional methods, In out example, calculating the acceleration times in Moogle took 0,3 seconds, whereas the time in INCA_FLOW took about 30seconds. This flexibility and faster analysis capability means we can easily re-analyse measure files. So for example, if a problem is discovered during the development process, all previous measure files can be analysed to determine when the failure first appeared. Alternatively, older measure data can be analysed to provide a bigger base for calibrating thresholds for OBD calibration.

SOLUTION TIMING

The solution itself will be available in 3 forms, as a service, as a server system and a PC version see figure 10.. As a service the Moogle system runs on our ETAS servers. The customer supplies the measure files and the conditions to be found, and ETAS returns the Moogle index.. The next step is to offer engineering consulting to build the Moogle system locally on customer servers. The official release is planned for 2016, but we can already build prototypes systems if people are interested, finally a smaller version of the crawler functionality is planned that will run on a pc..

Figure 10 The Moogle solutions

The Moogle roadmap is shown on figure 11.

Figure 11 The Moogle roadmap.

CONCLUSION

Developments of traditional and measure data analysis using traditional tools like MDA have been presented. The main developments are in the areas of performance to improve the data read times, and connectivity to other tools. In additional an alternative scripting method called INCA_FLOW was presented that allows calibrators with no programming experience script and be able to understand automated analysis routines. Finally a new analysis approach called Moogle was introduced, that uses fixed analysis routines and easily editable search criteria. With such an approach large amounts of data can be analysed quickly, and the method promotes the re-use of measure data.

REFERENCES

[1] http://www.etas.com/de/products/mda.php

[2] http://www.uniplot.de/

]3} Hentschel, R., Cernat, R. and Varchmin, J.-U. "Development of a Measurement Data Acquisition System for Optimized Automotive Diesel Engine Calibration". Institut für Elektrische Messtechnik und Grundlagen der Elektrotechnik, TU Braunschweig, 2002 (in German). [4] “Selbstkalibrierungvon Motorsteuergeräten“ Sven Meyer. ATZ Electronic June 2012

[5] “Development and Calibration of Engine and Transmission ECUs with the ETK ECU Interface” Burkhard Triess; Christoph P. Müller; Ulrich Lauff; Claus Mößner, ATZ worldwide eMagazines Edition: 2007-01

CONTACT

This is where main author information is typed, if desired, such as background, education, e-mail address, and web

Dr David Gary Hickman Techincal Specialist . Product Group Automotive Borsigstraße 14 70469 Stuttgart +49 711 89661 833 gary.hickman@etas.com www.etas.com

DEFINITIONS, ACRONYMS, ABBREVIATIONS

MDA Measure Data Analysis A tool to analyse measure data OEM Original Equipment Manufacturer Tier 1 A direct supplier to an OEM (for example Bosch) ECU Electronic Control Unit