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This study presents reliability and availability analysis of mining systems with hardware and human-error failures. The failures of proposed models representing the mining systems could be safe or unsafe. To understand model reliability and availability behaviours, different values of failure and repair rates were used at different number of parallel units and different repair policies. Also, a model with partial and complete failures was analysed beside two miscellaneous models representing systems used in mining sector. The failure and repair rates are assumed constant. Markov method was used to perform analysis of the models and Laplace transforms were used to solve associated equations.

This main objectives of this study were to examine human aspects of machining and to obtain an understanding of the issues within the broad context of manufacturing. Emphasis was placed on operator mismatches and the relationships of these to basic human characteristics and the preferred levels of automation from the operators' perspective with regard to turning operations.

Human error taxonomies have been implemented in numerous safety critical industries. These taxonomies have provided invaluable insight into understanding the underlying causes of human error; however, their utility for actually predicting future errors remains in question. A need has been identified for another approach to supplement what we can extrapolate from taxonomies and better predict human error. Task network modeling is a promising approach to human error prediction that had yet to be empirically evaluated. This study tested a task network modeling approach to predicting human error in the context of automotive assembly. The task network modeling architecture was expanded to include a set of predictors from the human error literature, and used to model part of an operational automotive assembly plant. This manuscript contains three studies. Study 1 tested separate task network models for two different target areas of an active automotive assembly line. Study 2 tested the validity of predictions made by the models from Study 1, both within and across samples. Study 3 tested predictions across both models on a larger sample of vehicles. The expanded architecture accounted for 21.9% to 36.5% of the variance in human error and identified 12 explanatory variables that significantly predicted the occurrence of human error. Model outputs were used to compute prediction equations that were tested using binary logistic regression and then cross-validated twice using both split-half and cross-sample validation. The predictors of Time Pressure, Visual Workload, Auditory Workload, Cognitive Workload, Psychomotor Workload, Task Frequency, Information Flow, Teamwork, and Equipment Feedback were significant predictors of human error in all three models that were tested. The variables of Information Presentation and Task Dependency varied in significance across samples, but both were significant in two out of the three models. The variables of Shift and Hour into Shift were never significant in any of the three models. The variables that were greatly stable across studies were all related to the tasks being performed by each worker at each station. The variables related to the timing of errors, on the other hand, were never significant. The results indicate that an expanded task network architecture is a great tool for predicting the situations and circumstances in which human errors will occur, but not the timing of when they will occur. Nevertheless, task network modeling demonstrated to provide useful, valid, and accurate predictions of human error and should continue to be developed as an error prediction tool.

Denna studie genomfördes i syfte att kartlägga och analysera mönster mellan olyckor sominvolverar svenska lotsbåtar. Studien utfördes som en kvantitativ litteraturstudie medkompletterande textanalys. Samtliga rapporter från lotsbåtsolyckor från de senaste 14 årenbegärdes ut från Transportstyrelsen. Dessa kategoriserades efter de vanligaste olyckstypernaoch analyserades därefter. Resultatet visade att flest olyckor sker i samband med transportenfrån kaj till fartyget som ska lotsas och vice versa, medan haverier och grundstötning är devanligaste incidenterna. Personskador sker näst intill uteslutande vid bordningsmomentet menär förhållandevis få. Det kunde fastslås att ”Human Error” är den vanligaste angivna orsaken iolycksrapporterna till att olyckor sker. Ett förslag till ökad säkerhet är fortsatt utbildning av”Maritime Resource Management” för en större medvetenhet om risken för olyckor,framförallt i samband med dåligt väder. Vidare finns tekniska problem med sprucknabränsleledningar och heta avgasrör, vilket möjligtvis kan lösas med dubbelmantladebränsleledningar. / This study was conducted in order to identify and analyze patterns between accidents involvingSwedish pilot boats. The study was conducted as a quantitative literature review withadditional textual analysis. All reports regarding pilot boat accidents from the last 14 yearswere requested from the Swedish Transport Agency. These were categorized by the mostcommon types of accident and subsequently analyzed. The results showed that most accidentsoccur during transportation from quay to the ship requesting pilotage and vice versa, whilebreak downs and groundings are the most common incidents. Personal injuries occur almostexclusively during boarding but are relatively few in number. It was concluded that "HumanError" is the most common reason given in the accident reports. A proposal for increasedsafety is continued training of "Maritime Resource Management" for a greater awareness ofthe risk of accidents, especially during bad weather. Furthermore, there are technical problemswith cracked fuel lines and hot exhaust pipes, which can possibly be solved with double-walledfuel lines.

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This study has been concerned with investigating several aspects of error, including human error, and the underlying causes in construction.It has attempted to calculate the realistic error rate and as a result has identified the optimum inspection rate in construction.Error prediction in construction is a new field of study, particularly with regard to repetitive construction processes.Several methodologies and probalistic approaches have been considered.Case studies have been presented and the findings shown. The results from several publications suggest that the learning rate varies with experience and with the skill of the worker, task complexity, environmental factors and interruptions due to inclement weather, inspection delays, and equipment breakdowns.A learning theory using a Straight-Line Power model was used to predict future performances,and the descrete event simulation model using 'iThink' simulation software,in conjunction with CPM, was developed in this study to calculate project durations.A simulation model was developed using the Event Tree Analysis (ETA) to calculate a more realistic error rate for the repetitive tasks. / Doctor of Philosophy (PhD)

The 'defects problem' has demanded considerable attention in recent years, with much emphasis given to the technical causes of failure. This research project examines the problem from a different point of view - that of human error. Taking as a starting point, technical publications in the construction industry, the research reviews human error literature from a variety of industries and perspectives and synthesises a model of error causation covering organisations in a construction project context. This model is then progressively tested in four studies, a general preliminary survey and three more detailed studies of house-building. Conclusions support the view that errors leading to failure in complex socio-technical systems often exhibit systems characteristics and involve the whole managerial structure. An improved model is proposed, which emphasises the importance of both project and general management errors.