Bearing Fault Diagnosis under Variable Rotational Speeds Using Stockwell Transform-Based Vibration Imaging and Transfer Learning

Abstract

In this paper, discrete orthonormal Stockwell transform (DOST)-based vibration imaging is proposed as a preprocessing step for supporting load and rotational speed invariant scenarios for signals of various health conditions. For any health condition, features can easily be extracted from its generated health pattern. To automate the feature selection process, a convolutional neural network (CNN)-based transfer learning (TL) approach for diagnosis has also been introduced. Transfer learning allows an established model to use feature knowledge obtained under one set of working conditions through hidden layers to diagnose faults that occur under other working conditions. The network learns from the massive source dataset, and that knowledge is applied to the target data to identify faults. Using the bearing dataset of Case Western Reserve University, the proposed approach yields an average 99.8% classification accuracy and, specifically, 99.99% for healthy condition (HC), 99.95% for inner race fault (IRF), 99.96% for ball fault (BF), 99.68% for outer race fault for 12 o’clock sensor position (ORF@12), 99.93% for outer race fault for 3 o’clock sensor position (ORF@3), and 99.89% for outer race fault for 6 o’clock sensor position (ORF@6). In this paper, the proposed approach is compared with conventional artificial neural networks (ANNs), support vector machines (SVMs), hierarchical CNNs, and deep autoencoders. The proposed approach outperforms these conventional methods in the accuracy under all working conditions.

Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

“…Transfer learning (TL) is an improvement for CNN, which transfers the pre-trained experience from the source domain to the target domain so that the CNN model can possess a better image recognition ability or deal with a new objective that has few labeled images [28,58,59]. It has been proven that TL forms of CNN have good generalization, and compared to the result of prototypes, CNN-TL models have a stronger image feature extraction ability outside the range of the training data [53,60,61]. However, as TL needs pre-trained experience transfer, CNN-TL models have a different working process and different efficiencies in training and testing compared to their prototypes.…”

Time Frequency Representation Enhancement via Frequency Matching Linear Transform for Bearing Condition Monitoring under Variable Speeds

“…The signal is displayed in Figure 2a and the shaft IF () s f  is plotted in Figure 2b. The conventional LT methods, STFT and CT, are applied to process the signal defined by Equations (12) and (13). Resulting TFR is presented in Figure 2c,d, respectively.…”

“…TFA is also an effective approach for vibration-based bearing fault diagnosis under time-varying speeds as it has strong potential to characterize both time and frequency features of vibration signals, in addition to order tracking [12]. LT is the extensively used technique for TFA of nonstationary signals.…”

An Improved ABC Algorithm and Its Application in Bearing Fault Diagnosis with EEMD

“…A bearing is an essential part in a rotating machine, which can be easily damaged. At present, the scholars' research on bearing fault diagnosis can be roughly divided into the following: signal analysis based on vibration [1][2][3][4], monitoring based on temperature [5], and analysis based on acoustic emission [6,7], etc. Among them, the analysis based on vibration signal is the main method for bearing fault diagnosis.…”