Effective Maintenance Policy Research Articles

Pengendalian Kualitas Produk Gula Studi Kasus PT Madubaru PG. PS. Madukismo Yogyakarta

PG Madukismo is a sugar factory that was established in 1955 and is the only sugar factory in the province of the Special Region of Yogyakarta. This factory is located in Padokan Village, Tirtonirmolo Village, Kasihan District, Bantul Regency, Special Region of Yogyakarta. PT Madubaru, a company engaged in sugar cane processing, is a sugar and spirits factory located in the Yogyakarta area. Companies that produce cane sugar with SHS IA quality. Product defects are often found, resulting in a decrease in quality, therefore it is necessary to carry out quality control. This study uses the Failure Mode and Effect Analysis (FMEA) method to identify failure modes, Logic Tree Analysis (LTA) to determine the consequences of failure arising from failure modes and determine effective preventive maintenance policies for each machine. For the production quality control section, the Statistical Processing Control (SPC) method is used to analyze product defects to determine whether product defects are still within controllable limits or not. While the Failure Mode and Effect Analysis (FMEA) method is used to determine the failure mode in sugar production. For the quality control section on raw materials using the Economic Order Quantity (EOQ), Re-Order Point (ROP), Safety Stock (SS), and Total Inventory Cost (TIC) methods. The conclusion of this study is regarding the results of the analysis and calculations carried out with several settlement methods and there are several causes of production defects from 5 factors, such as: human factors, work methods, environment, raw materials and machines used. And provide solutions to companies in order to reduce defects by controlling product quality.

Review of maintenance strategies for ship machinery systems

This study presents the state-of-art and future trends of the maintenance approaches to ship machinery systems. A systematic review process is conducted to identify relevant literature. Firstly, literature is examined by bibliometric analyses based on the determination of the distribution of papers by years, the top authors, papers, journals, and organisations. Then, studies in the literature are reviewed and presented. In addition, the advantages and difficulties of each maintenance strategy are discussed. Finally, an effective maintenance policy that can implement in new ship concepts such as autonomous marine vessels is introduced in line with the information from the literature. In the future, it is believed that maintenance practices will be performed via the condition-based approach when the evolution of followed maintenance operations in ship machinery systems is examined. However, it should be underlined that more effective and powerful maintenance management can be achieved by supporting the system performance monitoring with reliability and risk approaches.

Aircraft Engine Bleed Valve Prognostics Using Multiclass Gated Recurrent Unit

Prognostics and health management is an engineering discipline that aims to support system operation while ensuring maximum safety and performance. Prognostics is a key step of this framework, focusing on developing effective maintenance policies based on predictive methods. Traditionally, prognostics models forecast the degradation process using regression techniques that approximate a mapping function from input to continuous remaining useful life estimates. These models are typically of high complexity and low interpretability. Classification approaches are an alternative solution to these types of models. We propose a predictive classification model that translates the input into discrete output variables instead of mapping the input to a single remaining useful life estimate. Each discrete output variable corresponds to a range of remaining useful life values. In other words, each output class variable represents the likelihood or risk of failure within a specific time range. We apply this model to a real-world case study involving the unscheduled and scheduled removals of a set of engine bleed valves from a fleet of Boeing 737 aircraft. The model can reach an area under the (micro-average) receiver operating characteristic curve of 72%. Our results suggest that the proposed multiclass gated recurrent unit network can provide valuable information about the different fault stages (corresponding to intervals of residual lives) of the studied valves.

A Low-Cost Deep Learning System to Characterize Asphalt Surface Deterioration

Every day millions of people travel on highways for work- or leisure-related purposes. Ensuring road safety is thus of paramount importance, and maintaining good-quality road pavements is essential, requiring an effective maintenance policy. The automation of some road pavement maintenance tasks can reduce the time and effort required from experts. This paper proposes a simple system to help speed up road pavement surface inspection and its analysis towards making maintenance decisions. A low-cost video camera mounted on a vehicle was used to capture pavement imagery, which was fed to an automatic crack detection and classification system based on deep neural networks. The system provided two types of output: (i) a cracking percentage per road segment, providing an alert to areas that require attention from the experts; (ii) a segmentation map highlighting which areas of the road pavement surface are affected by cracking. With this data, it became possible to select which maintenance or rehabilitation processes the road pavement required. The system achieved promising results in the analysis of highway pavements, and being automated and having a low processing time, the system is expected to be an effective aid for experts dealing with road pavement maintenance.

A joint age-based system replacement and component reallocation maintenance policy: Optimization, analysis and resilience

The component reallocation (CR) policy is an emerging and effective preventive maintenance policy for improving the system performance, which has attracted much attention in reliability engineering and is independent from the research of age-based system replacement (ASR). This paper is to explore advantages and characteristics of a joint ASR and CR (JASRCR) preventive maintenance policy for a system, for which it is hard or uneconomical to execute repair or replacement at the component level but easy to reallocate the functionally-interchangeable and unevenly degraded components. In the JASRCR policy, the CR is implemented before the system failure and preventive ASR, and the corrective maintenance is to replace the system whenever it fails. The CR is of capability of balancing the degradation processes of components and thus has potential on improving the system resilience in resisting the component failures. An integrated mixed binary nonlinear optimization model is proposed to determine the optimal assignment of CR and optimal times for ASR and CR with the objective of minimizing the expected total maintenance cost per unit time. Numerical experiments demonstrate efficiency of the JASRCR policy on improving system resilience and reducing system maintenance cost.

Optimum condition-based maintenance policy with dynamic inspections based on reinforcement learning

During the service life, inspections and repairs should be applied timely to maintain the reliability level of deteriorating structures. Condition-based maintenance (CBM) is an effective maintenance policy to reduce the life cycle cost. When the number of inspections does not change regardless of the performance, the CBM is categorize as fixed inspection (FI), otherwise, the inspection policy is denoted as dynamic inspection (DI). Compared with FI policy, DI policy performs the inspections based on the actual state and can avoid the unnecessary or insufficient inspections. Reinforcement learning is an effective and advanced decision-making tool and provides a useful method to optimize DI policy. Meanwhile, reinforcement learning has two methods (model free and model based) distinguished by the interaction method of environment. Comparison of two methods can help select an appropriate method to derive DI policy. Here, model based dynamic inspection (MBDI) and model free dynamic inspection (MFDI) are investigated for their performances in integrity management of fatigue structures. A fatigue details of ship structure is applied to illustrate the proposed framework and comparison study between DI and FI is performed. Results show that dynamic inspections can effectively reduce the expected life cycle costs. Furthermore, MFDI has a better performance than MBDI under different deteriorating rate and cost conditions.

A dynamic condition-based maintenance optimization model for mission-oriented system based on inverse Gaussian degradation process

An effective maintenance policy optimization model can reduce maintenance cost and system operation risk. For mission-oriented systems, the degradation process changes dynamically and is monotonous and irreversible. Meanwhile, the risk of early failure is high. Therefore, this paper proposes a dynamic condition-based maintenance (CBM) optimization model for mission-oriented system based on inverse Gaussian (IG) degradation process. Firstly, the IG process with random drift coefficient is used to describe the degradation process and the relevant probability distributions are obtained. Secondly, the dynamic preventive maintenance threshold (DPMT) function is used to control the early failure risk of the mission-oriented system, and the influence of imperfect preventive maintenance (PM) on the degradation amount and degradation rate is analysed comprehensively. Thirdly, according to the mission availability requirement, the probability formulas of different types of renewal policies are obtained, and the CBM optimization model is constructed. Finally, a numerical example is presented to verify the proposed model. The comparison with the fixed PM threshold model and the sensitivity analysis show the effectiveness and application value of the optimization model.

Reliability and availability assessment of railway locomotive bogies under correlated failures

Railways play a pivotal role in transportation systems worldwide. Effective maintenance policies are crucial to guarantee the reliability and availability of such systems inserted in a challenging and competitive environment. In this scenario, condition-based and predictive maintenance strategies emerge as key components of the European rail traffic system, where common advanced monitoring solutions of railway assets serve as performance metrics for the development of maintenance rules. However, the effectiveness of a good maintenance policy is intrinsically dependent on a clear assessment of the system on target, including not only aspects related to its complexity and financial importance, but fundamentally the physical characteristics and the environmental and operational conditions to which the system is subject to. In this sense, simulation models are useful as they can mimic the behaviour of systems, especially those too complex to solve analytically, incorporating the characteristics and the inherent stochastic behaviour associated with them, including their correlation structure. This study focuses on the locomotive bogie, which is a complex subsystem of the train onto which the wheels of the vehicle are fixed. The main components of the bogie are described as well as the stochastic behaviour associated with the occurrence of failure. A Discrete Event Simulation (DES) model is proposed to study the impact on the system, in terms of reliability and availability, under different scenarios with varying assumptions on the underlying failure modes, repairs, and on the failures’ correlation structure. The results point out that the model is suitable for representing the main characteristics of this train subsystem, and it identifies the parameters and components that are more critical in terms of reliability and availability fluctuations when contrasted with the variations imposed on the model by considering different scenarios. Overall, the DES model allows the robust assessment of where the focus should be put according to the uncertainty embedded in the correlations of failures and/or in the maintenance durations.

Stochastic Model for Integrated Preventive Maintenance Planning

Preventive maintenance planning management is modelled with stochastic approach. It is aimed to prevent stoppages and quality disorders due to disturbances, carriage and maintenance processes in production. Different maintenance policy alternatives were considered in order to develop and sustain more effective maintenance policies. The preventive maintenance process includes the cost of inspection and maintenance status, the cost of repair and other losses in the accidents with operator injuries and damage to the possible value of the situation. The stochastic model approach is applied for determine the possible period intervals of the machinery and equipment and the maintenance process analyzed and discussed in detail. The preventive maintenance approach in the maintenance planning process is aimed to develop a sustainable maintenance policy without any disturbance in the quality of production from any disturbance and disruption of the system in the long term. However, it is aimed to take preventive maintenance measures as well as to analyze the current system condition and predict future situations.

Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs

In complex systems with stochastically dependent components which are not observed directly, determining an effective maintenance policy is a difficult task. In this paper, a dynamic Bayesian network based maintenance decision framework is proposed to evaluate proactive maintenance policies for such systems. Two preventive and one predictive maintenance strategies from a cost perspective are designed for multi-component dependable systems which aim to reduce maintenance cost while increasing system reliability at the same time. Tabu procedure is employed to avoid repetitive similar actions. The performances of the policies are compared with a reactive maintenance strategy and also with each other using different strategy parameters on a real life system confronted in thermal power plants for six different scenarios. The scenarios are designed considering different structures of system dependability and reactive cost. The results show that the threshold based maintenance which is the predictive strategy gives the minimum cost and maintenance number in almost all scenarios.

Reliability Evaluation by a Dependent Competing Failure Model Including a Time-Varying Rate for Sudden Degradation Increments

In this study, an effective dependent competing failure model is proposed for systems suffering from shocks. Under worse system degradation, shocks with the same magnitudes can bring larger sudden degradation increments. However, this relationship was ignored in most existing research. To address this problem, in the proposed failure model, a time-dependent rate is included for the sudden degradation increments by shocks. This time-varying rate is applied for the consideration that system degradation is closely related to operation time. Two dependent competing failure processes, i.e., soft failure and hard failure, are involved in the dependent competing failure model. The distribution of the total sudden degradation increments is then deduced, and its accuracy is verified by Monte Carlo simulation. The developed reliability model is illustrated by the reliability analysis of a microelectromechanical system. The sensitivity analyses of important parameters are also performed. The analysis results show that the proposed time-varying model effectively considers the impact of system degradation on sudden degradation increments, and by using this model, the change of sudden degradation increments can be well reflected under different system performances. These advantages make the reliability model more practical and help achieve more effective maintenance policies.

Mathematical determination of the optimal control and maintenance scheme for industrial processes

Modern industrial projects face many challenges in order to sustain their productivity in a capital effective manner. Operational costs and production lines maintenance policy is on the top factors that play critical roles in that challenge. Linear programming is utilized in this paper to examine the possible minimization of the operational cost and determine an effective optimal maintenance policy for middle-sized furniture manufacturing plant in Baghdad city, taking under consideration all alternatives, non-sensitivity, and solidness. A mixture of Markov decision processes and linear programming analysis is implemented for the actual site and operational data to help decision-makers in planning for their project’s mid and long term maintenance policy and performing Solidness and, as result, the tentative cost reduction scheme.

On preventive maintenance policies: a selection framework

PurposeThis paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.Design/methodology/approachThe critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.FindingsFor a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.Research limitations/implicationsOnly three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.Practical implicationsThe proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.Originality/valueThis research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

An study of cost effective maintenance policies: Age replacement versus replacement after N minimal repairs

In this paper we consider the inspection and maintenance of a system under two types of age-dependent failures, revealed minor failures (R) and unrevealed catastrophic failures (U). Periodic inspections every T units of time are carried out to detect U failures, leading to the system replacement when one is discovered. R failures are followed by a minor repair. In addition the system is preventively replaced at MT or after the Nth R failure whichever comes first. The costs of minimal repair and replacement after N minor failures depend on age and history of failures. Non-perfect inspections are assumed, providing false positives when no U failure has happened or false negatives when a U failure is present. The long-run cost per unit of time along with the optimum policy (T*, M*, N*) are obtained. We explore conditions under which both strategies of preventive maintenance are profitable, comparing with suboptimal policies when only one of them is performed. Maintenance of infrastructures illustrates the model conditions.

An End-to-End Reliability Framework of the Internet of Things.

The Internet of Things (IoT) paradigm feeds from many scientific and engineering fields. This involves a diversity and heterogeneity of its underlying systems. When considering End-to-End IoT systems, we can identify the emergence of new classes of problems. The best-known ones are those associated to standardization for better interoperability and compatibility of those systems, and those who gave birth of new paradigms like that of Fog Computing. Predicting the reliability of an End-to-End IoT system is a problem belonging to this category. On one hand, predicting reliability can be mandatory, most times, before the deployment stage. On another hand, it may help engineers at the design and the operational stages to establish effective maintenance policies and may provide the various stakeholders and decision-makers a means to take the relevant actions. We can find in the literature works which consider only fragments of End-to-End IoT systems such as those assessing reliability for Wireless Sensors Networks (WSN) or Cloud subsystems, to cite just a few. Some other works are specific to well-defined industries, like those targeting reliability study of E-health and Smart-Grid infrastructures. Works that aims to assess reliability for an End-to-End IoT system are remarkably rare and particularly restrained in terms of expressiveness, flexibility, and in their implementation time complexity. In this paper, we apply the Reliability Block Diagram (RBD) paradigm to set up a framework for End-to-End IoT system reliability modeling and analysis. Our contribution is four-fold: we propose an IoT network-based layered architecture, we model in depth each layer of the proposed architecture, we suggest a flow chart to deploy the proposed framework, and we perform a numerical investigation of simplified scenarios. We affirm that the proposed framework is expressive, flexible, and scalable. The numerical study reveals mission time intervals which characterize the behavior of an IoT system from the point of view of its reliability.

Reliability and condition-based maintenance modeling for systems operating under performance-based contracting

Performance-based contracting motivates service providers to implement effective maintenance policies so as to boost profits and improve system performance at a lower cost. This paper deals with reliability and condition-based maintenance modeling for single-unit systems operating under performance-based contracting. For a system subject to degradation and sudden shocks, there involves three states: normal, degraded and failed. Once entered into the degraded state, the system deteriorates faster and becomes more susceptible to shocks. The degradation conforms to a two-stage inverse Gaussian process with random effects characterizing unit-specific heterogeneity in the population. Furthermore, the arrival of sudden shocks follows a doubly stochastic Poisson process. A reliability model is developed based on degradation-based and shock-based failures modeling. Afterwards, the long-run maintenance cost rate and the average system availability are evaluated. Optimal inspection-based preventive replacement policy is obtained by maximizing the expected profit rate to the service provider. Finally, a numerical example along with sensitivity analysis of model parameters is presented to demonstrate the applicability and solution procedure of the proposed models.

Preventive Maintain of Ventilators and its Cost Consideration

Background: Maintenance is the core function of biomedical engineering and is essential for the optimum functioning of equipment. This study was undertaken to understand the current maintenance practices of ventilator and its cost implications from administrative point of view. Methodology: 179 Ventilators supplied and installed by one vendor in different wards of the hospital were studied. It was a retrospective, descriptive study. Equipment related data was taken from various service reports. This data was used to calculate spares failure and their costs implication. Results: A total of 692 maintenance visits were undertaken for 179 ventilators of 5 different modals over 27 months by 6 Bio- Medical Engineers (BME). The mean number of spare changed throughout the study was 2.73. The uptime was within satisfactory limits. The yearly repair to cost ratio was 3.50 %. The cost of spare changed was a factor of modal under consideration and the status of equipment concerning its coverage under maintenance contact. Conclusion: The findings of the study should enable researchers in the future to formulate an effective equipment maintenance policy for the hospital.

Investigating effective maintenance policies for urban networks of residential cities by using optimum and sensitivity analyses

Rural and urban networks have been rapidly expanding over time, so there is a rising demand for more optimum maintenance policies (MPs). The low traffic weights on the urban network of a residential city makes the appearance and density of load-associated distresses rare. This encourages the city’s transportation agency to depend on limited treatment choices and the subjective judgement of the agency’s engineers in managing maintenance and rehabilitation (M&R) activities. The engineers’ judgement reflects their extensive field experience and is usually used in this type of city rather than generating optimal computerized solutions. The present study proposes two alternative MPs that are more objective than the policy that depends on engineers’ judgement. One of the proposed alternative policies allows one M&R action along the planning horizon and the other allows multiple M&R actions. The optimum analysis showed that the preventive treatment is the optimal action for more than 50% of the network segments, which means that it plays a vital and nonnegligible role in improving the effectiveness of the M&R activities. Moreover, the sensitivity of the generated M&R plans to the initial performance of the pavement, traffic volume, and the objectives weights is investigated to determine the optimal time, suitable action, maintenance need, and optimal objective weights. The sensitivity analysis showed that varying objective weights leads to different optimal and rational solutions and the most cost-effective solution is not achieved at equal weights for objectives.

A DBN based reactive maintenance model for a complex system in thermal power plants

Thermal power plants consist of several complex systems having many interacting hidden components. Any unexpected failure may lead to prolonged downtime and serious lost profits. Therefore, implementing an effective maintenance policy is crucial for this sector. Although preventive maintenance has become a more popular strategy, it does not completely prevent the need for corrective maintenance. Our aim in this study is to tackle the corrective maintenance implementation problem of a multi-component partially observable dynamic system based on a regenerative air heater in a thermal power plant. We propose eight methods having different efficiency measures with respect to time, effect and probability criteria to minimize the total number of maintenance activities in a given planning horizon. Performances of these methods are evaluated under corrective maintenance strategy using dynamic Bayesian networks. The results show that fault effect methods with best working state probability measure perform better than the others considering both the total amount of maintenance activities and also the solution time. We also point out how the methods can be implemented in real-life and how the results can be used for requirements planning. Furthermore, the proposed methods can be used for the corrective maintenance of all systems having hidden interacting components.

A predictive association rule-based maintenance policy to minimize the probability of breakages: application to an oil refinery

Effective maintenance policies can support companies to deal with process interruptions and consequently, to prevent significant profit losses. Moreover, the proliferation of structured and unstructured data due to production plants validates the application of knowledge discovery in databases techniques to increase processes’ reliability. In this paper, an innovative maintenance policy is proposed. It aims at both predicting components breakages through association rule mining and determining the optimal set of components to repair in order to improve the overall plant’s reliability, under time and budget constraints. An experimental campaign is carried out on a real-life case study concerning an oil refinery plant. Finally, numerical results are discussed considering different blockage categories and number of components and by varying some significant input parameters.