Name
Abstract | ||
|---|---|---|
Alarm floods represent a widespread issue for modern chemical plants. During these conditions, the number of alarms may be unmanageable, and the operator may miss safety-critical alarms. Chattering alarms, which repeatedly change between the active and non-active states, are responsible for most of the alarm records within a flood episode. Typically, chattering alarms are only addressed and removed retrospectively (e.g. during periodic performance assessments). This study proposes a Machine-Learning based approach for alarm chattering prediction. Specifically, a method for dynamic chattering quantification has been developed, whose results have been used to train three different Machine Learning models - Linear, Deep, and Wide&Deep models. The algorithms have been employed to predict future chattering behavior based on actual plant conditions. Performance metrics have been calculated to assess the correctness of predictions and to compare the performance of the three models. (C) 2020 The Authors. Published by Elsevier Ltd. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.compchemeng.2020.107122 | COMPUTERS & CHEMICAL ENGINEERING |
Keywords | DocType | Volume |
Machine Learning, Data Mining, Alarm management, Alarm floods, Chattering alarms, Chattering prediction | Journal | 143 |
ISSN | Citations | PageRank |
0098-1354 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Tamascelli | 1 | 0 | 0.34 |
| Nicola Paltrinieri | 2 | 5 | 1.91 |
| Valerio Cozzani | 3 | 57 | 11.98 |