ENG  Vol.9 No.6 , June 2017
An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP
Abstract: Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine’s operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.
Cite this paper: Thobiani, F. , Tran, V. and Tinga, T. (2017) An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP. Engineering, 9, 524-539. doi: 10.4236/eng.2017.96033.

[1]   Toutountzakis, T., Tan, C.K. and Mba, D. (2005) Application of Acoustic Emission to Seeded Gear Fault Detection. NDT & E International, 38, 27-36.

[2]   Wu, J.D. and Chuang, C.Q. (2005) Fault Diagnosis of Internal Combustion Engines Using Visual Dot Patterns of Acoustic and Vibration Signals.NDT & E International, 38, 605-614.

[3]   Wang, J. and Hu, H. (2006) Vibration-Based Fault Diagnosis of Pump Using Fuzzy Technique. Measurement, 39, 176-185.

[4]   Yang, B.-S., Han, T. and An, J.L. (2004) ART-Kohonenneural Network for Fault Diagnosis of Rotating Machinery. Mechanical Systems and Signal Processing, 18, 645-657.

[5]   Lee, S.K. and White, P.R. (1997) Higher-Order Time-Frequency Analysis and Its Application to Fault Detection in Rotating Machinery. Mechanical Systems and Signal Processing, 11, 637-650.

[6]   Cheng, J., Yang, Y. and Yu, D. (2010) The Envelope Order Spectrum Based on Generalized Demodulation Time-Frequency Analysis and Its Application to Gear Fault Diagnosis. Mechanical Systems and Signal Processing, 24, 508-521.

[7]   Rmda, S. and Alhussein, A. (2013) Petroleum Pumps’ Current and Vibration Signatures Analysis Using Wavelet Coherence Technique. Advances in Acoustics and Vibration, 2013, Article ID: 659650, 6 p.

[8]   Baydar, N. and Ball, A. (2003) Detection of Gear Failures via Vibration and Acoustic Signals Using Wavelet Transform. Mechanical Systems and Signal Processing, 17, 787-804.

[9]   Bagavathiappan, S., Saravanan, T., George, N.P., Philip, J., Jayakumar, T. and Raj, B. (2007) Condition Monitoring of Exhaust System Blowers Using Infrared Thermography. Insight, 50, 512-515.

[10]   Leemans, V., Destain, M., Kilundu, B. and Dehombreux, P. (2011) Evaluation of the Performance of Infrared Thermography for On-Line Condition Monitoring of Rotating Machines. Engineering, 3, 1030-1039.

[11]   Younus, A.M.D. and Yang, B.-S. (2012) Intelligent Fault Diagnosis of Rotating Machinery Using Infrared Thermal Image. Expert Systems with Applications, 39, 2082-2091.

[12]   Younus, A.M.D., Widodo, A. and Yang, B.-S. (2010) Evaluation of Thermography Image Data for Machine Fault Diagnosis. Nondestructive Test Evaluation, 25, 231-247.

[13]   Mazioud, A., Ibos, L., Khlaifi, A. and Durastanti, J.F. (2008) Detection of Rolling Bearing Degradation Using Infrared Thermography. International Conference on Quantitative InfraRed Thermography, Krakow, 2-5 July 2008.

[14]   Bagavathiappan, S., Lahiri, B.B., Saravanan, T., Philip, J. and Jayakumar, T. (2013) Infrared Thermography for Condition Monitoring—A Review. Infrared Physics & Technology, 60, 35-55.

[15]   Sheshadri, H. and Kandaswamy, A. (2007) Experimental Investigation on Breast Tissue Classification Based on Statistical Feature Extraction of Mammograms. Computerized Medical Imaging and Graphics, 31, 46-48.

[16]   Materka, A. and Strzelecki, M. (1998) Texture analysis Methods—A Review. Institute of Electronics, Tech University of Lodz, Lodz.

[17]   Aggarwal, N. and Agrawal, R.K. (2012) First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images. Journal of Signal and Information Processing, 3, 146-153.

[18]   Haralick, R.M., Shanmugam, K. and Dinstein, I. (1973) Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, 3, 610-621.

[19]   Weszka, J., Deya, C. and Rosenfeld, A. (1976) A Comparative Study of Texture Measures for Terrain Classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 269-285.

[20]   Conners, R.W., Trivedi, M.M. and Harlow, C.A. (1984) Segmentation of a High-Resolution Urban Scene Using Texture Operators. Computer Vision, Graphics, and Image Processing, 25, 273-310.

[21]   Haralick, R.M. (1979) Statistical and Structural Approaches to Texture. Proceeding of the IEEE, 67, 786-804.

[22]   Soh, L.K. and Tsatsoulis, C. (1999) Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurrence Matrices. IEEE Transactions on Geoscience and Remote Sensing, 37, 780-795.

[23]   Jardine, A.K.S., Lin, D. and Banjevic, D. (2006) A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance. Mechanical Systems and Signal Processing, 20, 1483-1510.

[24]   Vapnik, V.N. (1999) The Nature of Statistical Learning Theory. Springer, Berlin.

[25]   Tran, V.T., Thobiani, F.A. and Ball, A. (2013) An Application to Transient Current Signal Based Induction Motor Fault Diagnosis of Fourier-Bessel Expansion and Simplified Fuzzy ARTMAP. Expert Systems with Applications, 40, 5372-5384.

[26]   Kasuba, T. (1993) Simplified Fuzzy ARTMAP. AI Expert, 8, 19-25.

[27]   Peng, H., Long, F. and Ding, C. (2005) Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1226-1238.

[28]   Ding, C. and Peng, H.C. (2003) Minimum Redundancy Feature Selection from Microarray Gene Expression Data. Proceedings of the Computational Systems Bioinformatics, 2003, 523-528.

[29]   Carpenter, C.A., Grossberg, S., Markuzon, N., Reynolds, J.H. and Rosen, D.B. (1992) Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps. IEEE Transactions on Neural Networks, 3, 698-713.

[30]   Tran, V.T., Yang, B.S., Gu, F. and Ball, A. (2013) Thermal Image Enhancement Using Bi-Dimensional Empirical Mode Decomposition in Combination with Relevance Vector Machine for Rotating Machinery Fault Diagnosis. Mechanical Systems and Signal Processing, 38, 601-614.

[31]   Guyon, I. and Elisseeff, A. (2003) An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research, 3, 1157-1182.

[32]   Gulgezen, G., Cataltepe, Z. and Yu, L. (2009) Stable and Accurate Feature Selection. Lecture Notes in Computer Science, 5781, 455-468.