Software architecture for condition monitoring of mobile underground mining machinery: A framework extensible to intelligent signal processing and analysis

In the mining sector, there are growing calls for the ability to monitor the health of operational assets like structures, mobile underground machinery, and complex stationary equipment. This work focuses on the development of a software architecture to monitor the entire range of industrial and mining equipment - all the while acknowledging the more generic problem of intelligent signal processing having applicability to a much broader class of problem. The implementation of condition monitoring for mobile underground mining equipment relies on previous work by the authors in advancing condition-monitoring techniques for vari able speed and load machinery. The design will permit flexible run-time system configuration with a variety of choices in signal processing and intelligent analysis techniques; it has been demonstrated to work well with an implementation in MATLAB object-oriented programming (OOP) from data collected on a real gearbox subject to varying loads and speeds. This success justifies the advancement of a full-fledged prototype in LabVIEW OOP. The aim is the development of the data analysis layer; the result should integrate seamlessly with such developing industrial standards as the International Rock Excavation Data Exchange Standard (IREDES).

[1]  Kari Koskinen,et al.  Service-Oriented Application Integration for condition-based maintenance with OPC Unified Architecture , 2011, 2011 9th IEEE International Conference on Industrial Informatics.

[2]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[3]  Andrew Y. C. Nee,et al.  An agent-based platform for Web-enabled equipment predictive maintenance , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[4]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[5]  Jin Chen,et al.  SOA-based remote condition monitoring and fault diagnosis system , 2010 .

[6]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[7]  Markus Timusk,et al.  Fault detection in variable speed machinery: Statistical parameterization , 2009 .

[8]  T. Jackson,et al.  An Architecture for Distributed Search and Data-Mining in Condition Monitoring Applications , 2007, 2007 IEEE Aerospace Conference.

[9]  J. Dunsdon,et al.  The Application of Open System Architecture for Condition Based Maintenance to Complete IVHM , 2008, 2008 IEEE Aerospace Conference.

[10]  B. Chidambaram,et al.  Condition-based monitoring of an electro-hydraulic system using open software architectures , 2005, 2005 IEEE Aerospace Conference.

[11]  Markus Timusk,et al.  Feature extraction for novelty detection as applied to fault detection in machinery , 2011, Pattern Recognit. Lett..

[12]  Min-Chun Pan,et al.  Remote online machine condition monitoring system , 2008 .

[13]  Ming Rao,et al.  Architecture of integrated distributed intelligent multimedia system for on-line real-time process monitoring , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[14]  Andrew Ball,et al.  Decisions in condition monitoring-an examplar for data fusion architecture , 2000, Proceedings of the Third International Conference on Information Fusion.

[15]  Kenneth D. Bever.  Understanding MIMOSA's Open Standards for On-board Health Assessment and Enterprise Application Integration , 2007 .

[16]  Michael G. Lipsett,et al.  Fault detection using transient machine signals , 2008 .

[17]  Liu Dong,et al.  Gearbox Diagnosis Based on N4SID Subspace Method , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

[18]  Erkki Jantunen,et al.  Flexible software for condition monitoring, incorporating novelty detection and diagnostics , 2006, Comput. Ind..

[19]  A. Halme,et al.  Multi-agent based information access services for condition monitoring in process automation , 2005, INDIN '05. 2005 3rd IEEE International Conference on Industrial Informatics, 2005..

[20]  Charles R. Farrar,et al.  Novelty detection under changing environmental conditions , 2001, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[21]  Lin Ma,et al.  Implementation of Asset Health Assessment System with Pattern-Oriented Design and Practice , 2006, 2006 3rd International IEEE Conference Intelligent Systems.

[22]  X. B. He,et al.  A Multi-Agent Architecture of Health Monitoring system for Long-Span Bridge , 2009, 2009 International Conference on Management and Service Science.

[23]  Michael G. Lipsett,et al.  Automated Duty Cycle Classification for Online Monitoring Systems , 2007 .

[24]  Andreas Gössling,et al.  Architecture of a Predictive Maintenance Framework , 2007, 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07).