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Autonomous learning for fuzzy systems: a review
As one of the three pillars in computational intelligence, fuzzy systems are a powerful
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …
Revisiting evolutionary fuzzy systems: Taxonomy, applications, new trends and challenges
Abstract Evolutionary Fuzzy Systems are a successful hybridization between fuzzy systems
and Evolutionary Algorithms. They integrate both the management of imprecision …
and Evolutionary Algorithms. They integrate both the management of imprecision …
A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment
Anomaly in mechanical systems may cause equipment to break down with serious safety,
environment, and economic impact. Since many mechanical equipment usually operates …
environment, and economic impact. Since many mechanical equipment usually operates …
Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system
Remaining useful life (RUL) prediction of machining tools is a typical multi-sensor
information fusion problem. It involves the use of the monitoring information acquired from …
information fusion problem. It involves the use of the monitoring information acquired from …
Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion
The remaining useful life (RUL) prediction has received increasing research attention in
recent years due to its essential role in improving industrial manufacturing systems' …
recent years due to its essential role in improving industrial manufacturing systems' …
Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery
D Dou, S Zhou - Applied Soft Computing, 2016 - Elsevier
Condition monitoring of rotating machinery is important to promptly detect early faults,
identify potential problems, and prevent complete failure. Four direct classification methods …
identify potential problems, and prevent complete failure. Four direct classification methods …
Online active learning in data stream regression using uncertainty sampling based on evolving generalized fuzzy models
In this paper, we propose three criteria for efficient sample selection in case of data stream
regression problems within an online active learning context. The selection becomes …
regression problems within an online active learning context. The selection becomes …
Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier
In this paper, we propose a two-stage algorithm for real-time fault detection and identification
of industrial plants. Our proposal is based on the analysis of selected features using …
of industrial plants. Our proposal is based on the analysis of selected features using …
Prediction and analysis of cold rolling mill vibration based on a data-driven method
Mill chatter is one of the most common problems in cold rolling. Thus, it is important to
investigate the mill chatter phenomenon to ensure a high-speed and stable rolling process …
investigate the mill chatter phenomenon to ensure a high-speed and stable rolling process …
An evolving approach to unsupervised and real-time fault detection in industrial processes
Fault detection in industrial processes is a field of application that has gaining considerable
attention in the past few years, resulting in a large variety of techniques and methodologies …
attention in the past few years, resulting in a large variety of techniques and methodologies …