Remote online machine condition monitoring system

Abstract The study aims at realizing a remote online machine condition monitoring system built up in the architecture of both the Borland C++ Builder (BCB) software-developing environment and Internet transmission communication. Various signal-processing computation schemes such as time–frequency analysis and order tracking for signal analysis and pattern recognition purposes are implemented based upon the Borland C++ Builder graphical user interface. Thus machine fault diagnostic capability can be extended by using the socket application program interface as the transmission control protocol/Internet protocol (TCP/IP). In the study, the effectiveness of the developed remote diagnostic system is justified by monitoring a transmission-element test rig. A complete monitoring cycle including data acquisition, signal-processing, feature extraction, pattern recognition through the artificial neural networks, and online video surveillance, is demonstrated.

[1]  Paul Sas,et al.  INTELLIGENT JOINT FAULT DIAGNOSIS OF INDUSTRIAL ROBOTS , 1998 .

[2]  Jinwu Xu,et al.  An Implementation of a Remote Diagnostic System on Rotational Machines , 2006 .

[3]  P. Tse,et al.  Remote machine maintenance system through Internet and mobile communication , 2006 .

[4]  Q. Ahsan,et al.  Distributed On-Line System for Process Plant Monitoring , 2006 .

[5]  Min-Chun Pan,et al.  Adaptive Vold Kalman filtering order tracking , 2007 .

[6]  Min-Chun Pan,et al.  Transmission noise identification using two-dimensional dynamic signal analysis , 2003 .

[7]  Min-Chun Pan,et al.  Transmission component monitoring and comparison of two artificial neural network schemes , 2004, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[8]  Min‐Chun Pan,et al.  Dynamic interpretation of transmission systems using advanced time‐frequency representations , 2004 .

[9]  R. A. Errath,et al.  Remote drive condition monitoring , 1999, 1999 IEEE/-IAS/PCA Cement Industry Technical Conference. Conference Record (Cat. No.99CH36335).

[10]  Min-Chun Pan,et al.  Remote online machine fault diagnostic system , 2004, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[11]  H. Van Brussel,et al.  Machine Condition Monitoring Using Signal Classification Techniques , 2003 .

[12]  Y.-F. Lin,et al.  Further exploration of Vold–Kalman-filtering order tracking with shaft-speed information—I: Theoretical part, numerical implementation and parameter investigations , 2006 .

[13]  Min-Chun Pan,et al.  Investigation on improved Gabor order tracking technique and its applications , 2006 .

[14]  Y.-F. Lin,et al.  Further exploration of Vold–Kalman-filtering order tracking with shaft-speed information—II: Engineering applications , 2006 .

[15]  Lifeng Xi,et al.  Remote multi-robot monitoring and control system based on MMS and web services , 2007, Ind. Robot.

[16]  Min-chun Pan.  Integrated Study of Time-Frequency Representations and Their Applications in Source Identification of Mechanical Noise , 2002 .