The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

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

We present a modification of Kohonens SOFMin order to simulate cortical plasticity induced by coactivation patterns. This is accomplished by introducing a probabilistic mode of stimulus presentation and by substituting the winner-takes- all mechanism by selecting the winner from a set of best matching neurons.

Modification of Kohonen's SOFM to Simulate Cortical Plasticity Induced by Coactivation Input Patterns

A system has been developed to extract diagnostic information from jet engine carcass vibration data The system consists of a number of modules, each of which focuses on particular subsets of the data known to hold valuable information Two of these modules, based on neural network techniques, are described in detail in this paper In the first module, novelty detection provides a measure of how unusual the shape of a vibration signature is, by learning a representation of normality based entirely on normal examples The low-dimensional vectors which encode vibration signatures are normalised by an appropriate transform before their distribution is modelled by a few kernels, whose placement is optimised by clustering techniques Novelty is then measured as the local distance from the nearest kernel centre This method provides good separation between usual and unusual vibration signatures but, given the small number of examples of normal engines, the resulting representation of normalitymay be overfitting the training data The severity of this effect is investigated for two different normalising transforms The second module detects sudden transitions in vibration signature curves A multi-layer-perceptron is trained to predict one step-ahead for curves without these unexpected transitions Sudden transitions in the test engine data are then reported whenever the prediction error exceeds a predetermined threshold

A System for the Analysis of Jet Engine Vibration Data

The application of bispectral and trispectral analysis in condition monitoring is discussed. Higher-order spectral analysis of machine vibrations for the provision of diagnostic features is investigated. Experimental work is based on vibration data collected from a small test rig subjected to bearing faults. The direct use of the entire bispectrum or trispectrum to provide diagnostic features is investigated using a variety of classification algorithms including neural networks, and this is compared with simpler power spectral and statistical feature extraction algorithms. A more detailed investigation of the higher-order spectral structure of the signals is then undertaken. This provides features which can be estimated more easily in practice and could provide diagnostic information about the machines.

S. Zhang

Papers

Self-organising neural networks for automated machinery monitoring systems

A least square regression based fault locator for unbalanced faults on a transmission line