Feasibility of using unsupervised learning, artificial neural networks for the condition monitoring of electrical machines

1994 ◽  
Vol 141 (6) ◽  
pp. 317 ◽  
Author(s):  
J. Penman
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Unsupervised Learning ◽  
Condition Monitoring ◽  
Electrical Machines ◽  
Artificial Neural

Condition Monitoring of an Electrohydraulic Position Control System Using Artificial Neural Networks

2004 ◽  
Author(s):  
K. Pollmeier ◽  
C. R. Burrows ◽  
K. A. Edge
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Condition Monitoring ◽  
Position Control ◽  
Linear Actuator ◽  
Modular Approach ◽  
Failure Characteristics ◽  
Actuator System ◽  
Artificial Neural ◽  
Position Control System

This paper investigates the condition monitoring of a servo-valve-controlled linear actuator system using artificial neural networks (NNs). The aim is to discuss techniques for the identification of failure characteristics and their source. It is shown that neural networks can be trained to identify more than one fault but these are larger and require more training patterns than networks for single fault diagnosis. This leads to much longer training times and to problems with scaleability. Therefore a modular approach has been developed. Several networks were trained each to identify an individual fault. The parallel outputs of these nets were then used as inputs to another network. This additional network was able to identify not only the correct faults but also the actual fault levels.

Unsupervised Learning in Artificial Neural Networks

2008 ◽  
pp. 48-90
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Unsupervised Learning ◽  
Input Data ◽  
Descent Methods ◽  
Training Set ◽  
Human Capabilities ◽  
Artificial Neural ◽  
Do So

With the artificial neural networks which we have met so far, we must have a training set on which we already have the answers to the questions which we are going to pose to the network. Yet humans appear to be able to learn (indeed some would say can only learn) without explicit supervision. The aim of unsupervised learning is to mimic this aspect of human capabilities and hence this type of learning tends to use more biologically plausible methods than those using the error descent methods of the last two chapters. The network must self-organise and to do so, it must react to some aspect of the input data - typically either redundancy in the input data or clusters in the data; i.e. there must be some structure in the data to which it can respond.

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