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Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm

Computers, Materials & Continua 2023年第5期75卷 2461-2477页

作者： Tarique Rafique memon tayab din memon Imtiaz Hussain Kalwar Bhawani Shankar Chowdhry NCRA-HHCMS Lab Mehran University of Engineering&Technology Jamshoro76062Pakistan Department of Electronic Engineering Quaid-e-Awam University of EngineeringScience&Technology Nawabshah67480Pakistan Design and Creative Technology Torrens University Australia196 Flinders StreetMelbourneVIC 3000Australia Department of Electronic Engineering Mehran University of Engineering and TechnologyJamshoro76062Pakistan Department of Electronics and Power Engineering Pakistan Navy Engineering CollegeNUST Karachi75350Pakistan

Derailment of trains is not unusual all around the world,especially in developing countries,due to unidentified track or rolling stock faults that cause massive casualties each *** this purpose,a proper condition monitoring system is essential to avoid accidents and heavy ***,the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy ***,in this paper,we present the development of a novel embedded system prototype for condition monitoring of railway *** proposed prototype system works in real-time by acquiring railway track surface images and performing two tasks a)detect deformation(i.e.,faults)like squats,shelling,and spalling using the contour feature algorithm and b)the vibration signature on that faulty spot by synchronizing acceleration and image data.A new illumination scheme is also proposed to avoid the sunlight reflection that badly affects the image acquisition *** contour detection algorithm is applied here to detect the uneven shapes and discontinuities in the geometrical structure of the railway track surface,which ultimately detects unhealthy *** works by converting Red,Green,and Blue(RGB)images into binary images,which distinguishes the unhealthy regions by making them white color while the healthy regions in black *** have used the multiprocessing technique to overcome the massive processing and memory *** embedded system is developed on Raspberry Pi by interfacing a vision camera,an accelerometer,a proximity sensor,and a Global Positioning System(GPS)sensors(i.e.,multi-sensors).The developed embedded system prototype is tested in real-time onsite by installing it on a Railway Inspection Trolley(RIT),which runs at an average speed of 15 km/*** functional verification of the proposed system is done successfully by detecting and recording the various railway track surface faul

关键词： Railway track surface faults condition monitoring system fault detection contour detection deep learning image processing rail wheel impact

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Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

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Computer Modeling in Engineering & Sciences 2022年第11期133卷 435-470页

作者： Majid Hussain tayab din memon Imtiaz Hussain Zubair Ahmed memon Dileep Kumar Department of Electronic Engineering Quaid-e-Awam University of Engineering Science and TechnologyNawabshah67480Pakistan NCRA Condition Monitoring Systems Lab Mehran University of Engineering and TechnologyJamshoro76062Pakistan Department of Electronic Engineering Mehran University of Engineering and TechnologyJamshoro76062Pakistan Design and Creative Technology Torrens University AustraliaMelbourneAustralia Department of Electronics and Power Engineering Pakistan Navy Engineering College NUSTKarachi75350Pakistan

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and ***,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)***,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the ***,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and ***,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature *** faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault *** is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction *** simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL *** the DL models are trained and validated with a suitable number of architecture *** simulation,the multiclass confusion

关键词： Condition monitoring motor fault diagnosis stator winding faults deep learning signal processing

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

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Computers, Materials & Continua 2023年第2期74卷 3351-3370页

作者： Khakoo Mal tayab din memon Imtiaz Hussain Kalwar Bhawani Shankar Chowdhry Department of Electronic Engineering Mehran University of Engineering&TechnologyJamshoro76062Pakistan Sukkur IBA University Sukkur65200Pakistan Design and Creative Technology Torrens University Australia196 Flinders StreetMelbourne3000VICAustralia Department of Electronics and Power Engineering Pakistan Navy Engineering College NUSTKarachi75350Pakistan NCRA-Condition Monitoring Systems Lab Mehran University of Engineering&TechnologyJamshoro76062Pakistan

It is necessary to know the status of adhesion conditions between wheel and rail for efficient accelerating and decelerating of railroad *** proper estimation of adhesion conditions and their real-time implementation is considered a challenge for *** this paper,the development of simulation model of extended Kalman filter(EKF)in MATLAB/Simulink is presented to estimate various railway wheelset parameters in different contact conditions of *** to concurrent in nature,the Xilinx®System-on-Chip Zynq Field Programmable Gate Array(FPGA)device is chosen to check the onboard estimation ofwheel-rail interaction parameters by using the National Instruments(NI)myRIO®development *** NImyRIO®development board is flexible to deal with nonlinearities,uncertain changes,and fastchanging dynamics in real-time occurring in wheel-rail contact conditions during vehicle *** simulated dataset of the railway nonlinear wheelsetmodel is tested on FPGA-based EKF with different track conditions and with accelerating and decelerating operations of the *** proposed model-based estimation of railway wheelset parameters is synthesized on FPGA and its simulation is carried out for functional verification on *** obtained simulation results are aligned with the simulation results obtained through *** the best of our knowledge,this is the first time study that presents the implementation of a model-based estimation of railway wheelset parameters on FPGA and its functional *** functional behavior of the FPGA-based estimator shows that these results are the addition of current knowledge in the field of the railway.

关键词： Adhesion force extended kalman filter FPGA implementation railway wheelset real-time estimation wheel-rail interaction

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A Novel Method Based on UNET for Bearing Fault Diagnosis

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Computers, Materials & Continua 2021年第10期69卷 393-408页

作者： Dileep Kumar Soother Imtiaz Hussain Kalwar Tanweer Hussain Bhawani Shankar Chowdhry Sanaullah Mehran Ujjan tayab din memon National Centre of Robotics and Automation HHCMS LabMehran University of Engineering&TechnologyJamshoro76020SindhPakistan Department of Electrical Engineering DHA SUFFA UniversityKarachiSindhPakistan Department of Electronic Engineering Mehran University of Engineering and TechnologyJamshoro76020SindhPakistan

Reliability of rotating machines is highly dependent on the smooth rolling of ***,it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring *** the recent past,Deep Learning(DL)has become applicable in condition monitoring of rotating machines owing to its *** paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration *** proposed method is the UNET model that is a recent development in DL *** model is applied to the 2D vibration images obtained by transforming normalized amplitudes of the time-series vibration data samples into the corresponding vibration *** UNET model performs pixel-level feature learning using the vibration images owing to its unique *** results demonstrate that the model can perform dense predictions without any loss of label information,generally caused by the sliding window labelling *** comparative analysis with other DL models confirmed the superiority of the UNET model which has achieved maximum accuracy of 98.91%and F1-Score of 99%.

关键词： Condition monitoring deep learning fault diagnosis rotating machines vibration

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