Abstract
This research is mainly about the abnormal data analysis in factories of process industries. In the processing factory, there are many sensors which transmit the values to each other. Workers in process factory need to be alerted when the values of some sensors are abnormal values. In our research, the main target is to detect the potential abnormal value from different sensors of process industries. Since the value is filled with noise and delays, we first use the cross-correlation and wavelet transformation to remove them. Then, use deep-learning method to train the model with processed data and use the model to detect potential abnormal value. Finally, we evaluate the model we trained by the data extracted from a real process factory. The result shows that our model performs well.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 |
| Publisher | IEEE Computer Society |
| Pages | 2356-2360 |
| Number of pages | 5 |
| Volume | 2017-December |
| ISBN (Electronic) | 9781538609484 |
| DOIs | |
| Publication status | Published - 2018 Feb 9 |
| Event | 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 - Singapore, Singapore Duration: 2017 Dec 10 → 2017 Dec 13 |
Other
| Other | 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017 |
|---|---|
| Country/Territory | Singapore |
| City | Singapore |
| Period | 17/12/10 → 17/12/13 |
Keywords
- Data analysis
- deep-learning
- wavelet-denoising
ASJC Scopus subject areas
- Business, Management and Accounting (miscellaneous)
- Industrial and Manufacturing Engineering
- Safety, Risk, Reliability and Quality