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| Fault Prediction Model for Process Industry based on GMM-MD Combinational Algorithm |
| Received:November 17, 2021 |
| DOI：10.11981/j.issn.1000-6842.2022.02.81 |
| Key Words:fault prediction;machine learning;papermaking;modeling and simulation |
| Fund Project:国家重点研发计划（2020YFE0201400）；人工智能与数字经济广东省实验室（广州）青年学者项目（PLZ2021KF0019）。 |
| Author Name | Affiliation | Postcode | | DU Jian | State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640 | 510640 | | ZHANG Lei | Guangdong Energy Conservation Center， Guangzhou， Guangdong Province， 510030 | 510030 | | LI Jigeng | State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640 | 510640 | | HONG Mengna | State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640
China-Singapore International Joint Research Institute， Guangzhou， Guangdong Province， 510555 | 510555 | | MAN Yi | State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou， Guangdong Province， 510640
Guangdong Artificial Intelligence and Digital Economy Laboratory （Guangzhou）， Guangzhou， Guangdong Province， 510335 | 510335 |
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| Abstract: |
| A process industry fault prediction model based on Gaussian mixture model （GMM） and Mahalanobis distance （MD） combinational algorithm was introduced. The model first removes redundant and irrelevant variables through the correlation coefficient， and then marks abnormal data before the fault through the K-means clustering algorithm to obtain core characteristic variables， and finally constructs health index based on the GMM-MD combinational algorithm to evaluate health degree of the production process. The model was verified by using the real-time production data of a domestic paper mill. The result shows that the predictive accuracy and recall rate of the model is 76.82% and 72.50%， respectively， indicating it could properly track the variation process of equipment running state during papermaking process and play the role of fault prediction in process industry. |
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