Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network

Abstract

Detecting bearing faults is very important in preventing non-scheduled shutdowns, catastrophic failures, and production losses. Localized faults on bearings are normally detected based on characteristic frequencies associated with faults in time and frequency spectra. However, missing such characteristic frequency harmonics in a spectrum does not guarantee that a bearing is healthy, or noise might produce harmonics at characteristic frequencies in the healthy case. Further, some defects on roller bearings could not produce characteristic frequencies. To avoid misclassification, bearing defects can be detected via machine learning algorithms, namely convolutional neural network (CNN), support vector machine (SVM), and sparse autoencoder-based SVM (SAE-SVM). Within this framework, three fault classifiers based on CNN, SVM, and SAE-SVM utilizing transfer learning are proposed. Transfer of knowledge is achieved by extracting features from a CNN pretrained on data from the imageNet database to classify faults in roller bearings. The effectiveness of the proposed method is investigated based on vibration and acoustic emission signal datasets from roller bearings with artificial damage. Finally, the accuracy and robustness of the fault classifiers are evaluated at different amounts of noise and training data.

Vibration analysis in bearings for failure prevention using CNN

“…Only two different adaptations of AlexNet can be found in the literature for bearing failure diagnosis using images. For example, in [13], AlexNet was slightly modified and combined with two SVM classifiers and two Sparse Auto-Encoder SAE-SVM classifiers. The experiments were performed with frequency-domain images.…”

“…Some of these methods are still used today to perform fault classification, such as SVM. However, these methods have to be combined with other more recent techniques, and considerably modified to improve their performance [7,13].…”

“…Henceforth, in recent years, researchers have studied bearing condition monitoring and failure diagnosis using Deep Learning. These studies have focused on feature extraction and condition recognition [17], where the use of Convolutional Neural Networks (CNN) have proven to be an excellent option for effectively learning features in the context of fault diagnosis through vibration analysis [2,12,13,[18][19][20][21][22][23].…”

Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders

“…Data-driven fault diagnosis methods based on machine learning have also been intensively developed in recent years [18]. Fault classifiers based on decision trees (DT) [19], [20], support vector machine (SVM) [21], [22], k-nearest neighbor (k-NN) [23], [24], convolutional neural network (CNN) [25]- [27] and deep belief networks (DBN) [28], [29] are well applied to deal with bearing fault detection. All mentioned machine learning based methods require historical failure data for training, which is hard to obtain in industry.…”

“…This suggests that diagnosis methods based on detection of defect characteristic frequencies alone are ineffective in detecting wear in large and slow axial bearings. Identifying this defect on the axial bearings is currently relying on offline monitoring methods such as lubricant analysis and visual inspection combined with precautionary maintenance actions [25], [30]. This practice requires interruptions of production and may allow failure to progress inconspicuously between inspections.…”