Preventive Maintenance Actions Research Articles

Failure Pattern and Reliability Analysis of Mobile Biomass Gasifier Engine (Prototype 3) using Weibull Calculation as a Basis for Updating the Preventive Maintenance Activity Schedule

Gasification is a technology that optimistically utilizes biomass to produce syngas (consisting of H2, CO, CO2, CH4) that can be used as an energy source while reducing excess CO2 emissions. However, if there are frequent failures due to unplanned maintenance activities, the engine performance will not be optimal. Therefore, an up-to-date reliability study is needed as a basis for updating preventive maintenance activities to maintain the quality of the engine so that it continues to operate properly and is durable. This research aims to analyze the reliability, unreliability, and availability of the Mobile Biomass Gasifier (Prototype3), as well as study the failure patterns measured using the shape parameter (?) with the Weibull distribution. The focus of this research is on the Suction blower which based on Pareto results is the equipment with the highest failure frequency. The results show that the average reliability is smaller than the failure rate. In addition, the value of the shape parameter (?) > 1, which means that the damage rate increases as the component ages. The implication of this study shows the need to update the preventive maintenance (PM) schedule by considering the addition of new PM activities that are in accordance with the duration of the life of the components that cause machine damage.

Preventive Maintenance Strategy Prediction of the Firewater Systems Based on the Pythagorean Fuzzy Cost–Benefit–Safety Analysis and Fuzzy Dematel

The firewater system is a complex system associated with the safety process of Hydrogen storage tanks. Predicting preventive maintenance strategies is essential to ensure the long-term reliability of this system. Therefore, it is necessary to evaluate the multistate reliability of the firewater system in order to predict preventive maintenance strategies and provide safety measures. A polymorphic fuzzy fault tree analysis (PFFTA) for the risk analysis of complex systems has attracted much attention because of its powerful evaluation capability and its ability to analyze relationships among basic events. However, obtaining multistate failure probability (MFP) data for basic events in PFFTA has always been a major challenge. It is also difficult to quantify the minimum cut set (MCS) in PFFTA and determine the critical components for selecting a preventive maintenance strategy. In this study, we propose the Pythagorean fuzzy cost–benefit–safety analysis by using the PFFTA, an improved consistency aggregation method (I-CAM), and fuzzy Dematel for a predictive preventive maintenance strategy. In the proposed approach, the I-CAM method was used to collect and aggregate weights of experts’ opinions to evaluate the MFP of basic events in PFFTA. As a result, a triptych cost–benefit–safety analysis based on Pythagorean fuzzy sets (PFSs) and the sum-product method (SPM) was estimated to reduce expert subjectivity, support an improved cost-effectiveness index to rank critical components, and fuzzy Dematel to evaluate influence of proposed preventive maintenance actions. To clarify the effectiveness and feasibility of the proposed methodology, a case study of the firewater system related to the plant is located in SONELGAZ electricity power plant (OUMACHE Unit) was demonstrated. Both evaluations of the cost–benefit–safety analysis of the critical component were performed, and selected the influence of preventive maintenance strategy of the firewater system was predicted.

Optimizing extended warranty options with preventive maintenance service under multinomial logit model

Extended warranty (EW) services have become the primary source of profit for manufacturers. How to design the appropriate EW service to balance customer user experience with the manufacturer’s profit issues has received great attention. Implementing preventive maintenance (PM) actions can not only reduce the customer’s lifecycle cost but also enhance the manufacturer’s brand image. Therefore, EW bundled with PM service has emerged in the after-sales market today. To enrich customers’ choice, we investigate the design of EW menus bundled with PM service options, and then we jointly determine EW prices and the frequency of PM actions for the provided menus. In the proposed model, we combine the multinomial logit model with prospect theory to characterize customer choice behavior when presented with different EW menu options. Based on that, with the aim of maximizing the manufacturer’s profit, we can derive sets of offers, the corresponding EW service prices, and the number of included PM actions. We also introduce two additional models that account for different customer purchase scenarios. Numerical results reveal that when the manufacturer chooses to offer a unified EW menu to all customers, it is consistently more profitable to present the menu at the point of product sales. Another interesting finding is that customized EW menus designed for various customer segments do not always outperform unified ones. Overall, this study will provide a foundation for manufacturers to make informed decisions regarding the selection and design of EW menus in various scenarios.

Integrated two-stage multi-factory assembly scheduling with maintenance considerations

ABSTRACT In the dynamic landscape of contemporary industry, integrating maintenance practices with production scheduling is essential for sustaining operational efficiency and competitiveness. This study addresses a two-stage multi-factory assembly scheduling problem, introducing an innovative approach that incorporates maintenance practices to enhance system reliability in the face of unexpected machine failures. For deterministic scheduling, a tailored mixed-integer programming model is presented to minimise the makespan. This model is extended to formulate a stochastic schedule, accommodating unforeseen machine breakdowns through stochastic distributions. The extension includes the integration of preventive and corrective maintenance activities into an integrated two-stage multi-factory assembly scheduling problem. To solve the resulting optimisation problem, a decomposition algorithm utilising an exact solver is proposed. This approach breaks down the main model into smaller models, addressing computational challenges. Comparative analyses against the widely adopted CPLEX software across various instances validate the effectiveness of our approach. A significant finding from this comparison is that our decomposition algorithm outperforms the exact solver, achieving the optimal solution at a faster rate. In sensitivity analyses, the results underscore the superior solution-finding capability of our integrated stochastic model compared to maintenance heuristics from existing literature.

A rolling horizon for rolling stock maintenance scheduling problem with cyclical activities

The rolling stock maintenance scheduling problem addressed here involves scheduling a set of cyclical preventive maintenance activities while considering resource capacity and geographically dispersed depots. The objective is to minimize both corrective and preventive maintenance costs using reliability features. We propose to use rolling horizon to decompose the problem into several successive subproblems such that each one tackles the scheduling of only the next cycle of activities instead of all cycles. The scheduled dates to be fixed should be as close as possible to their respective deadlines. Each train can have three status (in service, in maintenance, out of service). The activities deadlines are determined depending on component failure rate which has to be respected otherwise the train becomes out of service. An integer linear programming (ILP) model is proposed to solve the subproblem using a time oriented modeling to get an accurate maintenance cost (preventive and corrective) and respect a minimum of available trains. The rolling horizon allows to build a feasible schedule within a predefined planning horizon for moderate size problems with an interesting trade-off between computation time and solution quality.

Imperfect maintenance modelling and estimation for interval-censored data

The paper proposes a new maintenance model for a repairable system. The system is inspected at deterministic times. If a failure occurs between two successive inspections, a minimal repair is performed. At inspection times, two types of maintenance actions can be performed. The first originality of the paper is to consider that the choice between these two types of maintenance depends on the fact that there has been a failure or not since the previous inspection. If no failure has occurred since the last inspection, the maintenance, simply denoted PM, corresponds to a basic preventive maintenance action, whose effect is planned by the initial maintenance plan. If at least one failure has occurred since the last inspection, the maintenance, denoted PCM, includes both the preventive action planned by the initial maintenance plan, and a specific corrective action which aims to fix the defects that have happened since the last inspection. Both types of maintenances are assumed to be imperfect, of the ARA (Arithmetic Reduction of Age) type, with different parameters. Moreover, the paper derives several statistical inference methods for this model. The usual case of complete data is first considered. The second originality of the paper is to analyse interval-censored data in this imperfect maintenance framework. This situation holds when failure times are not observed, and the only information is the fact that failures have occurred or not between two inspections. The quality of the proposed methods is assessed on simulated data. Finally, a real dataset from a gas transmission system operator is provided and analysed with the proposed approach.

Integrated design and maintenance strategies for wind turbine gearboxes

PurposeThe purpose of this paper is to optimize the maintenance strategies for wind turbine (WT) gearboxes to minimize costs associated with PM actions, cooling, production loss and gearbox replacement. Two approaches, periodic imperfect maintenance and a novel design incorporating alternating gearboxes are compared to identify the most cost-effective solution.Design/methodology/approachThis study employs mathematical modeling to analyze the design, operation and maintenance of WT gearboxes. Two maintenance strategies are investigated, involving periodic imperfect maintenance actions and the incorporation of two similar gearboxes operating alternately. The models determine optimal preventive maintenance (PM) and switching periods to minimize total expected costs over the operating time span.FindingsThe research findings reveal, for the considered case of a moroccan wind farm, that the use of two similar gearboxes operating alternately is more cost-effective than relying on a single gearbox. The mathematical models developed enable the determination and comparison of optimal strategies for various WT gearbox scenarios and associated maintenance costs.Research limitations/implicationsLimitations may arise from simplifications in the mathematical models and assumptions about degradation, temperature monitoring and maintenance effectiveness. Future research could refine the models and incorporate additional factors for a more comprehensive analysis.Practical implicationsPractically, the study provides insights into optimizing WT gearbox maintenance strategies, considering the trade-offs between PM actions, cooling, production loss and gearbox replacement costs. The findings can inform decisions on maintenance planning and design modifications to enhance cost efficiency.Social implicationsWhile the primary focus is on cost optimization, the study indirectly contributes to the broader societal goal of sustainable energy production. Efficient maintenance strategies for WTs help ensure reliable and cost-effective renewable energy, potentially benefiting communities relying on wind power.Originality/valueThis paper introduces two distinct strategies for WT gearbox maintenance, extending beyond traditional periodic maintenance. The incorporation of alternating gearboxes presents a novel design approach. The developed mathematical models offer a valuable tool for determining and comparing optimal strategies tailored to specific WT scenarios and associated maintenance costs.

A Condition-based Maintenance Policy in Chance Space

A condition-based maintenance policy is considered for a deteriorating system including both of preventive and corrective maintenance actions. The gamma process is used to model stochastic degradation in the probability space. Although, the cost of preventive maintenance is considered as an uncertain variable due to incomplete information, and its distribution is estimated based on the opinions of some experts using the Delphi method. The optimal policy is determined by minimizing the expected cost rate function. Since in this function, there are both random variables discussing in a probability space, and an uncertain variable, which is considered in an uncertain space, we have to study the optimal policy in a chance space which is a combination of probability and uncertain spaces. The proposed methodology is explained in an illustrative example. Finally, the results are applied to a real data set.

Correlation analysis of degrading systems based on bivariate Wiener processes under imperfect maintenance

AbstractThis article focuses on the correlation between the degradation levels of the two components that form a system. The degradation evolution of each component is modeled using Wiener processes. Both components are dependent and this dependence is described using the trivariate reduction method. To reduce the degradation and extend the system lifetime, preventive maintenance actions are periodically performed. These preventive maintenance actions are imperfect and they are modeled by using an arithmetic reduction of degradation of infinite order model with a determined maintenance efficiency parameter. The evolution of the maintained system is analysed by assessing the expectation and variance of both degradation processes at successive maintenance times. The novelty of this work is the analysis of the Pearson correlation coefficient between the degradation levels of the two components. Different properties of the monotonicity of the Pearson correlation coefficient between the two degradation paths are obtained by considering equal maintenance efficiency and equal general time scales functions for the two Wiener degradation processes associated to each degrading component.

Assessment of thermal stability and application of engineering solutions to preserve the degrading road embankment near Hudson Bay Coast, Canada

There are 14 northern communities in Nunavik, the Arctic region of Quebec province, Canada. Transportation infrastructure plays a vital role in the social and economic development of these localities. The thawing of permafrost compromises the stability of northern transportation infrastructure. Harsh Arctic climate conditions limit the installation of effective monitoring systems to assess infrastructure stability. In Akulivik, the access road connects the Akulivik airport and the village of Akulivik. There is no monitoring to observe the thermal condition of the permafrost foundation of the access road, hindering the capacity to perform preventive maintenance activities, especially in the context of observed climate warming in Nunavik. This paper describes a project aiming at the assessment of the stability of the access road using a new approach and proposes adaptation solutions to stabilize the road, based on design tools recently developed. Particular attention was paid to the foundation soil under the side slope where relatively rapid permafrost degradation was occurring due to accumulated snow. The results indicate a positive thermal gradient of 0.29 °C/m under the side slope and a near-zero thermal gradient under the centerline. Projected climate warming was also considered to further investigate the thermal condition, providing a safety margin for the design of promising adaptation solutions. These results assist government agencies in evaluating the thermal conditions of underlying permafrost and deploying potential adaptation solutions in Akulivik.

Optimal maintenance policy design considering learning effects under successive performance-based contracts

Successive short-term performance-based contracts (PBCs) would not only drive the original equipment manufacturer (OEM) to improve maintenance service quality, but also decrease the financial burdens of the equipment owner. This paper presents the optimal maintenance policy incorporating both preventive maintenance (PM) and upgrade actions for continuously monitored degrading equipment under successive short-term PBCs. The improvement factor model is adopted to characterize the impact of the PM and upgrade actions. We also consider that the OEM’s learning effects will affect the cost and duration of the PM, as well as the upgrade cost. The learning effect increases as the period of the contract accumulates. To characterize the varied equipment production and degradation rates across different PBC periods, a Wiener process with covariates is developed. Meanwhile, we assume that the reward and penalty parameters of the performance-based revenue function varied each contract period, thus motivating the OEM to enhance the equipment availability. Based on that, the optimal maintenance plan for each contract period is determined to maximize the OEM’s expected net profit. Numerical examples show the superiority and examine the sensitivity of the proposed successive short-term PBCs. Managerial insights are also given to guide the implementation of the proposed maintenance policy.

Enhancing the maintenance strategy and cost in systems with surrogate assisted multiobjective evolutionary algorithms

Digital twins need efficient methodologies to design maintenance strategies for decision-making purposes. Recently, a methodology coupling computational simulation and multiobjective evolutionary algorithms has been proposed for developing maintenance strategies consisting in assigning times for preventive maintenance activities and designing the layout of components of a system, minimizing the unavailability of the system and the strategy cost.Here, surrogate assisted evolutionary algorithms (SAEAs) enhance the multiobjective optimization and improve the drawback of the computational cost of the maintenance strategy assessment based on discrete simulation. Several Kriging surrogates were tested.Two industrial test cases are handled in the experimental section, where the methodology succeed in obtaining nondominated designs improving previous benchmarks, and enhancing state-of-the-art multiobjective optimizers, with up to an order of magnitude in terms of the number of fitness function evaluations. Results show that using multiobjective SAEAs in the development of optimal maintenance strategies could foster and improve digital twins operations.

Advancing weather predictions for offshore wind farm maintenance through deep learning

Historical met-ocean data are widely used in the decision support tools to evaluate different operations & maintenance (O&M) strategies for offshore wind energy. Although effective, they are often very limited, which may not be able to represent prolonged offshore weather conditions at the wind farm sites. This hinders their application to the O&M planning. In this paper, a deep learning approach is proposed to build up the stochastic weather generator, in which the long short-term memory neural network is leveraged to simulate wind and wave time series data. The neural network is trained using the (limited) historical met-ocean dataset to accurately capture the statistical characteristics of wind and wave conditions. The results demonstrate the effectiveness of the proposed stochastic weather generator in delivering both open-loop and closed-loop forecasting for wind speed and significant wave height data, thereby supporting both corrective and preventive maintenance activities. The case study reveals that the open-loop forecast excels in short-term hourly met-ocean parameter predictions, while the closed-loop forecast proficiently captures the met-ocean patterns within a predefined window. Although the closed-loop forecast for wave parameters generally follows the measurements trend, it diverges from the actual measurements; a discrepancy likely due to the complex spatial-temporal dynamics of waves not completely captured by the LSTM model. The proposed LSTM model, considered as a complementary but connected solution, is able to enhance the utility of the limited historical data in O&M planning.

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.

A comprehensive framework for identifying safety-related and ageing sensitive sscs in low power nuclear research reactor

This work aims to establish a novel approach for the selection of structures, systems, and components (SSCs) important to safety and vulnerable to ageing degradation for low-power research reactors. For this purpose, the framework developed combines the Preliminary Risk Analysis (PRA) technique to categorize SSCs into four different safety classes, and Ageing Failure Mode and Effect Analysis (AFMEA) technique to find out ageing failure modes related to a specific SSC and evaluate them according to the Risk Priory Number (RPN). The intersection of the PRA and AFMEA outcomes then yields a list of SSCs that are both important to safety and strongly ageing sensitive, making it possible to optimize the selection process of SSCs to be prioritized in further preventive maintenance, periodic testing, and inspection activities. The present paper also conducts a case study to apply the proposed methodology to the Brazilian IPR-R1 TRIGA nuclear research reactor. The selected SSCs were then both AL and SS fuel rods from the reactor core, stainless steel pipe, thermocouples, pressure indicators, and water level indicators from the primary circuit, and power monitoring channels, radiation monitors, and cooling water temperature channels from the controlling system. Moreover, the approach was also able to identify SSCs that are important from a safety perspective but are not ageing sensitive (reactor tank and control rods) and components that are not important to safety but are strongly ageing sensitive (pipe filters). These findings may further serve as input for the comprehensive safety assessment conducted during the Periodic Safety Review of the facility.

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.

An optimal maintenance strategy for machining system considering production rates

In this paper, a maintenance strategy is developed to determine the optimal number of preventive maintenance actions to carry out over the finite operating time span H for a manufacturing system satisfying several demands. The system is composed of dry machining which produces two types of products (steel products and aluminum products). At any given time, the machine is able to produce one type of product, and then switches to another. This is done by modeling and minimizing the expected total maintenance cost considering the influence of production rates on the system degradation. A numerical example is presented to illustrate the use of the proposed approach and to discuss the obtained results.

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.