Journal ArticleDOI
A data mining approach for fault diagnosis: An application of anomaly detection algorithm
TLDR
In this paper, a data mining approach using a machine learning technique called anomaly detection (AD) is presented, which employs classification techniques to discriminate between defect examples and two features, kurtosis and non-Gaussianity score (NGS), are extracted to develop anomaly detection algorithms.About:
This article is published in Measurement.The article was published on 2014-09-01. It has received 104 citations till now. The article focuses on the topics: Anomaly detection & Fault detection and isolation.read more
Citations
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Journal ArticleDOI
Convolutional Neural Network Based Fault Detection for Rotating Machinery
Olivier Janssens,Viktor Slavkovikj,Bram Vervisch,Kurt Stockman,Mia Loccufier,Steven Verstockt,Rik Van de Walle,Sofie Van Hoecke +7 more
TL;DR: A feature learning model for condition monitoring based on convolutional neural networks is proposed to autonomously learn useful features for bearing fault detection from the data itself and significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier.
Journal ArticleDOI
A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products
TL;DR: An overall architecture of big data-based analytics for product lifecycle (BDA-PL) was proposed that integrated big data analytics and service-driven patterns that helped to overcome barriers in the implementation of CP.
Journal ArticleDOI
A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions
Shan Ren,Yingfeng Zhang,Yang Liu,Yang Liu,Tomohiko Sakao,Donald Huisingh,Cecília M.V.B. Almeida +6 more
TL;DR: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and more efficient as discussed by the authors. But, the benefits of smart manufacturing are limited.
Journal ArticleDOI
A framework for Big Data driven product lifecycle management
TL;DR: The results showed that the proposed framework was feasible to be adopted in industry, and can provide an overall solution for optimizing the decision-making processes in different phases of the whole lifecycle.
Journal ArticleDOI
Early Fault Detection Approach With Deep Architectures
TL;DR: A novel deep-structured framework to solve the early fault detection problem using deep neural network (DNN) and long short-term memory (LSTM) network and a circular indirect alarm assessment strategy designed for collecting deviation values and confirming the fault appearance only when a specified confidence level is reached.
References
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The Nature of Statistical Learning Theory
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An overview of statistical learning theory
TL;DR: How the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms are demonstrated and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems are demonstrated.
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A review on machinery diagnostics and prognostics implementing condition-based maintenance
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.