Abstract
The use of the convolutional neural network for fault diagnosis has been a common method of research in recent years. Since this method can automatically extract fault features, it has played a good role in some research studies. However, this method has a clear drawback that the signals will be significantly affected by working conditions and sample size, and it is difficult to improve diagnostic accuracy by directly learning faults, regardless of working conditions. It is therefore a research orientation worthy of a diagnosis of high precision defect in various working conditions. In this article, using a fine-grained classification algorithm, the operating conditions of the object system are considered an approximate classification. A specific failure in different working conditions is considered a beautiful classification. Samples of different faults in different working conditions are learned uniformly and the common characteristics are extracted from the convolutional network so that different faults of different working conditions can simultaneously be identified on the basis of the entire sample. Experimental results show that the method effectively uses the set of samples of the working conditions of the variables to obtain the dual recognition of defects and specific working conditions and the accuracy of the recognition is significantly higher than the method of learning regardless of working conditions.
Highlights
Traditional methods of fault diagnosis, whether in time domain or frequency domain analysis, are highly dependent on physical experience
Because convolution neural network (CNN) was proposed [1], deep learning algorithms have developed rapidly; their powerful end-toend learning ability enables feature extraction work that needs experience to be completed independently by CNN, which becomes a new direction of fault diagnosis research
Especially for gearbox, using CNN to learn the vibration signals was the main method of fault diagnosis [2,3,4,5]
Summary
Traditional methods of fault diagnosis, whether in time domain or frequency domain analysis, are highly dependent on physical experience. According to the traditional method (Figure 1(b)), the coupling of the working conditions to the fault is ignored, which has a significant impact In method, it seems that more samples are used to support the training of a single network, the modeling accuracy will often be seriously reduced instead. (i) High utilization efficiency of the data: all samples unified are used to train a single feature extractor, the sample utilization ratio is doubled compared with the traditional division of labor modeling, the convolution layer can obtain more adequate training to achieve better feature extraction effect, and the working condition label originally used for manual division is used in learning (ii) Under the constraint of fine-grained model, the fault under different working conditions is automatically distinguished, and the influence of working conditions on fault diagnosis is solved while all samples are trained uniformly. Experiments show that the gearbox is a system with significant differences in working conditions and rare effective fault samples and its fault diagnosis problem is solved
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
