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Li, S., Wunsch, D.C., O’Hair, E. and Giesselmann, M.G. (2002) Extended Kalman Filter Training of Neural Networks on a SIMD Parallel Machine. Journal of Parallel and Distributed Computing, 62, 544-562.
https://doi.org/10.1006/jpdc.2001.1807

has been cited by the following article:

• TITLE: A New Evolving Technology for Gearbox Condition Monitoring and Fault Diagnosis

  AUTHORS: Derek Y. Luo, Wilson Wang

  KEYWORDS: Evolving Systems, Pattern Classification, Gear System Condition Monitoring, Fault Diagnosis, Machine Learning

  JOURNAL NAME: Intelligent Control and Automation, Vol.16 No.4, November 5, 2025

  ABSTRACT: Gearboxes are commonly used in rotary machines. Reliable fault diagnostics in gearboxes is of great importance to industries to improve production quality and reduce maintenance costs. In this paper, an improved evolving fuzzy (iEF) technique is proposed for real-time gear system health monitoring and fault diagnosis. The architecture evolution is performed based on the comparison of the potential of the incoming data set and the existing cluster centers. The proposed evolving method has the ability of adding or subtracting clusters adaptively. An enhanced Kalman filter (EKF) method is suggested to improve parameter training efficiency and processing convergence. The effectiveness of the developed classifier is evaluated firstly by simulation tests and then by experimental tests under different gear conditions.