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This work is concerned with the problem of optimizing maintenance policies in terms of economical rewards and availability. We consider a system with multiple states in terms of healthy mode (good state, degraded state and failure state) and maintenance action (running state, stopped for maintenance). The level of maintenance (perfect or not) is also taken into account. We propose semi-Markovian model highlighting the effects of dwell times and transitions on economical rewards. We determine an optimal policy conditionally upon the current state according to eight decision parameters related to time intervals between two preventive maintenances and the level of maintenance. We show through a sensitive analysis that decision parameters have nonlocal effects that imply a multiple objective function. Hence, we propose a compromise by optimizing the asymptotic average reward.
David Jaures Fotsa Mbogne; Armand Fonkou; Wolfgang Nzie; Adolfo Crespo Marquez. Semi-Markovian approach to optimizing the time of systematic preventive maintenance and the level of maintenance over a finite horizon. 2021, 1 .
AMA StyleDavid Jaures Fotsa Mbogne, Armand Fonkou, Wolfgang Nzie, Adolfo Crespo Marquez. Semi-Markovian approach to optimizing the time of systematic preventive maintenance and the level of maintenance over a finite horizon. . 2021; ():1.
Chicago/Turabian StyleDavid Jaures Fotsa Mbogne; Armand Fonkou; Wolfgang Nzie; Adolfo Crespo Marquez. 2021. "Semi-Markovian approach to optimizing the time of systematic preventive maintenance and the level of maintenance over a finite horizon." , no. : 1.
The correct selection of the subset of features for the design of a CBM (Condition Based Maintenance) strategy may result in models working faster and producing more accurate predictions. This must be done avoiding a phenomenon known as the curse of dimensionality, that appears in Machine Learning when algorithms must learn from an ample feature volume with abundant values within each one. This paper deals precisely with feature selection problem when dealing with compressors failure modes detection, using machine learning (ML) models. To that end, several feature selection ranking (FSR) methods are considered. These methods are basically algorithms which include wrappers and filters and they are able to provide a ranking about all the analysed features. A very important issue of these methods, is to realise the feature selection unconstrained of the Machine Learning algorithm to be later applied, and that will be tested in this paper. Stability and scalability of these methods will be also defined and discussed in the paper. The paper case study evaluates the possibility of detecting and therefore diagnosing the rod drop failure mode appearance in Liquid Natural Gas (LNG) cryogenic reciprocating compressors by using artificial intelligence analysis techniques. This failure mode implies unavailability of this equipment, which are critic in the LNG industry due to the cost of flaring or, less common and desirable, venting of the boil-off gas (BOG) recovered by its compression in order to send it out or use as fuel. More than 90.000 running hours and thirteen representative features are evaluated as well as thirteen FSR methods. Three most-used classifiers have been employed in order to assess the feature rankers’ effect over the models development to diagnose the rod drop failure. Conclusions are about the possibility, not only to diagnose the appearance of a failure mode like rod drop, but also to do it with considering a reduced number of features.
Fernando Hidalgo-Mompeán; Juan Francisco Gómez Fernández; Gonzalo Cerruela-García; Adolfo Crespo Márquez. Dimensionality analysis in machine learning failure detection models. A case study with LNG compressors. Computers in Industry 2021, 128, 103434 .
AMA StyleFernando Hidalgo-Mompeán, Juan Francisco Gómez Fernández, Gonzalo Cerruela-García, Adolfo Crespo Márquez. Dimensionality analysis in machine learning failure detection models. A case study with LNG compressors. Computers in Industry. 2021; 128 ():103434.
Chicago/Turabian StyleFernando Hidalgo-Mompeán; Juan Francisco Gómez Fernández; Gonzalo Cerruela-García; Adolfo Crespo Márquez. 2021. "Dimensionality analysis in machine learning failure detection models. A case study with LNG compressors." Computers in Industry 128, no. : 103434.
This paper describes the optimization of preventive maintenance (PM) over a finite planning horizon in a semi-Markov framework. In this framework, the asset may be operating, and providing income for the asset owner, or not operating and undergoing PM, or not operating and undergoing corrective maintenance following failure. PM is triggered when the asset has been operating for τ time units. A number m of transitions specifies the finite horizon. This system is described with a set of recurrence relations, and their z-transform is used to determine the value of τ that maximizes the average accumulated reward over the horizon. We study under what conditions a solution can be found, and for those specific cases the solution τ* is calculated. Despite the complexity of the mathematical solution, the result obtained allows the analyst to provide a quick and easy-to-use tool for practical application in many real-world cases. To demonstrate this, the method has been implemented for a case study, and its accuracy and practical implementation were tested using Monte Carlo simulation and direct calculation.
Antonio Sánchez Herguedas; Adolfo Crespo Márquez; Francisco Rodrigo Muñoz. Optimizing preventive maintenance over a finite planning horizon in a semi-Markov framework. IMA Journal of Management Mathematics 2020, 1 .
AMA StyleAntonio Sánchez Herguedas, Adolfo Crespo Márquez, Francisco Rodrigo Muñoz. Optimizing preventive maintenance over a finite planning horizon in a semi-Markov framework. IMA Journal of Management Mathematics. 2020; ():1.
Chicago/Turabian StyleAntonio Sánchez Herguedas; Adolfo Crespo Márquez; Francisco Rodrigo Muñoz. 2020. "Optimizing preventive maintenance over a finite planning horizon in a semi-Markov framework." IMA Journal of Management Mathematics , no. : 1.
We present a comparative study on the most popular machine learning methods applied to the challenging problem of Liquefied Natural Gas pumps fault prediction in regasification plants. The proposed solution tries to address the problem of pump failure during operation, this failure makes the pump unavailable, with a high cost of corrective maintenance. It must be taken into account that the condition monitoring may be insufficient because they are cryogenic and inaccessible equipment once the tanks have been started up. The use of machine learning techniques allows us to anticipate the response time by detecting anomalies in the operation, and to be able to do the maintenance before the failure occurs. In our experiments, we predict the power consumption based on the parameters captured in real time during operation. For the composition of the dataset, data was collected between 2007 and 2017, resulting in a dataset of over 15,000 lines for training and validation. First, all models were applied and evaluated on a dataset collected from a real case study. In the second phase, the performance improvement offered by boosting was studied. In order to determine the most efficient parameter combinations we compare Root Mean Squared Error, Absolute Error, Relative Error, Squared Error, Correlation, Training Time and Scoring Time. Our results demonstrate clear superiority of the boosted versions of the models against the plain (non-boosted) versions. The fastest scoring and total time was the Decision Tree and the best overall was Gradient Boosted Trees.
Fuente A. De la; Márquez A. Crespo; E. Candón; J.F. Gómez; J. Serra. A comparison of machine learning techniques for LNG pumps fault prediction in regasification plants. IFAC-PapersOnLine 2020, 53, 125 -130.
AMA StyleFuente A. De la, Márquez A. Crespo, E. Candón, J.F. Gómez, J. Serra. A comparison of machine learning techniques for LNG pumps fault prediction in regasification plants. IFAC-PapersOnLine. 2020; 53 (3):125-130.
Chicago/Turabian StyleFuente A. De la; Márquez A. Crespo; E. Candón; J.F. Gómez; J. Serra. 2020. "A comparison of machine learning techniques for LNG pumps fault prediction in regasification plants." IFAC-PapersOnLine 53, no. 3: 125-130.
Nowadays, no business activity escapes the fourth industrial revolution, called industry 4.0, which is characterized by digitalization of processes. The possibility of simultaneously having systems with greater interconnection, more information and greater flexibility, allows companies to have a clearer view of their processes and consequently improve their effectiveness and efficiency. The digital transformation can no longer be based simply on making the processes more efficient, but on creating more sustainable and profitable customer relationships, continuously aligning the value of the product with the changing customer requirements. Even though managing assets over the Internet is increasingly common, much effort is needed to identify the functionality that should be provided by these platforms to enhance existing asset management practices. The effort of IT vendors is focused on the development of IoT platforms, which allow, among other functions, to create a connection between machinery and digital systems, protect all devices and data against hacking or attacks, control operations and maintenance of equipment or perform different analyses of assets or systems. The aim of this paper is to understand the functionalities of the existing IAMP platforms, providing a system that evaluates these functionalities based on the business objectives from the point of view of asset management. This methodology allows maintenance managers guiding the evolution of the life cycle of their assets according to the business value conception. This makes this methodology especially suitable for supporting new challenging scenarios of maintenance management. In this paper we first talk about the structure of an IAMP, then how they integrate the asset management model and a summary of the features and modules that have the most known IAMP platforms. Finally, an evaluation system of IAMP platforms and a case study is presented based on their content for asset management.
Pablo Martínez-Galán; Adolfo Crespo; Antonio de la Fuente; Antonio Guillén. A new model to compare intelligent asset management platforms (IAMP). IFAC-PapersOnLine 2020, 53, 13 -18.
AMA StylePablo Martínez-Galán, Adolfo Crespo, Antonio de la Fuente, Antonio Guillén. A new model to compare intelligent asset management platforms (IAMP). IFAC-PapersOnLine. 2020; 53 (3):13-18.
Chicago/Turabian StylePablo Martínez-Galán; Adolfo Crespo; Antonio de la Fuente; Antonio Guillén. 2020. "A new model to compare intelligent asset management platforms (IAMP)." IFAC-PapersOnLine 53, no. 3: 13-18.
The family of Standards ISO 55000 on Asset Management aims to support a kind of management oriented to obtain value from assets. Besides these standards, other frameworks have help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. With this purpose, the present paper links a Maintenance Management Model well known in this area, with the publication of ISO 55000. As a consequence, justifies that a proper implementation of the Maintenance Management Model fulfils the requirements stated by the Standards ISO 55000, improving consequently the decision making, costs reductions, quality of the operations and increasing business profitability and users satisfaction.
C. Parra; V. González-Prida; E. Candón; A. De La Fuente; P. Martínez-Galán; A. Crespo. Integration of Asset Management Standard ISO55000 with a Maintenance Management Model. Recent Advances in Computational Mechanics and Simulations 2020, 189 -200.
AMA StyleC. Parra, V. González-Prida, E. Candón, A. De La Fuente, P. Martínez-Galán, A. Crespo. Integration of Asset Management Standard ISO55000 with a Maintenance Management Model. Recent Advances in Computational Mechanics and Simulations. 2020; ():189-200.
Chicago/Turabian StyleC. Parra; V. González-Prida; E. Candón; A. De La Fuente; P. Martínez-Galán; A. Crespo. 2020. "Integration of Asset Management Standard ISO55000 with a Maintenance Management Model." Recent Advances in Computational Mechanics and Simulations , no. : 189-200.
In this Chapter, several efforts that the SIM Research Group, from the University of Seville, accomplished during the Covid-19 pandemic lockdown in March-June 2020, are introduced. These efforts had an important impact on media and were mainly concern with the pandemic recovery phase. When confinement ends, recovery phase must be accurate planned at a local level. Health System (HS) capacity, specially ICUs and plants capacity and availability, would remain the key stone in this pandemic life cycle phase. This Chapter describes: First the important of the action plans design by local level, while a unique decision-making center is considered by country; Second, a management tool based on a ICUs and plants capacity model to estimate ICUs and plants saturation, and with these results, set new local and temporal confinement measures. The tool allows a dynamic analysis to estimate maximum Ro saturation scenarios; Third, a practical management tools to tackle the deconfinement strategy design problem. A proper control system to follow the course of action, especially in a scenario with unprecedent uncertainty, is developed. In all cases, it is remarked the importance of R (the pandemic basic and effective reproductive number), first as a variable to monitor and control the pandemic, to ensure its decline; second as a target to score risks associated to start certain activities over, after confinement. Reducing the potential increase in the value of R, when any type of activity is re-opening, guides the strategy. One common objective in these initiatives: Applying asset management principles to accelerate as much as possible socioeconomic normalization with a strict control over HS relapses risk.
Adolfo Crespo Márquez. Strategies for COVID-19 Pandemic Recovery: Application of Engineering Asset Management Principles. Recent Advances in Computational Mechanics and Simulations 2020, 288 -305.
AMA StyleAdolfo Crespo Márquez. Strategies for COVID-19 Pandemic Recovery: Application of Engineering Asset Management Principles. Recent Advances in Computational Mechanics and Simulations. 2020; ():288-305.
Chicago/Turabian StyleAdolfo Crespo Márquez. 2020. "Strategies for COVID-19 Pandemic Recovery: Application of Engineering Asset Management Principles." Recent Advances in Computational Mechanics and Simulations , no. : 288-305.
Modern train fleets have very demanding requirements in passenger safety, train service reliability and availability, comfort and life cycle costs. To reach these goals, maintenance intervals of more than thirty thousand kilometers besides serious failure-free objectives exceeding one and a half million kilometers are becoming a standard. This requires manufacturers to develop bold designs and to use advanced engineering tools for the Operations and Maintenance (O&M) of such trains. Condition Based Maintenance (CBM) solutions, using condition monitoring systems and advanced algorithms to detect commencing deterioration, may allow sufficient time for maintenance before serious failures can develop, which increases safety, reliability and availability while helping to reduce operating and maintenance expenses and the total cost of ownership. This paper applies predictive analytics, big data processes and tools to design CBM Plans for train axle bearings, to increase both preventive maintenance (PM) intervals and dependability of the trains. The paper details how the machine learning predictive model is selected and how the model is trained with different data sets. Big data processes allow to test and accept a universal model per bearing position regardless the axle or train of the fleet, overcoming complexity that could be generated by the non-ergodicity of these assets. The originality of this work consists in the ability to identify bearing deterioration related anomalies, by an innovative modeling and prediction of axle bearing temperature using data analytics. Also, interpretation rules for early failure detection based on these advanced predictive analytics are compared to those already existing rules in the train on-board control monitoring system (TCMS) ensuring train’s safety. Conclusions of the work are related to the process followed and the validity of results.
Adolfo Crespo Márquez; Antonio De La Fuente Carmona; José Antonio Marcos; Javier Navarro. Designing CBM Plans, Based on Predictive Analytics and Big Data Tools, for Train Wheel Bearings. Computers in Industry 2020, 122, 103292 .
AMA StyleAdolfo Crespo Márquez, Antonio De La Fuente Carmona, José Antonio Marcos, Javier Navarro. Designing CBM Plans, Based on Predictive Analytics and Big Data Tools, for Train Wheel Bearings. Computers in Industry. 2020; 122 ():103292.
Chicago/Turabian StyleAdolfo Crespo Márquez; Antonio De La Fuente Carmona; José Antonio Marcos; Javier Navarro. 2020. "Designing CBM Plans, Based on Predictive Analytics and Big Data Tools, for Train Wheel Bearings." Computers in Industry 122, no. : 103292.
Maintenance Management is a key pillar in companies, especially energy utilities, which have high investments in assets, and so for its proper contribution has to be integrated and aligned with other departments in order to conserve the asset value and guarantee the services. In this line, Intelligent Assets Management Platforms (IAMP) are defined as software platforms to collect and analyze data from industrial assets. They are based on the use of digital technologies in industry. Beside the fact that monitoring and managing assets over the internet is gaining ground, this paper states that the IAMPs should also support a much better balanced and more strategic view in existing asset management and concretely in maintenance management. The real transformation can be achieved if these platforms help to understand business priorities in work and investments. In this paper, we first discuss about the factors explaining IAMP growth, then we explain the importance of considering, well in advance, those managerial aspects of the problem, for proper investments and suitable digital transformation through the adoption and use of IAMPs. A case study in the energy sector is presented to map, or to identify, those platform modules and Apps providing important value-added features to existing asset management practices. Later, attention is paid to the methodology used to develop the Apps’ data models from a maintenance point of view. To illustrate this point, a methodology for the development of the asset criticality analysis process data model is proposed. Finally, the paper includes conclusions of the work and relevant literature to this research.
Adolfo Crespo Marquez; Juan Francisco Gomez Fernandez; Pablo Martínez-Galán Fernández; Antonio Guillen Lopez. Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models. Energies 2020, 13, 3762 .
AMA StyleAdolfo Crespo Marquez, Juan Francisco Gomez Fernandez, Pablo Martínez-Galán Fernández, Antonio Guillen Lopez. Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models. Energies. 2020; 13 (15):3762.
Chicago/Turabian StyleAdolfo Crespo Marquez; Juan Francisco Gomez Fernandez; Pablo Martínez-Galán Fernández; Antonio Guillen Lopez. 2020. "Maintenance Management through Intelligent Asset Management Platforms (IAMP). Emerging Factors, Key Impact Areas and Data Models." Energies 13, no. 15: 3762.
The increasing demand for energy from renewable sources is entailing the development of technologies oriented to increase the profitability of such projects and thus the attractiveness for potential investors. Wind power constitutes one of the most relevant renewable energy sources; however, the costs of the wind farms associated with Operations & Maintenance are prominent along the life-cycle. This paper proposes an approach intended to reduce these costs and lower the Levelized Cost of Energy. In this context, it is presented an opportunistic maintenance policy based on more accurate reliability estimates of the wind turbines components. The reliability of the components is estimated through a model based on Artificial Neural Networks that dynamically calculates the impact of operational conditions on the failures of the wind turbines. The approach has been validated through a case study based on real field data which proposes a multi-objective optimization of the maintenance strategy for the life-cycle of a wind farm. The obtained results provide interesting findings from the perspective of wind farms investors, operators, and owners.
J. Izquierdo; Adolfo Crespo Márquez; J. Uribetxebarria; A. Erguido. On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects. Renewable Energy 2020, 153, 1100 -1110.
AMA StyleJ. Izquierdo, Adolfo Crespo Márquez, J. Uribetxebarria, A. Erguido. On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects. Renewable Energy. 2020; 153 ():1100-1110.
Chicago/Turabian StyleJ. Izquierdo; Adolfo Crespo Márquez; J. Uribetxebarria; A. Erguido. 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects." Renewable Energy 153, no. : 1100-1110.
This paper aims to define the different considerations and results obtained in the implementation in an Intelligent Maintenance System of a laboratory designed based on basic concepts of Industry 4.0. The Intelligent Maintenance System uses asset monitoring techniques that allow, on-line digital modelling and automatic decision making. The three fundamental premises used for the development of the management system are the structuring of information, value identification and risk management.
E. Candón; P. Martínez-Galán; Antonio De-La-Fuente-Carmona; V. González-Prida; Adolfo Crespo Márquez; J. Gómez; A. Sola; M. Macchi. Implementing Intelligent Asset Management Systems (IAMS) within an Industry 4.0 Manufacturing Environment. IFAC-PapersOnLine 2019, 52, 2488 -2493.
AMA StyleE. Candón, P. Martínez-Galán, Antonio De-La-Fuente-Carmona, V. González-Prida, Adolfo Crespo Márquez, J. Gómez, A. Sola, M. Macchi. Implementing Intelligent Asset Management Systems (IAMS) within an Industry 4.0 Manufacturing Environment. IFAC-PapersOnLine. 2019; 52 (13):2488-2493.
Chicago/Turabian StyleE. Candón; P. Martínez-Galán; Antonio De-La-Fuente-Carmona; V. González-Prida; Adolfo Crespo Márquez; J. Gómez; A. Sola; M. Macchi. 2019. "Implementing Intelligent Asset Management Systems (IAMS) within an Industry 4.0 Manufacturing Environment." IFAC-PapersOnLine 52, no. 13: 2488-2493.
Energy efficiency and reliability needs are growing in many economic sectors, where predictive analytics are becoming essential tools for these key variables forecasting. When predicting these variables, in many occasions, the problem to simplify the prediction model format when dealing with similar systems, which are placed in different functional locations, is a very complex problem due to model unavoidable dependency on changing operating conditions (per time and location). So effort is placed in this paper to develop tools that can easily adapt prediction models’ structure to existing operating conditions, for a given time period and place where the asset is located. Furthermore, these tools may allow the model to be easily trained and tested for automated implementation within the plant’s remote surveillance system. To this end, Artificial Intelligence (AI) techniques, and in particular artificial neural network (ANN) models, have been selected in this paper as prediction models, since their structure can be adapted to improve predictions accuracy and they can also learn from dynamic changes in environmental conditions. To demonstrate the adaptability for prediction accuracy and self-learning capabilities of the model, we have implemented an ANN with a backpropagation algorithm as a continuous time simulation model, which is then implemented using Vensim simulation environment, to benefit of the outstanding software optimization features for fast training. Using this model we provide predictions of asset degradation and operational risk under existing real time internal and locational variables. We can also dynamically release preventive maintenance activities. This prediction model is exemplified in an industrial case for failures in cryogenic pumps of LNG tanks.
Adolfo Crespo Márquez; Adolfo Crespo Del Castillo; Juan F. Gómez Fernández. Integrating artificial intelligent techniques and continuous time simulation modelling. Practical predictive analytics for energy efficiency and failure detection. Computers in Industry 2019, 115, 103164 .
AMA StyleAdolfo Crespo Márquez, Adolfo Crespo Del Castillo, Juan F. Gómez Fernández. Integrating artificial intelligent techniques and continuous time simulation modelling. Practical predictive analytics for energy efficiency and failure detection. Computers in Industry. 2019; 115 ():103164.
Chicago/Turabian StyleAdolfo Crespo Márquez; Adolfo Crespo Del Castillo; Juan F. Gómez Fernández. 2019. "Integrating artificial intelligent techniques and continuous time simulation modelling. Practical predictive analytics for energy efficiency and failure detection." Computers in Industry 115, no. : 103164.
Servitization is recognized as a key business strategy for original equipment manufacturers willing to move up the value chain. However, several barriers have to be overcome in order to successfully integrate products and services. Many of these barriers are caused by the technical challenges associated with the design and management of the product-service systems (PSSs), such as life cycle service level and cost estimation, risk management, or the system design and pricing. Asset management (AM) presents itself as a key research area in order to overcome these barriers as well as to integrate PSSs within the manufacturers’ operations management. It is the scope of this article to provide theoretical and practical insights with regards to the alignment of AM and PSS research areas. To support the alignment between both areas, a management framework which gathers specific technologies, including reliability analysis, simulation modeling, and multiobjective optimization algorithms, is presented. The purpose of the framework is to provide manufacturers with a decision-support tool that facilitates the main managerial challenges faced when implementing a servitization strategy. The article contributions are successfully applied to case studies in the railway and wind energy sectors based on real field data, thereby demonstrating their suitability for both facilitating manufacturer’s decision-making process and better satisfying stakeholders’ interests.
Asier Erguido; Adolfo Crespo Márquez; Eduardo Castellano; Ajith Kumar Parlikad; Juan Izquierdo. Asset Management Framework and Tools for Facing Challenges in the Adoption of Product-Service Systems. IEEE Transactions on Engineering Management 2019, 1 -14.
AMA StyleAsier Erguido, Adolfo Crespo Márquez, Eduardo Castellano, Ajith Kumar Parlikad, Juan Izquierdo. Asset Management Framework and Tools for Facing Challenges in the Adoption of Product-Service Systems. IEEE Transactions on Engineering Management. 2019; (99):1-14.
Chicago/Turabian StyleAsier Erguido; Adolfo Crespo Márquez; Eduardo Castellano; Ajith Kumar Parlikad; Juan Izquierdo. 2019. "Asset Management Framework and Tools for Facing Challenges in the Adoption of Product-Service Systems." IEEE Transactions on Engineering Management , no. 99: 1-14.
Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important effort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the different outputs for the different techniques.
Jesús Ferrero Bermejo; Juan Francisco Gómez Fernández; Rafael Pino; Adolfo Crespo Márquez; Antonio Jesús Guillén López. Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants. Energies 2019, 12, 4163 .
AMA StyleJesús Ferrero Bermejo, Juan Francisco Gómez Fernández, Rafael Pino, Adolfo Crespo Márquez, Antonio Jesús Guillén López. Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants. Energies. 2019; 12 (21):4163.
Chicago/Turabian StyleJesús Ferrero Bermejo; Juan Francisco Gómez Fernández; Rafael Pino; Adolfo Crespo Márquez; Antonio Jesús Guillén López. 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants." Energies 12, no. 21: 4163.
Equipment control represents a critical aspect of asset management because of its impact on asset’s reliability. Monitoring the behavior of the asset and being able to anticipate the occurrence of critical situation like asset’s failures play a vital role in the success of a company. Hence, in this paper, we propose a decision support system based on the development of an Artificial Neural Network and Association Rule Mining. The aim of this work is supporting the Operations and Maintenance managers in defining the best control policy on asset’s behavior, in order to anticipate the occurrence of failing situations and find relations among operating conditions’ values helpful for failures prediction and recognition. Implementing the proposed approach may support the decision maker in defining the best maintenance policy for the asset, knowing in advance the conditions leading to its failure. A brief example-case is provided for the discussion of the practical implications of the proposed approach.
Antonio De-La-Fuente-Carmona; Adolfo Crespo Márquez; S. Antomarioni; Maurizio Bevilacqua. Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) 2019, 2596 -2601.
AMA StyleAntonio De-La-Fuente-Carmona, Adolfo Crespo Márquez, S. Antomarioni, Maurizio Bevilacqua. Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). 2019; ():2596-2601.
Chicago/Turabian StyleAntonio De-La-Fuente-Carmona; Adolfo Crespo Márquez; S. Antomarioni; Maurizio Bevilacqua. 2019. "Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining." 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) , no. : 2596-2601.
In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption efficiency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence differs from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.
Adolfo Crespo Márquez; Antonio De-La-Fuente-Carmona; Sara Antomarioni. A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency. Energies 2019, 12, 3454 .
AMA StyleAdolfo Crespo Márquez, Antonio De-La-Fuente-Carmona, Sara Antomarioni. A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency. Energies. 2019; 12 (18):3454.
Chicago/Turabian StyleAdolfo Crespo Márquez; Antonio De-La-Fuente-Carmona; Sara Antomarioni. 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency." Energies 12, no. 18: 3454.
Operations and maintenance costs of the wind power generation systems can be reduced through the implementation of opportunistic maintenance policies at suitable indenture and maintenance levels. These maintenance policies take advantage of the economic dependence among the wind turbines and their systems, performing preventive maintenance tasks in running systems when some other maintenance tasks have to be undertaken in the wind farm. The existing opportunistic maintenance models for the wind energy sector follow a static decision making process, regardless of the operational and environmental context. At the same time, on some occasions policies do not refer to practical indenture and maintenance levels. In this chapter, a maintenance policy based on variable reliability thresholds is presented. This dynamic nature of the reliability thresholds, which vary according to the weather conditions, provides flexibility to the decision making process. Within the presented model, multi-level maintenance, capacity constraints and multiple failure modes per system have been considered. A comparative study, based on real operation, maintenance and weather data, demonstrates that the dynamic opportunistic maintenance policy significantly outperforms traditional corrective and static opportunistic maintenance strategies, both in terms of the overall wind farm energy production and the Life Cycle Cost.
Asier Erguido Ruiz; Adolfo Crespo Márquez; Eduardo Castellano; Juan F. Gómez Fernández. A Dynamic Opportunistic Maintenance Model to Maximize Energy-Based Availability While Reducing the Life Cycle Cost of Wind Farms. Value Based and Intelligent Asset Management 2019, 259 -287.
AMA StyleAsier Erguido Ruiz, Adolfo Crespo Márquez, Eduardo Castellano, Juan F. Gómez Fernández. A Dynamic Opportunistic Maintenance Model to Maximize Energy-Based Availability While Reducing the Life Cycle Cost of Wind Farms. Value Based and Intelligent Asset Management. 2019; ():259-287.
Chicago/Turabian StyleAsier Erguido Ruiz; Adolfo Crespo Márquez; Eduardo Castellano; Juan F. Gómez Fernández. 2019. "A Dynamic Opportunistic Maintenance Model to Maximize Energy-Based Availability While Reducing the Life Cycle Cost of Wind Farms." Value Based and Intelligent Asset Management , no. : 259-287.
In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this chapter, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the chapter is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities.
Fernando Olivencia Polo; Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Adolfo Crespo Márquez. Failure Mode Prediction and Energy Forecasting of PV Plants to Assist Maintenance Task by ANN Based Models. Value Based and Intelligent Asset Management 2019, 187 -209.
AMA StyleFernando Olivencia Polo, Jesús Ferrero Bermejo, Juan F. Gómez Fernández, Adolfo Crespo Márquez. Failure Mode Prediction and Energy Forecasting of PV Plants to Assist Maintenance Task by ANN Based Models. Value Based and Intelligent Asset Management. 2019; ():187-209.
Chicago/Turabian StyleFernando Olivencia Polo; Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Adolfo Crespo Márquez. 2019. "Failure Mode Prediction and Energy Forecasting of PV Plants to Assist Maintenance Task by ANN Based Models." Value Based and Intelligent Asset Management , no. : 187-209.
This Chapter deals with the process of criticality analysis in overhead power lines, as a tool to improve maintenance, felling and pruning programs management. Felling and pruning activities are tasks that utility companies must accomplish to respect the servitudes of the overhead lines, concerned with distances to vegetation, buildings, infrastructures and other networks crossings. Conceptually, these power lines servitudes can be considered as failure modes of the maintainable items under our analysis (power line spans), and the criticality analysis methodology developed, will therefore help to optimize actions to avoid these as other failure modes of the line maintainable items. The approach is interesting, but another relevant contribution of the Chapter is the process followed for the automation of the analysis. Automation is possible by utilizing existing companies IT systems and databases. The Chapter explains how to use data located in Enterprise Asset management Systems, GIS and Dispatching systems for a fast, reliable, objective and dynamic criticality analysis. Promising results are included and also discussions about how this technique may result in important implications for this type of businesses.
Adolfo Crespo Márquez; Antonio Sola Rosique; Pedro Moreu De León; Juan F. Gómez Fernández; Antonio González Diego; Eduardo Candón Fernández. Exploiting EAMS, GIS and Dispatching Systems Data for Criticality Analysis. Value Based and Intelligent Asset Management 2019, 139 -161.
AMA StyleAdolfo Crespo Márquez, Antonio Sola Rosique, Pedro Moreu De León, Juan F. Gómez Fernández, Antonio González Diego, Eduardo Candón Fernández. Exploiting EAMS, GIS and Dispatching Systems Data for Criticality Analysis. Value Based and Intelligent Asset Management. 2019; ():139-161.
Chicago/Turabian StyleAdolfo Crespo Márquez; Antonio Sola Rosique; Pedro Moreu De León; Juan F. Gómez Fernández; Antonio González Diego; Eduardo Candón Fernández. 2019. "Exploiting EAMS, GIS and Dispatching Systems Data for Criticality Analysis." Value Based and Intelligent Asset Management , no. : 139-161.
An Asset Health Index (AHI) is a tool that processes data about asset’s condition. That index is intended to explore if alterations can be generated in the health of the asset along its life cycle. These data can be obtained during the asset’s operation, but they can also come from other information sources such as geographical information systems, supplier’s reliability records, relevant external agent’s records, etc. The tool (AHI) provides an objective point of view in order to justify, for instance, the extension of an asset useful life, or in order to identify which assets from a fleet are candidates for an early replacement as a consequence of a premature aging. The purpose of this Chapter is to develop a generic procedure to easy obtaining an AHI that can reasonably measure the current degradation of the assets, offering the possibility to compare their status to the one expected for the at their age and functional location. As a result of the procedure, an organization will have an objective tool to prioritize interventions, attention and the renewal of significant equipment. In this Chapter we first review the concept and the most relevant models in the literature, then we introduce the methodology and different examples related to the different steps. Finally conclusions of the chapter are presented.
Adolfo Crespo Márquez; Antonio De La Fuente; Antonio J. Guillén López; Antonio Sola Rosique; Javier Serra Parajes; Pablo Martínez-Galán Fernández; Juan Izquierdo. Defining Asset Health Indicators (AHI) to Support Complex Assets Maintenance and Replacement Strategies. A Generic Procedure to Assess Assets Deterioration. Value Based and Intelligent Asset Management 2019, 79 -99.
AMA StyleAdolfo Crespo Márquez, Antonio De La Fuente, Antonio J. Guillén López, Antonio Sola Rosique, Javier Serra Parajes, Pablo Martínez-Galán Fernández, Juan Izquierdo. Defining Asset Health Indicators (AHI) to Support Complex Assets Maintenance and Replacement Strategies. A Generic Procedure to Assess Assets Deterioration. Value Based and Intelligent Asset Management. 2019; ():79-99.
Chicago/Turabian StyleAdolfo Crespo Márquez; Antonio De La Fuente; Antonio J. Guillén López; Antonio Sola Rosique; Javier Serra Parajes; Pablo Martínez-Galán Fernández; Juan Izquierdo. 2019. "Defining Asset Health Indicators (AHI) to Support Complex Assets Maintenance and Replacement Strategies. A Generic Procedure to Assess Assets Deterioration." Value Based and Intelligent Asset Management , no. : 79-99.