scholarly journals On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis

Energies ◽  
2016 ◽  
Vol 9 (7) ◽  
pp. 520 ◽  
Author(s):  
Francesc Pozo ◽  
Yolanda Vidal ◽  
Josep Serrahima
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Wind Turbines ◽  
Sensor Selection ◽  
Detection Time ◽  
Selection Algorithm ◽  
Time Reduction

scholarly journals Real-Time and Robust Hydraulic System Fault Detection via Edge Computing

2020 ◽  
Vol 10 (17) ◽  
pp. 5933 ◽  
Author(s):  
Dzaky Zakiyal Fawwaz ◽  
Sang-Hwa Chung
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Real Time ◽  
Hydraulic System ◽  
Noisy Data ◽  
Sensor Data ◽  
Feature Subset ◽  
Detection Time ◽  
Detection Model ◽  
Robust Fault Detection

We consider fault detection in a hydraulic system that maintains multivariate time-series sensor data. Such a real-world industrial environment could suffer from noisy data resulting from inaccuracies in hardware sensing or external interference. Thus, we propose a real-time and robust fault detection method for hydraulic systems that leverages cooperation between cloud and edge servers. The cloud server employs a new approach that includes a genetic algorithm (GA)-based feature selection that identifies feature-to-label correlations and feature-to-feature redundancies. A GA can efficiently process large search spaces, such as solving a combinatorial optimization problem to identify the optimal feature subset. By using fewer important features that require transmission and processing, this approach reduces detection time and improves model performance. We propose a long short-term memory autoencoder for a robust fault detection model that leverages temporal information on time-series sensor data and effectively handles noisy data. This detection model is then deployed at edge servers that provide computing resources near the data source to reduce latency. Our experimental results suggest that this method outperforms prior approaches by demonstrating lower detection times, higher accuracy, and increased robustness to noisy data. While we have a 63% reduction of features, our model obtains a high accuracy of approximately 98% and is robust to noisy data with a signal-to-noise ratio near 0 dB. Our method also performs at an average detection time of only 9.42 ms with a reduced average packet size of 179.98 KB from the maximum of 343.78 KB.

scholarly journals Determinant-based Fast Greedy Sensor Selection Algorithm

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yuji Saito ◽  
Taku Nonomura ◽  
Keigo Yamada ◽  
Kumi Nakai ◽  
Takayuki Nagata ◽  
...  
Keyword(s):  
Sensor Selection ◽  
Selection Algorithm

Fault detection algorithms for real-time diagnosis in large-scale systems

2001 ◽  
Author(s):  
Thiagalingam Kirubarajan ◽  
Venkatesh N. Malepati ◽  
Somnath Deb ◽  
Jie Ying
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Large Scale ◽  
Large Scale Systems ◽  
Detection Algorithms ◽  
Time Diagnosis
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