A Procedure for Practical Prognostics and Health Monitoring of Fully Electric Vehicles for Enhanced Safety and Reliability

2014 ◽  
Author(s):  
P. Baraldi ◽  
A. Galarza ◽  
M. Rigamonti ◽  
S. Rantala ◽  
I. Unanue ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Health Monitoring ◽  
Safety And Reliability ◽  
Prognostics And Health Monitoring

scholarly journals Piezoelectric Transducer-Based Structural Health Monitoring for Aircraft Applications

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 545 ◽  
Author(s):  
Xinlin Qing ◽  
Wenzhuo Li ◽  
Yishou Wang ◽  
Hu Sun
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Piezoelectric Transducer ◽  
Piezoelectric Materials ◽  
Development Trend ◽  
Practical Implementation ◽  
Damage State ◽  
Structural Health ◽  
Safety And Reliability ◽  
Aircraft Application

Structural health monitoring (SHM) is being widely evaluated by the aerospace industry as a method to improve the safety and reliability of aircraft structures and also reduce operational cost. Built-in sensor networks on an aircraft structure can provide crucial information regarding the condition, damage state and/or service environment of the structure. Among the various types of transducers used for SHM, piezoelectric materials are widely used because they can be employed as either actuators or sensors due to their piezoelectric effect and vice versa. This paper provides a brief overview of piezoelectric transducer-based SHM system technology developed for aircraft applications in the past two decades. The requirements for practical implementation and use of structural health monitoring systems in aircraft application are then introduced. State-of-the-art techniques for solving some practical issues, such as sensor network integration, scalability to large structures, reliability and effect of environmental conditions, robust damage detection and quantification are discussed. Development trend of SHM technology is also discussed.

scholarly journals Application of Machine Learning Techniques for Railway Health Monitoring

2010 ◽  
pp. 396-421
Author(s):  
G.M. Shafiullah ◽  
Adam Thompson ◽  
Peter J. Wolfs ◽  
A.B.M. Shawkat Ali
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Vertical Acceleration ◽  
Lateral Instability ◽  
Learning Techniques ◽  
Safety And Reliability ◽  
Efficient Data ◽  
Modern Machine

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions.

Application of Bayesian Sensor Placement Optimization for Real–Time Health Monitoring

2012 ◽  
Author(s):  
Masoud Pourali ◽  
Ali Mosleh
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Sensor Placement ◽  
The State ◽  
Physical Parameters ◽  
Real Time System ◽  
System Health Monitoring ◽  
Sensor Placement Optimization ◽  
Safety And Reliability ◽  
Real Time Information

Sensors are being increasingly used for real–time health monitoring of complex systems. The measured quantities are expected to provide real–time information about the state of the system, its subsystems, components, and internal and external physical parameters. A complex system normally requires many sensors to extract required information from the sensed environment. The increasing costs of aging systems and infrastructures have become a major concern and real–time health monitoring systems could ensure increased safety and reliability of these systems. Real–time system health monitoring, assesses the state of systems’ health and, through appropriate data processing and interpretation, can predict the remaining life of the system. This paper introduces a method based on Bayesian networks and attempts to find optimum locations of sensors for the best estimate a system health. Information metrics are used for optimized sensor placement based on the value of information that each possible sensor placement scenario provides.

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