INTEGRASI DAN PENGEMBANGAN SISTEM MACHINE LEARNING PADA KEGIATAN MAINTENANCE UNIT BGMF PT. FI

The Big Gossan Mill Facility (BGMF) unit has a vital function to deliver tailing supporting Big Gossan underground mining. Plant maintenance strategies have been implemented to support its availability. This study aims to study integration of machine learning model into the plant maintenance and to formulate development of Machine Learning System in BGMF unit. The maintenance planning standards is used to integrate Machine Learning model through interview. The Industrial Internet Reference Architecture (IIRA) is applied to develop machine learning system. It uses interview method to formulate business viewpoint and usage viewpoint and observation to elaborate functional viewpoint and implementation viewpoint. The study results integration of machine learning model is done by state it as PD-200 Propelling Liquid alarm. It then should be followed up by the planning crew. The machine learning system development starts with formulation of Key Objectives and Fundamental Capabilities on the business viewpoint. The usage viewpoint defines two scenarios on machine learning system. The functional viewpoint elaborates system functionality. The implementation viewpoint designed network topology. It then emphasizes on key system characteristics. This research concludes that model integration into plant maintenance can minimize PD-200’s downtime and it’s system design can be done by IIRA. Keywords: IIRA, machine learning, maintenance improvement, predictive maintenance, predictive mode

Description of rail track geometry deterioration process in Hungarian rail lines No. 1 and No. 140

The aim was the perfection of an analytic examination, which describes the track deterioration process, characterized the correspondences more precisely and better to use in practice. This method was based on the destruction’s theory of the railway track geometry and it exploited the possibilities of recent computer technology. More than one million measuring car (FMK-004) data were processed than analyzed and defined by configuring and programming a new method. The results of this method were descriptive functions, which afford interpretable information about the geometrically destruction’s occurrences of the different railway lines

Data-driven predictive maintenance scheduling policies for railways

Inspection and maintenance activities are essential to preserving safety and cost-effectiveness in railways. However, the stochastic nature of railway defect occurrence is usually ignored in literature; instead, defect stochasticity is considered independently of maintenance scheduling. This study presents a new approach to predict rail and geometry defects that relies on easy-to-obtain data and integrates prediction with inspection and maintenance scheduling activities. In the proposed approach, a novel use of risk-averse and hybrid prediction methodology controls the underestimation of defects. Then, a discounted Markov decision process model utilizes these predictions to determine optimal inspection and maintenance scheduling policies. Furthermore, in the presence of capacity constraints, Whittle indices via the multi-armed restless bandit formulation dynamically provide the optimal policies using the updated transition kernels. Results indicate a high accuracy rate in prediction and effective long-term scheduling policies that are adaptable to changing conditions

Model for predictive maintenance of railway switches and crossings

En las últimas décadas, se ha producido un crecimiento exponencial en la generación, recopilación y almacenamiento de datos impulsado por el avance de la tecnología y la digitalización en diversos ámbitos. La invención de dispositivos móviles inteligentes, el avance de la instrumentación electrónica, el desarrollo de la tecnología IoT, todo ello sumado a la evolución de la capacidad de almacenamiento y de procesamiento computacional, han sido algunos factores destacados en el crecimiento vertiginoso de los datos. La importancia de la evolución de los datos radica en su capacidad para proporcionar información y conocimientos que antes eran inimaginables. Los datos se han convertido en un activo invaluable para las organizaciones, permitiéndoles comprender mejor a sus clientes, identificar tendencias, detectar patrones, automatizar procesos y optimizar sus operaciones. La necesidad de gestionar y obtener valor de esta generación masiva de datos ha impulsado el desarrollo de técnicas de análisis de datos avanzadas, como el aprendizaje automático (Machine Learning (ML)), cuya función principal es extraer información valiosa de los datos con el objetivo de generar nuevos conocimientos que permitan la innovación, ayudar en la toma de decisiones, mejorar la eficiencia y generar nuevos servicios y modelos de negocio. A su vez, como consecuencia del aumento de la preocupación global por la emisión de gases contaminantes y el cambio climático, el sistema ferroviario ha adquirido en los últimos años mayor importancia debido a su eficiencia energética, bajas emisiones, menor impacto ambiental y uso eficiente del espacio. Este auge de la importancia del ferrocarril ha provocado que se adopten estrategias enfocadas a realizar una mayor inversión, destinada a la innovación y mejora de las infraestructuras ferroviarias. Este proyecto pretende poner en valor y aprovechar la evolución de las técnicas de análisis de datos para tratar de aportar una mejora en el plan de mantenimiento de un sistema tan importante como el sistema ferroviario. Para ello, se desarrollarán técnicas de Machine Learning basadas en datos como el Análisis de Componentes Principales (PCA), capaz de detectar patrones estadísticos o tendencias entre los conjuntos de datos. De esta manera, se pretende implementar un algoritmo que integre funciones de diagnóstico, detección y predicción de fallos con el fin de evolucionar el plan de mantenimiento de los aparatos de vía hacia una estrategia de mantenimiento predictiva que tenga un impacto notable en la seguridad y eficiencia del transporte ferroviario, así como en la reducción de los costes de operación.In recent decades, there has been an exponential growth in data generation, collection and storage driven by the advancement of technology and digitalization in various fields. The invention of smart mobile devices, the advancement of electronic instrumentation, the development of IoT technology, all this combined with the evolution of storage and computational processing capacity, have been some prominent factors in the dizzying growth of data. The importance of the evolution of data is based on in its ability to provide information and insights that were previously unimaginable. Data has become an invaluable asset for organizations, enabling them to a better comprehension of their customers, identifying trends, detecting patterns, automating processes and optimizing their operations. The need to manage and obtain value from this massive generation of data has driven the development of advanced data analysis techniques, such as Machine Learning (ML), whose main function is to extract valuable information from data in order to generate new knowledge that will enable innovation, decision-making assistance, improve efficiency and generate new services and business models. In turn, as a result of the increasing global concern about the emission of polluting gases and climate change, the railway system has become more important in recent years due to its energy efficiency, low emissions, lower environmental impact and efficient use of space. This rise in the importance of railroads has led to the adoption of strategies focused on greater investment, aimed at innovation and improvement of railway infrastructures. This project aims to value and take advantage of the evolution of data analysis techniques to try to bring an improvement in the maintenance plan of a system as important as the railway system. For this purpose, Machine Learning techniques based on data such as Principal Component Analysis (PCA) will be developed, capable of detecting statistical patterns or trends among data sets. In this way, it is intended to implement an algorithm that integrates diagnostic functions, detection and prediction of failures in order to evolve the maintenance plan of track devices towards a predictive maintenance strategy that has a significant impact on the safety and efficiency of rail transport, as well as on the reduction of operating costs.Máster en Ingeniería Industria

An ensemble classifier to predict track geometry degradation

Railway operations are inherently complex and source of several problems. In particular, track geometry defects are one of the leading causes of train accidents in the United States. This paper presents a solution approach which entails the construction of an ensemble classifier to forecast the degradation of track geometry. Our classifier is constructed by solving the problem from three different perspectives: deterioration, regression and classification. We considered a different model from each perspective and our results show that using an ensemble method improves the predictive performance