Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: a access to the machines on site is sometimes impracticable, and b the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system Melvin I, which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators KPIs. A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

[1]  Yingxu Wang.  Breakthroughs in Software Science and Computational Intelligence , 2012 .

[2]  Gautam S. Muralidhar,et al.  Computer-Aided Detection and Diagnosis for 3D X-Ray Based Breast Imaging , 2012 .

[3]  Yingxu Wang,et al.  An intelligent fault recognizer for rotating machinery via remote characteristic vibration signal detection , 2011, IEEE 10th International Conference on Cognitive Informatics and Cognitive Computing (ICCI-CC'11).

[4]  Vasant Honavar,et al.  Evolutionary Design of Neural Architectures -- A Preliminary Taxonomy and Guide to Literature , 1995 .

[5]  Jeff Heaton,et al.  Introduction to Neural Networks for C#, 2nd Edition , 2008 .

[6]  Gerald M. Knapp,et al.  An ARTMAP neural network‐based machine condition monitoring system , 2000 .

[7]  T. V. Geetha,et al.  Requirements Elicitation by Defect Elimination: An Indian Logic Perspective , 2009, Int. J. Softw. Sci. Comput. Intell..

[8]  D. P. Acharjya,et al.  Global Trends in Intelligent Computing Research and Development , 2013 .

[9]  Dipak Laha,et al.  Handbook of Computational Intelligence in Manufacturing and Production Management , 2007 .

[10]  Gerardo Reyes Salgado,et al.  An Enhanced Petri Net Model to Verify and Validate a Neural-Symbolic Hybrid System , 2009, Int. J. Softw. Sci. Comput. Intell..

[11]  Adam J. Conover,et al.  A Simulation of Temporally Variant Agent Interaction via Belief Promulgation , 2009 .

[12]  Eric Bouillet,et al.  Semantic Matching, Propagation and Transformation for Composition in Component-Based Systems , 2009, Int. J. Softw. Sci. Comput. Intell..

[13]  Huaqing Wang,et al.  Fault diagnosis and condition surveillance for plant rotating machinery using partially-linearized neural network , 2008, Comput. Ind. Eng..

[14]  Slawomir Zadrozny,et al.  Protoforms of Linguistic Database Summaries as a Human Consistent Tool for Using Natural Language in Data Mining , 2009, Int. J. Softw. Sci. Comput. Intell..

[15]  Paramartha Dutta,et al.  Handbook of Research on Computational Intelligence for Engineering, Science, and Business , 2012 .

[16]  Wei-Ying Ma,et al.  Annotating Images by Mining Image Search , 2009, Semantic Mining Technologies for Multimedia Databases.

[17]  Steven Y. Liang,et al.  BEARING CONDITION DIAGNOSTICS VIA VIBRATION AND ACOUSTIC EMISSION MEASUREMENTS , 1997 .

[18]  Mahil Carr,et al.  Software Cost Estimation using Soft Computing Approaches , 2010 .

[19]  Wei-Chiang Samuelson Hong.  Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation , 2013 .

[20]  Yingxu Wang,et al.  The Formal Design Model of an Automatic Teller Machine (ATM) , 2010, Int. J. Softw. Sci. Comput. Intell..

[21]  Norio Shiratori,et al.  Agent-Based Middleware for Advanced Communication Services in a Ubiquitous Computing Environment , 2010, Int. J. Softw. Sci. Comput. Intell..

[22]  Yingxu Wang,et al.  Software and Intelligent Sciences: New Transdisciplinary Findings , 2012 .

[23]  A. Al-Ghandoor,et al.  An Intelligent Machine Condition Monitoring System Using Time-Based Analysis: Neuro-Fuzzy Versus Neural Network , 2009 .

[24]  A. Nandi,et al.  A comparison of artificial neural networks and other statistical methods for rotating machine condition classification , 1996 .

[25]  Jinlong Zeng,et al.  ISW: Interest-Based Small World Network of P2P , 2012, Int. J. Softw. Sci. Comput. Intell..

[26]  G. Saranya,et al.  Fault detection in Induction motor using WPT and Multiple SVM , 2010 .

[27]  Ronald N. Bracewell,et al.  The Fourier Transform and Its Applications , 1966 .

[28]  Prakash Vinod,et al.  Standardization of Absolute Vibration Level and Damage Factors for Machinery Health Monitoring , 2002 .

[29]  Yonghong Peng,et al.  Intelligent condition monitoring using fuzzy inductive learning , 2004, J. Intell. Manuf..

[30]  Bibhuti Bhusan Biswal,et al.  Appropriate Evolutionary Algorithm for Scheduling in FMS , 2011, Int. J. Appl. Evol. Comput..

[31]  Xiangfeng Luo,et al.  Measuring Textual Context Based on Cognitive Principles , 2009, Int. J. Softw. Sci. Comput. Intell..