FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

Mal, Khakoo; Memon, Tayab Din; Kalwar, Imtiaz Hussain; Chowdhry, Bhawani Shankar

doi:10.32604/cmc.2023.032940

Cited by 4 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A nonlinear wheelset simulation model presented in [ 21 , 22 , 23 , 28 ] is used to develop the simulation model in Simulink to mimic the behavior of the actual wheelset dynamics. The model is modified to consider all the disturbances that are faced by an actual railway vehicle (e.g., irregularities in track in the lateral direction, variation in gauge and track geometry) to generate data that are close to real-time scenarios.…”

Section: Methodsmentioning

confidence: 99%

Wheel Defect Detection Using a Hybrid Deep Learning Approach

Shaikh

Kalwar

Chowdhry

2023

Sensors

View full text Add to dashboard Cite

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques’ cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections.

show abstract

Section: Methodsmentioning

confidence: 99%

Wheel Defect Detection Using a Hybrid Deep Learning Approach

Shaikh

Kalwar

Chowdhry

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…We have simulated these series in MATLAB by the programming code given in Appendix 5. Time delay leaves the modulus of the Fourier transform unchanged but introduces a phase shift in its Fourier transform, which is a linear function of time delay [15][16][17]. The real and imaginary parts of Fourier transformed Sinc function are given below.…”

Section: (A) Step Response By Taking Integral Of Built-in Sinc Functionmentioning

confidence: 99%

Simulation and analysis of ideal low pass filter response using MATLAB

Shaikh¹,

Mal²,

Kumar³

et al. 2023

Mehran Univ. res. j. eng. technol.

View full text Add to dashboard Cite

Filters are the crucial element of many modern electronic systems used in control engineering, wireless communication systems, and embedded systems designs. Filters in one form or another, are considered an important subsystem in developing communication systems. The ideal filter cannot be designed through any electronic circuit and the only way is to realize using simulations tools like MATLAB. In this paper, the simulation and analysis of the ideal low pass filter is presented. The MATLAB software is used to generate and perform analysis of the step response of ideal low-pass filter (Si function) and impulse response of ideal low-pass filter (Sinc function) in time and frequency domains. The aim of the work is to provide engineering students an easier understanding of the filter design process in an appropriate way with the help of simple programming. Where, it is easy to generate signals and analyze the ideal low pass filter response. To verify achievement of learning objectives, a survey is conducted from 45 engineering students of electronic and telecommunication which elicited students’ observations and interest in the learning process. Overall about 75% of students gave positive feedback, which indicates that such type of experimentation had a significant impact on the student academic outcomes.

show abstract

“…IT protection mechanisms for CPS are usually data processing and filtering modules to remove and correct malicious or corrupted data packets. Stochastic techniques and models [4], advanced filtering algorithms such as the Kalman filter [15], hardware-enabled algorithms such as parameter estimation [16], and pattern recognition techniques to identify unusual information [17] are the most common technologies. As well as game-theory and other common technologies for CPS protection, such as honeypots [18] or Software-Defined Networks [19].…”

Section: Related Workmentioning

confidence: 99%

“…However, current models are very limited, and analytical protection algorithms have a reduced impact in real applications. Numerical data filtering (Kalman) [16] Parameter estimation for numerical models and time series [17] Pattern recognition for numerical models and time series [18] Honeypots for attacker capture using the game theory [19] Software-Defined Network technologies to create intrusion detection systems [21,24,23] Decentralized In this paper, we address this challenge, with a continuous-time generic multidimensional nonlinear model, and a protection policy based on probabilistic decision-making schemes.…”

Section: Related Workmentioning

confidence: 99%

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Bordel S醤chez,

Alcarria,

Robles

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Cyber-Physical Systems are very vulnerable to sparse sensor attacks. But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely. Therefore, in this paper, we propose a new non-linear generalized model to describe Cyber-Physical Systems. This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and random effects in the physical and computational worlds. Besides, the digitalization stage in hardware devices is represented too. Attackers and most critical sparse sensor attacks are described through a stochastic process. The reconstruction and protection mechanisms are based on a weighted stochastic model. Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics (such as the Fourier transform, first-return maps, or the probability density function). A decision algorithm calculates the final reconstructed value considering the previous error probability. An experimental validation based on simulation tools and real deployments is also carried out. Both, the new technology performance and scalability are studied. Results prove that the proposed solution protects Cyber-Physical Systems against up to 92% of attacks and perturbations, with a computational delay below 2.5 s. The proposed model shows a linear complexity, as recursive or iterative structures are not employed, just algebraic and probabilistic functions. In conclusion, the new model and reconstruction mechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks, even in dense or pervasive deployments and scenarios.

show abstract

FPGA Implementation of Extended Kalman Filter for Parameters Estimation of Railway Wheelset

Cited by 4 publications

References 22 publications

Wheel Defect Detection Using a Hybrid Deep Learning Approach

Wheel Defect Detection Using a Hybrid Deep Learning Approach

Simulation and analysis of ideal low pass filter response using MATLAB

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Contact Info

Product

Resources

About