Date of this Version
Published in IEEE Electrification Magazine, September 2022, pp. 83–84.
Electric motors are widely used in the industrial, commercial, residential, and transportation sectors to power the systems that provide goods and services to end users. The failure of electric motors may cause significant production or service interruption and financial losses. To improve the quality of service of systems driven by electric motors, it is vital to continuously improve the reliability of electric motors. Driven by this demand, various condition monitoring and fault diagnostic techniques for electric motors have been developed by academia and industry over the past decades.
The article “Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review,” written by Subhasis Nandi, Hamid A. Toliyat, and Xiaodong Li and published by the IEEE Transactions on Energy Conversion, reviewed the major faults in electric motors and the corresponding fault diagnostic methods that were reported in journal and conference publications and books published between the 1980s and 2005. Such a review provides a bird’s-eye view on the signals used and signal processing methods developed before 2005 for the diagnosis of specific faults in electric motors. Such a bird’s-eye view helped researchers avoid repeating past work when carrying out research in this field.
Therefore, since the article was published, it has been used by researchers in the field as a major reference in their new publications to discuss the contributions of their work with respect to the techniques developed before 2005, as evidenced by 2,322 citations as of 8 May 2022, according to Google Scholar Citations. The number of annual citations of this article increased continuously from 2006 to 2015 and has stayed steady around 200 in the past five years. The high popularity of this article is mainly attributed to its high-quality, comprehensive review as well as the increasing interest and demand in the development of effective condition monitoring and prognostic health management techniques for electric motors, which have been driven by several major factors: the increasing applications of electric motors in complex tasks that require high reliability; new development of data analytic and artificial intelligence (AI) techniques; interdisciplinary and transformative nature of the research in this field; and significant improvements of computational resources for the implementation of condition monitoring systems.