Pablo Viveros Gunckel

The Graphical Analysis for Maintenance Management Method: A Quantitative Graphical Analysis to Support Maintenance Management Decision Making

This article proposes a logical support tool for maintenance management decision making. This tool is called the Graphical Analysis for Maintenance Management (GAMM), a method to visualize and analyze equipment dependability data in a graphical form. The method helps for a quick and clear analysis and interpretation of equipment maintenance (corrective and preventive) and operational stoppages. Then, opportunities can be identified to improve both operations and maintenance management (short–medium term) and potential investments (medium–long term). The method allows an easy visualization of parameters, such as the number of corrective actions between preventive maintenance, the accumulation of failures in short periods of time, and the duration of maintenance activities and sequence of stops of short duration. In addition, this tool allows identifying, a priori, anomalous behavior of equipment, whether derived from its own function, maintenance activities, misuse, or even equipment designs errors. In this method, we used a nonparametric estimator of the reliability function as a basis for the analysis. This estimator takes into account equipment historical data (total or partial) and can provide valuable insights to the analyst even with few available data. Copyright

Graphical Analysis for Operation Management: A Graphical Method to Support Operation Decision Making

This paper proposes a graphical method to easy decision-making in industrial plants operations. The proposed tool ‘Graphical Analysis for Operation Management (GAOM) method’ allows to visualizing and analyzing production-related parameters, integrating assets/systems maintenance aspects. This integration is based on the Total Productive Maintenance model, using its quantitative management techniques for optimal decision-making in day-to-day operations. On the one hand, GAOM monitors possible production target deviations, and on the other, the tool illustrates different aspects to gain control on the production process, such as availability, repair time, cumulative production, or overall equipment effectiveness. Through appropriate information filtering, individual analysis by class of intervention (corrective maintenance, preventive maintenance, or operational intervention) and production level can be developed. Graphical Analysis for Operation Management (GAOM) integrates maintenance information (number of intervention, type of intervention, required/not required stoppage) with production information (cumulative production, cumulative defective products, and cumulative production target) during a certain timeframe (cumulative calendar time, duration of intervention). Then the tool computes basic performance indicators supporting operational decision-making. GAOM provides interesting graphical outputs using scatter diagrams integrating indicators on the same graph. GAOM is inspired in the Graphical Analysis for Maintenance Management method, published by the authors (LB, AC, and PV) in 2012. Copyright

Reliability Stochastic Modeling for Repairable Physical Assets

The reliability modeling, calculating, and projecting for industrial equipment and systems are today a basic and fundamental task for reliability and maintenance engineers, regardless of the nature or genetics of those industrial assets. In this paper, the stochastic models Perfect Renewal Process (PRP), Nonhomogeneous Processes of Poison (NHPP), and GRP are explained in detail with the corresponding conceptual, mathematical, and stochastic development. For each model, the respective conceptualization and parameterization is analyzed in detail. The practical application is developed for a real case in the mining industry, which shows step by step the appropriate stochastic and mathematical development. Finally, this research becomes an analytical and explanatory procedure on the definition, calculation, methodology, and criteria to be considered for industrial assets parameterization with partial or null post maintenance degradation.

Influence of the Input Load on the Reliability of the Grinding Line

The management of physical resources in an organization involves several processes related to innovation and continuous improvement. For this reason, the proper study of the reliability and maintainability analysis is considered essential and is treated as one of the main pillars for decision-making at the tactical and operational levels. This paper proposes a useful support tool for decision-making in the field of maintenance management and reliability analysis, so that such decisions remain aligned with the vision, strategy and economic indicators of the business or industrial organization. This research clearly shows how the variability of different load levels (inflows) on the grinding lines, affects the reliability of a specific sulphur plant (located in Chile), determining after that, what the optimum load should be. The paper identifies the relationship between each line load ranges and the corresponding reliability, all through the development of a real case study conducted in a mining company located in northern Chile.

Propuesta metodológica para aumentar el grado de comprensión de un sistema complejo mejorando el proceso de toma de decisión

This paper deals with the formalization of knowledge of an organization, the structuring of a suitable logic sequence and the processing to achieve the applicability of this knowledge in the practical field for the organization. All this is done through a methodological proposal that allows increasing the organizational knowledge, which is based on the information found in the organization’s computer systems as well as on the knowledge and experience of experts, generating significant synergies. The motivation to develop this paper comes from the need to align organizational goals with the knowledge of the people and data in information systems related to the field of asset management and maintenance. This methodological proposal uses a recursive process of knowledge generation, where the iteration of processes and the permanent consultation regarding compliance with the objectives, generate a cyclic process whose results are materialized in a conceptual model that contains qualitative and quantitative information, in order to increase the understanding of the system.This paper deals with the formalization of knowledge of an organization, the structuring of a suitable logic sequence and the processing to achieve the applicability of this knowledge in the practical field for the organization. All this is done through a methodological proposal that allows increasing the organizational knowledge, which is based on the information found in the organization’s computer systems as well as on the knowledge and experience of experts, generating significant synergies. The motivation to develop this paper comes from the need to align organizational goals with the knowledge of the people and data in information systems related to the field of asset management and maintenance. This methodological proposal uses a recursive process of knowledge generation, where the iteration of processes and the permanent consultation regarding compliance with the objectives, generate a cyclic process whose results are materialized in a conceptual model that contains qualitative and quantitative information, in order to increase the understanding of the system. Key Words: Conceptual Model, Transformation of Data into Information, Understanding of Complex Systems.

MODELADO ESTOCÁSTICO DE CONFIABILIDAD PARA ACTIVOS FÍSICOS REPARABLES. CASO DE ESTUDIO APLICADO A LA MINERÍA CHILENA

Modelar, calcular y proyectar la confiabilidad de equipos y sistemas industriales es una tarea basica y fundamental hoy en dia para los ingenieros de confiabilidad y mantenimiento, independiente de la naturaleza o genetica de estos activos industriales. En el presente articulo se explican en detalle los modelos estocasticos PRP, NHPP y GRP, con el desarrollo conceptual, matematico y estocastico segun corresponda. Para cada modelo se analiza en detalle la respectiva conceptualizacion y parametrizacion. La aplicacion practica se desarrolla para un caso real en la industria mineria que evidencia paso a paso el desarrollo matematico y estocastico segun corresponda. Esta investigacion finalmente se transforma en un procedimiento analitico y explicativo sobre la definicion, metodologia de calculo y criterios que deben ser considerados para parametrizar activos industriales con degradacion nula o parcial post mantenimiento. Palabras Clave: Confiabilidad, Degradacion, Activos Reparables, Simulacion.

PROPUESTA METODOLÓGICA PARA AUMENTAR EL APRENDIZAJE ORGANIZACIONAL EN BASE AL CONOCIMIENTO DE EXPERTOS Y A LOS SISTEMAS DE INFORMACIÓN EN LA GESTIÓN DE ACTIVOS Y MANTENIMIENTO

This paper deals with the formalization of knowledge of an organization, the structuring of a suitable logic sequence and the processing to achieve the applicability of this knowledge in the practical field for the organization. All this is done through a methodological proposal that allows increasing the organizational knowledge, which is based on the information found in the organization’s computer systems as well as on the knowledge and experience of experts, generating significant synergies. The motivation to develop this paper comes from the need to align organizational goals with the knowledge of the people and data in information systems related to the field of asset management and maintenance. This methodological proposal uses a recursive process of knowledge generation, where the iteration of processes and the permanent consultation regarding compliance with the objectives, generate a cyclic process whose results are materialized in a conceptual model that contains qualitative and quantitative information, in order to increase the understanding of the system.

INFLUENCIA DE LA CARGA DE ALIMENTACIÓN EN LA FIABILIDAD DE LÍNEAS DE MOLIENDA. CASO DE ESTUDIO

RESUMEN: La gestion de los recursos fisicos en una organizacion involucra diversos procesos relacionados con la innovacion y la mejora continua. Por este motivo, el estudio apropiado de la fiabilidad y el analisis de la mantenibilidad se considera fundamental y es tratado como uno de los pilares principales para la toma de decisiones tanto a nivel tactico como operacional. El presente articulo propone una herramienta de apoyo util para la toma de decisiones en el ambito de la direccion de mantenimiento y analisis de confiabilidad, de manera que dichas decisiones permanezcan alineadas con la vision, la estrategia y los indicadores economicos del negocio u organizacion industrial. Este articulo muestra claramente como afecta a la confiabilidad de una planta de sulfuro (ubicada en Chile) la variabilidad de diferentes niveles de carga (flujos de entrada) en las lineas de molienda de dicha planta y, posteriormente, determina cual seria la carga optima. El articulo identifica la relacion existente entre los rangos de cargas de cada linea y la respectiva confiabilidad de las mismas con el desarrollo de un caso practico real llevado a cabo en una empresa minera ubicada en el norte de Chile. Palabras Clave: Confiabilidad por rango de carga, linea de molienda, gestion y optimizacion del mantenimiento, eficiencia en mantenimiento.

A Quantitative Graphical Analysis to Support Maintenance

This paper proposes a logical support tool for maintenance management decision-making. This tool is called GAMM (Graphical Analysis for Maintenance Management) and it is a method to visualize and analyze equipment dependability data in a graphical form. The method helps for a quick and clear analysis and interpretation of equipment maintenance (corrective and preventive) and operational stoppages. Then, opportunities can be identified to improve both operations and maintenance management (short-medium term) and potential investments (medium-long term). The method allows an easy visualization of parameters like: number of corrective actions between preventive maintenance, accumulation of failures in short periods of time, duration of maintenance activities and sequence of stops of short duration. In addition, this tool allows identifying, a priori, anomalous behavior of equipment, whether derived from its own functioning, maintenance activities, from misuse, or even as a result of equipment designs errors. In this method we use a nonparametric estimator of the reliability function as a basis for the analysis. This estimator takes into account equipment historical data (total or partial) and can provide valuable insights to the analyst even with few available data.

A Graphical Method to Support Operation Performance Assessment

This paper proposes a graphical method to easy decision-making in industrial plants operations. The proposed tool “Graphical Analysis for Operation Management Method” (GAOM) allows to visualize, and to analyze, production related parameters, integrating assets/systems maintenance aspects. This integration is based on the TPM model, using its quantitative management techniques for optimal decision-making in day-to-day operations. On the one hand, GAOM monitors possible production target deviations, and on the other, the tool illustrates different aspects to gain control on the production process, such as availability (A), repair time, cumulative production or overall equipment effectiveness. Through appropriate information filtering, individual analysis by class of intervention (corrective maintenance, preventive maintenance or operational intervention) and production level can be developed. GAOM integrates maintenance information (number of intervention, type of intervention, required/not required stoppage) with production information (cumulative production, cumulative defective products, and cumulative production target) during a certain timeframe (cumulative calendar time, duration of intervention). Then the tool computes basic performance indicators supporting operational decision-making. GAOM provides interesting graphical outputs using scatter diagrams integrating indicators on the same graph. GAOM is inspired in the GAMM (Graphical Analysis for Maintenance Management) method, published by the authors (LB, AC and PV) in 2012.