Abstract

Traditional techniques for accident investigation have hindsight biases. Specifically, they isolate the process of the accident event and trace backward from the event to determine the factors leading to the accident. Nonetheless, the importance of the contributing factors towards a successful operation is not considered in conventional accident modeling. The Safety-II approach promotes an examination of successful operations as well as failures. The rationale is that there is an opportunity to learn from successful operations, in addition to failure, and there is an opportunity to further differentiate failure processes from successful operations. The functional resonance analysis method (FRAM) has the capacity to monitor the functionality and performance of a complex socio-technical system. The method can model many possible ways a system could function, then captures the specifics of the functionality of individual operational events in functional signatures. However, the method does not support quantitative analysis of the functional signatures, which may demonstrate similarities as well as differences among each other. This paper proposes a method to detect anomalies in operations using functional signatures. The present work proposes how FRAM data models can be converted to graphs and how such graphs can be used to estimate anomalies in the data. The proposed approach is applied to human performance data obtained from ice-management tasks performed by a cohort of cadets and experienced seafarers in a ship simulator. The results show that functional differences can be captured by the proposed approach even though the differences were undetected by usual statistical measures.

Highlights

  • Human involvement is crucial to the success of many industrial operations

  • The purpose of the present work is to exploit similarity-based clustering to detect anomalies in human performance data represented in the form of functional resonance analysis method (FRAM) instantiations

  • The methodology developed here shows the potential to detect anomalies in human performance data presented in the form of FRAM instantiations

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Summary

Introduction

Human involvement is crucial to the success of many industrial operations. Many modern industrial operations can be characterized as (complex) socio-technical systems, operations where humans work synergistically with technologies to achieve their goals. Things are usually further complicated by under-specification of work [1], which leads to local adjustments that must be made to accommodate changing and unexpected work conditions. By applying these ideas to industrial workplaces, there is a complexity that makes an assignment of cause and/or blame for workplace accidents seem simplistic or imprecise. Contributing factors are often assigned without the study of their significance to successful operations This is a distinct difference from how the significance of factors is determined in other scientific domains. The matrix S2 keeps the pairwise node affinities for the graph G2. The DeltaCon algorithm [37] calculates the distance between the two graphs G1 and G2 , keeping in view the affinity scores of each graph by using the Matusita distance, d, as shown in Equation (1)

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