| Title: |
Comparative Performance of Machine Learning Architectures for Fault Detection and Diagnosis in Chemical Processes |
| Authors: |
Ammar Khodja, Rayane; Voisin, Alexandre; Costa, Victor; Celse, Benoit; Casteran, Fanny; Iung, Benoît |
| Contributors: |
IFP Energies nouvelles (IFPEN); Centre de Recherche en Automatique de Nancy (CRAN); Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS); Institute of Mathematics and its Applications |
| Source: |
Proceedings of the 13th IMA International Conference on Modelling in Industrial Maintenance and Reliability ; 13th IMA International Conference on Modelling in Industrial Maintenance and Reliability, MIMAR 2025 ; https://hal.science/hal-05401011 ; 13th IMA International Conference on Modelling in Industrial Maintenance and Reliability, MIMAR 2025, Institute of Mathematics and its Applications, Jul 2025, Vandœuvre-lès-Nancy, France. ⟨10.19124/ima.2025.01.54⟩ |
| Publisher Information: |
CCSD; Institute of Mathematics and its Applications |
| Publication Year: |
2025 |
| Collection: |
Université de Lorraine: HAL |
| Subject Terms: |
[SPI]Engineering Sciences [physics] |
| Subject Geographic: |
Vandœuvre-lès-Nancy; France |
| Description: |
International audience ; Fault Detection and Diagnosis (FDD) is crucial for both maintenance and control in chemical industries, where early fault detection can prevent costly failures and optimize operations. This is particularly critical in pilot units like those at IFP Energies Nouvelles (IFPEN), which operate under short-term experimental conditions with frequently varying operational parameters. This study conducts an extensive benchmarking analysis using the Tennessee Eastman Process (TEP), a widely used simulated chemical process dataset, to evaluate multiple approaches for fault detection and diagnosis, featuring numerous continuous operations with different sensors and faults. Several methods were implemented and compared including Multi Scale PCA (MS-PCA), AutoEncoder, Ensemble Learning, and LSTM models for fault detection, alongside Random Forest, XGBoost, and BLSTM (Bidirectional LSTM) for fault diagnosis.Using the TEP dataset, our results demonstrate that Ensemble Learning achieves detection rates ranging from 80% to 100% across various fault scenarios for the fault detection task. For the fault diagnosis task, BLSTM achieved a diagnosis accuracy of 98.76%. The study reveals that ensemble-based approaches consistently outperform individual models in handling the complex, multivariate nature of chemical process data, due to its robustness in combining multiple perspectives, comprehensive data capture, and localized detection capabilities. Furthermore, the superior performance of the BLSTM is due to its ability to capture both past and future temporal dependencies in the sequential data, particularly important in chemical processes where fault patterns may manifest with complex temporal relationships.This research contributes to the field of Fault Detection and Diagnosis by providing empirical evidence for the effectiveness of ensemble methods and Bidirectional LSTMs in order to address industrial FDD applications on chemical processes. |
| Document Type: |
conference object |
| Language: |
English |
| DOI: |
10.19124/ima.2025.01.54 |
| Availability: |
https://hal.science/hal-05401011; https://hal.science/hal-05401011v1/document; https://hal.science/hal-05401011v1/file/MIMAR25_full_Paper_AMMAR_KHODJA_Rayane.pdf; https://doi.org/10.19124/ima.2025.01.54 |
| Rights: |
http://hal.archives-ouvertes.fr/licences/copyright/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.CF601885 |
| Database: |
BASE |