Prof. Mehrdad Saif
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Academic Career Length
41Years (1986-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications17 September 2019 in IEEE Transactions on Reliability
Journal Article
Mojtaba KordestaniMehrdad SaifMOMarcos E. Orchard
Marcos E. Orchard
Roozbeh Razavi-Far
Khashayar KhorasaniFault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.
1 May 1991 in IEEE Transactions on Automatic Control
Journal Article
YGY. Guan
Y. Guan
Mehrdad Saif1 December 2001 in IEEE Transactions on Automatic Control
Journal Article
YXYi Xiong
Yi Xiong
Mehrdad SaifA new sliding mode observer for a class of nonlinear uncertain systems is proposed in this article. The proposed sliding mode observer works under much less conservative conditions than previous nonlinear unknown input observers. Also, a functional version of the observer is proposed in certain cases where it may not be possible to design an observer capable of estimating the entire state of the system.
5 February 2024 in IEEE Transactions on Big Data
Journal Article
EHEhsan Hallaji
Ehsan HallajiRoozbeh Razavi-FarMehrdad SaifBWBoyu Wang
Boyu WangQYQiang Yang
Qiang YangFederated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possible variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study.
12 October 2020 in IEEE Transactions on Instrumentation and Measurement
Journal Article
MRMilad Rezamand
Milad RezamandMojtaba Kordestani
Rupp CarriveauDTDavid Ting
David TingMOMarcos E. Orchard
Marcos E. OrchardMehrdad SaifAs wind energy is becoming a significant utility source, minimizing the Operation and Maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind Turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, etc. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the Remaining Useful Life (RUL) of wind farm assets. This paper provides a recent literature review on modeling developments for the remaining useful life prediction of critical WT components including physics-based, Artificial Intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physicsbased prognostics, and Bayesian application in the development of WT prognosis methods.
About Me
Dr. Mehrdad Saif is a distinguished figure in the field of electrical engineering, with a career spanning over three decades. He completed his bachelor's degree in 1982, followed by a master's in 1984 and a Ph.D. in 1987. Throughout his graduate studies, he was involved in pioneering research sponsored by NASA Lewis (now Glenn) Research Center and the Cleveland Advanced Manufacturing Program (CAMP).
In 1987, Dr. Saif joined Simon Fraser University's School of Engineering Science as an Assistant Professor. He rose through the ranks, becoming a Full Professor in 1997. From 2002 to 2011, he took on the role of Director of the School, spearheading its significant expansion during his tenure. Subsequently, he served as the Dean of the Faculty of Engineering at the University of Windsor from July 2011 until September 2021. There, he initiated major developments, including substantial enrollment growth and the addition of new programs in aerospace engineering, engineering management, B.Eng. technology, mechatronics, among others. Under his leadership, the faculty also saw an increase in both faculty numbers and research output.
Dr. Saif's research contributions are both vast and impactful, specializing in systems and control, estimation, observer theory, and AI/ML-based approaches to fault diagnostics and condition monitoring. His work has applications in a range of sectors, including automotive, power, and autonomous systems. To date, he has published over 400 refereed journal an