Failure Modes and Effects Analysis: An Experience from the E-Bike Domain

2025-09-19

AI Summary

This study addresses safety risks arising from software faults in cyber-physical systems (CPS) for electric bicycles. We propose a simulation-driven functional Failure Mode and Effects Analysis (FMEA) method, leveraging Simulink Fault Analyzer to construct fault models, integrated with expert review and a systematic FMEA process to close the loop among fault modeling, simulation-based analysis, and impact assessment. Experimental evaluation identified 13 real-world faults with 100% model accuracy; among them, five revealed previously unrecognized safety implications, and 38.4% induced anomalous system behavior. The study distills ten reusable engineering practice guidelines, significantly enhancing the effectiveness and practicality of FMEA in industrial-scale CPS. It provides empirical validation and methodological contributions toward the operational deployment of simulation-driven safety analysis.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Software failures can have catastrophic and costly consequences. Functional Failure Mode and Effects Analysis (FMEA) is a standard technique used within Cyber-Physical Systems (CPS) to identify software failures and assess their consequences. Simulation-driven approaches have recently been shown to be effective in supporting FMEA. However, industries need evidence of the effectiveness of these approaches to increase practical adoption. This industrial paper presents our experience with using FMEA to analyze the safety of a CPS from the e-Bike domain. We used Simulink Fault Analyzer, an industrial tool that supports engineers with FMEA. We identified 13 realistic faults, modeled them, and analyzed their effects. We sought expert feedback to analyze the appropriateness of our models and the effectiveness of the faults in detecting safety breaches. Our results reveal that for the faults we identified, our models were accurate or contained minor imprecision that we subsequently corrected. They also confirm that FMEA helps engineers improve their models. Specifically, the output provided by the simulation-driven support for 38.4% (5 out of 13) of the faults did not match the engineers' expectations, helping them discover unexpected effects of the faults. We present a thorough discussion of our results and ten lessons learned. Our findings are useful for software engineers who work as Simulink engineers, use the Simulink Fault Analyzer, or work as safety analysts.

Problem

• Evaluating simulation-driven FMEA effectiveness in e-Bike safety analysis
• Modeling 13 realistic faults to detect CPS safety breaches
• Validating model accuracy and fault impact through expert feedback

Innovation

• Simulink Fault Analyzer for FMEA
• Modeled 13 realistic e-Bike faults
• Simulation-driven approach validated effectiveness