Abstract
Bearings are key components in modern power machines. Effective diagnosis of bearing faults is crucial for normal operation. Recently, the deep convolutional neural network (DCNN) with 2D visualization technology has shown great potential in bearing fault diagnosis. Traditional DCNN-based fault diagnosis mostly adopts a single learner with one input and is time-consuming in sample and network construction to obtain a satisfied performance. In this paper, a scheme combining diverse DCNN learners and an AdaBoost tree-based ensemble classifier is proposed to improve the diagnosis performance and reduce the requirement of sample and network construction simultaneously. In this scheme, multiple types of samples can be constructed independently and employed for diagnosis simultaneously; next, the same number of DCNN learners are built for underlying features extraction and the obtained results are integrated and finally fed into the ensemble classifier for fault diagnosis. An illustration based on the Case Western Reserve University datasets is given, which proves the superiority of the proposed scheme in both accuracy and robustness. Herein, we present a universal scheme to improve the diagnosis performance, and give an example for practical application, where the signal preprocessing and image sample construction methods can also be applied in other vibration-based analysis.
Highlights
Bearings are important to any processing or manufacturing plant that uses rotating equipment
Samples are constructed separately and contribute to fault diagnosis simultaneously, which eliminates the situation that only one domain is focused or an elaborate feature integration across different domains must be determined in traditional vibration-based fault diagnosis, providing more freedom for the sample construction where each type of sample can concentrate on one aspect of the signal
Fea (i) isofthe integrated feature space corresponding ith signal segment, and Sp,q donates the qth element of features obtained from the Fully-Connected Layer (FC) layer of the pth deep convolutional neural network (DCNN)
Summary
Bearings are important to any processing or manufacturing plant that uses rotating equipment. On one hand, the above studies need careful engineering and substantial domain expertise in selecting the useful features related to faults, and on the other hand, information of single domain, such as time-frequency domain, is mostly adopted for fault diagnosis and the time domain and frequency domain information are not considered. For most of studies on DCNN-based fault diagnosis, a single network is used, which means there is only one input This leads to the problem of features integration across different domains, in other words, a careful construction of sample is needed to contain useful information as much as possible. × amplitude], corresponding to frequency domain and time-frequency domain, respectively Beside those mentioned above, in single DCNN-based fault diagnosis, plenty of work must be done with respect to structure designing and parameter tuning to obtain a satisfying performance (e.g., diagnosis accuracy). We present a universal framework to improve the diagnosis performance, and give instruction for the practical application of the framework where the proposed methodologies for 2D sample construction in this paper can be applied in other vibration-based analysis
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