Process: program for research on operator control in an experimental simulated setting

An experimental tool for the investigation of human control behavior of slowly responding dynamic systems is described. Process (Program for Research on Operator Control in an Experimental Simulated Setting) is a simulation of a dynamic water-alcohol distillation system that is especially useful in research on operator training. In particular, Process was developed to conduct research on fault management skill

A system performance throughput model applicable to advanced manned telescience systems

As automated space systems become more complex, autonomous, and opaque to the flight crew, it becomes increasingly difficult to determine whether the total system is performing as it should. Some of the complex and interrelated human performance measurement issues are addressed that are related to total system validation. An evaluative throughput model is presented which can be used to generate a human operator-related benchmark or figure of merit for a given system which involves humans at the input and output ends as well as other automated intelligent agents. The concept of sustained and accurate command/control data information transfer is introduced. The first two input parameters of the model involve nominal and off-nominal predicted events. The first of these calls for a detailed task analysis while the second is for a contingency event assessment. The last two required input parameters involving actual (measured) events, namely human performance and continuous semi-automated system performance. An expression combining these four parameters was found using digital simulations and identical, representative, random data to yield the smallest variance

Pilot interaction with automated airborne decision making systems

The use of advanced software engineering methods (e.g., from artificial intelligence) to aid aircraft crews in procedure selection and execution is investigated. Human problem solving in dynamic environments as effected by the human's level of knowledge of system operations is examined. Progress on the development of full scale simulation facilities is also discussed

A Bayesian-Influence Model for Error Probability Analysis of Combine Operations in Harvesting

Harvesting is one of the most important agricultural operations because it captures the value from the entire cropping season. In modern agriculture, grain harvesting has been mechanized through the combine harvester. A combine harvester enables highly productive crop harvesting. Combine harvesting performance depends on the highly variable skill of combine operators and associated operator error. An approach was developed to analyze the risk of the combine harvesting operation as it relates to operator error. Specifically, a risk analysis model was built based on a task analysis from operator interviews and estimates of the probability of operator error. This paper employs a Bayesian approach to assess risks in combine operation. This approach applies a Bayesian Belief Network to agriculture operations, which represents a new application for this risk analysis tool. Sensitivity analysis of different errors and operator skill levels was also performed. The preliminary results indicate that a reduction of human operator action errors can substantially improve the outcomes of the human-machine interaction

Methodical support for investigation of system behaviour by means of analysis techniques - overcoming non-transparency in embodiment design

One challenge in adaptive design of technical systems is insufficient understanding of the mechanical system behavior. The actual system behaviour often differs from the system behaviour expected by the designer. This is due, for example, to influences from manufacturing, wear, or errors in the designer’s understanding. For the analysis of the differences between expected and actual system behavior, the system behaviour can be observed. Special analysis techniques are often necessary for system observation. However, the missing methodical support in the system-specific use of analysis techniques is a challenge. In this contribution, a methodical support for the selection and adaptation of analysis techniques for system observation is developed. For this, known errors that occur during system observation are operationalised and provided as requirements for evaluation of analysis techniques. The support is provided as a Selection Matrix, in which the evaluated analysis techniques can be selected and adapted. This is evaluated considering an accompanying application to the non-transparent system wood screw connection. By using the analysis techniques selected with the method, it was possible to identify the actual system behaviour and gain new insights. Here, the Selection Matrix provided support through a structured evaluation of analysis techniques. The Selection Matrix also supported the adaptation of analysis techniques for improved observation of the system behaviour. No general statements on the quality of the support by the Selection Matrix are yet possible. Also, the operationalisation of the errors should be improved to reduce subjective influences. Therefore, these topics should be investigated in further studies

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 255)

This bibliography lists 278 reports, articles and other documents introduced into the NASA scientific and technical information system in January 1984

Which factors and situations for human functional failures? Developing grids for accident causation analysis

This report describes the work undertaken in Task 5.2 of the TRACE project. Human failures are explained by factors characterizing the state of the system and of their interactions. A grid of factors which could lead to these human functional failures is given along with a grid of pre-accident driving situations. In addition to this, an overview is included of the background work undertaken to establish a methodology for classification of these factors and situations. Factors related to the ‘User’, ‘Vehicle’ and ‘Environment’ are described and classifications for use at a ‘descriptive’, ‘generic’ and ‘in-depth’ level are determined, to allow analysis at different levels of detail of accident data. These factors and situations will be used along with the Task 5.1 functional failures to help identify typical failure generating scenarios in Task 5.3, and the subsequent analysis of real world accident data in other work packages in TRACE. They will also be a useful basis for future improvements in the collection of accident causation data, avoiding the common over simplification whereby road users are seen as the main reason for the ‘failure’ in the accident scenario

Classification and reduction of pilot error

Human error is a primary or contributing factor in about two-thirds of commercial aviation accidents worldwide. With the ultimate goal of reducing pilot error accidents, this contract effort is aimed at understanding the factors underlying error events and reducing the probability of certain types of errors by modifying underlying factors such as flight deck design and procedures. A review of the literature relevant to error classification was conducted. Classification includes categorizing types of errors, the information processing mechanisms and factors underlying them, and identifying factor-mechanism-error relationships. The classification scheme developed by Jens Rasmussen was adopted because it provided a comprehensive yet basic error classification shell or structure that could easily accommodate addition of details on domain-specific factors. For these purposes, factors specific to the aviation environment were incorporated. Hypotheses concerning the relationship of a small number of underlying factors, information processing mechanisms, and error types types identified in the classification scheme were formulated. ASRS data were reviewed and a simulation experiment was performed to evaluate and quantify the hypotheses

When the Machine Stops: The Impact of Information Technology Failure on Firm Value

Previous research on information technology (IT) failures has predominantly focused on determining the impact of security breaches on firm value. However, little is known about IT failures that emerge accidentally and are unrelated to security incidents. This study approaches this notable research gap by applying the resource weakness framework and utilizing event study methodology. Based on a sample of 571 failure events from publicly-traded European firms, we find that non-security-related IT failures result on average in a 0.32% decline in firm value over a two-day event window. Interestingly, this decline is diminishing in more recent years. Moreover, the loss in firm value is particularly pronounced if the failure is caused by a software error (1.08%). In sum, our findings suggest that the often-neglected resource weakness perspective – that complements resource strengths within resource-based view – has strong explanatory power regarding the contingency factors of IT failure events