Name
Title | ||
|---|---|---|
Fault diagnosis in industrial chemical processes using interpretable patterns based on Logical Analysis of Data. |
Abstract | ||
|---|---|---|
•Logical Analysis of Data is applied as a pattern-based diagnostic method.•It is used to detect and analyze faults in the industrial chemical processes.•It relies on extracting a set of strong and interpretable patterns.•LAD's patterns can help the user to relate the fault to its causes.•LAD shows a performance that is comparable to the common accurate methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.eswa.2017.11.045 | Expert Systems with Applications |
Keywords | Field | DocType |
Fault detection and diagnosis,Industrial chemical processes,Tennessee Eastman Process,Logical analysis of data,Machine learning and pattern recognition,Black liquor recovery boilers | k-nearest neighbors algorithm,Data mining,Decision tree,Chemical process,Computer science,Support vector machine,Artificial intelligence,Statistical classification,Random forest,Artificial neural network,Machine learning,Quadratic classifier | Journal |
Volume | ISSN | Citations |
95 | 0957-4174 | 6 |
PageRank | References | Authors |
0.41 | 29 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ahmed Ragab | 1 | 30 | 3.93 |
| Mohamed El-Koujok | 2 | 9 | 1.47 |
| Bruno Poulin | 3 | 6 | 0.41 |
| Mouloud Amazouz | 4 | 10 | 1.61 |
| Soumaya Yacout | 5 | 133 | 13.08 |