While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance…
Abstract
Purpose
While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Traditional preventive/planned maintenance is usually scheduled at a fixed interval based on maintenance personnel's experience and it can result in decreasing reliability. This paper deals with reliability analysis and prediction for mining machinery. A software tool called GenRel is discussed with its theoretical background, applied algorithms and its current improvements. In GenRel, it is assumed that failures of mining equipment caused by an array of factors (e.g. age of equipment, operating environment) follow the biological evolution theory. GenRel then simulates the failure occurrences during a time period of interest based on Genetic Algorithms (GAs) combined with a number of statistical procedures. The paper also discusses a case study of two mine hoists. The purpose of this paper is to investigate whether or not GenRel can be applied for reliability analysis of mine hoists in real life.
Design/methodology/approach
Statistical testing methods are applied to examine the similarity between the predicted data set with the real-life data set in the same time period. The data employed in this case study is compiled from two mine hoists from the Sudbury area in Ontario, Canada. Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.
Findings
The case studies shown in this paper demonstrate successful applications of a GAs-based software, GenRel, to analyze and predict dynamic reliability characteristics of two hoist systems. Two separate case studies in Mine A and Mine B at a time interval of three months both present acceptable prediction results at a given level of confidence, 5 percent.
Practical implications
Potential applications of the reliability assessment results yielded from GenRel include reliability-centered maintenance planning and production simulation.
Originality/value
Compared to conventional mathematical models, GAs offer several key advantages. To the best of the authors’ knowledge, there has not been a wide application of GAs in hoist reliability assessment and prediction. In addition, the authors bring discrete distribution functions to the software tool (GenRel) for the first time and significantly improve computing efficiency. The results of the case studies demonstrate successful application of GenRel in assessing and predicting hoist reliability, and this may lead to better preventative maintenance management in the industry.
Details
Keywords
The purpose of this paper is to formulate, develop and test a reliability assessment model (GenRel) based on genetic algorithms.
Abstract
Purpose
The purpose of this paper is to formulate, develop and test a reliability assessment model (GenRel) based on genetic algorithms.
Design/methodology/approach
Using genetic algorithm based modelling technique, a computer model was developed to predict mine equipment failures from historical data. Two different approaches in application of this technique are demonstrated.
Findings
A case study representing a test for convergence of the model was successfully performed. This is an indicator that GenRel can be used to predict equipment failures using a genetic algorithm based modeling technique.
Practical implications
The use of classical statistical techniques has proven to be an effective tool for reliability analysis of mining equipment. This paper presents an efficient alternative to these classical probability based reliability analysis methods. GenRel is a software solution which performs predictive reliability based upon genetic algorithms (GAs). The advantage of using this technique is the fact that the assumptions based on GAs are much simpler compared to classical statistical methods. The computer model is developed to accept a variety of user input data, most importantly, the ability to use real life historical data in the form of Time Between Failures (TBFs) or Time To Repair (TTRs).
Originality/value
The proposed research offers an alternative method to conventional statistically based reliability analysis and may lead to the foundation of a new approach for reliability assessment with potential applications in other industrial fields as well.
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Social movement organizations are concerned and cognizant of their public image and typically need to maintain positive public perception to gain and sustain support. White…
Abstract
Social movement organizations are concerned and cognizant of their public image and typically need to maintain positive public perception to gain and sustain support. White supremacist organizations believe that they are highly stigmatized, reviled, and surveilled groups and go to great lengths to protect their desired self-representation. Through a qualitative analysis of close to 2 million Discord chat messages from white supremacist organizations, I find that white nationalist groups attempt to cater their public appearances through three primary axes: organizational, activism, and individual/membership. This chapter uses concepts from Goffmanian sociology, such as Stigma, Impression Management, and Frontstage/Backstage, to highlight how political movements discuss, argue, and debate the public image they wish to deploy. Studies on right-wing movements tend to be “externalist” in the sense that they look at publicly available documents which privilege the views of leadership. This chapter uses a dataset which delves into the social movement “backstage,” enabling us to view white supremacists' private conversations, their impression management strategies, and how they wish to appear on the “frontstage.”
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Yue Wang and Sai Ho Chung
This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application…
Abstract
Purpose
This study is a systematic literature review of the application of artificial intelligence (AI) in safety-critical systems. The authors aim to present the current application status according to different AI techniques and propose some research directions and insights to promote its wider application.
Design/methodology/approach
A total of 92 articles were selected for this review through a systematic literature review along with a thematic analysis.
Findings
The literature is divided into three themes: interpretable method, explain model behavior and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed.
Practical implications
This study is of vital interest to industry practitioners and regulators in safety-critical domain, as it provided a clear picture of the current status and pointed out that some AI techniques have great application potential. For those that are inherently appropriate for use in safety-critical systems, regulators can conduct in-depth studies to validate and encourage their use in the industry.
Originality/value
This is the first review of the application of AI in safety-critical systems in the literature. It marks the first step toward advancing AI in safety-critical domain. The paper has potential values to promote the use of the term “safety-critical” and to improve the phenomenon of literature fragmentation.
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Marissa Bird, James Shaw, Christopher D. Brinton, Vanessa Wright and Carolyn Steele Gray
A synthesis of integrated care models classified by their aims and central characteristics does not yet exist. We present a collection of five “archetypes” of integrated care…
Abstract
Purpose
A synthesis of integrated care models classified by their aims and central characteristics does not yet exist. We present a collection of five “archetypes” of integrated care, defined by their aims, to facilitate model comparison and dialogue.
Design/methodology/approach
We used a purposive literature search and expert consultation strategy to generate five archetypes. Data were extracted from included articles to describe the characteristics and defining features of integrated care models.
Findings
A total of 25 examples of integrated care models (41 papers) were included to generate five archetypes of integrated care. The five archetypes defined include: (1) whole population models, (2) life stage models, (3) disease-focused models, (4) identity group-based models and (5) equity-focused models.
Research limitations/implications
The five presented archetypes offer a conceptual framework for academics, health system decision makers and patients, families, and communities seeking to develop, adapt, investigate or evaluate models of integrated care.
Originality/value
Two cross-cutting themes were identified, including (1) minimal reporting of patient, caregiver and community engagement efforts in integrated care development, implementation and evaluation, and (2) the nuanced emphasis and implementation of electronic data sharing methods across archetypes, and the need for further definition of the role of these data sharing methods.