Formalized Identification Of Key Factors In Safety-Relevant Failure Scenarios
February 28, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Tim Maurice Julitz, Nadine SchlΓΌter, Manuel LΓΆwer
arXiv ID
2402.18194
Category
cs.SE: Software Engineering
Cross-listed
eess.SY
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This research article presents a methodical data-based approach to systematically identify key factors in safety-related failure scenarios, with a focus on complex product-environmental systems in the era of Industry 4.0. The study addresses the uncertainty arising from the growing complexity of modern products. The method uses scenario analysis and focuses on failure analysis within technical product development. The approach involves a derivation of influencing factors based on information from failure databases. The failures described here are documented individually in failure sequence diagrams and then related to each other in a relationship matrix. This creates a network of possible failure scenarios from individual failure cases that can be used in product development. To illustrate the application of the methodology, a case study of 41 Rapex safety alerts for a hair dryer is presented. The failure sequence diagrams and influencing factor relationship matrices show 46 influencing factors that lead to safety-related failures. The predominant harm is burns and electric shocks, which are highlighted by the active and passive sum diagrams. The research demonstrates a robust method for identifying key factors in safety-related failure scenarios using information from failure databases. The methodology provides valuable insights into product development and emphasizes the frequency of influencing factors and their interconnectedness.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted