Name
Title | ||
|---|---|---|
A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching |
Abstract | ||
|---|---|---|
This paper presents a new online detection approach for rolling bearing’s incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm is first proposed based on singular value decomposition (SVD) and Kurtosis criterion. Based on the assessment results, a kind of deep learning algorithm, i.e., stacked denoising autoencoder (SDAE), is introduced to extract the common deep features of normal state and early fault state. Support vector data description (SVDD) is applied to establish the offline detection model using the obtained features. At the online stage, a self-adaptive matching strategy with 1-Dimensional anchor is proposed. By utilizing the SDAE model established at offline stage, this strategy can extract more representative deep features of the target bearing via generating various proposal fragments and then determining the fault occurrence time in a self-adaptive way by feeding the online features into the SVDD model. Experiments run on the bearing data set of IEEE prognostic and health management (PHM) Challenge 2012. The results show the proposed approach has good detection performance in real time and much lower false alarm rate, with no need to acquire fault characteristic frequency in advance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIM.2019.2903699 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Feature extraction,Fault detection,Deep learning,Fault diagnosis,Support vector machines,Employee welfare,Adaptation models | Singular value decomposition,Pattern recognition,Support vector machine,Control engineering,Bearing (mechanical),Self adaptive,Feature matching,Artificial intelligence,Constant false alarm rate,Deep learning,Kurtosis,Mathematics | Journal |
Volume | Issue | ISSN |
69 | 2 | 0018-9456 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentao Mao | 1 | 112 | 11.54 |
| Jiaxian Chen | 2 | 0 | 0.34 |
| Xihui Liang | 3 | 15 | 2.73 |
| Xinming Zhang | 4 | 0 | 0.34 |