Item Infomation
Title: | Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification |
Authors: | Luo, B. |
Participants: | Wang, H. Liu, H. Li, B. Peng, F. |
Issue Date: | 2019 |
Publisher: | IEEE Xplore |
Series/Report no.: | IEEE Transactions on Industrial Electronics, (2019), VOL. 66, NO. 1, pp 509-518 |
Abstract: | In modern digital manufacturing, nearly 79.6% of the downtime of a machine tool is caused by its mechanical failures. Predictive maintenance (PdM) is a useful way to minimize the machine downtime and the associated costs. One of the challenges with PdM is early fault detection under time-varying operational conditions, which means mining sensitive fault features from condition signals in long-term running. However, fault features are often weakened and disturbed by the time-varying harmonics and noise during a machining process. Existing analysis methods of these complex and diverse data are inefficient and time-consuming. This paper proposes a novel method for early fault detection under time-varying conditions. In this study, a deep learning model is constructed to automatically select the impulse responses from the vibration signals in long-term running of 288 days. Then, dynamic properties are identified fromthe selected impulse responses to detect the early mechanical fault under time-varying conditions. Compared to traditional methods, the experimental results in this paper have proved that our method was not affected by time-varying conditions and showed considerable potential for early fault detection in manufacturing. |
URI: | http://tailieuso.tlu.edu.vn/handle/DHTL/9806 |
Appears in Collections: | Tài liệu hỗ trợ nghiên cứu khoa học |
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