Abstract
In recent years, researchers seldom investigate how to boost the classification performance of any learning algorithm for fault signal detection. We propose a fault signal classification method based on adaptive boosting (adaboost) in this paper. Adaboost is able to select an optimal linear combination of classifiers to form an ensemble whose joint decision rule has relatively high accuracy on the training set. First, we extract statistical features from sample signals. And then we make use of a decision tree to identify optimal features, which are used to classify the sample set by adaboost algorithm. To verify its accuracy, we set up the roller bearing experiment. Practical results show that the method can precisely identify fault signals, and be comparable to SVM based traditional method.DOI: http://dx.doi.org/10.5755/j01.eee.18.8.2635
Highlights
Roller bearing is one of the most widely used in rotating machinery
We propose a fault signal classification method based on adaptive boosting (Adaboost)
The construction criterion of decision tree for feature selection is as follows: The extracted features are the input of the feature selection algorithm, and the output is a generated decision tree; Each node represents a subset of classes, which will be partitioned successively in the child nodes; Each leaf node is associated with a class label; The branch of the tree denotes a threshold, which originates from the attribute; In the decision tree, the optimal features are selected by an importance criterion of each node
Summary
Roller bearing is one of the most widely used in rotating machinery Condition monitoring of such an element is greatly advantageous for economical and security value, and can be regarded as a pattern recognition problem [1]. According to the traditional signal processing methods, we might calculate several symptom values under each condition, such as the wavelet energy coefficients [10], the Fourier coefficients [11], the statistical measurements [8], and the frequencies obtained from envelop analysis. These symptoms cannot be automatically recognized by computers. Boosting is a general methodology for improving the performance of any given learning algorithms, and adaboost is a representative one of boosting algorithms, which is firstly introduced by Freund and Schapire [3], and which is a machine learning meta-algorithm for performing supervised learning
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