Residual attention convolutional autoencoder for feature learning and fault detection in nonlinear industrial processes

2021 ◽  
Author(s):  
Xing Liu ◽  
Jianbo Yu ◽  
Lyujiangnan Ye
Keyword(s):  
Fault Detection ◽  
Feature Learning ◽  
Industrial Processes ◽  
Convolutional Autoencoder

scholarly journals Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes

2014 ◽  
Vol 62 (3) ◽  
pp. 571-582 ◽  
Author(s):  
J.M. Kościelny ◽  
M. Syfert
Keyword(s):  
Fault Detection ◽  
Large Scale ◽  
Fault Detection And Isolation ◽  
Industrial Processes ◽  
Industrial Practice ◽  
Potential Applicability ◽  
Diagnostic Approaches ◽  
Level Of Knowledge ◽  
High Level ◽  
Selection Of

Abstract The survey presents a selection of the methods of the fault detection and isolation suitable to be useful for the diagnostics of the complex, large scale industrial processes. The paper focuses on these methods that have appropriately high level of potential applicability in industrial practice. The novelty of the paper relies on the discussion of the dependency of the level of knowledge about diagnosed process and recommended diagnostic approaches. Appropriate recommendations were given in the convenient form of the table

scholarly journals Proactive Critical Energy Infrastructure Protection via Deep Feature Learning

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2622
Author(s):  
Konstantina Fotiadou ◽  
Terpsichori Helen Velivassaki ◽  
Artemis Voulkidis ◽  
Dimitrios Skias ◽  
Corrado De Santis ◽  
...  
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Short Term Memory ◽  
Feature Learning ◽  
Slight Modification ◽  
Critical Energy ◽  
Normal Operation ◽  
High Detection Rate ◽  
Energy Infrastructure ◽  
Critical Energy Infrastructure

Autonomous fault detection plays a major role in the Critical Energy Infrastructure (CEI) domain, since sensor faults cause irreparable damage and lead to incorrect results on the condition monitoring of Cyber-Physical (CP) systems. This paper focuses on the challenging application of wind turbine (WT) monitoring. Specifically, we propose the two challenging architectures based on learning deep features, namely—Long Short Term Memory-Stacked Autoencoders (LSTM-SAE), and Convolutional Neural Network (CNN-SAE), for semi-supervised fault detection in wind CPs. The internal learnt features will facilitate the classification task by assigning each upcoming measurement into its corresponding faulty/normal operation status. To illustrate the quality of our schemes, their performance is evaluated against real-world’s wind turbine data. From the experimental section we are able to validate that both LSTM-SAE and CNN-SAE schemes provide high classification scores, indicating the high detection rate of the fault level of the wind turbines. Additionally, slight modification on our architectures are able to be applied on different fault/anomaly detection categories on variant Cyber-Physical systems.

scholarly journals A Deep Feature Learning Method for Drill Bits Monitoring Using the Spectral Analysis of the Acoustic Signals

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2634 ◽  
Author(s):  
Caleb Vununu ◽  
Kwang-Seok Moon ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Power Spectrum ◽  
Feature Learning ◽  
Final Diagnosis ◽  
Extraction Process ◽  
Drill Bits ◽  
High Discrimination ◽  
Convolutional Autoencoder ◽  
Spectrum Density ◽  
High Level ◽  
Nonlinear Classifier

Machine fault diagnosis (MFD) has gained an important enthusiasm since the unfolding of the pattern recognition techniques in the last three decades. It refers to all of the studies that aim to automatically detect the faults on the machines using various kinds of signals that they can generate. The present work proposes a MFD system for the drilling machines that is based on the sounds they produce. The first key contribution of this paper is to present a system specifically designed for the drills, by attempting not only to detect the faulty drills but also to detect whether the sounds were generated during the active or the idling stage of the whole machinery system, in order to provide a complete remote control. The second key contribution of the work is to represent the power spectrum of the sounds as images and apply some transformations on them in order to reveal, expose, and emphasize the health patterns that are hidden inside them. The created images, the so-called power spectrum density (PSD)-images, are then given to a deep convolutional autoencoder (DCAE) for a high-level feature extraction process. The final step of the scheme consists of adopting the proposed PSD-images + DCAE features as the final representation of the original sounds and utilize them as the inputs of a nonlinear classifier whose outputs will represent the final diagnosis decision. The results of the experiments demonstrate the high discrimination potential afforded by the proposed PSD-images + DCAE features. They were also tested on a noisy dataset and the results show their robustness against noises.

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