Usulan Preventive Maintenance Unit Flat Bed Trailer 72 (FBT 72) dengan Metode Failure Mode And Effects Analysis (FMEA) di PT. U

PT U merupakan industri rekayasa alat berat untuk mendukung pertambangan maupun logistik. Produk yang dihasilkan bersifat customized disesuaikan dengan kebutuhan pelanggan. Setiap produk yang diproduksi dan dikirim pelanggan harus dilengkapi skema perawatan berkala (periodical services) dan kebutuhan suku cadang yang dibutuhkan. Dengan metode produksi bersifat customized setiap unit yang dihasilkan memiliki spesifikasi dasar berbeda menyesuaikan permintaan pelanggan. Metode produksi customized memunculkan varian produk baru dengan salah satu contoh produk Flat Bed Trailer 72 (FBT 72) yang belum memiliki skema perawatan berkala. Penelitian ini bertujuan untuk merancang skema perawatan unit FBT 72. Perancangan skema perawatan berkala pada varian produk baru untuk unit FBT 72 melalui analisis FMEA yang digunakan untuk menentukan komponen kritis. Komponen kritis dianalisis menggunakan data kerusakan unit sejenis dalam jangkauan waktu 5 tahun, didapatkan 124 kerusakan pada 8 komponen standar. Adapun 6 komponen kritis tersebut meliputi Coupling, Axle, Brake System, Body & Stucture, Suspension dan wheel. Berdasarkan analisis yang dilakukan didapatkan rancangan skema perawatan berkala untuk unit FBT 72 terdiri dari Format Commissioning, Standard Maintenance (Periodical Service), Format Check Sheet Maintenance & List Item Backup

Dynamic Information System for Failure Analysis with It’s Application on Ship Main Engine

Ships are often used to move cargo, and their main engines are crucial. Accidents and financial losses might result from the main engine being in poor condition. Before completing maintenance, conducting a failure analysis is necessary. The existing method is static and involves using a list of failure modes from the engine's manufacturing phase. This study proposes a preliminary design of dynamic system prototype that seeks to improve ship engine monitoring of status. It includes features such as a list of failure modes and codes based on ISO 14224:2016, data collection unit worksheet, and dynamic charts for visualizing the results. Two testing iterations were performed on the prototype. First, literature data obtained from the internet was used to generate annual and monthly report charts, confirming the functionality of the prototype. Second, real data from engine failures on the tanker ship were used to ensure logical correlations among failure causative factors. The result from real data testing included Structural Deficiency (STD), External Leakage Fuel (ELF), and Breakdown (BRD) were shown. Based on these results through the prototype simulation, can be taken into consideration for the ship's crew and shipping company management to plan oil monitoring, heating the oil properly, and conduct routine maintenance check as a preventive action to reduce the impact of engine damage in the future due to Engine Breakdown and Structural Defiency

A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry

Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises Machine Learning (ML) and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions. Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and Machine Learning (ML) algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels. Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years. Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom linePeer Reviewe

Caracterización del estado de aplicación de las técnicas de mantenimiento predictivo de la flota atunera industrial que opera en el Pacífico Oriental

The fishing activity is considered of great importance from a nutritional and economic perspective because it develops in a proportional way to the population growth; It is considered that the first link in this supply chain are tuna vessels, maintenance management being essential for their operation, especially of a predictive type, however, today the state of the fundamental elements of maintenance is not known. this vital fleet. Considering that a large part of the fleet is over 40 years old and that at the moment there are no defined studies on the development of this maintenance process, the main objective is to characterize the state of application of predictive maintenance techniques in the active tuna fleet. of the Eastern Pacific. The methodology applied for this research was based on the general characterization of the active fleet of the Eastern Pacific through documentary information that allowed to classify the vessels by flags and years of age and the study of the maintenance management variables used by the vessels, for which a study sample made up of 41 vessels that carry out logistics operations in Ecuadorian ports located in the cantons of Manta and Jaramijó was used. In general, the investigation details that the fleet is old, including its diesel engines and other equipment on board, which does not have current indicator control technologies, which are manually recorded in physical binnacle. Keywords: predictive maintenance, diesel engines, parameters, control, lubrication system, cooling system, fuel system.La actividad pesquera es considerada de gran importancia desde un enfoque alimenticio y económico debido a que se desarrolla de manera proporcional al crecimiento poblacional; se considera que el primer eslabón de esta cadena de suministro son las embarcaciones atuneras, siendo esencial para su funcionamiento la gestión de mantenimiento, sobre todo de tipo predictivo, sin embargo, hoy en día no se conoce el estado de los elementos fundamentales de mantenimiento de esta vital flota. Considerando que gran parte de la flota superan los 40 años de edad y que al momento no existen estudios definidos sobre el desarrollo de este proceso de mantenimiento, el objetivo principal es caracterizar el estado de aplicación de las técnicas de mantenimiento predictivo de la flota atunera activa del Pacífico Oriental. La metodología aplicada para esta investigación se fundamentó en la caracterización general de la flota activa del Pacífico Oriental a través de información documental que permitió clasificar a las embarcaciones por banderas y años de antigüedad y del estudio de las variables de gestión de mantenimiento utilizada por las embarcaciones, para lo cual se utilizó una muestra de estudio conformada por 41 embarcaciones que realizan operaciones logísticas en puertos ecuatorianos ubicados en los cantones de Manta y Jaramijó. En general la investigación detalla que la flota resulta antigua incluyendo sus motores diésel y resto de equipos a bordo, la misma que no cuenta con tecnologías actuales de control de indicadores, los mismos que son registrados manualmente en bitácoras físicas. Palabras clave: mantenimiento predictivo, motores diésel, parámetros, control, sistema de lubricación, sistema de enfriamiento, sistema de combustible. Abstract The fishing activity is considered of great importance from a nutritional and economic perspective because it develops in a proportional way to the population growth; It is considered that the first link in this supply chain are tuna vessels, maintenance management being essential for their operation, especially of a predictive type, however, today the state of the fundamental elements of maintenance is not known. this vital fleet. Considering that a large part of the fleet is over 40 years old and that at the moment there are no defined studies on the development of this maintenance process, the main objective is to characterize the state of application of predictive maintenance techniques in the active tuna fleet. of the Eastern Pacific. The methodology applied for this research was based on the general characterization of the active fleet of the Eastern Pacific through documentary information that allowed to classify the vessels by flags and years of age and the study of the maintenance management variables used by the vessels, for which a study sample made up of 41 vessels that carry out logistics operations in Ecuadorian ports located in the cantons of Manta and Jaramijó was used. In general, the investigation details that the fleet is old, including its diesel engines and other equipment on board, which does not have current indicator control technologies, which are manually recorded in physical binnacle. Keywords: predictive maintenance, diesel engines, parameters, control, lubrication system, cooling system, fuel system. Información del manuscrito:Fecha de recepción: 04 de octubre de 2021.Fecha de aceptación: 09 de noviembre de 2021.Fecha de publicación: 08 de diciembre de 2021

Propuesta de mantenimiento predictivo para reducir tasa de fallas de equipos críticos en Refrigerados Fisholg & Hijos SAC Paita 2021

Este informe de investigación tiene como título “Propuesta de mantenimiento predictivo para reducir tasa de fallas en los equipos críticos de la empresa Refrigerados Fisholg e Hijos SAC”, tiene como objetivo general la elaboración de una propuesta de mantenimiento predictivo para reducir la tasa de fallas de los equipos críticos de la empresa Refrigerados Fisholg & Hijos SAC. También tenemos como objetivo específico 1, elaborar una propuesta de mantenimiento predictivo para reducir la tasa de fallas mecánicas de los equipos críticos de la empresa refrigerados Fisholg & Hijos SAC y como objetivo específico 2, elaborar una propuesta de mantenimiento predictivo para reducir fallas por mala manipulación de equipos en la empresa refrigerados Fisholg & Hijos SAC, la metodología de este informe de investigación es de tipo aplicada, tiene un enfoque cuantitativo, así mismo, esta investigación tiene un nivel explicativo y su diseño es no experimental no propositivo. En este informe de investigación se tiene como resultados que la disponibilidad de los equipos incrementó de una media 78.66% a una media de 85.09%, el tiempo medio entre fallas (MTBF) se incrementó de una media de 5.4 horas a 7.19 horas, de todos los equipos que intervienen en el proceso productivo de pota y perico se determinaron los equipos críticos siguientes: Los equipos de los túneles de congelamiento 1,2,3 y 4; los equipos de los congeladores de placas 1,2 y 3; las peladoras de pota 1,2,3,4,5 y 6; termoformadora envasadora multivac; montacargas y apiladores eléctricos. También en este informe de investigación se redujo la tasa de fallas mecánicas de una media 53.04% a 40.11%. Como recomendación se debe capacitar al personal de mantenimiento y así mismo concientizar a este personal ya que ellos son los que estarán como base para desarrollo del mantenimiento predictivo

RADIS : a real-time anomaly detection intelligent system for fault diagnosis of marine machinery

By enhancing data accessibility, the implementation of data-driven models has been made possible to empower strategies in relation to O&M activities. Such models have been extensively applied to perform anomaly detection tasks, with the express purpose of detecting data patterns that deviate significantly from normal operational behaviour. Due to its preeminent importance in the maritime industry to adequately identify the behaviour of marine systems, the Real-time Anomaly Detection Intelligent System (RADIS) framework, constituted by a Long Short-Term Memory-based Variational Autoencoder in tandem with multi-level Otsu's thresholding, is proposed. RADIS aims to address the current gaps identified within the maritime industry in relation to data-driven model applications for enabling smart maintenance. To assess the performance of such a framework, a case study on a total of 14 parameters obtained from sensors installed on a diesel generator of a tanker ship is introduced to highlight the implementation of RADIS. Results demonstrated the capability of RADIS to be part of a diagnostic analytics tool that will promote the implementation of smart maintenance within the maritime industry, as RADIS detected an average of 92.5% of anomalous instances in the presented case study

A probabilistic model to evaluate the resilience of unattended machinery plants in autonomous ships

Over the next few years, digitalization and automation are expected to be key drivers for maritime transport innovation to be key drivers for maritime transportation innovation. This revolutionary shift in the shipping industry will heavily impact the reliability of the machinery which is intended to be operated remotely with minimum support from humans. Despite a large amount of research into autonomous navigation and control systems in maritime transportation, the evaluation of unattended engine rooms has received very little attention. For autonomous vessels to be effective during their unmanned mission, it is essential for the engine room understand its health condition and self-manage performance. The unattended machinery plant (UMP) should be resilient enough to have the ability to survive and recover from unexpected perturbations, disruptions, and operational degradations. Otherwise, the system may require unplanned maintenance or the operation will stop. Therefore, the UMP must continue its operation without human intervention and safely return the ship to port. This paper aims to develop a machine learning-based model to predict an UMP's performance and estimate how long the engine room can operate without human assistance. A Random Process Tree is used to model failures in the unattended components, while a Hierarchical Bayesian Inference is adopted to facilitate the prediction of unknown parameters in the process. A probabilistic Bayesian Network developed and evaluated the dependent relationship between active and standby components to assess the effect of redundant units in the performance of unattended machinery. The present framework will provide helpful additional information to evaluate the associate uncertainties and predict the untoward events that put the engine room at risk. The results highlight the model's ability to predict the UMP's trusted operation period and evaluate an unattended engine room's resilience. A real case study of a merchant vessel used for short sea shipping in European waters is considered to demonstrate the model's application.</p

Plan de mantenimiento basado en el RCM para mejorar la disponibilidad del sistema eléctrico en red de media tensión 10 kv

En la investigación se estableció como área de estudio al sector VIII “El Milagro”, distrito de Huanchaco, provincia de Trujillo, Departamento de La Libertad. Esta investigación tuvo la finalidad de mejorar la disponibilidad del sistema de suministro eléctrico a dicha zona y poder disminuir las interrupciones del servicio mediante la aplicación de un plan de mantenimiento preventivo en la Red de Media Tensión 10 KV, basado en la metodología RCM. Para iniciar el estudio, se formuló como pregunta respectiva ¿Cómo mejorar la disponibilidad del sistema eléctrico en la Red de Media Tensión 10 KV?, planteándose su respuesta mediante el objetivo general: evaluar la implementación de un Plan de Mantenimiento Basado en el RCM para mejorar la disponibilidad del sistema eléctrico en la Red de Media Tensión 10 KV en el Sector El Milagro, correspondiente al Alimentador TNO006, desde el recloser 302319 (Km 616 Panamericana Norte) hasta el seccionador 302637 - subestación HI.2835 10 KV, del Sector VIII. El desarrollo de actividades de la investigación se efectuó ordenadamente en función de una evaluación inicial actual de los principales indicadores de mantenimiento, identificación de los equipos y componentes más críticos, análisis modal y de efectos, prioridad de riesgos, programa de mantenimiento y finalmente proyectar reducciones de tiempos medios para reparación y aumento de tiempo medio entre fallas, determinando nuevos indicadores post mejora. Se concluye con un análisis financiero de costos y recuperación de la inversión

Systems reliability and data driven analysis for marine machinery maintenance planning and decision making

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies