Abstract
There have been many recent developments in the application of data-based methods to machine condition monitoring. A powerful methodology based on machine learning has emerged, where diagnostics are based on a two-step procedure: extraction of damage-sensitive features, followed by unsupervised learning (novelty detection) or supervised learning (classification). The objective of the current pair of papers is simply to illustrate one state-of-the-art procedure for each step, using synthetic data representative of reality in terms of size and complexity. The second paper in the pair will deal with novelty detection. Although there has been considerable progress in the use of outlier analysis for novelty detection, most of the papers produced so far have suffered from the fact that simple algorithms break down if multiple outliers are present or if damage is already present in a training set. The objective of the current paper is to illustrate the use of phase-space thresholding; an algorithm which has the ability to detect multiple outliers inclusively in a data set.
Highlights
The fundamental framework that describes the process of a Condition Monitoring (CM) strategy can be outlined in a sequence of steps [1]: operational evaluation, data acquisition, feature extraction and statistical modelling for feature discrimination
The second part of this paper demonstrates how a novel adaptive method for outlier detection can be used in combination with the genetically-optimised stochastic resonance approach, for the SADE-SR Output signal Outliers 0
Standard machine learning or statistics algorithms for novelty detection might under-perform in certain condition monitoring applications due to their training needs and the complexity of the data being analysed
Summary
The fundamental framework that describes the process of a Condition Monitoring (CM) strategy can be outlined in a sequence of steps [1]: operational evaluation, data acquisition, feature extraction and statistical modelling for feature discrimination. Supervised learning methods might provide much more informative results, they require training data describing all the damage classes or operational and environmental scenarios of a monitored machine, that in most cases are not available. For this reason, unsupervised learning approaches, might have an advantage over the supervised learning ones in many practical CM applications. One major challenge is that environmental and operational changes, that could affect the potential
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