Revolutionizing Electrical Safety Analysis in Automotive Design: A Layered Methodology

Sep 2024 | Automotive, Product Safety, Quality

Discover how a layered approach to automated electrical safety analysis can enhance the reliability and safety of automotive systems. This method allows continuous monitoring and early detection of potential issues, improving efficiency and reducing costs throughout the design process, from initial concepts to final implementation.

The automotive industry has witnessed a significant evolution in its electrical and electronic systems over the past five decades. This growth in complexity necessitates robust safety analysis mechanisms to prevent failures that can lead to catastrophic events. Traditionally, safety analysis methods like Failure Modes and Effects Analysis (FMEA) have been employed to identify potential hazards. However, these methods, when performed manually, are time-consuming and often only applied towards the end of the design process. This delay can make it difficult to implement changes without incurring significant costs. Automated safety analysis tools offer a solution by enabling continuous monitoring and assessment throughout the design process. This paper discusses a layered approach to automated electrical safety analysis in automotive environments, highlighting its benefits, methodologies, and potential applications beyond the automotive industry.

The Automation of Design Safety Analysis

1. Virtual Prototyping

Virtual prototyping allows engineers to simulate and test designs without the need for physical prototypes. By creating a digital model of the electrical system, engineers can perform tests such as turning the ignition on or checking light activation, ensuring that the design behaves as expected under various scenarios. This approach speeds up the identification of potential design flaws and allows for early intervention.

2. Failure Modes and Effects Analysis (FMEA)

FMEA is a structured approach used to identify and evaluate potential failures in a system. By simulating different component failures and their effects on the overall system, FMEA helps engineers prioritize issues based on severity, occurrence, and detectability. Automating FMEA using software tools allows for consistent and rapid analysis, reducing the manual effort required and enabling continuous assessment as the design evolves.

3. Sneak Circuit Analysis

Sneak circuits refer to unintended paths in a system that can cause unplanned activation or deactivation of functions. Identifying sneak circuits is crucial for ensuring the safety and reliability of automotive electrical systems. Automated sneak circuit analysis can detect these unintended paths by simulating all possible input combinations and identifying any unexpected functionality.

4. Functional Design Verification

This involves generating a state chart that depicts all possible states of the system. Engineers can use this chart to verify that the system operates as intended, without any unexpected behaviors. This verification process is essential for ensuring that the system meets its design specifications and safety requirements.

5. Workshop and On-Board Diagnosis

Model-based reasoning is used to generate diagnostic procedures for both on-board diagnostics (OBD) and workshop manuals. Automated diagnostics can identify failures and provide guidance on troubleshooting, which enhances vehicle maintenance and repair efficiency.

6. Process Implications of Automated Design Safety Analysis

Automating safety analysis not only reduces the time and effort required but also facilitates early detection of design flaws. This early detection is crucial for minimizing the cost of modifications and ensuring that safety issues are addressed before they reach production. Additionally, automated tools can continuously monitor the design as it evolves, providing real-time feedback on any changes.

%

Reduction in Design Time

Implementing automated electrical safety analysis can reduce design verification time by up to 30%, significantly enhancing efficiency and allowing engineers to focus on optimizing safety features early in the design process.
Source: Struss, P., & Price, C. (2003). Model-based systems in the automotive industry, AI Magazine, 24(4), 17-34.

%

Cost Savings

Early detection and resolution of design issues using automated safety analysis tools can lead to cost savings of up to 25% by minimizing late-stage changes and reducing the need for physical prototypes. Source: Ward, D., & Price, C. (2001). System functional safety through automated electrical design analysis. SAE 2001 Transactions, Section 7, Journal of Passenger Cars, 341-347.

Architecture of Automated Electrical Design Safety Analysis

1. Structure of the Architecture

The proposed approach utilizes a three-layered architecture for electrical safety analysis:

  • Functional Layer: This layer captures the intended functionality of the system, independent of its implementation. It provides a reusable representation of the system’s functional requirements.
  • Component Behavior Layer: This layer models the behavior of individual components, such as relays and switches, based on their operational states. It links the functional layer with the underlying electrical domain.
  • Qualitative Grid Reasoner Layer: The lowest layer simulates the electrical behavior using qualitative reasoning, which provides an abstract representation of electrical properties like resistance and current flow.

2. Simulation with the Architecture

The simulation process begins with the qualitative grid reasoner, which models the electrical network as a grid of qualitative resistors. The component behavior layer then adjusts the state of components based on the simulation results, while the functional layer abstracts these states to provide a high-level view of the system’s operation.

 

3. Advantages and Drawbacks of Qualitative Reasoning

  • Advantages: Qualitative reasoning enables early modeling with reusable components and provides valuable insights even when detailed numerical data is not available. It simplifies the identification of potential safety issues early in the design process.
  • Drawbacks: Purely qualitative models may not accurately capture all aspects of the system’s behavior, such as precise current levels or the exact timing of events. These limitations necessitate the integration of more detailed quantitative models as the design progresses.

Case Study: Power Windows System

A typical case study in automotive electrical safety analysis is the power windows system, which involves multiple switches, relays, motors, and fuses. The analysis focuses on ensuring proper operation, detecting sneak circuits, verifying voltage drops, and checking fuse integrity under various conditions, such as short circuits or motor stalls.

1. Early Qualitative Analysis

Initial simulations using qualitative reasoning can identify basic functional failures and sneak circuits. For example, a qualitative model can detect if a window motor fails to operate when a switch is activated, suggesting potential wiring or relay issues.

2. Enhanced Analysis with Resistor Levels

Adding multiple resistance levels allows the simulation to distinguish between signaling currents and power currents, improving the accuracy of the analysis. This enhancement can prevent false positives, such as incorrectly predicting a fuse blowout under normal operating conditions.

3. Quantitative Analysis

Using specific resistor values derived from selected components enables a more detailed examination of the power windows system. Numerical simulations can calculate precise current flows, validate fuse ratings, and ensure that voltage drops across motors remain within acceptable limits.

4. Complex Numerical Simulation

For the most critical analyses, detailed numerical models can simulate transient phenomena like motor inrush currents and provide insights into thermal effects on components. This level of detail is essential for ensuring the reliability and safety of high-performance automotive systems.

Incremental Design Safety Analysis

The ability to perform safety analysis incrementally throughout the design process is a significant advantage of the layered approach. Each time a change is made to the design, automated tools can re-run the analysis and highlight any new issues. This incremental approach minimizes the risk of introducing new hazards late in the design cycle and ensures continuous compliance with safety standards.

1. Tracking Design Changes

Automated tools can compare the results of successive analysis runs and report only the differences. This selective reporting reduces the workload for engineers, who can focus on addressing specific issues without being overwhelmed by a flood of data.

2. Real-Time Feedback

Continuous monitoring provides real-time feedback on design decisions, allowing engineers to make informed choices quickly. This responsiveness is particularly valuable in fast-paced development environments where design iterations are frequent.

3. Cost and Time Efficiency

Incremental analysis reduces the need for extensive re-testing and re-validation, saving time and resources. Early detection of issues also prevents costly redesigns and retrofits, improving overall project efficiency.

Application to Other Engineering Domains

While the focus of this paper is on automotive electrical systems, the layered approach to safety analysis is applicable to other engineering domains, such as aerospace, railway systems, and industrial automation. Any domain that involves complex interactions between electrical, mechanical, or hydraulic components can benefit from this methodology.

1. Cross-Domain Applicability

The principles of qualitative reasoning, layered simulation, and incremental analysis are not limited to electrical systems. They can be adapted to model and analyze fluid dynamics, thermal systems, and mechanical interactions.

2. Scalability

The modular nature of the layered approach makes it scalable to systems of varying complexity. Engineers can start with simple qualitative models and progressively incorporate more detail as required.

3. Enhanced Safety and Reliability

Applying these techniques across different domains enhances the safety and reliability of engineered systems. By providing early insights into potential failures and continuously tracking design changes, the layered approach helps ensure that safety is maintained throughout the lifecycle of a product.

Conclusion

The layered approach to automated electrical safety analysis offers significant improvements over traditional methods. By separating the types of knowledge into distinct layers, this methodology enables continuous monitoring and assessment of design changes, providing early feedback on potential safety issues. The integration of different simulation tools, from qualitative reasoning to detailed numerical analysis, ensures that safety analysis evolves alongside the design, maintaining accuracy and relevance at every stage. This approach not only enhances the safety and reliability of automotive systems but also holds promise for other engineering domains where complex interactions must be managed effectively.

 

References

  • A Layered Approach to Automated Electrical Safety Analysis in Automotive Environments – C.J. Price, N.A. Snooke, S.D. Lewis, University of Wales, Aberystwyth, UK. Published in Computers in Industry, 2006.
  • Struss, P., & Price, C. (2003). Model-based systems in the automotive industry. AI Magazine, 24(4), 17-34.
  • Ward, D., & Price, C. (2001). System functional safety through automated electrical design analysis. SAE 2001 Transactions, Section 7, Journal of Passenger Cars, 341-347.
  • Keown, J. (2000). OrCAD PSpice and Circuit Analysis (4th ed.). Prentice-Hall.
  • Lee, M. (1999). Qualitative circuit models in failure analysis reasoning. Artificial Intelligence, 111, 239-276.
  • Savakoor, S., Bowles, J., & Bonnell, D. (1993). Combining sneak circuit analysis and failure modes and effects analysis. Proceedings of the Annual Reliability and Maintainability Symposium, IEEE Press, 199-205.
  • Milde, H., Guckenbiehl, T., Malik, A., Neumann, B., & Struss, P. (2000). Integrating model-based diagnosis techniques into current work processes: three case studies from the INDIA project. AI Communications, 13, 99-123.
  • Price, C., & Taylor, N. (2002). Automated multiple failure FMEA. Reliability Engineering and System Safety Journal, 76(1), 1-10.
  • Snooke, N., & Bell, J. (2002). Abstracting automotive system models from component-based simulation with multi-level behavior. Proceedings of the 16th International Workshop on Qualitative Reasoning, 151-160.
  • Reiter, R. (1987). A theory of diagnosis from first principles. Artificial Intelligence, 32, 57-96.
  • Genesereth, M. (1984). The use of design descriptions in automated diagnosis. Artificial Intelligence, 24, 411-436.

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