Preventive Maintenance Actions Research Articles

Optimal Preventive Maintenance Policy for Equipment Rented under Free Leasing as a Contributor to Sustainable Development

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of “free leasing”. In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment’s consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer’s premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer’s planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor’s average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented.

A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems

The rise of mass customisation drives manufacturers to adopt leasing agreements for their machinery rather than outright their ownership. This industrial trend results in large-scale leased manufacturing systems, where the asset conditions and maintenance requirements for a fleet of machines and/or production lines from several manufacturers are continuously monitored and managed by a lessor. In this paper, we propose an operations and maintenance planning model that explicitly models (i) dynamic real-time failure rate predictions for machines based on sensor-driven degradation data, (ii) optimal routes for maintenance teams across a number of geographically-distributed manufacturing sites, and (iii) operational outcomes. The model can handle complex maintenance and operational outcome interdependencies between multiple machines, and incorporate demand for different products, heterogeneous machine capacities and capabilities, factory topology, and the resulting production flow capacities of products. To demonstrate the model’s practicality, it is applied to three experimental case studies with various numbers of machines, operating schedules, product types, and flow capacities. The results show that the proposed model significantly outperforms the traditional reliability-based maintenance model in key metrics such as the number of unexpected failures, the cost of preventive maintenance actions, machine downtime, the percentage of unsatisfied demand, and total cost.

Effect of Load on Sequential Imperfect Preventive Maintenance and Replacement Schedules of Mechanically Repairable Machines

This paper examines the impact of load on the operational time and maintenance cost of mechanically repairable machines. Three different levels of load with multiplicative impact on the hazard rate of the failure distribution were applied to the working of a cassava grinding machine using a two-parameter Weibull distribution with respective hazard and cumulative hazard functions. Their effect on the preventive maintenance (PM) and replacement schedules revealed that at above maximum load level, the length of the machine’s operational time decreased drastically compared to the decrease at maximum load level and relative decrease at the below maximum load level when compared to the machine’s operational time at the minimum load level. The application of load also results in frequent preventive maintenance actions and an increase in machine downtime for a given cost ratio. This implies that the influence of load on the PM and replacement maintenance schedule of mechanically repairable machines is essential to the design and operation of such machines. The results also provide maintenance engineers with an operational guide for PM and replacement maintenance actions in order to prevent failure maintenance and increase the machine’s availability for enhanced productivity.

Mathematical programming formulations for the reclaimer scheduling problem with sequence-dependent setup times and availability constraints

In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer Programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case.

Perhitungan Waktu Baku Proses Preventive Maintenance Mesin CNC Router

An efficient preventive maintenance requires scheduling process. The first step of this process is to calculate the standard time for each maintenance activity. This research aims to calculate the standard time for preventive maintenance activities for CNC Router machines at PT X through three stages: (1) Interviews and direct observation with the relevant machine operators to obtain a list of maintenance activities, (2) measure the cycle time of each maintenance activities, (3) Calculate Standard time based on cycle time measurement data. The research results show that it takes a total of 100 minutes to carry out the daily maintenance process, 95 minutes for the weekly maintenance process, 790 minutes for the maintenance process after 2000 hours, and 1140 minutes for the maintenance process after 4000 hours. This result can be used as input for the scheduling process for CNC Router machines maintenance at PT. X.

Group Maintenance for Numerical Control Machine Tools: A Case Study

The high-reliability requirement of numerical control (NC) machine tools emphasizes the effectiveness of preventive maintenance (PM). However, most existing PM decisions are made by treating them as single-component systems, resulting in the problems of “excessive maintenance” or “insufficient maintenance.” Moreover, real failure data has rarely been utilized for maintenance decision-making, which leads to idealized policies far from practice. This article presents a framework to derive the optimal group maintenance (GM) policy from real failure histories for an NC machine tool comprised of heterogeneous components. Criticality analysis is first conducted to identify high-risk and low-risk components. The lifetime of each high-risk component is characterized by a two-parameter Weibull model or by a Weibull competing-risk model according to whether its failures are independent of the other components. High-risk components with strictly increasing or bathtub-shaped failure rates are selected for PM decision-making. This article introduces a multilevel PM decision-making approach: 1) a repair-replacement model is developed at the component level to derive the optimal PM interval and the maximum number of PM actions; and 2) at the system level, a GM procedure is presented to obtain the optimal PM interval of the system. The failure histories of an NC machine tool with 13 components are used to illustrate the effectiveness of the proposed approach. A maintenance plan is finally made based on optimization results to guide maintenance implementation.

Rationalization of the operation process of intrusion and hold-up alarm systems

Intrusion and hold-up alarm systems (I&HAS) are part of electronic security systems. They are now installed in many facilities, including those classified as critical infrastructure. The variety of available alarm control panels and their configurations means that I&HAS are installed with the use of alarm control panels with various functional and diagnostic capabilities. In the process of operation of intrusion and hold-up alarm systems, maintenance actions are most often used when no damage has occurred. This is referred to as preventive maintenance. These activities are aimed at carrying out preventive maintenance that will reduce the likelihood of damage or deterioration of the system. An extension and also an evolution of preventive maintenance activities is the development of the eMaintenance concept. By implementing modern solutions in diagnosing intrusion and hold-up alarm systems and using advanced IT applications, it is possible to monitor them and manage the operational process in real-time with the possibility of observing the degradation of the I&HAS. This makes it possible to take action to prevent the transition to a state of partial fitness or unfitness. As a result of the analysis of the operation process of the intrusion and hold-up alarm systems, taking into account diagnostic information, a graph of relations occurring in the system was developed. Then, dependencies were determined to calculate the probabilities of the I&HAS being in a state of full ability, states of periodic service, states of partial ability, and a state of unfitness. The issues of modelling the operation process of intrusion and hold-up alarm systems presented by the authors make it possible to rationalize the intensity of periodic inspections. The result is an increase in the value of the readiness index of I&HAS, which is particularly important in the case of securing critical infrastructure facilities.

Aircraft fleet availability optimisation: a reinforcement learning approach

AbstractA fleet of aircraft can be seen as a set of degrading systems that undergo variable loads as they fly missions and require maintenance throughout their lifetime. Optimal fleet management aims to maximise fleet availability while minimising overall maintenance costs. To achieve this goal, individual aircraft, with variable age and degradation paths, need to operate cooperatively to maintain high fleet availability while avoiding mechanical failure by scheduling preventive maintenance actions. In recent years, reinforcement learning (RL) has emerged as an effective method to optimise complex sequential decision-making problems. In this paper, an RL framework to optimise the operation and maintenance of a fleet of aircraft is developed. Three cases studies, with varying number of aircraft in the fleet, are used to demonstrate the ability of the RL policies to outperform traditional operation/maintenance strategies. As more aircraft are added to the fleet, the combinatorial explosion of the number of possible actions is identified as a main computational limitation. We conclude that the RL policy has potential to support fleet management operators and call for greater research on the application of multi-agent RL for fleet availability optimisation.

Blockchain-based trusted accountability in the maintenance of medical imaging equipment

Preventative maintenance is imperative for ensuring the longevity and reliability of diagnostic medical imaging equipment. However, conventional maintenance systems face challenges related to centralization, fragmented information, and ineffective tracking mechanisms. In response to these challenges, this research presents a blockchain-based solution that introduces a robust framework for managing preventative maintenance tasks. The proposed solution leverages blockchain technology, smart contracts, IPFS, and timer oracles to promote transparency, accountability, and traceability in maintenance operations. To ensure the integrity and availability of maintenance data while preserving the confidentiality of sensitive information, a detailed system architecture is outlined. Sequence diagrams and algorithms are developed to define the flow and logic of the solution, focusing on automatic fault detection and diagnosis. The smart contracts, which are made publicly available on GitHub, were rigorously tested to guarantee their intended functionality, thus addressing software reliability concerns. Additionally, the solution includes a maintenance incentive mechanism, actively motivating stakeholders to engage in preventative maintenance activities, and establishing accountability. Security testing confirms the absence of vulnerabilities in smart contracts, aligning with the scope of automatic fault detection and diagnosis. Furthermore, potential generalizations of the solution are discussed, and future directions emphasize the importance of continuous improvement and innovation in maintenance practices.

An Improved Method for Optimizing the Timing of Preventive Maintenance of Pavement: Integrating LCA and LCCA

The optimal preventive maintenance timing of traditional asphalt pavement is based on the maximum benefit-cost ratio. For cost evaluation indicators, mainly agency cost and user cost, use life cycle cost analysis (LCCA) to determine. The environmental impact of pavement preventive maintenance activities is often overlooked. For the benefit evaluation index, only the new area under the performance curve is used to express the maintenance benefit, and the benefit evaluation index is single. This study proposes an integrated approach combining life cycle assessment (LCA) and LCCA to model preventive maintenance timing decisions considering economic, social, and environmental factors. In this paper, LCA is used to quantitatively analyze the energy consumption and emissions in the pavement operation and maintenance process, and the environmental costs are also included in the accounting scope of the costs. The costs that constitute pavement use are analyzed from three aspects: the agency cost, the user cost, and the environmental cost. At the same time, in the process of constructing the cost model, the costs caused by the maintenance construction process to users and the environment are also taken into consideration. Aiming at the benefit index and comprehensively considering the economic benefits, social benefits, and environmental benefits brought by maintenance, a comprehensive evaluation model of maintenance benefits was constructed based on the analytic hierarchy process (AHP). On the basis of accurately calculating the cost and benefit of maintenance in the life cycle, the model of the optimal preventive maintenance timing of asphalt pavement is constructed so as to calculate the optimal timing of preventive maintenance and provide certain information for the formulation of expressway pavement preventive maintenance programs. For reference. Taking a highway project in China as an example to verify the applicability of the model

Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders

Floating offshore wind turbines (FOWTs) show promise in terms of energy production, availability, and sustainability, but remain unprofitable due to high maintenance costs. This work proposes a deep learning algorithm to detect mooring line degradation and failure by monitoring the dynamic response of the publicly available DeepCWind OC4 semi-submersible platform. This study implements an autoencoder capable of predicting multiple forms of damage occurring at once, with various levels of severity. Given the scarcity of real data, simulations performed in OpenFAST, recreating both healthy and damaged mooring systems, are used to train and validate the algorithm. The novelty of the proposed approach consists of using a set of key statistical metrics describing the platform’s displacements and rotations as input layer for the autoencoder. The statistics of the responses are calculated at 33-minute-long sea states under a broad spectrum of metocean and wind conditions. An autoencoder is trained using these parameters to discover that the proposed algorithm is capable of detecting mild anomalies caused by biofouling and anchor displacements, with correlation coefficients up to 98.51% and 99.16%, respectively. These results are encouraging for the continuous health monitoring of FOWT mooring systems using easily measurable quantities to plan preventive maintenance actions adequately.

Statistical Modeling of the Effectiveness of Preventive Maintenance for Repairable Systems

Preventive maintenance (PM) is commonly adopted in practice to improve a system’s health condition and reduce the risk of unexpected failures. When a PM action is poorly performed, however, it is likely to have adverse effects on system reliability. We observe this phenomenon when evaluating the effectiveness of a PM program for a fleet of service vehicles based on their four-year operating data. This phenomenon is also commonly reported in the maintenance of vehicles and aircraft. Motivated by this observation, we propose a statistical model for repairable systems by taking potential PM adverse effects into account. In the formulation, the baseline failure process without PM effects is modeled by a nonhomogeneous Poisson process. When a PM action is performed, its effect on the failure process is modeled as a multiplicative random effect on the system rate of occurrence of failures. Statistical inference under the proposed model is discussed, and we further develop goodness-of-fit test procedures to validate the adequacy of this model. The above-mentioned service vehicle operating data are used to demonstrate the proposed methods.

Imperfect Preventive Maintenance Optimization with Variable Age Reduction Factor and Independent Intervention Level

Maintenance policies are crucial for ensuring the reliability, safety, and longevity of a system, as well as reducing the risk of accidents. Preventive maintenance (PM) is an effective strategy to keep equipment and systems in good working order by fixing potential issues before they cause downtime or safety hazards. However, optimizing the time intervals between PM activities is essential for minimizing the overall maintenance cost. This paper proposes an innovative approach that considers the intervention level of maintenance activities as an independent variable of PM times. The approach provides greater flexibility in creating maintenance plans, as it considers practical aspects that may impact maintenance activities beyond the time interval between PMs. The proposed approach uses a reliability model that incorporates imperfect preventive maintenance and a variable improvement factor based on age reduction. The improvement factor of each preventive maintenance activity (PMA) is defined based on the intervention level of the activity itself, which is determined by the number of tasks performed, execution time, and the number of items replaced in the maintenance plan. The proposed maintenance strategy determines not only the optimal times for PMAs and the respective intervention level but also the optimal number of maintenance activities that minimize the total maintenance cost along a fixed and user-defined planning horizon. The effectiveness and precision of the approach have been demonstrated through a series of numerical examples and a comprehensive case study involving three heat exchangers situated within the hydroelectric power plant.

Reliability-Centered Maintenance Framework for Distribution Grids Based on Fault-Tree Analysis

Reliability is the key issue in the supply of electrical energy in modern society, which is jeopardized by the failures occurring in different sections of distribution grids. To address this challenge, this paper presents a reliability-centered maintenance framework for transformers, switchgear panels and power cables in medium-voltage distribution grids. First, fault tree models for the different equipment are established in this paper, with which the impacts of different failures and the effects of maintenance actions are analyzed in a quantitative manner. Using the fault tree models, the influences of different maintenance strategies on the reliability indexes of equipments in long-term operations can be estimated, which provides references for the selection and prioritization of preventive maintenance actions. This research work provides a generalized and practical framework for designing reliability-centered maintenance plans for distribution grids.

Simultaneous optimization of design and maintenance for systems using multi-objective evolutionary algorithms and discrete simulation

When projecting and building new industrial facilities, getting integrated design alternatives and maintenance strategies are of critical importance to achieve the physical assets optimal performance, which is needed to be competitive in the actual global markets. Coupling Evolutionary Algorithms with Discrete Event Simulation has been explored both in relation to systems design and their maintenance strategy. However, it was not simultaneously considered when both the corrective and the preventive maintenance—consisting of achieving the optimum period of time to carry out a preventive maintenance activity—are taken into account before being considered by the authors of the present paper. This work couples Multi-objective Evolutionary Algorithms with Discrete Event Simulation in order to enhance the knowledge and efficiency of the methodology presented, which consists of exploring and optimizing simultaneously systems design alternatives and their preventive maintenance strategies. The aim consists of finding the best set of non-dominated solutions by using the system availability (first maximized objective function) with taking into consideration associated operational cost (second minimized objective function), while automatically selecting the system devices. Each solution proposed by the Multi-Objective Evolutionary Algorithm is analyzed by using Discrete Event Simulation in a procedure that looks at the effect of including periodic preventive maintenance activities all along the mission time. An industrial application case study is solved, and a comparison of the performance of five state-of-the-art and three more recently developed Multi-objective Evolutionary Algorithms is handled; moreover, the gap in the literature reviewed about the analysis regarding the effect of the discrete event simulation sampling size is faced with useful insights about the synergies of Multi-objective Evolutionary Algorithms and Discrete Event Simulation. Finally, the methodology is expanded to more complex systems which are successfully solved.

Optimal production and non-periodic maintenance plan for a degrading serial production system with uncertain demand

Over the past few decades, economic globalisation has intensified the competition among international enterprises. So, firms are working hard to improve their performances to stay competitive in markets. In the industrial domain, firms are continuously looking for the improvement of their production systems’ availability and the optimisation of production planning to assure the satisfaction of a random demand. Therefore, joint maintenance and production optimisation have been receiving increasing attention from researchers and firms. This study deals with joint nonperiodic preventive maintenance and production planning optimisation with regard to a manufacturing system composed of several machines mounted in series. The relationship between failure rate and production is considered to indicate the influence of variation of the production rate on the failure rate along with the average number of failures. The non-periodic preventive maintenance with respect to the production planning is proposed, to assure the continuity of the machines’ reliability by minimising the failures. In this paper, a mixed-integer-linear programme is developed to jointly find the optimal planning of integrated non-periodic preventive maintenance to production by minimising the total cost. Experimental results are presented to study the impact of the failure rate threshold on the number and periods of preventive maintenance actions.

Optimal Maintenance Schedule for a Wind Power Turbine with Aging Components

Wind power is one of the most important sources of renewable energy available today. A large part of the cost of wind energy is due to the cost of maintaining wind power equipment. When a wind turbine component fails to function, it might need to be replaced under circumstances that are less than ideal. This is known as corrective maintenance. To minimize unnecessary costs, a more active maintenance policy based on the life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modeling. In this paper, an optimal preventive maintenance algorithm is designed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance that allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones. The proposed optimization algorithm is applied to a four-component model of a wind turbine, and the optimal maintenance plans are computed for various initial conditions. The modeling results clearly show the benefit of PM planning compared to a pure CM strategy (about 30% lower maintenance cost).

Dynamic opportunistic maintenance grouping in a lot streaming based job-shop scheduling problem

This paper aims to model maintenance planning with a dynamic opportunistic approach for a job-shop production system. One issue in such a system is the positive or negative economic dependency. That is grouping maintenance activities may decrease or increase system costs. Furthermore, many maintenance models consider the planning of maintenance only based on a long-term horizon. While short-term and real circumstances such as system characteristics and constraints, workload, number of available maintenance teams, and variable maintenance cost and time are almost ignored. To address these issues, a rolling-horizon approach based on a long-term maintenance plan is proposed so that subsequent scheduling of maintenance and production activities are performed as events unfold through the time. Hence, we have developed a mixed-integer nonlinear mathematical model to simultaneously make decisions on maintenance selection, maintenance grouping, lot sizing and production scheduling. The objective function includes the costs of preventive and corrective maintenance activities as well as various production costs such as production and setup, tardiness penalty, and safety stock penalty. A self-adaptive Cuckoo Optimization Algorithm has been used to solve the proposed model. Numerical experiments were conducted to demonstrate the validity of the model and investigate the efficiency and effectiveness of the optimization algorithm.

Fault Tree Analysis for Reliability Analysis of Wind Turbines Considering the Imperfect Repair Effect

Wind turbines are complex and expensive equipment, requiring high reliability and low maintenance costs. However, most of the existing fault tree analysis (FTA) methods for reliability analysis of wind turbines assume that the repair of wind turbines can restore them to as good as new condition, which is called perfect repair. This assumption may not be realistic in practice, as the repair may not fully recover the original performance or functionality of the equipment or may introduce new defects or errors. This phenomenon is called imperfect repair, which can reduce the reliability of wind turbines over time. To consider the imperfect repair effect in reliability analysis, we present a new FTA approach in this study. In order to predict and assess the failure intensity and dependability of wind turbines under imperfect repair, the proposed FTA technique uses a log-linear proportional intensity model (LPIM). Failure probability, failure rate, and mean time to failure can all be improved with the suggested FTA technique for wind turbines operating with poor repair. The proposed FTA method can also identify the critical components or failure modes most affected by the imperfect repair effect and suggest preventive maintenance actions to improve the reliability of wind turbines. We demonstrate the applicability and effectiveness of the proposed FTA method through a case study on a real or hypothetical wind turbine system under imperfect repair. The findings indicate that the proposed FTA method offers a more precise and authentic assessment of the reliability of wind turbines in the presence of imperfect repair, in contrast to existing FTA methods that assume perfect repair. The findings also demonstrate that the electrical system, hydraulic system, gearbox, generator, and blade are the most critical components or failure modes affecting the system's reliability.

Proposal of a predictive and preventive maintenance plan in electrical substations through the use of thermography

The objective of this study was to make a proposal for a predictive and preventive maintenance plan in the electrical substations La Propicia, Las Palmas, and La Pradera belonging to CNEL-EP Esmeraldas Business Unit through the use of thermography. The methodology was of a qualitative approach, under the type of documentary-bibliographical research. The search for information was carried out online in scientific and academic databases. It was found that the information on the maintenance plans in the subtransmission system of CNEL EP Esmeraldas Business Unit, is meager, preventive and predictive maintenance actions are carried out such as revision, washing, readjustment and replacement of elements in poor condition, but no information was found on the analysis technique used leading to the maintenance activities of electrical equipment and installations. The results of the consulted investigations agree that the thermographic technique and its tools such as drones, infrared cameras and software programs are very useful for the diagnosis, collection and processing of data in electrical systems to determine maintenance requirements. Finally, the investigation yielded substantial information that allowed the preparation of a maintenance plan for the aforementioned La Propicia, Las Palmas and La Pradera electrical substations of CNEL-EP Esmeraldas Business Unit through the use of infrared thermography, to assist in their better operation. and performance by allowing the evaluation of the state of the equipment that makes up the electrical system of the aforementioned substations.