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.
Highlights
IntroductionMotion is usually determined by mechanical device structures (e.g., rotating machines or induction motors), which leads to satisfactory records of nearly 70% of the gross energy ingestion in modern manufacturing economics [1,2]
In electromechanical engineering, motion is usually determined by mechanical device structures, which leads to satisfactory records of nearly 70% of the gross energy ingestion in modern manufacturing economics [1,2]
Vibration imaging works as the preprocessing step for the input data to generate identical patterns, and the convolutional neural network (CNN) is used to save and transfer knowledge between the source and target tasks to achieve the classification for the bearing fault diagnosis
Summary
Motion is usually determined by mechanical device structures (e.g., rotating machines or induction motors), which leads to satisfactory records of nearly 70% of the gross energy ingestion in modern manufacturing economics [1,2]. In contrast to existing methods, this study focuses on a signal processing technique to create an invariant scenario under different load and rpm levels to extract automated features by employing an advanced neural network mechanism To address this issue, the Stockwell transform (S-transform) is employed in this paper [30]. The main contributions of the current work are: (1) identical health pattern formations for different health types employing discrete orthonormal Stockwell transform (DOST)-based vibration imaging to create load-invariant and rpm-invariant scenarios, and (2) a transfer learning-based convolutional neural network approach to automate the feature extraction process from those identical health patterns in a short amount of training time.
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