Self-Organising Neural Networks for Automated Machinery Monitoring Systems

Zhang, S.; Ganesan, Rajamohan; Xistris, G. D.

doi:10.1006/mssp.1996.0036

Cited by 23 publications

(16 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…F or example, M urray and Penman [10] used data features extracted from higher-order statistics as input variables to a neural network. Zhang et al [11] used features from time and frequency domain analyses. In addition to being used as a post-processing tool, arti cial neural networks have been used by G u et al [12] for combustion process modelling using raw data as the input.…”

mentioning

confidence: 99%

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Webber

Payne

et al. 2003

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

This paper (Part 1) describes the principles of a novel unsupervised adaptive neural network anomaly detection technique, called componential coding, in the context of condition monitoring of electrical machines. N umerical examples are given to illustrate the technique' s capabilities. The companion paper (Part 2), which follows, assesses componential coding in its application to real data recorded from a known machine and an entirely unseen machine (a conventional induction motor and a novel transverse ux motor respectively). Componential coding is particularly suited to applications in which no machine-speci c tailored techniques have been developed or in which no previous monitoring experience is available. This is because componential coding is an unsupervised technique that derives the features of the data during training, and so requires neither labelling of known faults nor pre-processing to enhance known fault characteristics. Componential coding offers advantages over more familiar unsupervised data processing techniques such as principal component analysis. In addition, componential coding may be implemented in a computationally ef cient manner by exploiting the periodic convolution theorem. Periodic convolution also gives the algorithm the advantage of time invariance; i.e. it will work equally well even if the input data signal is offset by arbitrary displacements in time. This means that there is no need to synchronize the input data signal with respect to reference points or to determine the absolute angular position of a rotating part.

show abstract

mentioning

confidence: 99%

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Webber

Payne

et al. 2003

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

“…The use of the SOM for novelty detection has been relatively well documented [8,12]. SOM is a neural-network model well known for its application in high-dimensional data analysis and mapping.…”

Section: The Kohonen Sommentioning

confidence: 99%

Modified self-organising map for automated novelty detection applied to vibration signal monitoring

Wong

Jack

Nandi

2006

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

“…Self-Organising Maps (SOMs) and related techniques offer a potentially productive means of assessing data in which analytical relationships connecting the parameters which influence the data are largely or wholly unknown [11][12][13][14][15][16][17][18]. This is the situation with PCB biodegradation data.…”

Section: Implementation Of the Selforganising Mapmentioning

confidence: 99%

Investigation of Structure – Biodegradability Relationships in Polychlorinated Biphenyls Using Self-Organising Maps

Cartwright

2002

Neural Comput Applic

View full text Add to dashboard Cite

Self-Organising Neural Networks for Automated Machinery Monitoring Systems

Cited by 23 publications

References 0 publications

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Componential coding in the condition monitoring of electrical machines Part 1: Principles and illustrations using simulated typical faults

Modified self-organising map for automated novelty detection applied to vibration signal monitoring

Investigation of Structure – Biodegradability Relationships in Polychlorinated Biphenyls Using Self-Organising Maps

Contact Info

Product

Resources

About