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Juan F. Gómez Fernández
E.T.S. de Ingeniería. C. de los Descubrimientos, s/n. Pabellón Pza, de América, 41092, Sevilla, Spain

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Journal article
Published: 10 March 2021 in Computers in Industry
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Fernando 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.

Journal article
Published: 22 July 2020 in Energies
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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.

ACS Style

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 Style

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 (15):3762.

Chicago/Turabian Style

Adolfo 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.

Journal article
Published: 05 December 2019 in Computers in Industry
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Adolfo 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.

Review
Published: 31 October 2019 in Energies
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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.

ACS Style

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 Style

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 (21):4163.

Chicago/Turabian Style

Jesús 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.

Review
Published: 05 May 2019 in Applied Sciences
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The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

ACS Style

Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Fernando Olivencia Polo; Adolfo Crespo Márquez. A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources. Applied Sciences 2019, 9, 1844 .

AMA Style

Jesús Ferrero Bermejo, Juan F. Gómez Fernández, Fernando Olivencia Polo, Adolfo Crespo Márquez. A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources. Applied Sciences. 2019; 9 (9):1844.

Chicago/Turabian Style

Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Fernando Olivencia Polo; Adolfo Crespo Márquez. 2019. "A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources." Applied Sciences 9, no. 9: 1844.

Journal article
Published: 08 March 2019 in IEEE Transactions on Network and Service Management
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ACS Style

Juan Fco. Gomez; Juan Francisco Gómez Fernández; Antonio J. Guillen; Adolfo Crespo Márquez. Risk-Based Criticality for Network Utilities Asset Management. IEEE Transactions on Network and Service Management 2019, 16, 755 -768.

AMA Style

Juan Fco. Gomez, Juan Francisco Gómez Fernández, Antonio J. Guillen, Adolfo Crespo Márquez. Risk-Based Criticality for Network Utilities Asset Management. IEEE Transactions on Network and Service Management. 2019; 16 (2):755-768.

Chicago/Turabian Style

Juan Fco. Gomez; Juan Francisco Gómez Fernández; Antonio J. Guillen; Adolfo Crespo Márquez. 2019. "Risk-Based Criticality for Network Utilities Asset Management." IEEE Transactions on Network and Service Management 16, no. 2: 755-768.

Journal article
Published: 06 September 2018 in IFAC-PapersOnLine
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This paper deals with the process of criticality analysis in overhead power lines, as a tool to improve maintenance, felling & pruning programs. Felling & 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 paper is the process followed for the automation of the analysis. Automation is possible by utilizing existing companies IT systems and databases. The paper explains how to use data located in Enterprise Assets 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.

ACS Style

A. Crespo; A. Sola; P. Moreu; J.F. Gómez; Antonio De La Fuente; A. Guillén; V. González-Prida. Criticality Analysis for improving maintenance, felling and pruning cycles in power lines. IFAC-PapersOnLine 2018, 51, 211 -216.

AMA Style

A. Crespo, A. Sola, P. Moreu, J.F. Gómez, Antonio De La Fuente, A. Guillén, V. González-Prida. Criticality Analysis for improving maintenance, felling and pruning cycles in power lines. IFAC-PapersOnLine. 2018; 51 (11):211-216.

Chicago/Turabian Style

A. Crespo; A. Sola; P. Moreu; J.F. Gómez; Antonio De La Fuente; A. Guillén; V. González-Prida. 2018. "Criticality Analysis for improving maintenance, felling and pruning cycles in power lines." IFAC-PapersOnLine 51, no. 11: 211-216.

Journal article
Published: 06 September 2018 in IFAC-PapersOnLine
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The aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management.

ACS Style

Antonio De-La-Fuente-Carmona; V. González-Prida; A. Crespo; J.F. Gómez; A. Guillén. Advanced Techniques for Assets Maintenance Management. IFAC-PapersOnLine 2018, 51, 205 -210.

AMA Style

Antonio De-La-Fuente-Carmona, V. González-Prida, A. Crespo, J.F. Gómez, A. Guillén. Advanced Techniques for Assets Maintenance Management. IFAC-PapersOnLine. 2018; 51 (11):205-210.

Chicago/Turabian Style

Antonio De-La-Fuente-Carmona; V. González-Prida; A. Crespo; J.F. Gómez; A. Guillén. 2018. "Advanced Techniques for Assets Maintenance Management." IFAC-PapersOnLine 51, no. 11: 205-210.

Journal article
Published: 01 December 2017 in Renewable Energy
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ACS Style

Asier Erguido; A. Crespo Márquez; E. Castellano; Juan Francisco Gómez Fernández. A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms. Renewable Energy 2017, 114, 843 -856.

AMA Style

Asier Erguido, A. Crespo Márquez, E. Castellano, Juan Francisco Gómez Fernández. A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms. Renewable Energy. 2017; 114 ():843-856.

Chicago/Turabian Style

Asier Erguido; A. Crespo Márquez; E. Castellano; Juan Francisco Gómez Fernández. 2017. "A dynamic opportunistic maintenance model to maximize energy-based availability while reducing the life cycle cost of wind farms." Renewable Energy 114, no. : 843-856.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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The purpose of this paper is to establish a basis for a criticality analysis, considered here as a prerequisite, a first required step to review the current maintenance programs, of complex in-service engineering assets. Review is understood as a reality check, a testing of whether the current maintenance activities are well aligned to actual business objectives and needs. This paper describes an efficient and rational working process and a model resulting in a hierarchy of assets, based on risk analysis and cost–benefit principles, which will be ranked according to their importance for the business to meet specific goals. Starting from a multi-criteria analysis, the proposed model converts relevant criteria impacting equipment criticality into a single score presenting the criticality level. Although detailed implementation of techniques like root cause failure analysis (RCFA) and reliability centered maintenance (RCM) will be recommended for further optimization of the maintenance activities, the reasons why criticality analysis deserves the attention of the engineers, maintenance and reliability managers are here precisely explained. A case study is presented to help the reader to understand the process and to operationalize the model.

ACS Style

Adolfo Crespo Márquez; Pedro Moreu De León; Antonio Sola Rosique; Juan Francisco Gómez Fernández. Criticality Analysis for Maintenance Purposes. Advanced Maintenance Modelling for Asset Management 2017, 143 -166.

AMA Style

Adolfo Crespo Márquez, Pedro Moreu De León, Antonio Sola Rosique, Juan Francisco Gómez Fernández. Criticality Analysis for Maintenance Purposes. Advanced Maintenance Modelling for Asset Management. 2017; ():143-166.

Chicago/Turabian Style

Adolfo Crespo Márquez; Pedro Moreu De León; Antonio Sola Rosique; Juan Francisco Gómez Fernández. 2017. "Criticality Analysis for Maintenance Purposes." Advanced Maintenance Modelling for Asset Management , no. : 143-166.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems’ condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition-Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.

ACS Style

Antonio Jesús Guillén López; Adolfo Crespo Márquez; Marco Macchi; Juan Francisco Gómez Fernández. Prognostics and Health Management in Advanced Maintenance Systems. Advanced Maintenance Modelling for Asset Management 2017, 79 -106.

AMA Style

Antonio Jesús Guillén López, Adolfo Crespo Márquez, Marco Macchi, Juan Francisco Gómez Fernández. Prognostics and Health Management in Advanced Maintenance Systems. Advanced Maintenance Modelling for Asset Management. 2017; ():79-106.

Chicago/Turabian Style

Antonio Jesús Guillén López; Adolfo Crespo Márquez; Marco Macchi; Juan Francisco Gómez Fernández. 2017. "Prognostics and Health Management in Advanced Maintenance Systems." Advanced Maintenance Modelling for Asset Management , no. : 79-106.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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In this paper a reliability model based on artificial neural networks and the generalized renewal process is developed. The model is used for failure prediction, and is able to dynamically adapt to changes in the operating and environmental conditions of assets. The model is implemented for a thermal solar power plant, focusing on critical elements of these plants: heat transfer fluid pumps. We affirm that this type of model can be easily automated within the plant’s remote monitoring system. Using this model we can dynamically assign reference values for warnings and alarms and provide predictions of asset degradation. These in turn can be used to evaluate the associated economic risk to the system under existing operating conditions and to inform preventive maintenance activities.

ACS Style

Juan Francisco Gómez Fernández; Jesús Ferrero Bermejo; Fernando Agustín Olivencia Polo; Adolfo Crespo Márquez; Gonzalo Cerruela García. Dynamic Reliability Prediction of Asset Failure Modes. Advanced Maintenance Modelling for Asset Management 2017, 291 -309.

AMA Style

Juan Francisco Gómez Fernández, Jesús Ferrero Bermejo, Fernando Agustín Olivencia Polo, Adolfo Crespo Márquez, Gonzalo Cerruela García. Dynamic Reliability Prediction of Asset Failure Modes. Advanced Maintenance Modelling for Asset Management. 2017; ():291-309.

Chicago/Turabian Style

Juan Francisco Gómez Fernández; Jesús Ferrero Bermejo; Fernando Agustín Olivencia Polo; Adolfo Crespo Márquez; Gonzalo Cerruela García. 2017. "Dynamic Reliability Prediction of Asset Failure Modes." Advanced Maintenance Modelling for Asset Management , no. : 291-309.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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CBM (Condition-Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.

ACS Style

Antonio Jesús Guillén López; Juan Francisco Gómez Fernández; Adolfo Crespo Márquez. A Framework for Effective Management of CBM Programs. Advanced Maintenance Modelling for Asset Management 2017, 107 -141.

AMA Style

Antonio Jesús Guillén López, Juan Francisco Gómez Fernández, Adolfo Crespo Márquez. A Framework for Effective Management of CBM Programs. Advanced Maintenance Modelling for Asset Management. 2017; ():107-141.

Chicago/Turabian Style

Antonio Jesús Guillén López; Juan Francisco Gómez Fernández; Adolfo Crespo Márquez. 2017. "A Framework for Effective Management of CBM Programs." Advanced Maintenance Modelling for Asset Management , no. : 107-141.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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Reliability requirements are increasingly demanded in all economic sectors, practical applications of the reliability theory are pursued often as a tailor-made suit for each case, in order to manage the assets effectively according to specific operating and environmental conditions. In sectors like renewable energy, these conditions can be changing importantly over time and reliability analysis is periodically required. At the same time, adapting a unique model to similar systems placed in different plants has proven to be troublesome. This paper adapts reliability models in order to incorporate monitored assets operating and environmental conditions. This paper introduces a logic decision tool which is based on two artificial neural networks models; allowing updating assets reliability analysis in relation to changes in operating and/or environmental conditions, and even more, this model could be easily automated within a SCADA system. Thus, by using the model, reference values and the corresponding warnings and alarms can be dynamically generated, serving as an online diagnostic or prediction of a potential degradation of the asset. The models are developed according to the available amount of failure data and they are used to detect early degradation in the energy production due to power inverter and solar tracker failures, and to evaluate the associated economic risk to the system under existing conditions. This information can then trigger preventive maintenance activities.

ACS Style

Fernando Agustín Olivencia Polo; Jesús Ferrero Bermejo; Juan Francisco Gómez Fernández; Adolfo Crespo Márquez. Assistance to Dynamic Maintenance Tasks by Ann-Based Models. Advanced Maintenance Modelling for Asset Management 2017, 387 -411.

AMA Style

Fernando Agustín Olivencia Polo, Jesús Ferrero Bermejo, Juan Francisco Gómez Fernández, Adolfo Crespo Márquez. Assistance to Dynamic Maintenance Tasks by Ann-Based Models. Advanced Maintenance Modelling for Asset Management. 2017; ():387-411.

Chicago/Turabian Style

Fernando Agustín Olivencia Polo; Jesús Ferrero Bermejo; Juan Francisco Gómez Fernández; Adolfo Crespo Márquez. 2017. "Assistance to Dynamic Maintenance Tasks by Ann-Based Models." Advanced Maintenance Modelling for Asset Management , no. : 387-411.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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This paper presents a methodology and a case study where the proportional hazard model is used to determine reliability and risk of existing repairable systems in a distribution network utility. After introducing different issues relating to conditioning maintenance management in these companies, we discuss the modeling possibilities to obtain online values for systems reliability and risk. In this case we try to model reliability considering current operating time of the equipment, the number of maintenance interventions, and the value of specific monitored parameters. Reliability is then used to calculate equipment risk for a given failure mode during a certain period, and in order to do so, failure mode affection to the network is estimated from historical data. Finally, the paper presents a possible business process to schedule preventive maintenance activities according to previous findings and a case study.

ACS Style

Adolfo Crespo Márquez; Juan Francisco Gómez Fernández; Pedro Moreu De León; Antonio Sola Rosique. Online Reliability and Risk to Schedule the Preventive Maintenance in Network Utilities. Advanced Maintenance Modelling for Asset Management 2017, 245 -261.

AMA Style

Adolfo Crespo Márquez, Juan Francisco Gómez Fernández, Pedro Moreu De León, Antonio Sola Rosique. Online Reliability and Risk to Schedule the Preventive Maintenance in Network Utilities. Advanced Maintenance Modelling for Asset Management. 2017; ():245-261.

Chicago/Turabian Style

Adolfo Crespo Márquez; Juan Francisco Gómez Fernández; Pedro Moreu De León; Antonio Sola Rosique. 2017. "Online Reliability and Risk to Schedule the Preventive Maintenance in Network Utilities." Advanced Maintenance Modelling for Asset Management , no. : 245-261.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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This chapter aims to investigate technical and economic factors related to failure costs (non reliability costs) within the life-cycle cost analysis (LCCA) of a production asset. Life-cycle costing is a well-established method used to evaluate alternative asset options. It is a structured approach that addresses all the elements of this cost and can be used to produce a spend profile of the assets over its anticipated life span. The results of an LCC analysis can be used to assist management in the decision-making process where there is a choice of options. The main costs can be classified as the ‘capital expenditure’ (CAPEX) incurred when the asset is purchased, and the ‘operating expenditure’ (OPEX) incurred throughout the asset’s life. This chapter will explore different aspects related to the “failure costs” within the LCCA, and will describe the most important aspects of the stochastic model called: Non-homogeneous Poisson Process (NHPP). This model will be used to estimate the frequency of failures and the impact that could cause diverse failures in the total costs of a production asset. This paper also contains a case study for the Rail Freight Industry (Chile) and in the Oil Industry (PETRONOX, Venezuela) where the above-mentioned model and concepts will be applied, and respectively compared in terms of results. Finally, the model presented provides maintenance managers with a decision tool that optimizes the LCCA of an asset and will increase the efficiency of the decision-making process related to the control of failures.

ACS Style

Carlos Parra Márquez; Adolfo Crespo Márquez; Vicente González-Prida Díaz; Juan Francisco Gómez Fernández; Fredy Kristjanpoller Rodríguez; Pablo Viveros Gunckel. Economic Impact of a Failure Using Life-Cycle Cost Analysis. Advanced Maintenance Modelling for Asset Management 2017, 213 -243.

AMA Style

Carlos Parra Márquez, Adolfo Crespo Márquez, Vicente González-Prida Díaz, Juan Francisco Gómez Fernández, Fredy Kristjanpoller Rodríguez, Pablo Viveros Gunckel. Economic Impact of a Failure Using Life-Cycle Cost Analysis. Advanced Maintenance Modelling for Asset Management. 2017; ():213-243.

Chicago/Turabian Style

Carlos Parra Márquez; Adolfo Crespo Márquez; Vicente González-Prida Díaz; Juan Francisco Gómez Fernández; Fredy Kristjanpoller Rodríguez; Pablo Viveros Gunckel. 2017. "Economic Impact of a Failure Using Life-Cycle Cost Analysis." Advanced Maintenance Modelling for Asset Management , no. : 213-243.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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In this last chapter of the book, as editors of the work, we would like to summarize for the reader the main contributions included in this manuscript. We want to remark the importance of new and advanced techniques supporting decision-making in different business processes for maintenance and assets management, but mainly we recall the reader the basic need of the adoption of a certain management framework, with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement.

ACS Style

Adolfo Crespo Márquez; Vicente González-Prida Díaz; Juan Francisco Gómez Fernández. Summary of Results and Conclusions. Advanced Maintenance Modelling for Asset Management 2017, 455 -467.

AMA Style

Adolfo Crespo Márquez, Vicente González-Prida Díaz, Juan Francisco Gómez Fernández. Summary of Results and Conclusions. Advanced Maintenance Modelling for Asset Management. 2017; ():455-467.

Chicago/Turabian Style

Adolfo Crespo Márquez; Vicente González-Prida Díaz; Juan Francisco Gómez Fernández. 2017. "Summary of Results and Conclusions." Advanced Maintenance Modelling for Asset Management , no. : 455-467.

Chapter
Published: 13 July 2017 in Advanced Maintenance Modelling for Asset Management
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For companies that distribute services such as telecommunications, water, energy, gas, etc., quality perceived by the customers has a strong impact on the fulfillment of financial goals, positively increasing the demand and negatively increasing the risk of customer churn (loss of customers). Failures by these companies may cause customer affection in a massive way, augmenting the intention to leave the company. Therefore, maintenance performance and specifically service reliability has a strong influence on financial goals. This paper proposes a methodology to evaluate the contribution of the maintenance department in economic terms based on service unreliability by network failures. The developed methodology aims to provide an analysis of failures to facilitate decision-making about maintenance (preventive/predictive and corrective) costs versus negative impacts in end customer invoicing based on the probability of losing customers. Survival analysis of recurrent failures with the General Renewal Process distribution is used for this novel purpose with the intention to be applied as a standard procedure to calculate the expected maintenance financial impact, for a given period of time. Also, geographical areas of coverage are distinguished, enabling the comparison of different technical or management alternatives. Two case studies in a telecommunications services company are presented in order to illustrate the applicability of the methodology.

ACS Style

Juan Francisco Gómez Fernández; Adolfo Crespo Márquez; Mónica Alejandra López-Campos. Customer-oriented Risk Assessment in Network Utilities. Advanced Maintenance Modelling for Asset Management 2017, 263 -290.

AMA Style

Juan Francisco Gómez Fernández, Adolfo Crespo Márquez, Mónica Alejandra López-Campos. Customer-oriented Risk Assessment in Network Utilities. Advanced Maintenance Modelling for Asset Management. 2017; ():263-290.

Chicago/Turabian Style

Juan Francisco Gómez Fernández; Adolfo Crespo Márquez; Mónica Alejandra López-Campos. 2017. "Customer-oriented Risk Assessment in Network Utilities." Advanced Maintenance Modelling for Asset Management , no. : 263-290.

Journal article
Published: 01 March 2016 in Reliability Engineering & System Safety
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ACS Style

Juan Francisco Gómez Fernández; Adolfo Crespo Márquez; Mónica A. López-Campos. Customer-oriented risk assessment in network utilities. Reliability Engineering & System Safety 2016, 147, 72 -83.

AMA Style

Juan Francisco Gómez Fernández, Adolfo Crespo Márquez, Mónica A. López-Campos. Customer-oriented risk assessment in network utilities. Reliability Engineering & System Safety. 2016; 147 ():72-83.

Chicago/Turabian Style

Juan Francisco Gómez Fernández; Adolfo Crespo Márquez; Mónica A. López-Campos. 2016. "Customer-oriented risk assessment in network utilities." Reliability Engineering & System Safety 147, no. : 72-83.

Journal article
Published: 01 April 2015 in Renewable Energy
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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 paper, 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 paper 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.

ACS Style

Fernando A. Olivencia Polo; Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Adolfo Crespo Márquez. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy 2015, 81, 227 -238.

AMA Style

Fernando A. Olivencia Polo, Jesús Ferrero Bermejo, Juan F. Gómez Fernández, Adolfo Crespo Márquez. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy. 2015; 81 ():227-238.

Chicago/Turabian Style

Fernando A. Olivencia Polo; Jesús Ferrero Bermejo; Juan F. Gómez Fernández; Adolfo Crespo Márquez. 2015. "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models." Renewable Energy 81, no. : 227-238.