Data-Driven Rate Optimization Under Geologic Uncertainty

2020 ◽  
Author(s):  
Deepthi Sen ◽  
Hongquan Chen ◽  
Akhil Datta-Gupta ◽  
Joseph Kwon ◽  
Srikanta Mishra
Keyword(s):  
Data Driven ◽  
Rate Optimization ◽  
Geologic Uncertainty

Data-driven rate optimization under geologic uncertainty

2021 ◽  
pp. 109116
Author(s):  
Deepthi Sen ◽  
Hongquan Chen ◽  
Akhil Datta-Gupta ◽  
Joseph Kwon ◽  
Srikanta Mishra
Keyword(s):  
Data Driven ◽  
Rate Optimization ◽  
Geologic Uncertainty

MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method

2020 ◽  
Vol 34 (14) ◽  
pp. 2059048
Author(s):  
Tong Gao ◽  
Wei Sheng ◽  
Mingliang Zhou ◽  
Bin Fang ◽  
Liping Zheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Optimization Method ◽  
Learning Rate ◽  
Data Driven ◽  
Adaptive Learning Rate ◽  
Improved Performance ◽  
Rate Optimization

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

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