Nourani, Mehrdad

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Mehrdad Nourani Is Professor of Electrical and Computer Engineering. He is also co-founder of Quality of Life Technology Laboratory, and Director of the Predictive Analytics & Technologies (PAT) Lab. His research interests include:
Healthcare Technology and Bioinformatics

  • Wearable Devices
  • Circuits and Systems
  • Biometric Signal/Image Sensing and Processing Algorithms for Monitoring Various Medical Conditions
  • Machine Learning Techniques for Biomedical Data Analytics
  • Feature Extraction
  • Risk Assessment and Prediction
VLSI & System-On-Chip Design and Test
  • Design for Testability
  • Aging of MOS Transistors
  • Design for Reliability
  • Fault Diagnostic and Prognostic Methodologies for Mission-Critical Applications
Embedded System Design
  • Special-Purpose Biomedical IC/System Design
  • Cache/Memory Architectures for High Speed and Low Power Applications
  • High-Speed Data Traffic Analysis and Content Inspection
  • Application Specific Processor Architectures

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Recent Submissions

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    Vibration-Based Bearing Fault Diagnosis Using Reflection Coefficients of the Autoregressive Model
    (Institute of Electrical and Electronics Engineers Inc.) Heydarzadeh, Mehrdad; Nourani, Mehrdad; Azimi, V.; Kashani-Pour, A. R.; 0000-0001-5077-4424 (Nourani, M); Heydarzadeh, Mehrdad; Nourani, Mehrdad
    Bearing faults are one of the main reasons of rotary machines failure. Monitoring vibration signal is an effective method for diagnosing faulty bearings and preventing thus catastrophic failures. However, existing algorithms neither offer satisfactory accuracy nor are efficient for real-time implementation due to complexity in feature extraction part. In this paper, we propose an accurate method for bearing diagnosis customized for real-time implementation. The proposed system estimates power spectral density of vibration signal using an autoregressive model for feature extraction. This is a novel use of autoregressive model for fault diagnosis which reduces the dimensionality of vibration signal and captures its frequency contents simultaneously. The proposed system can diagnose different bearing faults under variable load conditions with above 99 % accuracy.

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