Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

Polo, Fernando Agustín Olivencia; Bermejo, Jesús Ferrero; Fernández, Juan Pablo Murga; Márquez, Adolfo Crespo

doi:10.1016/j.renene.2015.03.023

Cited by 55 publications

(20 citation statements)

References 33 publications

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“…Before concluding this phase and regardless of the fact our results are promising, we realize that the selection of a given technique, like ANN, should not be done without discussing whether we are delivering results that can be benchmarked to those provided by other available advanced machine learning techniques for predictive analytics. This certainly supports the tool selection and provides more elements for a well-informed decision for the management of assets [33].…”

Section: Results Of the Prediction Error (As Inmentioning

confidence: 54%

“…In comparison to other methods, ANNs are well suited for solving problems where explicit knowledge is difficult to specify or define, but where there are enough data [31,32]. For these reasons ANN are a very popular tool for prediction and classification problems, but we agree that more research is needed to make their implementation more practical in real life applications, taking advantage of existing maintenance engineering tools and management systems [33].…”

Section: Rational For the Ann And Dm Techniques Selectionmentioning

confidence: 99%

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A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

2019

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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.

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Section: Results Of the Prediction Error (As Inmentioning

confidence: 54%

Section: Rational For the Ann And Dm Techniques Selectionmentioning

confidence: 99%

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

2019

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“…Concerning failure diagnosis and prognosis, an extra effort is required to properly measure the different failure mode consequences in order to identify the cause and expected behavior pattern of the failure modes [33,71].…”

Section: Ann Models In Photovoltaic (Pv) Energymentioning

confidence: 99%

“…based on collected information from sensors in each particular asset (with an approach very similar to current studies on the Internet of Things-IoT) [30][31][32]. ANN models are mathematical tools emulating human reasoning, learning from past experiences and coping with rather complex non-linear behaviors [33]. These models are especially well suited to replicate certain behavioral patterns where relationship among input and output variables cannot be explained by other mathematical techniques [34,35].…”

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confidence: 99%

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

Bermejo¹,

Fernández

Polo³

et al. 2019

Applied Sciences

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154

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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.

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“…This finding means that it is necessary to consider more extreme measures in terms of both the prediction and the anticipation of failure. Thus, predictive maintenance engineering has developed and perfected technologies for condition monitoring and predicting failures before breakage occurs [8][9][10]. Although this approach is more operational and requires more resources and investments than following the scheme [11], it cannot 2 Complexity be established in an entire strategic productive area without critical equipment, facilities, or machine parts.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning

2017

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The uncertainty of demand has led production systems to become increasingly complex; this can affect the availability of the machines and thus their maintenance. Therefore, it is necessary to adequately manage the information that facilitates decisionmaking. This paper presents a system for making decisions related to the design of customized maintenance plans in a production plant. This paper addresses this tactical goal and aims to provide greater knowledge and better predictions by projecting reliable behavior in the medium-term, integrating this new functionality into classic Balance Scorecards, and making it possible to extend their current measuring function to a new aptitude: predicting evolution based on historical data. In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. In addition, this extension enables the prediction of indicator outcomes such as overall equipment efficiency and mean time to failure.

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Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

Cited by 55 publications

References 33 publications

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

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

An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning

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