التفاصيل البيبلوغرافية
| العنوان: |
Ensemble Deep Learning for Detecting Onset of Abnormal Operation in Industrial Multi-component Systems |
| المؤلفون: |
Balaji Selvanathan, Sri Harsha Nistala, Venkataramana Runkana, Saurabh Jaywant Desai, Shashank Agarwal |
| المصدر: |
International Journal of Prognostics and Health Management, Vol 13, Iss 2 (2022) |
| بيانات النشر: |
The Prognostics and Health Management Society, 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Systems engineering |
| مصطلحات موضوعية: |
industrial, multi-component systems, abnormal operation onset detection, deep learning, ensemble, Engineering machinery, tools, and implements, TA213-215, Systems engineering, TA168 |
| الوصف: |
Breakdowns and unplanned shutdowns in industrial processes and equipment can lead to significant loss of availability and revenue. It is imperative to perform optimal maintenance of such systems when signs of abnormal behavior are detected and before they propagate and lead to catastrophic failure. This is particularly challenging in systems with interconnected multiple components as it is difficult to isolate the effect of one component on the operation of other components in the system. In this work, an ensemble approach based on Cascaded Convolutional neural network and Long Short-term Memory (CC-LSTM) network models is proposed for detecting and predicting the time of onset of faults in interconnected multicomponent systems. The performance of the ensemble CC-LSTM model was demonstrated on an industrial 4-component system and was found to improve the accuracy of onset time predictions by ~15% compared to individual CC-LSTM models and ~25-40% compared to commonly used deep learning techniques such as dense neural networks, convolutional neural networks and LSTMs. The CC-LSTM and the ensemble models also had the lowest missed detection rates and zero false positive rates making them ideal for real-time monitoring and fault detection in multicomponent systems. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2153-2648 |
| Relation: |
https://doaj.org/toc/2153-2648 |
| DOI: |
10.36001/ijphm.2022.v13i2.3093 |
| URL الوصول: |
https://doaj.org/article/9373b0f9b9734eba9a533b4e79eeedda |
| رقم الانضمام: |
edsdoj.9373b0f9b9734eba9a533b4e79eeedda |
| قاعدة البيانات: |
Directory of Open Access Journals |