Abstract
In this study, we developed a novel data-driven fault detection and diagnosis (FDD) method for bearing faults in induction motors where the fault condition data are imbalanced. First, we propose a bearing fault detector based on convolutional neural networks (CNN), in which the vibration signals from a test bench are used as inputs after an image transformation procedure. Experimental results demonstrate that the proposed classifier for FDD performs well (accuracy of 88% to 99%) even when the volume of normal and fault condition data is imbalanced (imbalance ratio varies from 20:1 to 200:1). Additionally, our generative model reduces the level of data imbalance by oversampling. The results improve the accuracy of FDD (by up to 99%) when a severe imbalance ratio (200:1) is assumed.
Highlights
Fault detection and diagnosis (FDD) in manufacturing facilities is very important for (1) improving productivity by preventing undesired downtime and (2) guaranteeing safe working conditions [1]
To verify the capability of FDD in data imbalanced condition, the number of images for normal conditions was much greater than the number of images of bearing faults
We found that CNN-based bearing fault detector (CBFD) with BF-nested scatter plot (NSP) had 95% accuracy, even when the imbalance ratio was 20:1
Summary
Fault detection and diagnosis (FDD) in manufacturing facilities is very important for (1) improving productivity by preventing undesired downtime and (2) guaranteeing safe working conditions [1]. To overcome the problems of physical models, assorted data-driven FDD methods that use machine learning and statistics, such as support vector machine [7,8] and fuzzy logic [9], have been proposed. To realize wide adoption of data-driven FDD to industry, simpler and more efficient methods are required in both data-processing and DNN models. Data imbalance is common in FDD because normal condition data are more prevalent than faulty condition data in real manufacturing environments [20] Such imbalanced conditions degrade data-driven FDD, especially for convolutional neural network (CNN)-based classifiers.
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