Dragan Komljenovic

Risk assessment for loader- and dozer-related fatal incidents in U.S. mining

The paper presents the results of research aimed at developing a risk assessment process that can be used to more thoroughly characterise risks associated with loader- and dozer-related fatal incidents in US mining. The assessment is based on historical data obtained from the US Mine Safety and Health Administration investigation reports, which includes 77 fatal incidents that occurred from 1995 to 2006. The Preliminary Hazard Assessment method is used in identifying and quantifying risks. Risk levels are then developed using a pre-established risk matrix that ranks them according to probability and severity. The resulting assigned risk value can then be used to prioritise risk control strategies. A total of 10 hazards were identified for loaders. The hazards ‘failure to follow adequate maintenance procedure’ and ‘failure of mechanical/electrical/hydraulic components’ were the most severe and frequent hazards and they fell into the category of ‘high’ risk. The same number of hazards was identified for dozers. The hazard ‘failure to identify adverse site/geological conditions’ was the most severe and frequent hazard and it fell into the category of ‘high’ risk.

Multi-attribute selection method for materials handling equipment

Rail-mounted boom type bucket wheel reclaimers and stacker-reclaimers are primary means of materials handling at stockyards. Therefore, the selection of the best possible equipment type (model) is of crucial importance for the decision-makers. The authors contribute to the body of knowledge by developing a novel methodology for the selection of this equipment type. Both the Coefficient of Technical Level (CTL) and Analytic Hierarchy Process (AHP) methods were used in the selection process. This methodology may be used by material handling system decision-makers to help in the selection of a specific type when acquiring a new equipment.

Development of risk-informed, performance-based asset management in mining

This paper discusses possibilities of developing a holistic risk-informed, performance-based asset management in mining (RIPBAMM). This process would consist of modelling and probabilistic quantification regarding decision support performance indicators. It assists decision-makers in determining not only which mine improvement investment options should be implemented, but also how to prioritize resources for their implementation based on their predicted levels of profitability. The RIPBAMM approach will complement and integrate existing main mine activities such as exploration, ore body modelling, mine design, planning and scheduling, exploitation (all the phases of the mine life), mineral treatment, cost and market model, operational safety and health, environmental issues, mining equipment reliability and maintenance process, equipment selection model, security, etc. RIPBAMM will involve an integrated assessment of dominant influence factors and performance measures related to mining operations. This process is intended to maximize both net present value (NPV) of the mine, and long-term profitability through a continuous support to a decision-making process. It may be particularly useful while optimizing several mine sites belonging to the same mining company. Initial risk informed asset management (RIAM) applications have been initially developed for the nuclear power industry. Afterwards, this process has been adapted to provide decision-making support to other types of power stations, complex facilities (usually capital-intensive), or even groups of such facilities across a wide variety of industries. RIPBAMM is introducing numerous (stochastic) models and supporting performance metrics that can ultimately be employed in order to support decisions that affect the allocation and management of mine resources (i.e. financial support, employment, scheduling, etc.).

Risk management programme for occupational safety and health in surface mining operations

This paper describes a systematic risk analysis process for occupational safety and health (OSH). A concept of technological risk management and risk assessment is applied. A review of published risk management and assessment applications for various industries is presented and a generalised approach to risk management for OSH in surface mining is proposed. The approach consists of six phases. This process can be used to help judge the tolerability of risk and aid in choosing between potential risk reduction and/or risk avoidance measures. The paper also underlines the benefits of applying risk management concepts from the decision makers perspective.

A hydrogenerator model-based failure detection framework to support asset management

Electrical utilities in North America significantly increased their installed capacities between 1960 and 1990. This ageing fleet is now forcing the producers to begin to use a holistic asset management in a more systematic way by introducing diagnostic and prognostic tools to support them in their decision-making process. For the last few decades, the Hydro-Quebec Research Institute has been working to understand ageing mechanisms and developing a diagnostic and prognostic causal graph model for hydrogenerators based on expert knowledge and diagnostic data. This paper proposes asset and asset system metrics based on graph theory to estimate the probability of detecting a failure using the number of detectable early warning signs. Proposed indicators intend to inform operators and decision makers on the failure detection probability for each individual asset and to identify critical failure detection of assets at an asset system level. An analysis has been carried out on a real hydropower plant for each of its sixteen hydrogenerators. Some results will be presented and critical failure detection rates for hydrogenerators will be identified. A framework will be proposed to improve asset management.

A holistic multi-failure mode prognosis approach for complex equipment

Abstract The aim of this paper is to propose a holistic multi-failure mode prognosis approach that takes into account the complexity of failure mechanisms as a system. Model assumptions are first proposed by experts and then formalized using graph theory and stochastic models. The prognosis approach relies on a diagnostic algorithm that combines diagnostic information from different sources (e.g., measurements and inspections) to detect active failure mechanisms and track their progression, and a prognostic algorithm that predicts failure mode occurrences dynamically as new information becomes available. Furthermore, the approach identifies undetectable failure mechanisms where no symptoms have yet been measured. The relative simplicity of the algorithms and graphical representation of the results helps to build decision-makers’ trust. In addition, the approach is a means of capturing acquired knowledge and available data. A case study of a hydroelectric generator stator is proposed. The resulting multi-state degradation model identified more than 150 failure mechanisms discretized in 70 physical states and leading to three failure modes. Three historical failure and one online case studies are presented, based on diagnostic data from Hydro-Quebecs generating fleet. In two of the case studies, the failure mode occurrence could have been predicted more than eight years in advance.