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Reasoning-supported Robustness Validation of Automotive E/E Components

Jan Novacek, Alexander Viehl, Oliver Bringmann, Wolfgang Rosenstiel

arXiv:2608.16421v1cs.AI

TL;DR

Automotive robustness-validation planning is error-prone and largely manual, while mission-profile formats provide limited semantic information. This paper maps mission profiles and domain knowledge into OWL to automate analysis selection, reporting accelerated validation and fewer manual-selection errors.

  • Problem

    Robustness-validation planning remains error-prone and largely manual, while mission-profile formats provide limited semantic information for integrating models, data, and analyses.

  • Method

    The approach maps mission profiles and component characteristics into OWL, formalizes background knowledge, and uses reasoning for semantic querying and automated analysis selection.

  • Results

    The industrial power-module application accelerated the robustness-validation process and avoided errors from manual analysis selection.

  • Takeaways & Limitations

    Ontology-supported reasoning can reduce manual effort and improve mission-profile integration in the demonstrated robustness-validation application.

  • Takeaways & Limitations

    The approach is limited by OWL’s Open World Assumption, coarse property propagation, and the need for sufficiently many analyses.

Abstract

from arXiv · show

This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs). In contrast to the error-prone industrially established manual procedure, we show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process. The proposed approach is based on the idea of mapping MPs to an OWL representation so to allow to perform semantic queries against MP data to improve their integration into the RV process. The resulting ontology-supported application framework has been applied to an industrial use-case from automotive power electronics. We present experimental results showing that the RV process can be significantly improved in terms of reduced design time and increased exhaustiveness by automating the analyses selection step and the provisioning of all the relevant data to be used.

I. INTRODUCTION

The introduction identifies manual robustness-validation planning as error-prone and proposes OWL-based knowledge formalization and Mission Profile mapping to automate analysis selection and provide relevant data. It also highlights propagation of component characteristics across mounting points as part of reducing RV complexity and manual effort.

  • Motivation and problem: Robustness Validation demonstrates that a product performs intended functions with sufficient margin under specified environmental conditions and supports reliability improvement.The process requires developers to identify all analyses needed to prove component robustness.
  • Motivation and problem: Mission Profiles capture relevant environmental stress factors and provide data required for analyses in the RV process.They support requirements propagation along the automotive development process.
  • Motivation and problem: Manual RV-plan creation is error-prone because analysis requirements are complex or change over time, causing analyses to be missed or unnecessarily conducted.RV and MP consideration remain mainly manual, while consolidating models, data, and methodologies for nonfunctional properties is difficult.
  • Contributions: The work automates RV analysis selection and provides corresponding Mission Profile data to decrease required manual effort.The approach also considers propagation of component characteristics across component mounting points.
  • Contributions: The main contributions are reducing RV-process complexity, mapping Mission Profiles to OWL representations, formalizing domain knowledge in OWL, and propagating component characteristics.These contributions establish the stated knowledge-management foundation for engineering-process improvement.

II. BACKGROUND · A. Robustness Validation

The background introduces Robustness Validation (RV) and Mission Profiles (MPs) in automotive E/E development. RV involves multiple analyses and manual selection and retrieval of required data, with analysis identification forming a key process step.

  • II. BACKGROUND: The section frames RV and Mission Profiles as central background concepts for this work.The introduction specifically presents a short overview of both concepts in relation to the paper.
  • II. BACKGROUND: Mission Profiles provide available data that developers must search manually for required information during RV.The passage identifies manual MP-data searching as part of the current process.
  • A. Robustness Validation: RV developers conduct various analyses with the Device Under Test, including Physical Stress Analysis.These analyses are currently performed manually for the most part.
  • A. Robustness Validation: Developers manually select the analyses required for a given component or system.Manual selection is identified as a current challenge in the RV process.
  • II. BACKGROUND: The RV process spans steps from determining and defining applications through production monitoring.The process flow is defined in the Handbook for Robustness Validation of Automotive Electrical/Electronic Modules.
  • A. Robustness Validation: Creating the robustness validation plan requires identifying all analyses needed to approve robustness according to the Handbook.This activity is step 6 on the system level.

B. Mission Profiles … B. Mission Profile Aware Design

Mission Profiles represent lifecycle exposure conditions and diverse stress data, motivating semantic integration into Robustness Validation. Prior work established MPAD concepts, MP representations, and systems-engineering ontologies, while leaving domain-specific RV aspects insufficiently covered.

  • B. Mission Profiles: Mission Profiles define application-specific component contexts and simplify the relevant conditions experienced throughout a component’s life cycle.They contain multiple stress factors from different sources.
  • B. Mission Profiles: The ResCar 2.0 MP format has core, template, and extension layers, but it is not yet an official standard.The autoSWIFT project aims to improve and standardize the format.
  • B. Mission Profiles: MP data can be large and diverse, including temperature and vibration data that analysts must search for during RV analyses.Semantic technologies are proposed to improve integration of this data into the RV process.
  • A. Systems Engineering: Van Ruijven’s Systems Engineering ontology uses ISO 15926 to model ISO 15288 processes but does not cover RV-specific aspects such as MPAD.Integrating it into the presented process flow could explicitly describe the process using defined semantics.
  • B. Mission Profile Aware Design: Prior work introduced Mission Profile Aware Design and described its key differences and enhancements compared with existing design approaches.Related studies also addressed extracting finite state machines from measured vehicle data and integrating them into MPs.
  • III. RELATED WORK: Nirmaier et al. described synthesizing system robustness values into component robustness values, while Katzschke et al. addressed propagating constraints between design domains.The supplied passage presents these as related MPAD methodology contributions.

IV. APPROACH · A. Mission Profile to OWL mapping · 1) Ontology structure:

The approach maps Mission Profile data and formalized component characteristics into OWL so reasoning and semantic queries can automate robustness-validation analysis selection and relevant-data provisioning. Its ontology structure represents MP documents, components, operating states, and functional-load specifications.

  • IV. APPROACH: The approach supports robustness-validation planning by using reasoning to identify required analyses and corresponding data during the Analysis, Development & Test phase.This phase is step 5 in the RV process flow.
  • A. Mission Profile to OWL mapping: Mission Profile data is mapped to OWL, enabling semantic queries such as retrieving temperature-related data.The ontology separates knowledge from programming and improves maintainability and knowledge sharing.
  • IV. APPROACH: Analysis selection compares analysis coverage with component attributes, including characteristics partially propagated from mounting points.The selected analysis determines which Mission Profile data is required.
  • A. Mission Profile to OWL mapping: A prototype mapping mechanism creates a corresponding OWL individual and properties for each Mission Profile element.It uses OWL API3, Scala, and Eclipse Modeling Framework models derived from the MP-format XML Schema Definition; vectors follow a specified encoding.
  • 1) Ontology structure:: The Mission Profile ontology structure follows the current format draft and defines three major objects: DocumentHeader, Component, and OperatingStateSet.DocumentHeader captures metadata, while Component specifies document loads and contains a PortSet for load specification.
  • 1) Ontology structure:: PropertySet and Property objects form the main building block for specifying functional loads.Property objects include an ID, name, and description, while each property can use a load-specification template.
  • 1) Ontology structure:: A TemperatureTimeProfileTemplate specifies temperature profiles through a matrix containing percent vectors for distributions of temperature ranges.This illustrates how MP properties encode functional-load information using templates.

2) Specification of functional loads: · B. Metadata extraction

The paper formalizes functional-load specifications in an MP ontology and uses metadata extraction to make MP data semantically identifiable, searchable, classifiable, and suitable for analysis selection. This extraction addresses missing semantic, provenance, and contextual information in the MP format, including units, value ranges, data types, and collection context.

  • 2) Specification of functional loads:: A PropertySet represents functional-load properties, whose PropertyValue objects specify simple values and whose templates specify complex structures.Each PropertyValueTemplate specialization is a subclass of PropertyValueTemplate, and instances may form complex structures depending on the functional load.
  • 2) Specification of functional loads:: The ontology graph models property specification for a functional load, illustrated with a business vehicle’s driving profile.The model graph excerpt is presented in VOWL, while the broader Mission Profile ontology structure is shown as a UML class diagram.
  • B. Metadata extraction: Metadata extraction determines data type and recognizes content, enabling machine interpretation of MP information.These purposes are part of the rationale for creating ontological representations of Mission Profiles.
  • B. Metadata extraction: Metadata extraction improves search, supports provenance information, filters data by content, and enables change management.These functions are listed as purposes of metadata extraction in ontological MP representations.
  • B. Metadata extraction: Metadata extraction can recognize component properties and associated MP data for classification, such as a TemperatureTimeProfileTemplate containing temperature ranges and distributions.The template contains a matrix with a TemperatureRangeVector for actual temperature ranges and a PercentVector for temperature distribution.
  • B. Metadata extraction: Metadata extraction is required because MP structure alone provides little semantic information needed to identify correct analysis data.Without metadata, functional-load specifications may contain incorrect units or values outside valid ranges, and data types are conveyed only through XSD templates.
  • B. Metadata extraction: MP documents omit contextual statements about component environments, so neighbor components and weather conditions must be extracted from other sources.The work uses a current MP-format draft whose limitations may change during the autoSWIFT project.

C. Analysis selection based on component properties · 1) Modeling of components:

The approach models component properties and integrates each component into a system model to support automated selection of relevant robustness analyses. Analysis recommendations are based on characteristics such as pin count, dimensions, self-heating capability, and mounting-point context.

  • C. Analysis selection based on component properties: 64 pins or 2.54 cm per side are example thresholds for selecting a Physical Stress Analysis.The Robustness Validation Handbook recommends this coverage for components exceeding either threshold.
  • C. Analysis selection based on component properties: Component characteristics form the basis for selecting appropriate analyses in the proposed system.The approach uses prescribed analysis coverage associated with component types.
  • 1) Modeling of components:: Component models provide the foundation for identifying relevant analyses for a given component.The developer supplies characteristics including pin count and self-heating capabilities.
  • 1) Modeling of components:: Component models should define properties such as pin count and self-heating capabilities.These properties are provided by the developer and exemplified in List. 1 of section V.
  • 1) Modeling of components:: Formalizing component models can ideally be automated by harvesting existing specifications from formats other than OWL.The passage presents automated harvesting as the preferred formalization approach.
  • 1) Modeling of components:: Each component is integrated into a system model representing the overall system structure.Mounting points are positions where components can be installed in a car and are important for propagating mounting-point characteristics.
  • 1) Modeling of components:: Mounting points support the propagation of characteristics associated with component installation positions.The propagation of mounting-point characteristics is described in section IV-D.

2) Modeling of background knowledge: · 3) Modeling of analyses coverages:

The approach formalizes robustness-validation background knowledge in OWL and separately models analysis coverages, enabling coverage rules to use specified component characteristics and remain adjustable.

  • 2) Modeling of background knowledge:: The RV Handbook’s guidelines are formalized as background knowledge comprising facts that define robustness-validation rules.This formalization provides the basis for expressing component characteristics such as pin-count categories.
  • 2) Modeling of background knowledge:: Axiom 1 expresses that subjects with more than 64 pins have a high pin count.The example illustrates how a component characteristic is represented as an ontology axiom.
  • 2) Modeling of background knowledge:: Finer-grained models can represent each pin as an instance of Pin and apply cardinality constraints through axioms.This supports more detailed modeling when individual pins must be represented explicitly.
  • 2) Modeling of background knowledge:: Additional background knowledge is formalized in the same manner.The paper presents the examples as part of a broader formalization of the knowledge required by the RV process.
  • 3) Modeling of analyses coverages:: Coverage modeling uses formalized background knowledge to specify that high-pin-count components have Physical Stress Analysis coverage.This relationship is expressed through axiom 3 as a PSACoverage.
  • 3) Modeling of analyses coverages:: Background-knowledge modeling is separated from coverage modeling under separation of concerns, allowing coverage definitions to be adjusted using specified characteristics.Other analysis coverages are formalized likewise, while the separation keeps coverage rules independently adaptable.

D. Propagation of mounting point characteristics

The ontology models characteristics propagated from locally neighboring components, including EMI, temperature, and vibration. Complex role inclusion axioms enable a reasoner to infer these characteristics for nearby components.

  • D. Propagation of mounting point characteristics: Component characteristics such as EMI, temperature, and vibration can be induced by locally neighboring components.The approach models propagation among components that are locally close to one another.
  • D. Propagation of mounting point characteristics: Complex role inclusion axioms model characteristic propagation among neighboring components.These axioms allow a reasoner to derive characteristics that are not explicitly assigned to the target component.
  • D. Propagation of mounting point characteristics: A reasoner infers high EMI for C1 because C2, mounted on a neighboring mounting point, has high EMI.In the example, C1 and C2 are mounted on M1 and M2 respectively, and axiom 4 propagates C2’s high EMI to C1.

E. Selecting appropriate data to support analyses · V. EXAMPLE APPLICATION

The approach automatically maps Mission Profiles into OWL representations, enabling semantic queries, reasoning-based analysis selection, and direct delivery of relevant data to analysts. In an automotive power-module application, it selected analyses for the FTCO3V455A1, reduced manual-selection errors, and supported updates when specifications changed.

  • E. Selecting appropriate data to support analyses: Mission Profiles containing functional and environmental component loads are passed directly to analysts after metadata extraction and reasoning-based analysis selection.The prototype maps MP documents to OWL representations so their contents can be queried semantically.
  • E. Selecting appropriate data to support analyses: The prototype automatically maps available Mission Profile documents into semantically enriched OWL representations for querying and analysis support.Metadata extraction supports classification and proper data selection.
  • V. EXAMPLE APPLICATION: The FTCO3V455A1 3-Phase inverter automotive power module was used to construct an ontological representation for a robustness-validation plan.The module is used in applications including electric power steering, electro-hydraulic power steering, electric water pumps, and electric oil pumps.
  • V. EXAMPLE APPLICATION: Reasoning inferred that FTCO3V455A1 requires CaSystems, EMCaSI, Physical Stress, and E/E Power and Load analyses, along with corresponding Mission Profile data.The inferred axioms also identify HighEMI and ElectronicEngine as a sibling component.
  • V. EXAMPLE APPLICATION: The EMI value for FTCO3V455A1 is induced by the locally close ElectronicEngine through the property chain defined in the approach.This demonstrates how relationships between components contribute to inferred analysis-relevant axioms.
  • V. EXAMPLE APPLICATION: The selected analyses accelerate the RV process and avoid errors from manual analysis selection, while allowing the RV plan to be updated when specifications change.The example specifically considers a slight inverter-module specification change during development.
  • V. EXAMPLE APPLICATION: Property chains can aggregate required analyses across submodules, and Robustness Indicator Figures can likewise be aggregated to summarize overall module robustness.These aggregation mechanisms extend the approach from individual components to modules composed of several submodules.

VI. GENERALIZATION

The approach generalizes to other analysis methodologies involving analysis and corresponding data selection. Its core maps component models, Mission Profile data, and standard literature into OWL representations that support analysis selection.

  • Generalization: The approach can be generalized to other analysis methodologies wherever analysis and corresponding data selection are performed.Its foundations are the component under examination, corresponding Mission Profile data, and standard literature containing analysis guidelines and procedures.
  • Generalization: Mapping engines transform component models, Mission Profiles, and standard literature into OWL representations used by the analysis selection process.These OWL representations provide the inputs for selecting analyses.

VII. CONCLUSIONS AND FUTURE WORK

The approach automates parts of the Robustness Validation process by mapping Mission Profiles to OWL representations for semantic querying, while remaining limited by ontology expressiveness and property-propagation granularity. Deployment requires automatic component-property formalization, broader analysis coverage, and metadata extraction for Mission Profiles.

  • Limitations: The Open World Assumption prevents expressing that a component lacks a property, while property-propagation granularity may be too coarse.Closure axioms can address the limitation but introduce further problems.
  • Future Work: Automatic formalization of component properties is identified as necessary to ease the transition from manual Robustness Validation toward automation.The proposed system would automatically extract component descriptions.
  • Future Work: The system should cover a broader set of analyses because analysis selection becomes more useful as Robustness Validation planning complexity increases.The approach might also be generalized to common analyses selection.
  • Future Work: Metadata extraction for various Mission Profile metadata types is needed to improve the selection of appropriate data.Metadata extraction is listed as a necessary deployment step.
  • Contributions: The approach reduces manual effort in Robustness Validation and propagates mounting-point characteristics through a Mission Profile-to-OWL mapping engine that enables semantic querying.Tooling was created for application within a project.
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