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arxiv: 2509.15893 · v1 · submitted 2025-09-19 · 💻 cs.SE

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

Andrea Bombarda , Federico Conti , Marcello Minervini , Aurora Zanenga , Claudio Menghi This is my paper

Pith reviewed 2026-05-18 15:46 UTC · model grok-4.3

classification 💻 cs.SE
keywords failure mode and effects analysiscyber-physical systemse-bikefault modelingsimulationsafety analysisindustrial experience
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The pith

Simulation-driven failure analysis of an e-bike control system uncovered unexpected safety effects in five of thirteen modeled faults and prompted corrections to the models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reports an industrial case study applying functional failure mode and effects analysis to evaluate software safety in an electric bicycle cyber-physical system. Thirteen realistic faults were selected, modeled, and examined through simulation to determine their consequences on system behavior. Expert engineers reviewed the results and found the models accurate enough to require only minor adjustments, while the simulation outputs for roughly 38 percent of the faults contradicted initial expectations and exposed previously unrecognized safety risks. This matters for practitioners because it supplies direct evidence that simulation-supported analysis can improve model fidelity and surface latent fault impacts that manual review might overlook. If the experience generalizes, it offers a practical route for safety teams to strengthen assessments before deployment in similar embedded control applications.

Core claim

The authors show that modeling thirteen representative faults in an e-bike system and running them through simulation-driven analysis produced outputs that matched expert expectations for most cases but diverged for five faults, enabling the team to identify unexpected effects and refine the models accordingly.

What carries the argument

Simulation-driven support for modeling faults and tracing their downstream effects on system safety properties.

If this is right

  • Engineers obtain concrete evidence of fault behaviors that differ from their prior assumptions.
  • Iterative comparison of simulation results against expectations leads to targeted model improvements.
  • Safety analysis gains an additional mechanism for detecting latent risks in control logic.
  • The process yields lessons that can be reused when applying similar analysis to other embedded systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending the fault set beyond the original thirteen could reveal whether the rate of unexpected effects remains stable across larger samples.
  • Pairing the qualitative expert review with automated checks for safety invariants might reduce reliance on human judgment alone.
  • Applying the same workflow to other vehicle domains would test whether the observed model refinement benefit transfers beyond e-bikes.

Load-bearing premise

The thirteen selected faults are representative enough of real faults in e-bike systems that expert qualitative review alone can validate both model accuracy and the usefulness of the discovered effects.

What would settle it

Repeating the analysis on a broader set of faults drawn from field data or adding quantitative safety metrics such as failure probability bounds and finding substantially fewer unexpected effects or lower model accuracy would undermine the reported benefits.

Figures

Figures reproduced from arXiv: 2509.15893 by Andrea Bombarda, Aurora Zanenga, Claudio Menghi, Federico Conti, Marcello Minervini.

Figure 1
Figure 1. Figure 1: Simulink® model for the e-Bike. including e-Bikes, and involving several companies, such as Brembo [34] and Pirelli [35]. E-Bikes are vehicles that support riders with power from an electric motor. Our case study is relevant because (a) the e-Bike software is often designed in Simulink® [36]; (b) e-Bikes have a considerable market size (27.15 USD billion in 2022, expected to grow to USD 82.84 billion by 20… view at source ↗
Figure 2
Figure 2. Figure 2: Adding new faults with the Simulink [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A portion of the Fault Table for our e-Bike. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The desired speed and the speed of the e-Bike when [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Flag enabling the use of event-based triggers. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The measured speed of the e-Bike when fault F15 is [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Mitigation of fault F2. Two sensors are used to measure [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports an industrial experience applying simulation-driven Functional FMEA using Simulink Fault Analyzer to an e-bike CPS. The authors selected and modeled 13 realistic faults, ran simulations to analyze effects, and collected expert engineer feedback on model appropriateness and safety-breach detection. Results indicate that models were accurate or needed only minor corrections, while simulation outputs mismatched expectations for 5 of 13 faults (38.4%), revealing unexpected effects; the paper discusses these outcomes and presents ten lessons learned for Simulink users and safety analysts.

Significance. If the findings hold, the work supplies concrete industrial evidence that simulation-based FMEA tooling can improve model fidelity and surface unanticipated fault consequences in a CPS domain. The specific counts (13 faults, 5 mismatches), expert validation process, and practitioner-oriented lessons learned constitute a useful addition to the empirical literature on safety analysis tools for software engineers working with Simulink or similar environments.

major comments (2)
  1. [Results] Results section: The central claims that models were 'accurate or contained minor imprecision' and that simulation 'helped them discover unexpected effects' rest solely on qualitative expert review. No quantitative safety metrics (e.g., computed failure rates, severity scores, or risk priority numbers produced by the tool) or baseline comparison against conventional non-simulation FMEA are reported, weakening the ability to substantiate effectiveness beyond subjective mismatch counts.
  2. [Case Study] Case study / fault modeling description: The criteria and process for selecting the 13 'realistic' faults are not specified. This omission is load-bearing for the representativeness claim and leaves open the possibility that the reported 38.4% mismatch rate reflects selection rather than a general property of the simulation-driven approach.
minor comments (2)
  1. [Results] A summary table listing each of the 13 faults, modeled injection method, expected effect, observed simulation output, and expert assessment would substantially improve readability and allow readers to trace the 5 unexpected-effect cases directly.
  2. [Discussion] The abstract states that 'ten lessons learned' are presented; numbering or grouping them explicitly in the discussion section would make the practical takeaways easier to locate and cite.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our industrial experience report. We address each major comment below and will revise the manuscript to incorporate clarifications and additional discussion where appropriate.

read point-by-point responses

  1. Referee: [Results] Results section: The central claims that models were 'accurate or contained minor imprecision' and that simulation 'helped them discover unexpected effects' rest solely on qualitative expert review. No quantitative safety metrics (e.g., computed failure rates, severity scores, or risk priority numbers produced by the tool) or baseline comparison against conventional non-simulation FMEA are reported, weakening the ability to substantiate effectiveness beyond subjective mismatch counts.

    Authors: We agree that the evaluation relies on qualitative expert feedback and does not include quantitative safety metrics or a baseline comparison to conventional FMEA. This aligns with the scope of an industrial experience report, which prioritizes sharing practical outcomes from tool application in a real CPS rather than controlled quantitative experiments. To address the concern, we will revise the Results and Discussion sections to explicitly note the qualitative nature of the evidence, acknowledge the lack of quantitative metrics and baseline data as a limitation, and suggest avenues for future work that could include such comparisons or metrics. revision: yes

  2. Referee: [Case Study] Case study / fault modeling description: The criteria and process for selecting the 13 'realistic' faults are not specified. This omission is load-bearing for the representativeness claim and leaves open the possibility that the reported 38.4% mismatch rate reflects selection rather than a general property of the simulation-driven approach.

    Authors: We acknowledge that the manuscript does not explicitly detail the criteria and process used to select the 13 faults. In the revised version, we will expand the Case Study section to describe the selection process: the faults were identified through collaboration with domain experts, drawing on historical system data, known safety-critical components in the e-bike CPS, and expert judgment regarding faults likely to produce observable effects in simulation. We will also add discussion of potential selection biases and implications for generalizability of the mismatch rate. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical experience report

full rationale

The paper is an industrial experience report on applying FMEA with Simulink Fault Analyzer to an e-bike CPS. It describes identifying 13 realistic faults, modeling them, running simulations to analyze effects, and obtaining expert feedback on model appropriateness and fault detection. No mathematical derivations, equations, predictions, fitted parameters, or first-principles results are present. Claims rest on concrete system modeling and external expert qualitative review rather than any self-referential construction, self-citation chains, or reductions of outputs to inputs by definition. The account is self-contained against external benchmarks of expert validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical industrial experience report that introduces no free parameters, unproven axioms, or new postulated entities; it relies on standard FMEA methodology and commercial tool usage.

pith-pipeline@v0.9.0 · 5781 in / 1124 out tokens · 49648 ms · 2026-05-18T15:46:20.811784+00:00 · methodology

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Reference graph

Works this paper leans on

76 extracted references · 76 canonical work pages · 1 internal anchor

  1. [1]

    Software Failure Modes and Effects Analysis,

    D. J. Reifer, “Software Failure Modes and Effects Analysis,”IEEE Transactions on Reliability, vol. R-28, no. 3, pp. 247–249, 1979

    work page 1979

  2. [2]

    FMEA variables of software failure risks based on journals and metrics,

    D. Valyayev, A. Mukasheva, D. Yedilkhan, B. Aigerim, and D. Mukham- mejanova, “FMEA variables of software failure risks based on journals and metrics,” in2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST), 2024, pp. 302–307

    work page 2024

  3. [3]

    Automatic assessment of an industrial safety-critical system,

    Modelwise, “Automatic assessment of an industrial safety-critical system,” 2025. [Online]. Available: https://modelwise.ai/automatic-ass essment-of-an-industrial-safety-critical-system/

    work page 2025

  4. [4]

    Procedure for Failure Mode, Effects and Criticality Analysis (FMECA),

    Office of manned space flight, Apollo program, Apollo Reliability and Quality Assurance Office, “Procedure for Failure Mode, Effects and Criticality Analysis (FMECA),” 1966

    work page 1966

  5. [5]

    Effects and Criticality Analysis (FMECA),

    F. Mode, “Effects and Criticality Analysis (FMECA),”Reliability Anal- ysis Center, 1993

    work page 1993

  6. [6]

    Ford Motor Company Customer-specific Re- quirements, for Use with ISO/TS 16949: 2002,

    Ford Motor Company, “Ford Motor Company Customer-specific Re- quirements, for Use with ISO/TS 16949: 2002,” 2003

    work page 2002

  7. [7]

    On safety, assurance, and reliability: a software engineering perspective (keynote),

    M. Chechik, “On safety, assurance, and reliability: a software engineering perspective (keynote),” inProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE 2022. New York, NY , USA: Association for Computing Machinery, 2022, p. 2. [Online]. Available: https://doi.o...

    work page doi:10.1145/3540250.3569443 2022

  8. [8]

    SAFA: A Tool for Supporting Safety Analysis in Evolving Software Systems,

    A. D. Rodriguez, T. Newman, K. R. Dearstyne, and J. Cleland- Huang, “SAFA: A Tool for Supporting Safety Analysis in Evolving Software Systems,” inProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, ser. ASE ’22. New York, NY , USA: Association for Computing Machinery, 2023. [Online]. Available: https://doi.org/10.1...

    work page doi:10.1145/3551349.3559535 2023

  9. [9]

    Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment,

    J. Zhu, L. Wang, and X. Han, “Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment,” inProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, ser. ASE ’22. New York, NY , USA: Association for Computing Machinery, 2023. [Online]. Available: https://doi.org/10.1145...

    work page doi:10.1145/3551349.3556906 2023

  10. [10]

    Model-based safety analysis,

    A. Joshi, M. P. Heimdahl, S. P. Miller, and M. W. Whalen, “Model-based safety analysis,” Tech. Rep., 2006

    work page 2006

  11. [11]

    Model-based safety assessment: Review of the discipline and its challenges,

    O. Lisagor, T. Kelly, and R. Niu, “Model-based safety assessment: Review of the discipline and its challenges,” inInternational Conference on Reliability, Maintainability and Safety. IEEE, 2011, pp. 625–632

    work page 2011

  12. [12]

    Software FMEA techniques,

    P. Goddard, “Software FMEA techniques,” inAnnual Reliability and Maintainability Symposium. International Symposium on Product Qual- ity and Integrity (Cat. No.00CH37055), 2000, pp. 118–123

    work page 2000

  13. [13]

    Software system reliability and safety assessment: an extended FMEA approach,

    S. Rebello and N. K. Goyal, “Software system reliability and safety assessment: an extended FMEA approach,”International Journal of Reliability and Safety, vol. 4, no. 4, p. 366, 2010

    work page 2010

  14. [14]

    An Integrated System Design and Safety Framework for Model-Based Safety Analysis,

    R. Krishnan and S. V . Bhada, “An Integrated System Design and Safety Framework for Model-Based Safety Analysis,”IEEE Access, vol. 8, pp. 146 483–146 497, 2020

    work page 2020

  15. [15]

    FIM: fault injection and mutation for Simulink,

    E. Bartocci, L. Mariani, D. Ni ˇckovi´c, and D. Yadav, “FIM: fault injection and mutation for Simulink,” inJoint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE. ACM, 2022, p. 1716–1720

    work page 2022

  16. [16]

    Simulation-Driven Failure Modes and Effects Analysis of Flight Control System Architec- tures,

    J. Rhein, M. Bimbi, G. Miraglia, and F. Holzapfel, “Simulation-Driven Failure Modes and Effects Analysis of Flight Control System Architec- tures,” inDigital Avionics Systems Conference (DASC). IEEE, 2024, pp. 1–10

    work page 2024

  17. [17]

    Simulink Fault Analyzer,

    Mathworks, “Simulink Fault Analyzer,” 2003. [Online]. Available: https://www.mathworks.com/products/simulink-fault-analyzer.html

    work page 2003

  18. [18]

    A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation,

    S. Ali, L. C. Briand, H. Hemmati, and R. K. Panesar-Walawege, “A Systematic Review of the Application and Empirical Investigation of Search-Based Test Case Generation,”IEEE Transactions on Software Engineering, vol. 36, no. 6, pp. 742–762, 2010

    work page 2010

  19. [19]

    Evidence-Based Soft- ware Engineering for Practitioners,

    T. Dyba, B. A. Kitchenham, and M. Jorgensen, “Evidence-Based Soft- ware Engineering for Practitioners,”IEEE Software, vol. 22, no. 1, pp. 58–65, 2005

    work page 2005

  20. [20]

    Evidence-Based Software Engineering,

    B. Kitchenham, T. Dyba, and M. Jorgensen, “Evidence-Based Software Engineering,” inInternational Conference on Software Engineering, 2004, pp. 273–281

    work page 2004

  21. [21]

    A large scale empirical comparison of state-of-the-art search-based test case generators,

    A. Panichella, F. M. Kifetew, and P. Tonella, “A large scale empirical comparison of state-of-the-art search-based test case generators,”Infor- mation and Software Technology, vol. 104, pp. 236–256, 2018

    work page 2018

  22. [22]

    On Parameter Tuning in Search Based Software Engineering: A Replicated Empirical Study,

    A. S. Sayyad, K. Goseva-Popstojanova, T. Menzies, and H. Ammar, “On Parameter Tuning in Search Based Software Engineering: A Replicated Empirical Study,” inInternational Workshop on Replication in Empirical Software Engineering Research, ser. RESER. IEEE, 2013, p. 84–90

    work page 2013

  23. [23]

    Empirical Re- search on Concurrent Software Testing: A Systematic Mapping Study,

    S. M. Melo, J. C. Carver, P. S. Souza, and S. R. Souza, “Empirical Re- search on Concurrent Software Testing: A Systematic Mapping Study,” Information and Software Technology, vol. 105, pp. 226–251, 2019

    work page 2019

  24. [24]

    Mapping Software Testing Practice With Software Testing Research — SERP-Test taxonomy,

    E. Engström and K. Petersen, “Mapping Software Testing Practice With Software Testing Research — SERP-Test taxonomy,” inIEEE Inter- national Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2015, pp. 1–4

    work page 2015

  25. [25]

    The Acceptance of Search-Based Software Engineering Techniques: An Empirical Evaluation Using the Technology Acceptance Model,

    V . Mezhuyev, M. Al-Emran, M. A. Ismail, L. Benedicenti, and D. A. P. Chandran, “The Acceptance of Search-Based Software Engineering Techniques: An Empirical Evaluation Using the Technology Acceptance Model,”IEEE Access, vol. 7, pp. 101 073–101 085, 2019

    work page 2019

  26. [26]

    An industrial experience report on applying search-based boundary input generation to cyber-physical systems,

    P. Valle, V . Riccio, A. Arrieta, P. Tonella, and M. Arratibel, “An industrial experience report on applying search-based boundary input generation to cyber-physical systems,”Empirical Software Engineering, vol. 30, no. 4, p. 112, 2025

    work page 2025

  27. [27]

    Characteristics, Potentials, and Limitations of Open-Source Simulink Projects for Empirical Research,

    A. Boll, F. Brokhausen, T. Amorim, T. Kehrer, and A. V ogelsang, “Characteristics, Potentials, and Limitations of Open-Source Simulink Projects for Empirical Research,”Software and Systems Modeling, vol. 20, no. 6, pp. 2111–2130, 2021

    work page 2021

  28. [28]

    Replicability Study: Corpora For Understanding Simulink Models & Projects,

    S. L. Shrestha, S. A. Chowdhury, and C. Csallner, “Replicability Study: Corpora For Understanding Simulink Models & Projects,” in ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). IEEE, 2023, pp. 1–12

    work page 2023

  29. [29]

    On the Replicability of Experimental Tool Evaluations in Model-Based Development: Lessons Learnt from a Sys- tematic Literature Review Focusing on MATLAB/Simulink,

    A. Boll and T. Kehrer, “On the Replicability of Experimental Tool Evaluations in Model-Based Development: Lessons Learnt from a Sys- tematic Literature Review Focusing on MATLAB/Simulink,” inSystems Modelling and Management. Springer, 2020, p. 111–130

    work page 2020

  30. [30]

    Replicability of Experimental Tool Evaluations in Model-Based Software and Systems Engineering With MATLAB/Simulink,

    A. Boll, N. Vieregg, and T. Kehrer, “Replicability of Experimental Tool Evaluations in Model-Based Software and Systems Engineering With MATLAB/Simulink,”Innovations in Systems and Software Engineering, vol. 20, no. 3, pp. 209–224, 2024

    work page 2024

  31. [31]

    Modeling Practices in Open Source Software,

    O. Badreddin, T. C. Lethbridge, and M. Elassar, “Modeling Practices in Open Source Software,” inOpen Source Software: Quality Verification. Springer, 2013, pp. 127–139

    work page 2013

  32. [32]

    How Do Open Source Communities Document Software Architecture: An Exploratory Survey,

    W. Ding, P. Liang, A. Tang, H. v. Vliet, and M. Shahin, “How Do Open Source Communities Document Software Architecture: An Exploratory Survey,” inInternational Conference on Engineering of Complex Computer Systems, ser. ICECCS. IEEE Computer Society, 2014, p. 136–145. [Online]. Available: https://doi.org/10.1109/ICECCS.2014.26

    work page doi:10.1109/iceccs.2014.26 2014

  33. [33]

    MOST Spoke 5 — ’Light Vehicle and Active Mobility’

    “MOST Spoke 5 — ’Light Vehicle and Active Mobility’.” [Online]. Available: https://centronazionalemost.it/Spoke5.html

    work page

  34. [34]

    Brembo Homepage,

    B. N.V ., “Brembo Homepage,” https://www.brembo.com/en/, 2024, accessed: July 21, 2025

    work page 2024

  35. [35]

    Pirelli Homepage,

    Pirelli, “Pirelli Homepage,” http://www.pirelli.com, 2024, accessed: July 21, 2025

    work page 2024

  36. [36]

    Bosch eBike Systems Develops Electric Bike Controller with Model-Based Design,

    M. C. Stories, “Bosch eBike Systems Develops Electric Bike Controller with Model-Based Design,” https://www.mathworks.com/company/user _stories/bosch-ebike-systems-develops-electric-bike-controller-with-m odel-based-design.html, 2024, accessed: November 7, 2024

    work page 2024

  37. [37]

    F. B. Insights, “E-Bike Drive Unit Market Size, Share & COVID-19 Impact Analysis, By Product Type (Mid-drive Motors and Hub Motors), By Application (OEM and Aftermarket), and Regional Forecasts, 2023- 2030,” https://www.fortunebusinessinsights.com/e-bike-drive-unit-mar ket-107520, 2024, accessed: July 21, 2025

    work page 2023

  38. [38]

    Software Takes eBikes to New Heights,

    E. Times, “Software Takes eBikes to New Heights,” https://www.eeti mes.eu/software-takes-ebikes-to-new-heights/, 2025, accessed: July 21, 2025

    work page 2025

  39. [39]

    The safety of e-bikes in The Netherlands,

    P. Schepers, K. K. Wolt, and E. Fishman, “The safety of e-bikes in The Netherlands,” Paris, International Transport Forum Discussion Paper 2018-02, 2018. [Online]. Available: https://hdl.handle.net/10419/194065

    work page 2018

  40. [40]

    The German Naturalistic Cycling Study — Comparing cycling speed of riders of different e-bikes and conventional bicycles,

    K. Schleinitz, T. Petzoldt, L. Franke-Bartholdt, J. F. Krems, and T. Gehlert, “The German Naturalistic Cycling Study — Comparing cycling speed of riders of different e-bikes and conventional bicycles,” Safety Science, vol. 92, p. 290–297, 2017. [Online]. Available: http://dx.doi.org/10.1016/j.ssci.2015.07.027

    work page doi:10.1016/j.ssci.2015.07.027 2017

  41. [41]

    Software Takes eBikes to New Heights,

    “Software Takes eBikes to New Heights,” 2025, https://www.eetimes. eu/software-takes-ebikes-to-new-heights/

    work page 2025

  42. [42]

    Regenerative Braking Capabilities in E-Bike Vehicles: Comparison Between two Drive Architectures,

    M. Minervini, P. Giangrande, F. Corti, P. Malighetti, and L. Mantione, “Regenerative Braking Capabilities in E-Bike Vehicles: Comparison Between two Drive Architectures,” inIEEE International Conference on 11 Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehi- cles & International Transportation Electrification Conference (ESARS- ITEC...

    work page 2024

  43. [43]

    Simulink-Based Simulation of Electric Bicycle Dynamics and Regenerative Braking for Battery State of Charge Assessment,

    F. Corti, M. Minervini, P. Giangrande, A. Reatti, P. Malighetti, and L. Pugi, “Simulink-Based Simulation of Electric Bicycle Dynamics and Regenerative Braking for Battery State of Charge Assessment,” in Mediterranean Electrotechnical Conference (MELECON). IEEE, Jun. 2024, p. 803–808

    work page 2024

  44. [44]

    Simscape Model and simulate multidomain physical systems,

    MathWorks, “Simscape Model and simulate multidomain physical systems,” accessed: July 21, 2025. [Online]. Available: https: //www.mathworks.com/products/simscape.html

    work page 2025

  45. [45]

    Sensor Fault Diagnosis Method Based on Rotor Slip Applied to Induction Motor Drive,

    C. D. Tran, M. Kuchar, M. Sobek, V . Sotola, and B. H. Dinh, “Sensor Fault Diagnosis Method Based on Rotor Slip Applied to Induction Motor Drive,”Sensors, vol. 22, no. 22, 2022. [Online]. Available: https://www.mdpi.com/1424-8220/22/22/8636

    work page 2022

  46. [46]

    A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment,

    E. Murgano, R. Caponetto, G. Pappalardo, S. D. Cafiso, and A. Severino, “A Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment,”Sensors, vol. 21, no. 12, 2021. [Online]. Available: https://www.mdpi.com/1424-8220/21/12/4183

    work page 2021

  47. [47]

    Rider control identification in cycling taking into account steering torque feedback and sensory delays,

    G. Dialynas, C. Christoforidis, R. Happee, and A. Schwab, “Rider control identification in cycling taking into account steering torque feedback and sensory delays,”Vehicle System Dynamics, vol. 61, no. 1, pp. 200–224, 2023

    work page 2023

  48. [48]

    Blocks that Support Fault Modeling,

    MathWorks, “Blocks that Support Fault Modeling,” accessed: June 22,

    work page

  49. [49]

    Available: https://www.mathworks.com/help/simscape/ ug/block-support.html

    [Online]. Available: https://www.mathworks.com/help/simscape/ ug/block-support.html

    work page

  50. [50]

    Variable Resistor,

    ——, “Variable Resistor,” https://www.mathworks.com/help/simscape/ ref/variableresistor.html, 2024, accessed: July 21, 2025

    work page 2024

  51. [51]

    Simulate Models with Faults by Using the Multiple Simulations Panel,

    ——, “Simulate Models with Faults by Using the Multiple Simulations Panel,” accessed: July 21, 2025. [Online]. Available: https://www.math works.com/help/fault-analyzer/ug/simulate-models-with-faults.html

    work page 2025

  52. [52]

    How Acceleration Modes Work,

    ——, “How Acceleration Modes Work,” https://www.mathworks.co m/help/simulink/ug/how-the-acceleration-modes-work.html, 2024, accessed: July 21, 2025

    work page 2024

  53. [53]

    European Union, “Regulation (EU) No 168/2013 of the European Parliament and of the Council of 15 January 2013 on the approval and market surveillance of two- or three-wheel vehicles and quadricycles Text with EEA relevance,” https://eur-lex.europa.eu/legal-content/E N/TXT/HTML/?uri=CELEX:02013R0168-20241127, 2024, accessed: July 21, 2025

    work page 2013

  54. [54]

    Deceleration on the Runway,

    SKYbrary, “Deceleration on the Runway,” https://skybrary.aero/articles /deceleration-runway, 2025

    work page 2025

  55. [55]

    E-bike safety. A review of Empirical European and North American Studies,

    C. R. Cherry and J. H. MacArthur, “E-bike safety. A review of Empirical European and North American Studies,”Light Electric Vehicle Education and Research Initiative, 2019

    work page 2019

  56. [56]

    Enhancing Mobile Robot Safety Evaluation with Simulation-Driven Model-Based Safety Analysis,

    G. Miraglia, M. Bimbi, and M. Nanjundappa, “Enhancing Mobile Robot Safety Evaluation with Simulation-Driven Model-Based Safety Analysis,” inRSS Safe Autonomy Workshop, 2024. [Online]. Available: https://sites.google.com/view/rss2024-safe-autonomy/home

    work page 2024

  57. [57]

    Procedures for Performing a Failure Mode Effects and Criticality Analysis

    MIL–P–1629, “Procedures for Performing a Failure Mode Effects and Criticality Analysis.”U.S. Department of Defense., 1949

    work page 1949

  58. [58]

    Failure Mode and Effects Analysis by Using the House of Reliability- Based Rough VIKOR Approach,

    Z. Wang, J.-M. Gao, R.-X. Wang, K. Chen, Z.-Y . Gao, and W. Zheng, “Failure Mode and Effects Analysis by Using the House of Reliability- Based Rough VIKOR Approach,”IEEE Transactions on Reliability, vol. 67, no. 1, pp. 230–248, 2018

    work page 2018

  59. [59]

    A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach,

    B. Ervural and H. I. Ayaz, “A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach,” Engineering Failure Analysis, vol. 152, p. 107525, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1350630 72300479X

    work page 2023

  60. [60]

    Using cost based FMEA to enhance reliability and serviceability,

    S. J. Rhee and K. Ishii, “Using cost based FMEA to enhance reliability and serviceability,”Advanced Engineering Informatics, vol. 17, no. 3, pp. 179–188, 2003. [Online]. Available: https: //www.sciencedirect.com/science/article/pii/S1474034604000072

    work page 2003

  61. [61]

    Model-based safety analysis final report,

    A. Joshi and M. Whalen, “Model-based safety analysis final report,” 02 2006

    work page 2006

  62. [62]

    Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System,

    J. Ivan ˇcan, D. Lisjak, D. Pavleti ´c, and D. Kolar, “Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System,”Machines, vol. 11, no. 7, 2023. [Online]. Available: https://www.mdpi.com/2075-1702/11/7/739

    work page 2023

  63. [63]

    En- hanced GO methodology to support failure mode, effects and criticality analysis,

    L. Liu, D. Fan, Z. Wang, D. Yang, J. Cui, X. Ma, and Y . Ren, “En- hanced GO methodology to support failure mode, effects and criticality analysis,”Journal of Intelligent Manufacturing, vol. 30, pp. 1451–1468, 2019

    work page 2019

  64. [64]

    Multi-perspective failure mode and effects analysis based on rough number projection,

    H. An, G. Xu, Z. Sun, W. Chen, and J. Pan, “Multi-perspective failure mode and effects analysis based on rough number projection,” Engineering Failure Analysis, vol. 169, p. 109192, 2025

    work page 2025

  65. [65]

    Exploration-Driven Reinforcement Learning for Avionic System Fault Detection (Experience Paper),

    P.-A. Le Tolguenec, E. Rachelson, Y . Besse, F. Teichteil-Koenigsbuch, N. Schneider, H. Waeselynck, and D. Wilson, “Exploration-Driven Reinforcement Learning for Avionic System Fault Detection (Experience Paper),” inSIGSOFT International Symposium on Software Testing and Analysis, ser. ISSTA. ACM, 2024, p. 920–931

    work page 2024

  66. [66]

    Simulation-based testing of simulink models with test sequence and test assessment blocks,

    F. Formica, T. Fan, A. Rajhans, V . Pantelic, M. Lawford, and C. Menghi, “Simulation-based testing of simulink models with test sequence and test assessment blocks,”IEEE Transactions on Software Engineering, vol. 50, no. 2, pp. 239–257, 2023

    work page 2023

  67. [67]

    Test Case Generation for Drivability Requirements of an Automotive Cruise Controller: An Experience with an Industrial Simulator,

    F. Formica, N. Petrunti, L. Bruck, V . Pantelic, M. Lawford, and C. Menghi, “Test Case Generation for Drivability Requirements of an Automotive Cruise Controller: An Experience with an Industrial Simulator,” inJoint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ser. ESEC/FSE

    work page

  68. [68]

    1949–1960

    Association for Computing Machinery, 2023, p. 1949–1960

    work page 2023

  69. [69]

    Incorporating software failure in risk analysis,

    C. A. Thieme, A. Mosleh, I. B. Utne, and J. Hegde, “Incorporating software failure in risk analysis,”Reliability Engineering & System Safety, vol. 198, p. 106804, 2020

    work page 2020

  70. [70]

    EBike / EScooter modular DIY OpenSource electronics and software,

    O. EBike, “EBike / EScooter modular DIY OpenSource electronics and software,” https://github.com/openSourceEBike, 2024, accessed: July 21, 2025

    work page 2024

  71. [71]

    Introduction to the Special Issue on Automotive CPS Safety & Security: Part 2,

    S. Chakraborty, S. Jha, S. Samii, and P. Mundhenk, “Introduction to the Special Issue on Automotive CPS Safety & Security: Part 2,”ACM Trans. Cyber-Phys. Syst., vol. 8, no. 2, May 2024. [Online]. Available: https://doi.org/10.1145/3650210

    work page doi:10.1145/3650210 2024

  72. [72]

    ISO 26262-6:2018 — Road vehicles — Functional safety — Part 6: Product development at the software level,

    International Standard Organization, “ISO 26262-6:2018 — Road vehicles — Functional safety — Part 6: Product development at the software level,” 2024, accessed: 21 July 2025. [Online]. Available: https://www.iso.org/standard/68388.html

    work page 2018

  73. [73]

    EN 15194 Cycles — Electrically power assisted cycles — EPAC Bicycles,

    European Standard, “EN 15194 Cycles — Electrically power assisted cycles — EPAC Bicycles,” 2023, accessed: 21 July 2025. [Online]. Available: https://www.en-standard.eu/ilnas-en-15194-cycles-electricall y-power-assisted-cycles-epac-bicycles/?srsltid=AfmBOorJ582mAq4zK CiFsLYUqBxriXM60vYdcDct0TdRM26dr1eGDz9d

    work page 2023

  74. [74]

    Cheating VanMoof e-bikes will be slowed outside the US,

    T. Ricker, “Cheating VanMoof e-bikes will be slowed outside the US,” https://www.theverge.com/2020/11/10/21558235/vanmoof-slows-s3-x 3-europe-japan-speed-limit, 2024, accessed: November 7, 2024

    work page 2020

  75. [75]

    Test Case Generation for Simulink Models: An Experience from the E-Bike Domain

    M. Marzella, A. Bombarda, M. Minervini, N. M. Bisceglia, A. Gargan- tini, and C. Menghi, “Test Case Generation for Simulink Models: An Experience from the E-Bike Domain,”arXiv preprint arXiv:2501.05792, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  76. [76]

    Replication Package,

    “Replication Package,” https://github.com/foselab/SimulationDrivenF MEA_Bikes, 2025, accessed: July 21, 2025. 12

    work page 2025