Abstract
In recent years, the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as high efficiency, portability, and so on. However, at present, most kinds of intelligent fault diagnosis algorithms mainly focus on the diagnosis of a single fault component, and few intelligent diagnosis models can simultaneously carry out comprehensive fault diagnosis for a rotating system composed of a shaft, bearing, gear, and so on. In order to solve this problem, a novel stacked auto encoders sparse filter rotating component comprehensive diagnosis network (SAFC) was proposed to extract domain invariant features of various health conditions at different speeds. The model clusters domain invariant features at different speeds through the self-coding network, and then classifies fault types of various parts through sparse filtering. The SAFC model was validated by the vibration data collected, and the results show that this model has higher diagnostic performance than other models.
Highlights
With the coming of the IoT (Internet of Things) era, the reliability of mechanical equipment is more and more demanding [1]
From what has been discussed above, it can be seen that the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as its high efficiency, portability, and so on [17]
The first 20% of the sample was selected in the stacked auto encoders (SAE) section, and after the dimension reduction, the first 10% of the sample was selected in the sparse filtering (SF) section
Summary
To further prove the effectiveness of the proposed method in distinguishing different parts, To further the of proposed method different fault fault. To further prove prove the effectiveness effectiveness of the the proposedaccording method in intodistinguishing distinguishing fault parts, parts, the previously measured test data were regrouped the different different fault locations
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.