Maintenance Management Decision Research Articles

Detection approach for unusable shared bikes enabled by reinforcement learning and PageRank algorithm

Existing research models can neither indicate the availability of shared bikes nor detect unusable ones owing to a lack of information on bike maintenance and failure. To improve awareness regarding the availability of shared bikes, we propose an innovative approach for detecting unusable shared bikes based on reinforcement learning and the PageRank algorithm. The proposed method identifies unusable shared bikes depending on the local travel data and provides a ranking of the shared bikes according to their availability levels. Given a sliding time window, the value function for the reinforcement learning model was determined by considering the cumulative number of unavailable shared bikes, the proportion of rental cancelations at the same stations, and the mean time between the cancelations. Reinforcement learning was then used to identify shared bikes with the worst availability. An availability ranking for the shared bikes below the reward threshold was performed using the PageRank algorithm. The proposed detection approach was applied to a trip dataset of a real-world bike-sharing system to illustrate the modeling process and its effectiveness. The detection results of unusable shared bikes in the absence of failure and feedback data can provide essential information to support the maintenance management decisions regarding shared bikes.

Combining maximum likelihood estimation and LSTM neural network to forecast reliability distributions: a study based on real data from the sugar-energy sector

The recent advances in reliability engineering and assets management allow industries to use advanced mathematical and statistical techniques to improve their maintenance strategies. Conversely, many of these methods requires the previous knowledge of the failure history for the assets, which are not always available. This study shows that is possible to support maintenance management decisions providing the distributions for assets with known and unknown failures history. The approach combines maximum likelihood estimation with a LSTM neural network to find and predict optimal reliability and failure distributions for data actual assets data and failures history. It is shown as an efficient and probably useful approach for practical business situations in the results section.

Remaining Useful Life Prediction from 3D Scan Data with Genetically Optimized Convolutional Neural Networks.

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, convolutional neural network-based deep neural network techniques are investigated for the remaining useful life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pretrained models, using a classic machine learning approach, i.e., support vector regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE = 0.058) to that of transfer learning, which, instead, remains at a lower or slightly higher level (MAPE = 0.416) than support vector regression (MAPE = 0.857).

Ontology-augmented Prognostics and Health Management for shopfloor-synchronised joint maintenance and production management decisions

In smart factories, guaranteeing shopfloor-synchronised and real-time decision-making is essential to be responsive to the ever-changing internal environment, namely the shopfloor of the production system and assets. At operational level, decisions should balance counter acting objectives of maintenance and production; therefore, their decision-making processes should be joint and coordinated, to fulfil production requirements considering the health state of the assets. The knowledge of the current state is promoted by the application of Prognostics and Health Management (PHM) as an aid to support informed decision-making. Nevertheless, PHM-purposed information is usually not complete in terms of production requirements. To support joint maintenance and production management decisions, an ontological approach is proposed. The ontology, called ORMA (Ontology for Reliability-centred MAintenance), has a modular structure, including formalisation of asset, process, and product knowledge. Via suitable relationships, rules, and axioms, ORMA can infer product feasibility based on the current health state of the assets and their functional units. ORMA is implemented in a Flexible Manufacturing Line at a laboratory scale. Therein, an integrated solution, involving a health state detection algorithm that interacts with the ontology, supports human decision-making via a web-based dashboard; joint maintenance and production management decisions can be then taken, relying on diversified information provided by the PHM algorithm as well as the augmentation via ontology reasoning. The proposed ontology-based solution represents a step towards reconfigurability of smart factories where human and automated decision-making processes work in synergy.

Management decisions to support and maintain the socio-economic development of the territories

The basis of the formation of a theoretical-methodological and practical reason for making effective management decisions to support and maintain the process of socio-economic development of territories is studied in the article. It was found that in order to solve this problem, it should be necessary to form an effective management policy through the improvement of mechanisms to support and maintain the process of socio-economic development of territories in the context of significant changes in the format of national and municipal management of territorial communities. The essence of the “socio-economic development of territories” category has been clarified. The functional responsibilities of subjects of the system of socio-economic development of territories supporting and maintaining the process of socio-economic development of territories by making effective management decisions are systematized. The main characteristics of the elements of the system of socio-economic development of the territory are summarized and it is concluded that the key for making management decisions is planning, mechanisms of public governance and diagnostics of the effectiveness of the developed system of socio-economic development of the territory. It was concluded that the process of supporting and maintenance management decisions on the socio-economic development of the territories of Ukraine is a complex, multidimensional and mutually agreed system, which includes many elements and relationships between them and the basis for the functioning of which is the effective distribution of functional responsibilities among the subjects and objects of this system.

Optimization Model of Network‐Level Pavement Maintenance Decision considering User Travel Time and Vehicle Fuel Consumption Costs

The maintenance management decisions of network‐level asphalt pavements have long been a challenge to highway agencies, and a great amount of factors have been involved. In this study, a network‐level optimization method was established by integrating the maintenance benefits into the zero‐one programming optimization model. An optimized performance evaluation method of asphalt pavement was proposed which contains 11 different kinds of combinations. The benefit model quantifies the cost savings of user travel time and vehicle fuel consumption to the pavement condition index (PCI) and ride quality index (RQI), respectively. Based on the simplified evaluation method as well as the quantified maintenance benefit model, an optimization model was established by employing the zero‐one programming. This optimization model aimed to maximize the improvements/price ratio of pavement maintenance for the whole pavement network. The calculation results present the optimal strategies of maintenance for every road section in the network. The applicability of the newly proposed model was validated by a case study. The methodology developed in this study helps to offer guidelines to highway agencies in managing and making decisions about network‐level pavement maintenance.

Classical, fuzzy, hesitant fuzzy and intuitionistic fuzzy analytic hierarchy processes applied to industrial maintenance management

A multi-criteria problem involves the consideration of two or more criteria in the prioritization of alternative solutions. The Analytic Hierarchy Process (AHP) is a leading multi-criteria method. Consistency checking is a great advantage of AHP. Since in AHP priorities come from pairwise compariso ns, it is possible to check the consistency of these comparisons. However, a problem occurs when comparisons fail the consistency check. Then, the excluding options are to review some comparisons (Option 1) or to keep the comparisons (Option 2). This paper presents an AHP application in the maintenance management of an industrial plant. Industrial maintenance is not in the core business of an organization. However, maintenance costs can account over 50% of production costs. One of the first maintenance management decisions is on the maintenance strategy. Shall maintenance anticipate the occurrence of failure? Or shall maintenance be performed after an equipment breakdown? Answering those questions with classical AHP resulted in inconsistent comparison matrices. In that case, Fuzzy AHP (FAHP) were applied, avoiding this situation. Therefore, the purpose of this paper is to present the applications of four AHP models: Classical AHP and three models of FAHP, including hesitant fuzzy sets and intuitionistic fuzzy sets. The application of Hesitant FAHP (HFAHP) and Intuitionistic FAHP (IFAHP) are the novelty of this paper. The four AHP models were also applied in the same case of maintenance management of an industrial plant. Results were very similar, but experts could express their preferred model.

Risk-Based Criticality for Network Utilities Asset Management

This paper describes the design and the implementation of a process of asset criticality analysis for distribution network services providers (DNSPs), also named network utilities. The proposed method is based on two points: 1) the elicitation of business-value factors and 2) the risk-based evaluation of assets, considering potential impacts of their failures on network value. Thus, it provides the capability to take maintenance management decision in terms of value and risk, considering the whole network under unique and homogeneous criteria. A hierarchy of assets ranked will come out of this process, which represents a fundamental result serving as input of the subsequent steps of the asset management process, including maintenance and renewal/reinvestments decisions. The proposed methodology is aligned with ISO 55000 family of standards approach, and therefore it can be understood as a practical asset management tool. Specific attention is paid to particular network utilities issues, characterizing assets in these companies, and the services that they provide (topology influence, reputational impact, operational losses, etc.). In addition to this, high requirements established by the service level agreements (SLAs), that are characteristics of network services contracts, make this method especially suitable in this application, considering in an overall way all these key aspects, providing great advantages for managing network utilities. In order to illustrate method applicability, an example extracted from a real telecommunication network use case is included.

Criticality Analysis for Network Utilities Asset Management

The proposed work describes the main part of asset criticality analysis for Distribution Network Services Providers (DNSP), also known as Network Utilities, the severity-value factors definition. The methodology is based on the risk-based evaluation of assets, considering potential impacts of their failures on network value. Thus, it provides the capability to take maintenance management decision in terms of value and risk, considering the whole network under unique and homogeneous criteria. A hierarchy of assets ranked according to with value and risk will come out of this process, which represents a fundamental result serving as input of the subsequent steps of the asset management process. Specific attention is paid to network utilities issues, characterizing assets in these companies, and the services that they provide. In addition to this, high requirements established by the Service Level Agreements (SLA), that are characteristics of network services contracts, make this methodology especially suitable in this application. In order to illustrate method applicability, an example extracted from a real electrical network use case is included.

Development of a real time intelligent health monitoring platform for aero-engine

In this paper an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. In a first approach the numerical tool is able to predict engine measurable data from flight data, in order to create a dataset of expected values. Then, in the case of a mismatch between expected values and measured data coming from a real engine, a second part of the tool can be activated to detect the component under degradation. In order to evaluate the performance prediction artificial neural networks (ANN) have been implemented. The tool is able to recognize the degradation due to compressor fouling and turbine erosion. Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The used data have been generated with a model based on gas path analysis. The training of the model is focused on components deterioration due to a combination of fouling and erosion. Different scenarios have been compared in order to carry out a sensitivity analysis and to choose the best parameters for the network input and output. Obviously the knowledge of the real engine health status can be crucial for maintenance and fleet management decisions.

Comprehensive Bridge Health Evaluation Method Based on Information Fusion

In the bridge maintenance management decision, the health evaluation on bridge structure is far more than determination of abnormal index exceed fixed threshold, or evaluation on damage status and carrying capacity, the on-site human inspection should also be paid attention. In order to achieve more accurate and reliable health status evaluation result, it needs to take full advantage of various information and parameter. The D-S evidence information fusion was used in the paper to present comprehensive bridge health evaluation method. It fuses bridge operation status using abnormal status identification, bridge durability with residual life estimation, as well as human inspection rating, to arrive at health evaluation. The actual application examples to verify the method is also provided.

Effects of Missing Runway Performance Data on Maintenance Management Decision Making

AbstractA problem commonly encountered in airport pavement management is how to deal with pavement condition and performance databases with various extents of missing data. While robust imputation techniques are now available to handle the problem of missing data, there are little to no guidelines available to assist airport engineers in evaluating the level of impact that data imputation has on the decisions made with respect to airport pavement maintenance. This research attempts to address the aforementioned issue by analyzing how different degrees of missing data would affect the maintenance decision concerning runway pavement sections in need of friction improvement. Using the runway friction data sample for illustration, 200 sets of data with varying degrees of missingness were generated. The generated data sets cover the following 10 levels of missingness: 5.55, 16.66, 25, 36.11, 44.44, 55.55, 66.66, 75, 86.11, and 91.66%. For each level of missingness, a total of 20 data sets with different patter...

Application of Fuzzy Logic and Clustering Techniques for Pavement Maintenance

When a large number of pavement stretches are to be maintained, the decision making becomes complicated and prioritization of individual stretches is not really useful in taking maintenance management decisions. In such situations, grouping or clustering the pavement stretches having similar quantified distress characteristics would be the practical and effective approach. Since a number of distresses are observed on pavements and usually they are being represented in different levels of severity and extent, it gets rather hard to cluster them. The problem becomes further aggravated as the weights of various distresses also play an important role and it is difficult to express them objectively. In this paper, a methodology has been proposed to quantify the pavement distresses and clustering the pavement stretches. The technique has been explained with the aid of a case study carried out in a few selected stretches in the state of Rajasthan, India.

Maintenance Management Decision Model for Reduction of Losses in Water Distribution Networks

Maintenance Management Decision Model for Reduction of Losses in Water Distribution Networks

Asset maintenance planning in electric power distribution network using statistical analysis of outage data

The problems faced by electric power utilities in developing countries today is that the power demand is increasing rapidly whereas the supply growth is constrained by aging generating and distributing assets, scarce resources for constructing new ones and other societal issues. This has resulted in the need for constructing new additional generating plants and a more economic ways of planning and maintaining existing Generating and Electric power distribution assets. System planning and maintenance that is based on reliability – centred asset management approach had been adopted in this paper.Maintenance of critical asset is an essential part of asset management in distribution network. In most Electric utilities, planning for maintenance constitutes an essential parts of asset management. In this paper, an enhanced RCM methodology that is based on a quantitative statistical analysis of outage data Performed at system/component level for overall system reliability was applied for the identification of distribution components critical to system reliability. The conclusion from this study shows that it is beneficial to base asset maintenance management decisions on processed, analyzed and tested outage data.

The Graphical Analysis for Maintenance Management Method: A Quantitative Graphical Analysis to Support Maintenance Management Decision Making

This article proposes a logical support tool for maintenance management decision making. This tool is called the Graphical Analysis for Maintenance Management (GAMM), a method to visualize and analyze equipment dependability data in a graphical form. The method helps for a quick and clear analysis and interpretation of equipment maintenance (corrective and preventive) and operational stoppages. Then, opportunities can be identified to improve both operations and maintenance management (short–medium term) and potential investments (medium–long term). The method allows an easy visualization of parameters, such as the number of corrective actions between preventive maintenance, the accumulation of failures in short periods of time, and the duration of maintenance activities and sequence of stops of short duration. In addition, this tool allows identifying, a priori, anomalous behavior of equipment, whether derived from its own function, maintenance activities, misuse, or even equipment designs errors.In this method, we used a nonparametric estimator of the reliability function as a basis for the analysis. This estimator takes into account equipment historical data (total or partial) and can provide valuable insights to the analyst even with few available data. Copyright © 2012 John Wiley & Sons, Ltd.

Sensitivity of Design Parameters on Optimal Pavement Maintenance Decisions at the Project Level

Sensitivity analyses are important parts of both studying complex systems and measuring the variation in input parameters on the response. They are useful to decision makers for understanding the robustness of the optimal solution that they are to adapt to variations of the parameters of the problem. The sensitivity of the optimal solution of a project-level pavement management problem is analyzed, and the robustness of the optimal solution to the interventions and the timing, cost, and benefit are investigated. The input parameters, which affect the optimal maintenance solution, are identified as the structural and functional condition parameters (defined in terms of deflection and roughness, respectively, at the beginning of the analysis period), traffic volume, growth rate, and discount rate. The problem of computing the optimal treatment and timing for a given budget level is modeled as a mixed integer nonlinear optimization problem and solved by using a computationally efficient network-optimization technique. The benefits are evaluated by considering the pavement performance and are quantified as the area between the performance curve and the threshold values. The optimal budget required for pavements in different structural and functional conditions as well as traffic levels is presented. The effect of initial pavement condition on the optimal maintenance actions as well as their timings is studied. The result of the sensitivity analysis showed that the cumulative standard axle loads and traffic growth rate have a significant effect on the selection and timing of rehabilitation and preventive maintenance actions. The effect of the discount rate on the maintenance management decisions is also presented.

Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost*

Cost-competent maintenance and management of civil infrastructure requires balanced consideration of both the structure performance and the total cost accrued over the entire life-cycle. Most existing maintenance and management systems are developed on the basis of life-cycle cost minimization only. The single maintenance and management solution thus obtained, however, does not necessarily result in satisfactory long-term structure performance. Another concern is that the structure performance is usually described by the visual inspection-based structure condition states. The actual structure safety level, however, has not been explicitly or adequately considered in determining maintenance management decisions. This paper reviews the recent development of life-cycle maintenance and management planning for deteriorating civil infrastructure with emphasis on bridges using optimization techniques and considering simultaneously multiple and often competing criteria in terms of condition, safety and life-cycle cost. This multiple-objective approach leads to a large pool of alternative maintenance and management solutions that helps active decision-making by choosing a compromise solution of preferably balancing structure performance and life-cycle cost.

The OR/MS contribution to maintenance management Comment on “Maintenance management decision making”

The OR/MS contribution to maintenance management Comment on “Maintenance management decision making”

Maintenance management decision making

Maintenance management of industrial equipment is an important but still relatively neglected business function. This paper identifies and discusses the most important elements of its decision making environment. As such the paper contributes to the structuring of the maintenance management area and it provides a frame of reference for further research in maintenance policy optimization. The paper starts with a brief description of maintenance management in a business context. In the remainder of the paper three parts may be distinguished. The first addresses the system design aspects of maintenance management in the broader operations management environment, the second examines the most important issues in maintenance decision making and the last part reviews the managerial tools available for decision making in this area. An extensive list of references is provided.