Convolutional Long Short-Term Memory Autoencoder-Based Feature Learning for Fault Detection in Industrial Processes

2021 ◽  
Vol 70 ◽  
pp. 1-15
Author(s):  
Jianbo Yu ◽  
Xing Liu ◽  
Lyujiangnan Ye
Keyword(s):  
Fault Detection ◽  
Short Term Memory ◽  
Feature Learning ◽  
Industrial Processes ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Design of an Novel Spectrum Sensing Scheme Based on Long Short-Term Memory and Experimental Validation

2021 ◽  
Vol 15 ◽  
pp. 26-32
Author(s):  
Nupur Choudhury ◽  
Kandarpa Kumar Sarma ◽  
Chinmoy Kalita ◽  
Aradhana Misra
Keyword(s):  
Deep Learning ◽  
Spectrum Sensing ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
Feature Learning ◽  
Noise Model ◽  
Critical Element ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Spectrum sensing allows cognitive radio systems to detect relevant signals in despite the presence of severe interference. Most of the existing spectrum sensing techniques use a particular signal-noise model with certain assumptions and derive certain detection performance. To deal with this uncertainty, learning based approaches are being adopted and more recently deep learning based tools have become popular. Here, we propose an approach of spectrum sensing which is based on long short term memory (LSTM) which is a critical element of deep learning networks (DLN). Use of LSTM facilitates implicit feature learning from spectrum data. The DLN is trained using several features and the performance of the proposed sensing technique is validated with the help of an empirical testbed setup using Adalm Pluto. The testbed is trained to acquire the primary signal of a real world radio broadcast taking place using FM. Experimental data show that even at low signal to noise ratio, our approach performs well in terms of detection and classification accuracies, as compared to current spectrum sensing methods.

scholarly journals Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4612 ◽  
Author(s):  
Pangun Park ◽  
Piergiuseppe Di Marco ◽  
Hyejeon Shin ◽  
Junseong Bang
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Short Term Memory ◽  
Fault Detection And Diagnosis ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Detection And Diagnosis ◽  
Lstm Network ◽  
Types Of Faults

Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.

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