El propósito de este trabajo es presentar un estudio de mantenimiento centrado del TPM (mantenimiento productivo total) y RCM (mantenimiento centrado en la confiabilidad), que llevó a cabo dentro de un proyecto de instalación de una red sanitaria a través de equipos de construcción pesada en Lima, Perú. Su enfoque se basa en los primeros pilares del TPM, mantenimiento preventivo y autónomo, así como el análisis FMEA del RCM para el análisis de mantenimiento, que se lleva a cabo en estudio. La aplicación del TPM tiene éxito en su aplicación de diversas políticas de Mantenimiento Preventivo (MP) asignados a los activos y demuestra que su aplicación en la industria de la construcción podría reducir la excesiva acumulación de mantenimiento por la misma optimización eficaz, apoyado del análisis del RCM y su análisis de los sistemas de los equipos pesados. El excesivo mantenimiento correctivo representa un alto índice de inversión y retrasos en tiempos de trabajo del proyecto asignado. Los métodos tradicionales de garantía de Disponibilidad, como el mantenimiento reactivo o de rutina, son insuficientes para satisfacer un plan de mantenimiento de equipos pesados, por lo tanto, cuenta para la aplicación sistemática RCM y TMP, permitirán seleccionar y aplicar tareas efectivas de PM. Se utiliza un enfoque que desarrolla y analiza a fondo las estrategias de mantenimiento correctivo y preventivo continuo con el ambiente de incertidumbre y datos operativos limitados por la crítica. Los resultados nos arrojan una mejora de la disponibilidad al 90%. / The purpose of this paper is to present a maintenance study focused on total productive maintenance (TPM) and reliability-centered maintenance (RCM). Its approach is based on the first pillars of TPM, preventive and autonomous maintenance, as well as the FMEA analysis of RCM for maintenance analysis, which was conducted in this study. The implementation of TPM was successful in that various preventive maintenance (PM) policies assigned to the assets were implemented and it was demonstrated that TPM application in the construction industry could reduce the excessive accumulation of maintenance with the same effective optimization, and with support from RCM analysis and its heavy equipment systems analysis. Excessive corrective maintenance accounts for high investment and delay rates in work times of the assigned project. Traditional methods of availability guarantee, such as reactive or routine maintenance, are insufficient to satisfy a heavy equipment maintenance plan; therefore, what is called for is the systematic application of RCM and TMP because they allow the selection and application of effective PM tasks. An approach that develops and thoroughly analyzes the strategies of continuous corrective and PM is used with an atmosphere of uncertainty and with operational data limited by criticism. Results show a 90% improvement in availability. / Trabajo de Suficiencia Profesional

BPL technology is quite widespread nowadays and the public is also somewhat aware of it. This technology can be used as a means of communication without the need to install additional media and thus use the existing infrastructure. In an effort to use the full potential of the technology, BPL communication began to be used to monitor the current state of cables in distribution networks online. Where the technology is already fully implemented, the distributor can use the data to evaluate the current situation. In this way, BPL technology can be used as a diagnostic tool to test the quality of a cable connection on which not only time but also humidity and other external influences could have a significant effect.

Con base en el estudio del sector armamentístico, podemos definir la gestión de mantenimiento como uno de los problemas más relevantes dentro de su cadena de producción. La deficiente planificación y los errores en las entregas de pedidos a tiempo afectan en gran medida la eficacia de la empresa y su retorno de inversión. Por tal motivo, surge la necesidad de encontrar soluciones que permitan el crecimiento del sector armamentístico peruano y metalmecánico. La presente investigación propone una solución basada en las herramientas TPM y Spare Parts Management que permitirán una buena gestión del flujo de actividades de mantenimiento preventivo o correctivo, gestionar eficientemente el movimiento de los repuestos y su respectiva ubicación e inventarios en el proceso de ingreso de nuevos repuestos, eliminar los desperdicios innecesarios y controlar de manera óptima los repuestos de las máquinas. La empresa en estudio es una empresa con régimen estatal, pero con dirección privada, la cual presenta problemas en su gestión de mantenimiento, debido al exceso de paradas inesperadas y atascos, alta producción de productos no conformes y baja eficacia de producción de municiones a tiempo. Esta propuesta logró incrementar la eficacia de la producción de casquillos y balas en un 6.99% pasando de un 66.52% a 73.51%. / Based on the study of the arms sector, we can define maintenance management as one of the most relevant problems within its production chain. Poor planning and errors in order delivery on time greatly affect the efficiency of the company and its return on investment. For this reason, the need arises to find solutions that allow the growth of the Peruvian arms and metalworking sector. The present investigation proposes a solution based on the TPM and Spare Parts Management tools that will allow a good management of the flow of preventive or corrective maintenance activities, efficiently manage the movement of spare parts and their respective location and inventories in the process of entering new spare parts, eliminate unnecessary waste and optimally control machine spare parts. The company under study is a company with a state regime, but with private management, which presents problems in its maintenance management, due to the excess of unexpected stops and jams, high production of non-conforming products and low efficiency of ammunition production on time. . This proposal managed to increase the efficiency of the production of casings and bullets by 6.99%, going from 66.52% to 73.51%. / Trabajo de Suficiencia Profesional

Detta arbete har genomförts på uppdrag av SCA Munksund, för att undersöka möjligheten att tillståndsövervaka hydraulpumpar med hjälp av smarta sensorer som mäter vibrationer och temperatur. Hydraulpumpar är svåra att tillståndsövervaka, och möjligheten att prediktera ett haveri i ett tidigt skede är väldigt svårt. Haverier på hydraulpumpar sker oftast inte direkt efter att ett fel har uppstått, utan efter en längre period, med däremot sker haverier på hydraulpumpar oftast utan förvarning och är mycket kostsamma för processindustrin, där materialkostnaden utgör en liten del jämfört med kostnaden som följd av produktionsförlusten. En möjlighet för bättre och effektivare tillståndsövervakning är att använda vibrationssensorer som i ett tidigt skede indikerar när pumpen avviker från sitt normaltillstånd, och på så sätt planera in underhållsarbete vid exempelvis nästkommande underhållsstopp, utan att behöva stoppa produktionen. Just därför har det valts att undersöka olika sensorparametrar för att identifiera hydraulpumparnas normaltillstånd, och utifrån det bestämma lämpliga larmgränser. Vibrationsmätningarna har genomförts under ca en månad på två hydraulpumpar, en som är relativt ny och en som är äldre. Huvudfokuset har varit att övervaka vibration och temperatur under varierande pumpbelastning/effekt och även att övervaka kolvslitage och lagertillstånd. Med användning av smarta vibrationssensorer har larmgränser kunnat identifierats för båda pumparna. Däremot måste fler mätningar utföras för att säkerställaatt de larmgränser som presenteras i denna rapport är korrekta och kan användas i framtiden. SCA Munksund har nu tillgång till alla mätningar och dokumentation som har utförts för båda hydraulpumparna, och kan nu arbeta vidare för att på sikt tillhandhålla mer specifika larmgränser. / This work has been carried out on behalf of SCA Munksund, to investigate the possibility of condition monitoring hydraulic pumps using smart sensors that measure vibrations and temperature. Hydraulic pumps are difficult to condition monitor, and the ability to predict a failure at an early stage is very difficult. Breakdowns on hydraulic pumps usually do not happen immediately after a fault has occurred, but after a longer period, but on the other hand, breakdowns on hydraulic pumps usually happen without warning and are very costly for the process industry, where the cost of materials constitutes a small part compared to the cost resulting from the loss of production. One possibility for better and more efficient condition monitoring is to use vibration sensors that indicate at an early stage when the pump deviates from its normal state, and in this way plan maintenance work at, for example, the next maintenance stop, without having to stop the production. Precisely for this reason, it has been chosen to investigate various sensor parameters to identify the normal state of the hydraulic pumps and based on that to determine suitable thresholds. Vibration measurements have been carried out for about a month on two hydraulic pumps, one that is relatively new and one that is older. The main focus has been on monitoring vibration and temperature under varying pump load/effect and also to monitor piston wear and bearing condition. With the use of smart vibration sensors, thresholds have been identified for both pumps. However, more measurements must be performed to ensure that the thresholds presented in this report are correct and can be used in the future. SCA Munksund now has access to all measurements and documentation that have been carried out for both hydraulic pumps and can now work further to provide more specific thresholds.

In today’s industry, where manufacturing is fast and efficient, uptime for manufacturing machines plays a key role for companies to stay relevant with their competitors. Although manufacturing equipment today is very advanced and sometimes automated, they may still run into problems and wear out. By maintaining and servicing the equipment well, one can avoid unexpected equipment failure and unnecessary downtime. Keeping track and documenting maintenance can play a crucial role in how efficiently a manufacturer can produce their product. Maintenance management software exists on the market today, but they are large and expensive. As Saab, a manufacturer of highly advanced military technology, does not have a well-established system for managing their maintenance, there is a need for an evaluation and development of a customised option. In this thesis, the maintenance needs for Saab’s operations were analysed, and options for developing maintenance software that fits these needs were investigated. The result was a web application hosted on a Python-based web server, which uses MongoDB as a database to store data in an object-oriented manner, allowing flexibility within the system. The application is not fully developed but provides an overall picture of how a smaller software could serve the same purpose as the larger ones. After evaluating the developed application alongside the existing maintenance software, it is concluded that with further development, the application could serve the same function and be a competitive contender alongside the larger software. However, the safer and more reliable option for Saab would be to instead implement one of the existing software solutions. / I dagens industri, där tillverkning är snabb och effektiv, spelar drifttid för tillverkningsmaskiner en nyckelroll för att företag ska kunna hålla sig relevanta gentemot sina konkurrenter. Trots att tillverkningsutrustning idag är mycket avancerad och ibland automatiserad, kan de fortfarande stöta på problem och slitas ut. Genom att underhålla och serva utrustningen på rätt sätt kan man undvika oväntade maskinhaverier och onödig driftstopp. Att ha kontroll och dokumentera underhåll kan spela en avgörande roll för hur effektivt en tillverkare kan producera sin produkt. Underhållshanteringssystem finns på marknaden idag, men de är stora och dyra. Eftersom Saab, tillverkare av högteknologisk militärteknik, inte har ett väletablerat system för hur de hanterar sitt underhåll, finns det ett behov av en utvärdering och utveckling av ett anpassat alternativ. I denna avhandling analyserades underhållsbehoven för Saabs verksamhet, och alternativ för hur en underhållsprogramvara kunde utvecklas för att passa dessa behov undersöktes. Resultatet blev en webbapplikation som är värd på en Python-baserad webbserver och använder MongoDB som en databas för att lagra data på ett objektorienterat sätt, vilket ger flexibilitet inom systemet. Applikationen är inte fullt utvecklad men ger en övergripande bild av hur en mindre programvara kan tjäna samma syfte som de större. När den utvecklade applikationen utvärderas i förhållande till den befintliga underhållsprogramvaran dras slutsatsen att applikationen, med ytterligare utveckling, skulle kunna fylla samma funktion och vara en konkurrenskraftig utmanare till de större programvarorna. Dock skulle det säkrare och mer pålitliga alternativet för Saab vara att istället implementera en av de befintliga programvarorna.

We consider a class of periodic preventive maintenance (PM) optimization problems, for a single piece of equipment that deteriorates with time or use, and can be repaired upon failure, through corrective maintenance (CM). We develop analytical and simulation-based optimization models that seek an optimal periodic PM policy, which minimizes the sum of the expected total cost of PMs and the risk-averse cost of CMs, over a finite planning horizon. In the simulation-based models, we assume that both types of maintenance actions are imperfect, whereas our analytical models consider imperfect PMs with minimal CMs. The effectiveness of maintenance actions is modeled using age reduction factors. For a repairable unit of equipment, its virtual age, and not its calendar age, determines the associated failure rate. Therefore, two sets of parameters, one describing the effectiveness of maintenance actions, and the other that defines the underlying failure rate of a piece of equipment, are critical to our models. Under a given maintenance policy, the two sets of parameters and a virtual-age-based age-reduction model, completely define the failure process of a piece of equipment. In practice, the true failure rate, and exact quality of the maintenance actions, cannot be determined, and are often estimated from the equipment failure history. We use a Bayesian approach to parameter estimation, under which a random-walk-based Gibbs sampler provides posterior estimates for the parameters of interest. Our posterior estimates for a few datasets from the literature, are consistent with published results. Furthermore, our computational results successfully demonstrate that our Gibbs sampler is arguably the obvious choice over a general rejection sampling-based parameter estimation method, for this class of problems. We present a general simulation-based periodic PM optimization model, which uses the posterior estimates to simulate the number of operational equipment failures, under a given periodic PM policy. Optimal periodic PM policies, under the classical maximum likelihood (ML) and Bayesian estimates are obtained for a few datasets. Limitations of the ML approach are revealed for a dataset from the literature, in which the use of ML estimates of the parameters, in the maintenance optimization model, fails to capture a trivial optimal PM policy. Finally, we introduce a single-stage and a two-stage formulation of the risk-averse periodic PM optimization model, with imperfect PMs and minimal CMs. Such models apply to a class of complex equipment with many parts, operational failures of which are addressed by replacing or repairing a few parts, thereby not affecting the failure rate of the equipment under consideration. For general values of PM age reduction factors, we provide sufficient conditions to establish the convexity of the first and second moments of the number of failures, and the risk-averse expected total maintenance cost, over a finite planning horizon. For increasing Weibull rates and a general class of increasing and convex failure rates, we show that these convexity results are independent of the PM age reduction factors. In general, the optimal periodic PM policy under the single-stage model is no better than the optimal two-stage policy. But if PMs are assumed perfect, then we establish that the single-stage and the two-stage optimization models are equivalent. / text

Um sistema reparável opera sob uma estratégia de manutenção que exige ações de recuperação preventiva em tempos pré-definidos e ações de reparo quando ocorre a perda de função do sistema. A manutenção preventiva (MP) é programada periodicamente e muitas vezes possui um intervalo de tempo fixo para ações. No entanto, as atividades de MP podem não restaurar o sistema para uma condição similar ao início de vida deste, mas para uma situação intermediária. Nesse caso, a MP é denominada de imperfeita. Além disso, ao longo da vida do sistema, são executados diferentes planos de manutenção com condições e atividades distintas que podem afetar a intensidade de falha de diferentes maneiras. Para modelar essas características da MP em um sistema reparável, propõe-se uma nova classe de modelo de fator de melhoria, denominado fator de melhoria variável que possibilita a modelagem da situação de manutenção perfeita. A formulação da função de verossimilhança foi desenvolvida para estimação dos parâmetros bem como desenvolvidos testes de verificação da qualidade de ajuste, intervalos de confiança para os parâmetros e otimização da periodicidade de realização da MP com base no enfoque dos novos modelos propostos. Os resultados foram aplicados em dados reais e verificou-se uma parametrização mais flexível a MP imperfeita e maior versatilidade nas análises de confiabilidade do sistema quando utilizado os novos modelos. / A repairable system operates under a maintenance strategy that calls for preventive repair actions at prescheduled times and the repair actions that restore system when failure occurs. The preventive maintenance (PM) is scheduled periodically and it often holds a fixed time interval for PM actions. However, PM activities are generally imperfect and cannot restore the system to as good as new condition but to an intermediate situation, which is called imperfect PM. In addition, throughout system life are implemented diverse maintenance policies with different activities and conditions that may affect the failure intensity in different ways. To model these PM characteristics, proposes a new model class of improvement factor called variable improvement factor that also enables modeling perfect maintenance situation. The likelihood function is developed for parameter estimation as well as goodness-of-fit tests and confidence intervals for the parameters are developed, and optimization of the PM intervals based on the proposed models is presented. The proposed model was applied to a data set and a more flexible parameterization for imperfect PM and greater versatility in the system reliability analysis were verified with the use of the new model.

This research has developed several models and methodologies with the aim of improving the accuracy and applicability of reliability predictions for complex repairable systems. A repairable system is usually defined as one that will be repaired to recover its functions after each failure. Physical assets such as machines, buildings, vehicles are often repairable. Optimal maintenance strategies require the prediction of the reliability of complex repairable systems accurately. Numerous models and methods have been developed for predicting system reliability. After an extensive literature review, several limitations in the existing research and needs for future research have been identified. These include the follows: the need for an effective method to predict the reliability of an asset with multiple preventive maintenance intervals during its entire life span; the need for considering interactions among failures of components in a system; and the need for an effective method for predicting reliability with sparse or zero failure data. In this research, the Split System Approach (SSA), an Analytical Model for Interactive Failures (AMIF), the Extended SSA (ESSA) and the Proportional Covariate Model (PCM), were developed by the candidate to meet the needs identified previously, in an effective manner. These new methodologies/models are expected to rectify the identified limitations of current models and significantly improve the accuracy of the reliability prediction of existing models for repairable systems. The characteristics of the reliability of a system will alter after regular preventive maintenance. This alternation makes prediction of the reliability of complex repairable systems difficult, especially when the prediction covers a number of imperfect preventive maintenance actions over multiple intervals during the asset's lifetime. The SSA uses a new concept to address this issue effectively and splits a system into repaired and unrepaired parts virtually. SSA has been used to analyse system reliability at the component level and to address different states of a repairable system after single or multiple preventive maintenance activities over multiple intervals. The results obtained from this investigation demonstrate that SSA has an excellent ability to support the making of optimal asset preventive maintenance decisions over its whole life. It is noted that SSA, like most existing models, is based on the assumption that failures are independent of each other. This assumption is often unrealistic in industrial circumstances and may lead to unacceptable prediction errors. To ensure the accuracy of reliability prediction, interactive failures were considered. The concept of interactive failure presented in this thesis is a new variant of the definition of failure. The candidate has made several original contributions such as introducing and defining related concepts and terminologies, developing a model to analyse interactive failures quantitatively and revealing that interactive failure can be either stable or unstable. The research results effectively assist in avoiding unstable interactive relationship in machinery during its design phase. This research on interactive failures pioneers a new area of reliability prediction and enables the estimation of failure probabilities more precisely. ESSA was developed through an integration of SSA and AMIF. ESSA is the first effective method to address the reliability prediction of systems with interactive failures and with multiple preventive maintenance actions over multiple intervals. It enhances the capability of SSA and AMIF. PCM was developed to further enhance the capability of the above methodologies/models. It addresses the issue of reliability prediction using both failure data and condition data. The philosophy and procedure of PCM are different from existing models such as the Proportional Hazard Model (PHM). PCM has been used successfully to investigate the hazard of gearboxes and truck engines. The candidate demonstrated that PCM had several unique features: 1) it automatically tracks the changing characteristics of the hazard of a system using symptom indicators; 2) it estimates the hazard of a system using symptom indicators without historical failure data; 3) it reduces the influence of fluctuations in condition monitoring data on hazard estimation. These newly developed methodologies/models have been verified using simulations, industrial case studies and laboratory experiments. The research outcomes of this research are expected to enrich the body of knowledge in reliability prediction through effectively addressing some limitations of existing models and exploring the area of interactive failures.

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No. of bitstreams: 1 quintella_lc_me_bauru.pdf: 4453734 bytes, checksum: b3c4369bb61bf3c735292c7280dfc961 (MD5) Previous issue date: 2016-07-04 / A função manutenção vem sendo considerada como fator estratégico para as empresas, pois através do alinhamento de suas políticas corporativas e integração de seus programas de gestão de ativos, de riscos e de ciclo de vida de suas unidades de negócios, as empresas vêm buscando a constante redução de custos e a melhoria de seus resultados operacionais. E, assim, obtendo maior competitividade. A Manutenção Centrada em Confiabilidade (RCM) é um método já bem disseminado por todo o mundo e que, ao longo dos anos, vem promovendo estes diferenciais estratégicos através de preceitos que possibilitam a elaboração de Programas de Manutenção Preventiva de custo-eficaz, através de um método para a definição de políticas de manutenção mais adequadas, com o foco na manutenção da função dos ativos em seu contexto operacional. Ao longo dos anos, o método RCM vem sendo aplicado em inúmeros estudos de casos em diferentes empresas de diversos seguimentos, onde podem ser observadas novas adaptações ou simplificações do método RCM clássico. Estas adaptações buscam uma maior adequação as particularidades destas empresas e/ou um retorno mais rápido de resultados. O setor nuclear de geração de energia foi um dos pioneiros na adoção e disseminação do RCM, e vem desenvolvendo processos simplificados de aplicação do RCM, como o “Streamlinned RCM” e o “método PMO” (Otimização do Programa de Manutenção, do inglês: Preventive Maintenance Optimization). Estes estudos mostram que o método PMO apresenta uma maior flexibilidade, o que permite a adoção de diferentes estratégias de aplicação que, por sua vez, têm trazido resultados expressivos para as empresas, através da otimização dos Programas de Manutenção já existentes. Com base na literatura, neste trabalho são abordadas questões referentes ao RCM e sua contextualização na área nuclear, estudos sobre os métodos simplificados do RCM e o desenvolvimento do método PMO. Por fim, é realizada uma aplicação prática do método PMO sobre os sistemas relacionados e subsistemas do Sistema de Remoção de Calor Residual (JN), mais especificamente, sobre as Bombas de injeção de segurança do Sistema de Injeção de Alta Pressão (JND) da Usina Nuclear Angra 2. Através dos resultados obtidos com esta aplicação, pretende-se otimizar o Programa de Manutenção da Planta (PMP) referente a estes equipamentos e, assim, validar o método PMO como ferramenta para a melhoria contínua do Programa de Gestão de Ativos e Ciclo de Vida da Usina Nuclear Angra 2. / Corporations tend to consider maintenance work a strategic element. It is through maintenance—especially the alignment between corporate policies and integration of their programs to manage assets, risks, and life cycles of business units—that corporations try to continuously improve their results, reduce their operational costs, and, therefore, increase their competitiveness. Reliability Centered Maintenance (RCM) is a popular method across the world; it has been promoting competitiveness through concepts that allow the design of cost-effective Preventive Maintenance Programs These programs are effective because they entail appropriate maintenance policies that are focused on the preservation of the assets functions in their operational context and on the formation of technical knowledge basis supported by hard data. Throughout the years, RCM has been applied in numerous case studies and in different companies engaged in a variety of market segments. Such diversity in the application of RCM allowed us to observe new adaptations and variations of the classic method. Such adaptations aim to better respond to specific operational contexts in and within production units, as well as achieve faster results. The nuclear power sector has pioneered regarding the adoption and dissemination of RCM; it has been developing simplified versions of RCM, such as the “Streamlined RCM” and the PMO (Preventive Maintenance Optimization). These studies demonstrate that the PMO presents enhanced flexibility, which allows the adoption of different strategies; such enhanced flexibility brings expressive results to corporations as pre-existing maintenance programs are optimized. Based on the currently available literature, this dissertation addresses numerous questions regarding RCM and its application to nuclear power segments. It also addresses studies about simplified versions of RCM and the development of the PMO method. The discussion is supplemented with a practical application of the PMO method regarding auxiliary systems and sub-systems of Removal of Residual Heat System (JN), especially those regarding the security injection pumps of the High Pressure Injection System (JND) at Angra II Nuclear Plant. Through the results obtained from this application, it is possible to optimize the Maintenance Program of these equipments, and therefore, validate the PMO method as a tool of continuous improvement of the Assets and Life Cycle Program of Angra II.

Um sistema reparável opera sob uma estratégia de manutenção que exige ações de recuperação preventiva em tempos pré-definidos e ações de reparo quando ocorre a perda de função do sistema. A manutenção preventiva (MP) é programada periodicamente e muitas vezes possui um intervalo de tempo fixo para ações. No entanto, as atividades de MP podem não restaurar o sistema para uma condição similar ao início de vida deste, mas para uma situação intermediária. Nesse caso, a MP é denominada de imperfeita. Além disso, ao longo da vida do sistema, são executados diferentes planos de manutenção com condições e atividades distintas que podem afetar a intensidade de falha de diferentes maneiras. Para modelar essas características da MP em um sistema reparável, propõe-se uma nova classe de modelo de fator de melhoria, denominado fator de melhoria variável que possibilita a modelagem da situação de manutenção perfeita. A formulação da função de verossimilhança foi desenvolvida para estimação dos parâmetros bem como desenvolvidos testes de verificação da qualidade de ajuste, intervalos de confiança para os parâmetros e otimização da periodicidade de realização da MP com base no enfoque dos novos modelos propostos. Os resultados foram aplicados em dados reais e verificou-se uma parametrização mais flexível a MP imperfeita e maior versatilidade nas análises de confiabilidade do sistema quando utilizado os novos modelos. / A repairable system operates under a maintenance strategy that calls for preventive repair actions at prescheduled times and the repair actions that restore system when failure occurs. The preventive maintenance (PM) is scheduled periodically and it often holds a fixed time interval for PM actions. However, PM activities are generally imperfect and cannot restore the system to as good as new condition but to an intermediate situation, which is called imperfect PM. In addition, throughout system life are implemented diverse maintenance policies with different activities and conditions that may affect the failure intensity in different ways. To model these PM characteristics, proposes a new model class of improvement factor called variable improvement factor that also enables modeling perfect maintenance situation. The likelihood function is developed for parameter estimation as well as goodness-of-fit tests and confidence intervals for the parameters are developed, and optimization of the PM intervals based on the proposed models is presented. The proposed model was applied to a data set and a more flexible parameterization for imperfect PM and greater versatility in the system reliability analysis were verified with the use of the new model.