56,202 research outputs found
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
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
A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.
As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification
This paper presents a study on power grid disturbance classification by Deep
Learning (DL). A real synchrophasor set composing of three different types of
disturbance events from the Frequency Monitoring Network (FNET) is used. An
image embedding technique called Gramian Angular Field is applied to transform
each time series of event data to a two-dimensional image for learning. Two
main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent
Neural Network) are tested and compared with two widely used data mining tools,
the Support Vector Machine and Decision Tree. The test results demonstrate the
superiority of the both DL algorithms over other methods in the application of
power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018
IEEE International Conference on Energy Internet (ICEI), Beijing, Chin
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