Suitable Maintenance Strategy Research Articles

Maintenance of Public Buildings in the Central Region of Ghana

Building maintenance ensures functionality, stability, and appearance and extends the structure’s lifespan. This study examined suitable maintenance strategies for public buildings in the Central Region of Ghana. Out of four hundred (400) questionnaires administered to maintenance officers and personnel of public buildings in the Central Region, two hundred and ninety-six (296) were returned, representing 74%. The findings showed that the respondents considered the deterioration of buildings and the maintenance quality as very important factors. The Kaiser-Meyer-Olkin (KMO) measure of sample adequacy for p-values varies from 0.914 to 0.971, indicating that the constructs or factors strongly impact the prediction of building maintenance management. Further findings show that all the constructs or factors have strong (0.700 – 0.800) factor loadings, indicating a strong influence in predicting building maintenance management. Only one construct or factor (maintenance budgeting and auditing) with one variable (end-of-job documentation) had strong (0.940) factor loadings. Three constructs/factors (maintenance techniques, maintenance culture, and maintenance management) were found to have strong (7.00 - 8.00) factor loadings, which strongly influence predicting building maintenance management. It is concluded that attention should be paid to the developed constructs or factors in any future work in the Central Region and should involve Development Officers.

Multi-Parametric Delineation Approach for Homogeneous Sectioning of Asphalt Pavements

The demand for preserving existing roadway infrastructure has been increasing to regulate expensive reconstruction activities. The maintenance of homogeneous road sections is one of the approaches to economize the overall management of pavement systems. The existing homogeneous delineation methods consider one or two parameters for segmenting the pavements based on similar characteristics, which are found to be a repetitive process. Also, there is a need to consider multiple parameters that represent the functional, structural, and traffic characteristics in segmentation process. Therefore, the objective of this study was to develop a multi-parameter-based delineation approach (MPDA) to segment the pavements into subsections with similar features considering functional, structural, and traffic characteristics. Deflection bowl parameters, unified pavement health index (functional performance metric), surface layer modulus, and traffic reported in terms of AADT were employed for developing a multi-parametric delineation index (MPDI). A total of 1781 datapoints covering 26 road sections in the State of Andhra Pradesh, India, were used. The C-charts method-based segmentation for MPDI was applied to obtain the homogeneous sections. The devised approach was found to be efficient in segmenting the pavements as well as robust in selecting suitable maintenance strategies for each group of the homogeneous sections. Further, the segmentation processes were automated for easier implementation by the agencies.

State-of-the-art review on asset management methodologies for oil-immersed power transformers

Owing to their vital function and high cost, oil-immersed power transformers represent key links in electricity grids. While extensive effort has been invested by industry in developing various condition monitoring and fault diagnosis apparatuses, it is not economically feasible to apply rigorous inspection and extensive testing to all power transformer fleets in the network. The fundamental purpose of asset management is to balance cost and reliability of the system. Maintaining the reliability of power transformers involves regular condition monitoring, fault diagnosis and overall health assessment. In addition, estimating the remnant life of power transformers can help asset managers plan capital investments. By reviewing the methods used for power transformer asset management, assets integrity team can decide the most suitable maintenance strategy for each transformer in the network. This paper is aimed to present state-of-the-art review for power transformer asset management, health assessment and remnant life methodologies. While many review papers on transformer fault diagnosis have been published in the literature, not much attention was given to present a state-of-the-art review on the three modules of the transformer asset managements that include fault diagnosis, reliability assessment and life management modules. These three modules are comprehensively presented in this paper that also presents future challenges and improvement in this field of research.

Stratégia údržby a efektívny výrobný systém

In industrial areas, key maintenance tasks have become an essential part of an organization's overall profitability. Maintenance techniques currently have the ability to significantly increase an organization's competitive advantage in the global marketplace. The first important step is to correctly set up a suitable maintenance strategy. The maintenance strategy of the production support process must, as part of the company's strategy, be based on and support its goals and strategy, perspectives and starting points. Choosing the right maintenance strategy is important for every business. He must determine which strategy will be advantageous for him from the point of view of costs, downtime and other important factors

Application of the reliability-centered maintenance techniques for dynamic equipment in the petrochemical ammonia-related systems

The reliability-centered maintenance (RCM) analysis method and the RCM implementation process for dynamic equipment in petrochemical ammonia-related systems were introduced. The operation situation, key roles, and potential risks were clarified through data analysis and system screening in ammonia dynamic equipment, and FMEA was used to analyze the failure mode. Then the risk of each failure mode was managed hierarchically, a suitable risk assessment standard and risk matrix were developed for the enterprise, and a suitable maintenance strategy was formulated for the risk level of each failure mode.

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide.

Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches

Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches

A novel decision support system for managing predictive maintenance strategies based on machine learning approaches

Nowadays, the industrial environment is characterised by growing competitiveness, short response times, cost reduction and reliability of production to meet customer needs. Thus, the new industrial paradigm of Industry 4.0 has gained interest worldwide, leading many manufacturers to a significant digital transformation. Digital technologies have enabled a novel approach to decision-making processes based on data-driven strategies, where knowledge extraction relies on the analysis of a large amount of data from sensor-equipped factories. In this context, Predictive Maintenance (PdM) based on Machine Learning (ML) is one of the most prominent data-driven analytical approaches for monitoring industrial systems aiming to maximise reliability and efficiency. In fact, PdM aims not only to reduce equipment failure rates but also to minimise operating costs by maximising equipment life. When considering industrial applications, industries deal with different issues and constraints relating to process digitalisation. The main purpose of this study is to develop a new decision support system based on decision trees (DTs) that guides the decision-making process of PdM implementation, considering context-aware information, quality and maturity of collected data, severity, occurrence and detectability of potential failures (identified through FMECA analysis) and direct and indirect maintenance costs. The decision trees allow the study of different scenarios to identify the conditions under which a PdM policy, based on the ML algorithm, is economically profitable compared to corrective maintenance, considered to be the current scenario. The results show that the proposed methodology is a simple and easy way to implement tool to support the decision process by assessing the different levels of occurrence and severity of failures. For each level, savings and the potential costs have been evaluated at leaf nodes of the trees aimed at defining the most suitable maintenance strategy implementation. Finally, the proposed DTs are applied to a real industrial case to illustrate their applicability and robustness.

Modelling and availability analysis of container manufacturing industry with Petri Nets

The present work deals with the modeling and availability analysis of Container Manufacturing industry by applying Petri Net (PN) technique and attains the various availability levels by varying various operating parameters that affects the overall availability of the system. This system consist of four subsystems/components namely Gas Cutting machine, Hydraulic Press, Rolling machine and Submerged arc welding (SAW) welding machine. For the mathematical recreation of the system pertaining to the real environment, a Petri module of GRIF 2021 software was used. The model of the system has been made with Generalized Stochastic Petri Net (GSPN) considering exponential distribution for the failure of the components. In the existing research, an attempt has been made to identify the most critical component of system and suggest the repair priorities of different components on the basis of effect of repair rate on overall system availability. Also an attempt has been to find out the optimum number of repairmen. In the present work Hydraulic Press is the critical subsystem having more effect on availability of the system as compared to other subsystems. The optimum no of repairmen required are four thereafter, no increase in system availability seen in the present case. The results of present research work are might be helpful for plant management to design a suitable maintenance strategy to meet one of the objectives i.e. economically maintenance with maximum availability of the system.

A New Implementation of Digital Twins for Fault Diagnosis of Large Industrial Equipment

Refurbishment and remanufacturing play a vital role in the sustainability of the large industrial field, which aims at restoring the equipment that is close to the end of their life. The EU-funded project RECLAIM proposes new approaches and techniques to support these two activities in order to achieve saving valuable materials and resources by renewing and recycling the mechanical equipment rather than scraping them when they exceed the end of the lifetime. As the most critical part of predictive maintenance in RECLAIM, the fault diagnosis technique could provide the necessary information about the identification of the failure type, thus making suitable maintenance strategies. In this paper, we propose a novel implementation method that can combine the digital twins with the fault diagnosis of large industrial equipment. Experiment result and analysis demonstrate that the proposed framework performs well for the fault diagnosis of rolling bearing.

Availability analysis of Tube Light Manufacturing industry using numerical technique

Continuous improvement in the performance of any industrial system is highly desirable. But failure is an inevitable phenomenon. In order to run industrial systems without failure reliability and availability of each subsystem needs to be maintained. So the maintenance of the working units of the system plays a very important role to make system available for a pre define period of time. The system performance can be increased to the maximum extent by adopting the suitable maintenance strategy. In this paper a computational technique is used for the analysis of Time Dependant System Availability (TDSA) of Tube Light manufacturing system has been addressed.. This system comprises of seven subsystems namely End Sealing cum Coating Machine, Baker Machine, Wiper Machine, Sealing Machine and Exhaust Machine, Capping Machine and Activator machine. Mathematical formulation of the system is done using Markov Approach (MA). The model has been solved TDSA with the help of Adaptive step size control Runge-Kutta Method. After carrying out the TDSA analysis it is found that subsystem- II i.e Baker machine is critical subsystem having more effect on TDSA of the system on basis of repair rate on as compared to other subsystems. Further, present research is also helpful in deciding maintenance repair priorities of different subsystems.

Frequency Hoyer attention based convolutional neural network for remaining useful life prediction of machinery

Remaining useful life (RUL) prediction based on vibration data is a vital part of bearing prognostic and health management, which can be applied to formulate a suitable maintenance strategy. Recently, attention mechanism has been widely studied and applied in the field of prognostics, which can adaptively enhance the features for RUL prediction and weaken the features interfering with the accurate estimation of the condition. However, insufficient priori information has been provided to the deep learning model via the attention mechanism. The domain knowledge of the special structure and the characteristics of the bearing vibration signal is underutilized in the conventional attention mechanism. An innovative attention based RUL prediction model, called frequency Hoyer attention based convolutional neural network (FHA-CNN), is proposed in this study, which combines a deep learning model and signal processing method organically. The 1D convolutional layer and isometric empirical wavelet transform are developed to extract the latent representation of vibration signals from different scales. The proposed FHA is applied to calculate the weight of the feature map adaptively, in which three types of Hoyer index are adopted to comprehensively evaluate the contribution of each frequency part to the degradation of rolling bearings from the frequency domain perspective. To verify the superiority of the proposed method, two run-to-failure experimental dataset case studies are analysed. The obtained results indicate that the proposed FHA-CNN model exhibits a better performance than conventional deep learning-based RUL prediction methods. In addition, the proposed method concentrates on the special structure of the bearing vibration signal and provides a novel insight into the decision-making processes of deep neural networks.

Improving the Identification and prioritization of the most important risks of safety equipment in FMEA with a hybrid multiple criteria decision-making technique

The purpose of this paper is to develop a new Failure Mode and Effect Analysis (FMEA) framework for identification, prioritization and improvement of failure modes. A hybrid multiple criteria decision-making (MCDM) method combining Stepwise Weight Assessment Ratio Analysis (SWARA) and VlseKriterijumska optimizacija I KOm-promisno Resenje (VIKOR) is used to rank the risk of failure modes identified in FMEA. For this purpose, the SWARA method is utilized to obtain the influential weights and then VIKOR technique is employed to give the prioritization levels for the failure modes in safety equipment. A case study of a gas company in Lorestan Province (Iran) is provided to illustrate the potential application and benefits of the proposed FMEA approach. The obtained results show that the new risk priority model can be effective to find high risk failure modes and create suitable maintenance strategies. The proposed FMEA also can overcome the shortcomings, improve the effectiveness of the traditional FMEA and provide useful information to help in managing risks of safety equipment.

Developing prescriptive maintenance strategies in the aviation industry based on a discrete-event simulation framework for post-prognostics decision making

The aviation industry is facing an ever-increasing competition to lower its operating cost. Simultaneously, new factors, such as sustainability and customer experience, become more important to differentiate from competitors. As aircraft maintenance contributes about 20% to the overall cost of airline operations and can significantly influence other objectives of an airline as well, maintenance providers are required to constantly lower their cost share and contribute to a more reliable and sustainable aircraft operation. Subsequently, new condition-monitoring technologies have emerged that are expected to improve maintenance operations by reducing cost and increasing the aircraft’s availability. As many of these technologies are still in their technological infancy, it is necessary to determine the expected benefit for the airline operations with the given technological maturity and to develop suitable maintenance strategies that incorporate the newly gained insights. With this paper, a discrete-event simulation framework is developed that uses established parameters to describe a condition-monitoring technology’s performance and subsequently develops a suitable prescriptive maintenance strategy. Therefore, it enables the adjustment of the optimization goal for the developed strategy to incorporate performance features beyond the frequently used financial indicators. The developed capabilities will be demonstrated for the tire pressure measurement task of an Airbus A320.

A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy selection of safety critical assets

In most industries, such as aerospace, manufacturing, transport and energy sectors, maintenance plays a vital role in improving the performance of safety critical equipment and facilities. It also helps industries achieve the largest possible efficiency, ensure workplace and environmental safety, and reduce unnecessary breakdowns and costs. Therefore, it is crucial for industries to adopt an optimal maintenance strategy for their critical systems and infrastructure. In this study, we aim to propose a novel analytical multi-criteria decision-making (MCDM) methodology for selecting the most suitable maintenance strategy in distillation units of oil refinery plants. The alternative maintenance strategies include run-to-failure (RTF), preventive maintenance (PM), condition-based maintenance (CBM), and reliability centered maintenance (RCM), which are evaluated with respect to 12 sub-criteria in three categories of economical, safety, and sustainability issues. The MCDM methodology consists of a DEMATEL-based analytic network process (ANP) method to determine the importance weights of decision criteria and a VIKOR method to rank the maintenance strategies. Also, interval type-2 fuzzy sets are used to capture uncertainty in experts’ individual judgments. Finally, a real case study is provided to show the applicability of the proposed methodology to an oil refinery plant. The results show that, thanks to advances in degradation modeling, sensor technology, and data analytics platforms, the RCM and CBM are the superior maintenance strategy for crude oil distillation systems.

Maintenance therapy for untreated diffuse large B-cell lymphoma: a systematic review and network meta-analysis.

Background:Several clinical trials have been conducted to evaluate the role of maintenance therapy in untreated diffuse large B-cell lymphoma (DLBCL) patients with complete response or partial response following standard immunochemotherapy; however, the effect of maintenance therapy remains uncertain, and a suitable maintenance strategy has not been determined because of the lack of direct/indirect comparisons.Methods:We performed a systematic review and Bayesian network meta-analysis (NMA) to analyze and compare the effectiveness of different maintenance regimens in newly diagnosed DLBCL patients. We searched the PubMed Medline, Embase, Web of Science, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials for relevant papers from inception to 18 March 2021. Our study was prospectively registered in the International Prospective Register of Systematic Reviews (CRD42020168864). Data on overall survival (OS) were extracted and the treatments were ranked using the surface under the cumulative ranking (SUCRA) curve.Results:Eight trials and seven treatments involving 3525 patients were analyzed. OS analysis indicated that none of the drugs showed any benefit compared with non-maintenance therapy. Maintenance therapy with lenalidomide (SUCRA 69.3%) was ranked first in terms of OS.Conclusion:Based on the OS results observed in this NMA, we do not recommend maintenance therapy in patients with newly diagnosed DLBLC after first-line therapy.

Reliability-centered maintenance and cost optimization for offshore oil and gas components

Reliability-Centered Maintenance (RCM) is widely used for enterprise asset management in the Oil and Gas sector. The traditional RCM process encompasses the development of equipment hierarchy, the determination of the functional failure of assets, identification of the critical failure modes and the development of suitable maintenance strategies for these failure modes through RCM logic tree analysis. However, the current industry standards and conditions call for greater strides of production availability and reliability of the assets. The aim of the paper is to deliver RCM oriented cost optimization framework for the assets through the implementation of probabilistic and statistical analysis. Probabilistic analysis of the most critical components predicts the failure over time. The mean time to failure, mean time between failure and mean time to repair parameters are determined from the failure history. The parameters of the probability distribution, for instance, the shape and scale parameters of Weibull distribution determine the failure pattern of the failure mode over time. The cost optimization modules allow the analyst to determine the optimum intervals for maintenance or replacement of the maintainable items. The integrated RCM methodology, therefore, provides quantitative and cost-effective solutions for asset maintenance and management. A case study of an equipment in the Oil and Gas sector will be presented to illustrate the optimization framework.

RAM analysis and availability optimization of thermal power plant water circulation system using PSO

This paper presents reliability, availability, and maintainability (RAM) analysis framework for evaluating the performance of a circulation system of water (WCS) used in a coal-fired power plant (CFPP). The performance of WCS is evaluated using a reliability block diagram (RBD), fault tree analysis (FTA), and Markov birth–death probabilistic approach. In this work, the system under study consists of five subsystems connected in series and parallel configuration namely condensate extraction pump (CEP), low-pressure feed water heater (LPH), deaerator (DR), boiler feed pump (BFP), high-pressure feed water heater (HPH). The reliability block diagram (RBD) and fault tree approach (FTA) have been employed for the performance evaluation of WCS. The Markov probabilistic approach based simulation model is developed. The transition diagram of the proposed model represented several states with full working capacity, reduced capacity, and failed state. The ranking of critical equipment is decided on the basis of criticality level of equipment. The study results revealed that the boiler feed pump affects the system availability at most, while the failure of deaerator affects it least. The availability of the system is optimized using the particle swarm optimization method. The optimized availability parameter (TBF, TTR) based modified maintenance strategy is recommended to enhance the availability of the plant system. The optimized failure rate and repair rate parameters of the subsystem are used to suggest a suitable maintenance strategy for the water circulation system of the thermal power plant. The proposed RAM framework helps the decision-makers to plan the maintenance activity as per the criticality level of subsystems and allocate the resources accordingly.

Strategic formulation of industrial maintenance based on equipment reliability in a sugar and ethanol production plant

Maintenance and management have substantial importance in the search of the company’s competitive advantages. In this direction, a reliability analysis of equipments is very important for the definition of the most suitable maintenance strategy. The main goal of this paper is to assess the reliability-centered maintenance of the industrial reliability curve of a sugar cane department of an industry located in São Paulo State, Brazil. The proposed research method was based on the application of existing statistical modeling for the times between failures (TBF) of all reported equipment failures or the interruption times in the production line. These times were modeled by standard lifetime probability distributions as log-normal and Weibull distributions. The results showed that the company's strategy for preventive maintenance during the off-season is not adequate and the statistical analysis also identified important factors that affect the company's maintenance strategy. These results could be of great interest for the company and for engineering applications in general.

Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization

The work reported in this paper evaluates the performance of the turbo-generator subsystem of a thermal power plant by employing a particle swarm optimization method. The majorly selected systems considered for the study are turbine governing, turbine lubrication, generator oil system, generator gas system, and generator excitation system. The Markov based availability modeling is adopted for the turbo-generator system. The Markov probabilistic approach based availability simulation model is reported. Through this study, the Markov based results reveal that the turbine lubrication and generator excitation subsystems affect the system availability at most. Therefore, these critical subsystems are on higher priority from a maintenance perspective. Furthermore, system availability is optimized to get the optimized availability parameters using the particle swarm optimization method. The PSO based results revealed that the maximum optimized availability of 98.9394 % is obtained for 30 particle size. The results reveal that with an increase in particle size of more than 30, the system availability remains constant. Further, the optimized availability parameters are used to recommend a suitable maintenance strategy for the plant's turbo-generator system.