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Fault diagnosis of automotive machine control

Posted: 05 Dec 2013     Print Version  Bookmark and Share

Keywords:Fault diagnosis  early fault detection  hybrid automobiles  DC starter  algorithm 

The current source is sampled by a Delta Sigma ADC embedded in the PSoC machine. This is a high quality analogue-to-digital converter with the added capabilitiy of multi-sampling, i.e., the same ADC can be switched to different sampling rates with different bit widths. This supports different scenarios where we may have to deal with multiple motors with dramatically different requirements.

While this method is significantly powerful, this model for fault detection in electrical machines has significant problems when it comes to dealing with transients that are in the form of very sharp receding impulses, since the FFT fails to identify sharp vanishing impulses in the frequency spectrum. A loss of information occurs at this stage, which can create significant hurdles in designing a system that can identify faults in electrical machines.

This problem can be addressed by employing a Wavelet Transform, an efficient technique to resolve time domain signals into the time-frequency spectrum while preserving information related to sharp impulses as well as high frequency harmonics.

By carefully designing features and using machine learning methods, significant improvements can be made in fault diagnostics in electrical machines. Another trend that has recently emerged is the use of graphical systems for fault diagnostics and prognosis, first reported by Zaidi et el [2], who used Hidden Markov Models for this purpose. These models provide a significant challenge in their porting to embedded systems because of extensive usage of computational resources.

References
1. "A Micropower Support Vector Machine Based Seizure Detection Architecture for Embedded Medical Device"s by Ali Shoeb, Dave Carlson, Eric Panken Timothy Denison. Published in the Proceedings of 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA, September 2-6. 2009
2. "Fault Diagnosis and Failure Diagnosis for Electrical Machines" By Syed Sajjad Haider Zaidi – Dissertation submitted to Michigan State University—2010

About the author
Salman Javaidis a graduate student in Electrical Engineering at the National University of Science and Technology, Pakistan. He has been working with Cypress' Programmable System on Chip design for the last year. His current research involves designing robust and accurate embedded designs for fault diagnostics in electrical machines using Cypress' PSoC.

Syed Sajjad H Zaidiis the Head of Electronics and Power Engineering Laboratories in the Pakistan Navy Engineering College (PNEC, Karachi), National University of Sciences and Technology (NUST), Pakistan. He has a doctorate degree from Michigan State University in the electrical machines fault diagnosis and prognosis using non-intrusive methods. He specialises in Electrical Machines, Signal Processing, Pattern Recognition and Time Frequency transformation. His research interests in the areas of condition-based maintenance, fault prediction and estimation, system engineering and designing.

Ahmed Majeed Khanis an engineer experienced in working with cross-functional groups to push the envelope of technology implemented in electronic products. Mr. Khan is from a consumer electronics background and his expertise includes embedded systems and wireless multimedia communication. He personally developed and led teams to develop several high volume, high quality products. Currently, Mr. Khan is an engineer at Cypress Semiconductor Corporation, where he developed and assisted the development of multiple programmable solutions. He also established CY-SEECS Joint Research Centre at National University of Sciences and Technology (NUST) in Islamabad, Pakistan. Mr. Khan holds an MS in Electrical Engineering from Michigan State University and has over 8 years of experience working with microcontrollers and embedded applications.

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