Journal
MEASUREMENT
Volume 152, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.107371
Keywords
Deep learning; Moving window-based stacked auto-encoder; Exponential function with slope local minimum point; Roller bearings
Funding
- [CityU 11206417]
- [R5020-18]
Ask authors/readers for more resources
Building a smooth degradation curve for a bearing can provide a good basis for predicting its remaining useful life, but the traditional models need to fuse multiple models. The stacked auto-encoder (SAE) can extract the potential features of the data from the frequency domain directly, but the oscillation of the original data diminishes the smoothness and monotonicity of the extracted degradation curve. Moreover, the degradation curve extracted by SAE shows slight oscillation, which needs to be further eliminated to improve its monotonicity. However, some methods, such as an exponentially weighted moving average, require parameter setting as outlier detection can only detect abnormal points for local curves. To solve these problems, we proposed a moving window-based stacked auto-encoder (MASAE) with an exponential function, which incorporates a slope local minimum point (ESLMP) to extract the degradation trends and improve its monotonicity. The experimental data results demonstrated the superiority of the proposed model. (C) 2019 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available