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Chris K. Mechefske

Chris K. Mechefske

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
37
Citations
4723
World Ranking
2430
National Ranking
103

Overview

Chris K. Mechefske is a researcher affiliated with Queen's University in Canada, specializing primarily in the field of Engineering. Mechefske's work focuses on several subfields, including Mechanical Engineering, Control and Systems Engineering, Mechanics of Materials, Electrical and Electronic Engineering, and Civil and Structural Engineering.

Their research covers diverse topics with significant attention to Machine Fault Diagnosis Techniques, Advanced Machining Processes and Optimization, Gear and Bearing Dynamics Analysis, Advanced Machining and Optimization Techniques, Advanced Surface Polishing Techniques, Structural Health Monitoring Techniques, and Hydraulic and Pneumatic Systems.

Mechefske has contributed to scholarly work published in reputable venues frequently, including:

  • Mechanical Systems and Signal Processing
  • arXiv (Cornell University)
  • Measurement
  • International Journal of Hydromechatronics
  • The International Journal of Advanced Manufacturing Technology

Recent published papers by Mechefske or closely associated collaborations include:

  • Analysis of different RNN autoencoder variants for time series classification and machine prognostics (2020) in Mechanical Systems and Signal Processing
  • Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals (2020) in ISA Transactions
  • Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling (2021) in Measurement
  • An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation (2021) in Reliability Engineering & System Safety
  • Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder (2021) in International Journal of Hydromechatronics

Collaboration is a notable aspect of Mechefske's research activities, with frequent co-authors including Yimin Shao, Tim von Hahn, Wennian Yu, Liming Wang, and Dingqiang Peng.

Best Publications

  • Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

    Wennian Yu;Ii Yong Kim;Chris Mechefske

  • An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme

    Wennian Yu;Il-Yong Kim;Chris K. Mechefske

  • Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey

    Houman Hanachi;Christopher Mechefske;Jie Liu;Avisekh Banerjee

  • USING FUZZY LINGUISTICS TO SELECT OPTIMUM MAINTENANCE AND CONDITION MONITORING STRATEGIES

    Chris K. Mechefske;Zheng Wang

  • Detection of Induction Motor Faults: A Comparison of Stator Current, Vibration and Acoustic Methods

    Weidong Li;Chris K. Mechefske

  • Analysis of different RNN autoencoder variants for time series classification and machine prognostics

    Wennian Yu;Il Yong Kim;Chris Mechefske

  • Dynamic characteristics of helical gears under sliding friction with spalling defect

    Hanjun Jiang;Yimin Shao;Chris K. Mechefske

  • The effects of spur gear tooth spatial crack propagation on gear mesh stiffness

    Wennian Yu;Yimin Shao;Chris K. Mechefske

  • Experimental investigation of reflection in guided wave-based inspection for the characterization of pipeline defects

    Xiaojuan Wang;Peter W. Tse;Chris K. Mechefske;Meng Hua

  • Hybrid data-driven physics-based model fusion framework for tool wear prediction

    Houman Hanachi;Wennian Yu;Il Yong Kim;Jie Liu;Jie Liu

  • Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals

    Xiuzhi He;Xiuzhi He;Xiaoqin Zhou;Wennian Yu;Yixuan Hou

  • OBJECTIVE MACHINERY FAULT DIAGNOSIS USING FUZZY LOGIC

    C.K. Mechefske

  • Fault detection using transient machine signals

    Markus Timusk;Mike Lipsett;Chris K. Mechefske

  • A study of vibration and vibration control of ship structures

    Tian Ran Lin;Jie Pan;Peter J. O'Shea;Chris K. Mechefske

  • Tool wear prediction in high-speed turning of a steel alloy using long short-term memory modelling

    Mohsen Marani;Mohammadjavad Zeinali;Victor Songmene;Chris K. Mechefske

  • An analytical model to investigate skidding in rolling element bearings during acceleration

    Wenbing Tu;Yimin Shao;Chris K. Mechefske

  • Optimal damping layout in a shell structure using topology optimization

    Sun Yong Kim;Chris K. Mechefske;Il Yong Kim

  • Fault detection and diagnosis in low speed rolling element bearings Part I: The use of parametric spectra

    C.K. Mechefske;J. Mathew

  • Modeling of a Fully Flexible 3PRS Manipulator for Vibration Analysis

    Zili Zhou;Jeff Xi;Chris K. Mechefske

  • Analytical modeling of spur gear corner contact effects

    Wennian Yu;Chris K. Mechefske

  • Drive axle housing failure analysis of a mining dump truck based on the load spectrum

    Yimin Shao;Jing Liu;Chris K. Mechefske

  • Robust detection of gearbox deterioration using compromised autoregressive modeling and Kolmogorov–Smirnov test statistic—Part I: Compromised autoregressive modeling with the aid of hypothesis tests and simulation analysis

    Yimin Zhan;Chris K. Mechefske

Frequent Co-Authors

Yimin Shao
Yimin Shao Chongqing University
Jie Liu
Jie Liu Hunan University
Jie Pan
Jie Pan University of Western Australia
Jing Liu
Jing Liu Chongqing University
Robert B. Randall
Robert B. Randall University of New South Wales
Zhongxiao Peng
Zhongxiao Peng University of New South Wales
Ming J. Zuo
Ming J. Zuo University of Alberta
Peter W. Tse
Peter W. Tse City University of Hong Kong
Joseph S. Gati
Joseph S. Gati University of Western Ontario
Wade A. Smith
Wade A. Smith University of New South Wales

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