133 research outputs found
Self-Supervised Encoder for Fault Prediction in Electrochemical Cells
Predicting faults before they occur helps to avoid potential safety hazards.
Furthermore, planning the required maintenance actions in advance reduces
operation costs. In this article, the focus is on electrochemical cells. In
order to predict a cell's fault, the typical approach is to estimate the
expected voltage that a healthy cell would present and compare it with the
cell's measured voltage in real-time. This approach is possible because, when a
fault is about to happen, the cell's measured voltage differs from the one
expected for the same operating conditions. However, estimating the expected
voltage is challenging, as the voltage of a healthy cell is also affected by
its degradation -- an unknown parameter. Expert-defined parametric models are
currently used for this estimation task. Instead, we propose the use of a
neural network model based on an encoder-decoder architecture. The network
receives the operating conditions as input. The encoder's task is to find a
faithful representation of the cell's degradation and to pass it to the
decoder, which in turn predicts the expected cell's voltage. As no labeled
degradation data is given to the network, we consider our approach to be a
self-supervised encoder. Results show that we were able to predict the voltage
of multiple cells while diminishing the prediction error that was obtained by
the parametric models by 53%. This improvement enabled our network to predict a
fault 31 hours before it happened, a 64% increase in reaction time compared to
the parametric model. Moreover, the output of the encoder can be plotted,
adding interpretability to the neural network model
Recent advances in the theory and practice of logical analysis of data
Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning methods by the fact that it analyzes a significant subset of combinations of variables to describe the positive or negative nature of an observation and uses combinatorial techniques to extract models defined in terms of patterns. In recent years, the methodology has tremendously advanced through numerous theoretical developments and practical applications. In the present paper, we review the methodology and its recent advances, describe novel applications in engineering, finance, health care, and algorithmic techniques for some stochastic optimization problems, and provide a comparative description of LAD with well-known classification methods
Is Fragmentation a Threat to the Success of the Internet of Things?
The current revolution in collaborating distributed things is seen as the
first phase of IoT to develop various services. Such collaboration is
threatened by the fragmentation found in the industry nowadays as it brings
challenges stemming from the difficulty to integrate diverse technologies in
system. Diverse networking technologies induce interoperability issues, hence,
limiting the possibility of reusing the data to develop new services. Different
aspects of handling data collection must be available to provide
interoperability to the diverse objects interacting; however, such approaches
are challenged as they bring substantial performance impairments in settings
with the increasing number of collaborating devices/technologies.Comment: 16 pages, 2 figures, Internet of Things Journal
(http://ieee-iotj.org
Data Preparation in Machine Learning for Condition-based Maintenance
ABSTRACT: Using Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate the effect of data preparation on the ML prediction accuracy of Gas Turbines (GTs) performance decay. First a data cleaning technique for robust Linear Regression imputation is proposed based on the Mixed Integer Linear Programming. Then, experiments are conducted to compare the effect of commonly used data cleaning, normalization and reduction techniques on the ML prediction accuracy. Results revealed that the best prediction accuracy of GTs decay, found with the k-Nearest Neighbors ML algorithm, considerately deteriorate when changing the data preparation steps and/or techniques. This study has shown that, for effective CBM application in industry, there is a need to develop a systematic methodology for design and selection of adequate data preparation steps and techniques with the proposed ML algorithms
Cloud computing based unsupervised fault diagnosis system in the context of Industry 4.0
ABSTRACT: New online fault monitoring and alarm systems, with the aid of Cyber-Physical Systems (CPS) and Cloud Technology (CT), are examined in this article within the context of Industry 4.0. The data collected from machines is used to implement maintenance strategies based on the diagnosis and prognosis of the machines' performance. As such, the purpose of this paper is to propose a Cloud Computing Platform containing three layers of technologies forming a Cyber-Physical System which receives unlabelled data to generate an interpreted online decision for the local team, as well as collecting historical data to improve the analyzer. The proposed troubleshooter is tested using unlabelled experimental data sets of rolling element bearing. Finally, the current and future Fault Diagnosis Systems and Cloud Technologies applications in the maintenance field are discussed
Par une discipline rigoreuse et illustrés: l'inspection dans la capitale de l'empire Brésilien
O objetivo deste estudo é analisar os procedimentos manejados na fiscalização de professores primários, por meio do estudo a respeito do serviço de inspeção da instrução na Corte Imperial, a partir de sua institucionalização com base no Regulamento da Instrução Primária e Secundária da Corte em 1854. Na pesquisa procurou-se discutir como a visibilidade dada à escola e aos seus atores, via inspeção, permitiram inseri-los em um esquema disciplinar, cujos efeitos encontram-se articulados e justificados em nome de um projeto de ordenação e civilização da capital do Império brasileiro
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