Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping

Mrowca, Artur; Moser, Barbara; Günnemann, Stephan

doi:10.1007/978-3-030-10997-4_6

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…In modern vehicles communication between Electronic Control Units (ECUs) is performed within its intra-vehicular networks to execute functionality, e.g. sensor data is exchanged during wiper usage (Mrowca et al 2018a). Analyzing such data in terms of correctness is essential to guarantee functional quality of subsystems at a low cost.…”

Section: Introductionmentioning

confidence: 99%

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Mrowca

Gyrock

Günnemann

2021

Data Min Knowl Disc

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Many systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.

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Section: Introductionmentioning

confidence: 99%

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Mrowca

Gyrock

Günnemann

2021

Data Min Knowl Disc

Self Cite

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“…A. Mrowca et al identify groups of signals in in-vehicle network traces [14]. In this approach, redundant signals are discovered to reduce potentially identical information on the bus load.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the feature subset with the best prediction quality is found. With the help of the most important feature subset, the signals can be clustered in several groups [14]. J.…”

Section: Introductionmentioning

confidence: 99%

Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging

2019

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Predictive maintenance has become important for avoiding unplanned downtime of modern vehicles. With increasing functionality the exchanged data between Electronic Control Units (ECU) grows simultaneously rapidly. A large number of in-vehicle signals are provided for monitoring an aging process. Various components of a vehicle age due to their usage. This component aging is only visible in a certain number of in-vehicle signals. In this work, we present a signal selection method for in-vehicle signals in order to determine relevant signals to monitor and predict powertrain component aging of vehicles. Our application considers the aging of powertrain components with respect to clogging of structural components. We measure the component aging process in certain time intervals. Owing to this, unevenly spaced time series data is preprocessed to generate comparable in-vehicle data. First, we aggregate the data in certain intervals. Thus, the dynamic in-vehicle database is reduced which enables us to analyze the signals more efficiently. Secondly, we implement machine learning algorithms to generate a digital model of the measured aging process. With the help of Local Interpretable Model-Agnostic Explanations (LIME) the model gets interpretable. This allows us to extract the most relevant signals and to reduce the amount of processed data. Our results show that a certain number of in-vehicle signals are sufficient for predicting the aging process of the considered structural component. Consequently, our approach allows to reduce data transmission of in-vehicle signals with the goal of predictive maintenance.

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Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping

Cited by 2 publications

References 14 publications

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Signal Pre-Selection for Monitoring and Prediction of Vehicle Powertrain Component Aging

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