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Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

Conference · · 2023 International Joint Conference on Neural Networks (IJCNN)
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  1. Old Dominion University,Vision Lab,Department of Electrical and Computer Engineering,Norfolk,VA,USA
  2. Jefferson Laboratory,Newport News,VA,USA
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The superconducting radio-frequency cavities are a crucial component of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. When a cavity faults, beam delivery to experimental end users is disrupted. Prediction of cavity faults prior to onset is essential to reduce operation and maintenance costs. In this work, a parallel long short-term memory (LSTM)-convolution neural network (CNN)-based deep learning (DL) model is proposed to predict impending faults using pre-fault signals. Further, we introduce an uncertainty quantification approach using Monte Carlo dropout with the LSTM-CNN model to ascertain confidence in the prediction. The model was tested using multivariate time series signals from stable cavity operations and before faults. Initial results show that on the test dataset, the model can identify impending faults before their onset with an average 10-fold cross validation accuracy of 97.39% and a standard deviation of 0.12% using a 100-ms time window. It is also observed that the model performs better as the prediction time moves closer to the fault onset. For additional context, we compare the performance of the model with three machine-learning-based (ML) fault prediction models. Our proposed parallel LSTM-CNN-based DL method shows better performance than the ML-based methods.

Research Organization:
Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-06OR23177
OSTI ID:
2283148
Report Number(s):
JLAB-ACC-23-3992; DOE/OR/23177-7390
Journal Information:
2023 International Joint Conference on Neural Networks (IJCNN), Conference: 2023 International Joint Conference on Neural Networks (IJCNN), 18-23 June 2023, Gold Coast, Australia
Country of Publication:
United States
Language:
English

References (18)

Fault prediction algorithm for offshore wind energy conversion system based on machine learning December 2021
Machine Learning-Based Approach for Hardware Faults Prediction November 2020
A Fault Prediction Of Equipment Based On CNN-LSTM Network May 2019
Two-stage prediction of machinery fault trend based on deep learning for time series analysis October 2021
Motor Fault Detection and Feature Extraction Using RNN-Based Variational Autoencoder January 2019
Mechanical fault time series prediction by using EFMSAE-LSTM neural network March 2021
Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory January 2022
SRF Cavity Fault Classification Using Machine Learning At CEBAF May 2019
Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM January 2018
A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators March 2021
Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning January 2022
Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification June 2020
Improving rail network velocity: A machine learning approach to predictive maintenance August 2014
A systematic literature review of machine learning methods applied to predictive maintenance November 2019
Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data January 2019
Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry June 2020
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory November 2020
Machine-learning techniques and their applications in manufacturing May 2005

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