scholarly journals Data Driven Hybrid Approach for Health Monitoring and Fault Detection in Military Ground Vehicles

2021 ◽  
Author(s):  
Indu Shukla ◽  
Antoinette Silas ◽  
Haley Dozier ◽  
Brandon Hansen ◽  
W. Bond
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Hybrid Approach ◽  
Data Driven ◽  
Ground Vehicles

Data Driven Hybrid Approach for Health Monitoring and Fault Detection in Military Ground Vehicles

2021 ◽  
Author(s):  
Indu Shukla ◽  
Antoinette Silas ◽  
Haley Dozier ◽  
Brandon Hansen ◽  
W. Bond
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Hybrid Approach ◽  
Data Driven ◽  
Ground Vehicles

scholarly journals Fault Detection, Isolation And Prognosis For Control Moment Gyros Onboard Satellites

2021 ◽  
Author(s):  
Venkatesh Muthusamy
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Detection Algorithm ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Path Model ◽  
Data Driven ◽  
Chebyshev Neural Network ◽  
Control Moment ◽  
Computational Budget

Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.

Fault Detection, Isolation And Prognosis For Control Moment Gyros Onboard Satellites

2021 ◽  
Author(s):  
Venkatesh Muthusamy
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Detection Algorithm ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Path Model ◽  
Data Driven ◽  
Chebyshev Neural Network ◽  
Control Moment ◽  
Computational Budget

Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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