A method for calculation of optimum data size and bin size of histogram features in fault diagnosis of mono-block centrifugal pump
Mono-block centrifugal pump plays a key role in various applications. Any deviation in the functions of centrifugal pump would lead to a monetary loss. Thus, it becomes very essential to avoid the economic loss due to malfunctioning of centrifugal pump. It is clear that the fault diagnosis and condition monitoring of pumps are important issues that cannot be ignored. Over the past 25 years, much research has been focused on vibration based techniques. Machine learning approach is one of the most widely used techniques using vibration signals in fault diagnosis. There are set of connected activities involved in machine learning approach namely, data acquisition, feature extraction, feature selection, and feature classification. Training and testing the classifier are the two important activities in the process of feature classification. When the histogram features are used as the representative of the vibration signals, a proper guideline has not been proposed so far to choose number of bins and number of samples required to train the classifier. This paper illustrates a systematic method to choose the number of bins and the minimum number of samples required to train the classifier with statistical stability so as to get best classification accuracy. In this study, power analysis method was employed to find the minimum number of samples required and a decision tree algorithm namely J48 was used to validate the results of power analysis and to find the optimum number of bins. (C) 2011 Elsevier Ltd. All rights reserved.
histogram features fault diagnosis centrifugal pump machine learning vibration signals number of bins power analysis minimum sample size