CASTT Project s Final Report

Transcription

1 CASTTProject sfinalreport Title Acronym Projectimpact Projectgoals Industrial: Academy:

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4 Realtimeanalysisofdata

5 Dates Projectleader: Projectteam: Acknowledgements

6 Publications Alzghoul, A, Löfstrand, M & Backe, B 2012, 'Data stream forecasting for system fault prediction' Computers & Industrial Engineering, vol 62, nr 4, s Alzghoul, A, Löfstrand, M, Karlsson, L & Karlberg, M 2011, 'Data stream mining for increased functional product availability awareness'. J Hesselbach & C Herrmann (red), i: Functional Thinking for Value Creation: Proceedings of the 3rd CIRP International Conference on Industrial Product Service Systems, Technische Universität Braunschweig, Braunschweig, Germany, May 5th - 6th, Springer, Berlin, s Products Software o o o Services Cooperation TestEntrepreneurs Cooperationwithcarmanufactures Universities Massimo Mecella Stephan Lukosch Seminars&workshops

7 Courses Discussion Appendices

8 CASTT project description: Real Time Analysis of Data from Wireless Sensor Networks in Automotive Testing Lennart Karlsson, Datorstödd maskinkonstruktion Jan van Deventer, EISLAB Mars Introduction Vehicle testing, and in our specific case winter vehicle testing, is a necessity to verify that a modern vehicle s performance meets the automotive standards. To achieve this goal, vehicles are tested in different conditions. These conditions include different maneuvers on different surfaces and varying temperatures. The surfaces range from deformable texture (sand, gravel, mud and snow) to homogeneous surfaces with different coefficient of friction when in contact with the tires (ice, wet tiles, wet asphalt, dry asphalt, etc). To be able to compare vehicles and refer to standards, there is a specification to test the vehicles. The results of these tests, when systematically evaluated, are a set of data which can be plotted in a graph. To be able to get this data, we need to instrument the vehicles. 1.1 Sensors and loggers Data collection in vehicles has evolved quite a bit during the last two decades; but that is mostly on the logging side of it. Sensors, such as temperature and pressure, are attached to their targets and long wires transmit their information to data loggers. The loggers, 20 years ago, were UV recorders able to record less than 10 signals. Nowadays these recorders are fast computers that can record many more analog signals and what is more important communicate with the CAN bus of the vehicle. The CAN bus is a two-wire network that allows the controllers in the car to communicate with each other (CAN stands for Controller Area Network). Modern data loggers can record hundreds of sensors and state information from the different controllers of the car via the CAN bus. These include wheel speeds, engine speed, signal lights, etc. The loggers are able to send data over the Internet in real time if the test tracks have a Wi-Fi coverage Wired Sensors The wires carrying the information from the sensors to loggers can generate problems to the data acquisition. They are susceptible to interference as they act as large antenna. We can discuss twisted pairs and shielding but we must remember that all these wires add weight to the car which is an issue when you are trying to test a car at lightly loaded weight. Furthermore, connectors to the sensors and loggers age quickly unless they are of military specification (also heavy and bulky). Calibration is also an issue: consider a pressure sensor on a brake line. The sensor is attached there for at least the duration of the test season. The logger is tuned by a technician to conditions the signal for each sensor specifically (e.g. full scale is 5 V) and is calibrated for it (200 bar is 4.2V). However the logger might be needed in another vehicle for which each acquisition channels must be set up correctly and when the logger is returned the procedure has to be repeated for each sensor and channel pairs The case of the wireless sensor It is easy to make the case of the wireless sensor. Imagine a sensor that can communicate with the logger in the car without the need of connecting several wires to it (ref, Passenger environment of temperature for Volvo and SAAB). The only wires that might be present are power cables to power the sensor. The wireless sensor is able to self calibrate (e.g. shunt in a pressure sensor). The wireless communication is much more robust to disturbance than wired. The sensor becoming a node can also keep track of its time in use. Wireless sensor network using TCP/IP in test cars are obviously the way of the future. They offer higher quality of data at a lower cost which includes operation and maintenance. The remote but secure

9 communication directly to the sensor enables new methods of testing and evaluating results. The wireless sensor network has the ability to have a distributed intelligence to preprocess the data into information. Previous work in CASTT has demonstrated the feasibility of using wireless sensor networks for automotive testing applications [1]. This project will build further on these results. 1.2 Real-time Measurement data stream analysis When sensor networks become more ubiquitous in the automotive testing industry, it will be increasingly important to be able to process the potentially very large data sets generated. To do this, real-time analysis (or at least real-time preprocessing of data) will be required. To analyze large amounts of streaming data, a so called Data Stream Management System [2, 3] can be used for specifying the desired computations over the streams and to perform data filtering based on these computations. With this approach, only transformed and filtered interesting stream sequences are immediately uploaded all the way to the remote analysis site. The filters are expressed as predefined filter functions that the engineer can use when searching on-line measurement data. Such filter functions are called query functions and they are implemented beforehand and registered for the DSMS. The DSMS provides means for executing these operators at different locations of the distributed system to obtain acceptable performance. 2. Project description and goals The project is dedicated to studying how wireless sensor networks can be used to collect data in real time from test vehicles, and furthermore how this data can be processed and analyzed in real-time. Previous efforts in CASTT have demonstrated how measurement data can be accessed from vehicles and transported over a combination of wireless and wired networks to an engineering site for analysis [4, 5]. To take one step further, we will now add wireless sensor networks to the picture, where the sensors and the data collection units are small built-in embedded systems. The other part of the project aims to study how large data streams produced by the sensor networks can be searched and processed directly in real time using different online data searching techniques. The ultimate goal is to improve automotive testing, so that more testing, with higher quality, can be done in less time, using fewer vehicle prototypes. 2.1 Project activities The project will consist of the following activities: Testbed establishment This activity will establish a demonstration and experimentation testbed for wireless sensor networks and real-time analysis of automotive data. It will be set-up at an automotive winter testing site (Arctic Falls). Wireless automotive sensor network technology This activity is devoted to studying and developing wireless sensor networks for automotive applications. Prototype wireless sensor networks will be developed and experiments with wireless automotive data communication will be performed. Real time automotive data processing and analysis This activity will study different approaches for real time analysis of automotive data. DSMS technology will be one core technology. Prototype systems will be designed and integrated with the wireless sensor network technology. Automotive applications This activity is intended to study specific automotive applications, such as climate tests, motor test, etc, that can benefit from wireless sensor networks and real-time analysis technology. The work will be done in close collaboration with automotive test engineers and car developers.

10 2.2 Expected results Industrial - New technology for automotive manufacturer's and automotive testing specialists - New tools and methods that make it possible to analysis complex, high volume vehicle data - Improved automotive testing processes, reduced development cycles for automotive companies - Improved technology and methods for quickly establishing wireless sensor networks for monitoring and analyzing automotive data Scientific - Scientific publishing of research in wireless senor networks and real-time analysis - Improved knowledge about wireless network technology and real-time analysis in automotive industry 2.3 Industrial partners Arctic Falls AB Alkit Communications AB Volvo Car Corporation 2.4 Scientific partners Luleå University of Technology, Division of Computer Aided Design Luleå University of Technology, EISLAB Department of Information Technology, Uppsala University 3. Project budget Cost type 2009 [ksek] 2010 [ksek] 2011 [ksek] 2012 [ksek] Labour, researchers DMK Labour, researchers EISLAB Equipment Travel Total Cost Funding CASTT DMK / VINNOVA, SSF EISLAB Volvo Cars / Faste Laboratory

11 References: [1] J. Eliasson, J. van Deventer, M. Johanson, "An Ad-Hoc Bluetooth Sensor Network for Automotive Testing," 5th IEEE Consumer Communications and Networking Conference, (CCNC 2008), Las Vegas, Nevada, USA, January [2] E. Zeitler and T. Risch: Processing high-volume stream queries on a supercomputer, [3] E. Zeitler and T. Risch: Using stream queries to measure communication performance of a parallel computing environment, [4] M. Johanson and L. Karlsson: Improving vehicle diagnostics through wireless data collection and statistical analysis. IEEE International Symposium on Wireless Vehicular Communications, October 2007, Baltimore, USA, October [5] M. Johanson and L. Karlsson: A framework for distributed collaborative automotive testing. 4th Workshop on Challenges in Collaborative Engineering, Prague, Czech Republic, April 2006.