IEEE 2668 Compliant Adaptive Quantitative Risk Analysis Strategy using STPA : An Elevator Safety Perspective

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Original languageEnglish
Pages (from-to)78-87
Journal / PublicationIEEE Transactions on Consumer Electronics
Issue number1
Online published1 Jan 2024
Publication statusPublished - Feb 2024


The diagnosis (i.e., risk analysis) of the elevator system is a significant challenge with the rapid growth of the elevator market in consumer electronics. The traditional elevator diagnosis strategies focus on detecting faults or predicting potential malfunctions. However, these solutions largely hinge on qualitative risk assessments, delivering risky or risk-free outcomes. As a result, the task of comprehending the level of risk and subsequently developing countermeasures that are appropriately adaptable presents a formidable challenge. To address this challenge, an IEEE 2668-compliant Adaptive Quantitative Risk Analysis strategy (AQRAS) using system theoretical process analysis (STPA) is proposed in this paper. The AQRAS analyzes the risk of the elevator system with a safety index (SDex) ranging from 0 to 5, using measurements derived from the Internet-of-Things (IoT) network. The SDex was inspired by the IoT maturity index (IDex) created by the IEEE 2668 global standard for evaluating IoT-related applications. Consequently, the AQRAS is an IEEE 2668-compliant development for elevator diagnosis. Specifically, incorporating STPA into the AQRAS improves the key assessment feature identification compared to the original framework. The feature recognition in the original IDex framework lacks an instructive technique. To complete this, the STPA finds out the key features by analyzing the interactions among intra-system components, which makes it more exhaustive and methodical. Moreover, the complex network is utilized to investigate the relationships between the features and therefore identify the riskiest components that could be impacted by the features. This allows for the adaptive design of countermeasures to mitigate the risks. Leveraging both the experimental and simulation data, two case studies are presented to demonstrate the application of AQRAS in two different scenarios. © 2024 IEEE

Research Area(s)

  • Elevator Diagnosis, Adaptive Quantitative Risk Analysis, IEEE 2668, IDex, IoT

Bibliographic Note

Information for this record is supplemented by the author(s) concerned.