13,110 research outputs found
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
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 e ort 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 di erent
outputs for the di erent techniques
Business analytics in industry 4.0: a systematic review
Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We
would like to thank to the three anonymous reviewers for their helpful suggestions
Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools
n recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor envi- ronments. The new Industry-4.0 model allows smart factories to become very advanced IT industries, generating an ever- increasing amount of valuable data. As a consequence, the neces- sity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision making process.
This paper discusses the latest software technologies needed to collect, manage and elaborate all data generated through innovative IoT architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life-cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step towards the rich landscape of literature for readers approaching this field, and as a global yet detailed overview of the current state-of-the-art in the Industry 4.0 domain for experts. As a case study, we discuss in detail the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs
Continuous maintenance and the future – Foundations and technological challenges
High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security
Fleet Prognosis with Physics-informed Recurrent Neural Networks
Services and warranties of large fleets of engineering assets is a very
profitable business. The success of companies in that area is often related to
predictive maintenance driven by advanced analytics. Therefore, accurate
modeling, as a way to understand how the complex interactions between operating
conditions and component capability define useful life, is key for services
profitability. Unfortunately, building prognosis models for large fleets is a
daunting task as factors such as duty cycle variation, harsh environments,
inadequate maintenance, and problems with mass production can lead to large
discrepancies between designed and observed useful lives. This paper introduces
a novel physics-informed neural network approach to prognosis by extending
recurrent neural networks to cumulative damage models. We propose a new
recurrent neural network cell designed to merge physics-informed and
data-driven layers. With that, engineers and scientists have the chance to use
physics-informed layers to model parts that are well understood (e.g., fatigue
crack growth) and use data-driven layers to model parts that are poorly
characterized (e.g., internal loads). A simple numerical experiment is used to
present the main features of the proposed physics-informed recurrent neural
network for damage accumulation. The test problem consist of predicting fatigue
crack length for a synthetic fleet of airplanes subject to different mission
mixes. The model is trained using full observation inputs (far-field loads) and
very limited observation of outputs (crack length at inspection for only a
portion of the fleet). The results demonstrate that our proposed hybrid
physics-informed recurrent neural network is able to accurately model fatigue
crack growth even when the observed distribution of crack length does not match
with the (unobservable) fleet distribution.Comment: Data and codes (including our implementation for both the multi-layer
perceptron, the stress intensity and Paris law layers, the cumulative damage
cell, as well as python driver scripts) used in this manuscript are publicly
available on GitHub at https://github.com/PML-UCF/pinn. The data and code are
released under the MIT Licens
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