Observer-biased bearing condition monitoring
Chuan Li,José Valente de Oliveira,Mariela Cerrada,Fannia Pacheco,Diego Cabrera,Vinicio Sanchez,Grover Zurita +6 more
TLDR
A novel method allowing for interactive clustering in bearing fault diagnosis is proposed and experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2016-04-01 and is currently open access. It has received 46 citations till now. The article focuses on the topics: Fuzzy clustering & Cluster analysis.read more
Citations
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Journal ArticleDOI
A review on data-driven fault severity assessment in rolling bearings
Mariela Cerrada,Mariela Cerrada,René-Vinicio Sánchez,Chuan Li,Fannia Pacheco,Diego Cabrera,José Valente de Oliveira,Rafael E. Vásquez +7 more
TL;DR: In this article, a review of fault severity assessment of rolling bearing components is presented, focusing on data-driven approaches such as signal processing for extracting proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions.
Journal ArticleDOI
Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
TL;DR: A novel method called improved convolutional deep belief network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing and results confirm that the developed method is more effective than the traditional methods.
Journal ArticleDOI
Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
TL;DR: A novel method called deep wavelet auto-encoder (DWAE) with extreme learning machine (ELM) is proposed for intelligent fault diagnosis of rolling bearing and the results confirm that the proposed method is superior to the traditional methods and standard deep learning methods.
Journal ArticleDOI
A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis
Chuan Li,José Valente de Oliveira,Mariela Cerrada,Diego Cabrera,René-Vinicio Sánchez,Grover Zurita +5 more
TL;DR: The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis of the available approaches resorting to fuzzy formalisms in this trendy topic.
Journal ArticleDOI
Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities
TL;DR: A real case study in the pharmaceutical industry validates the proposed anomaly detection methodology, using a 10 months database of 16 process parameters from a granulation process, validating its absolute necessity.
References
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Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
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On comparing partitions
Marjan Cugmas,Anuška Ferligoj +1 more
TL;DR: In this paper, Hubert and Arabie corrected the Rand Index for chance (Adjusted Rand Index) and presented some alternative indices, which do not assume one set of units for two partitions.
Journal ArticleDOI
Variable selection using random forests
TL;DR: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection, and proposes a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.