In-vehicle data logging system for fatigue analysis of drive shaft

Ilić, Slobodan; Katupitiya, Jayantha; Tordon, M.

doi:10.1109/rose.2004.1317610

Cited by 4 publications

(3 citation statements)

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“…The vehicles were test-driven for up to 100,000 kilometers, during which the tapes with gigabytes of data were regularly sent back to the company for evaluation. The same company, only a few years later (2004), has put effort into smallfootprint logging systems for drive shaft analysis, where data are compressed and approximated prior to recording [4]. As opposed to data-intensive logging systems, there are a number of systems with a small footprint.…”

Section: A Industry Practicesmentioning

confidence: 99%

“…The tuples of windows are less or equal than ω apart, so that for the first tuple t f and the last tuple t l of a time based window, the expression t l .τ − t f .τ ≤ ω is always valid 4 .…”

Section: B Window Operatorsmentioning

confidence: 99%

“…The window size is sometimes also called a window range[19] 4. To simplify matters, we use the windowing attribute time in our examples.…”

mentioning

confidence: 99%

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Flexible in-vehicle stream processing with distributed automotive control units for engineering and diagnosis

Schweppe

Zimmermann

Grilly

2008

2008 International Symposium on Industrial Embedded Systems

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This paper introduces a method for selectively preprocessing and recording sensor data for engineering testing purposes in vehicles. In order to condense data, methodologies from the domain of sensor networks and stream processing are applied, which results in a reduction of the quantity of data, while maintaining information quality. A situation-dependent modification of recording parameters allows for a detailed profiling of vehicle-related errors. We developed a data-flow oriented model, in which data streams are connected by processing nodes. These nodes filter and aggregate the data and can be connected in nearly any order, which permits a successive composition of the aggregation and recording strategy. The integration with an event-condition-action model provides adaptability of the processing and recording, depending on the state of the vehicle. In a proof-of-concept system, which we implemented on top of the automotive diagnostic protocols KWP and UDS, the feasibility of the approach was shown. The target platform was an embedded on-board computer that is connected to the OBD-II 1 interface of the vehicle. As the scope of recording can be adjusted flexibly, the recording system can not only be used for diagnostic purposes, but also serves objectives in development, quality assurance, and even marketing.

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Section: A Industry Practicesmentioning

confidence: 99%

Section: B Window Operatorsmentioning

confidence: 99%