Condition Based Monitoring of Machine Using Mamdani Fuzzy Network

In recent years, learning algorithms have been proved to learn abstract hierarchical pattern in the data and provides a better generalization. In this paper, intricate fuzzy patterns in the data are learned with the help of fuzzy network using Mamdani fuzzy inference system. The underlying architecture constituted with layers of Mamdani fuzzy inference system as nodes is called Mamdani Fuzzy Network (MFN). This architecture has an input layer, hidden layers and an output layer. Each layer has Mamdani fuzzy inference system as its basic building unit. A training is carried out using Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm to achieve the identification of all parameters in the architecture according to training data. The proposed architecture is implemented in two class air compressor vibration data containing 1200 samples with 5 features. These features are passed to fuzzy network to learn abstract fuzzy representations in the form of multiple distinct fuzzy rule bases which intelligible to human begins. The identified fuzzy rule bases consist of linguistic information of IF-THEN rules with both premise and consequent parts as linguistic labels. The effectiveness of proposed methodology is validated on vibration data sets collected from air compressor. These vibration data sets have been recorded from most sensitive positions of air compressor under various health conditions.

[1]  Jie Liu,et al.  Fusion of Low-level Features with Stacked Autoencoder for Condition based Monitoring of Machines , 2018, 2018 IEEE International Conference on Prognostics and Health Management (ICPHM).

[2]  T. G. Habetler,et al.  A method for sensorless on-line vibration monitoring of induction machines , 1997, IAS '97. Conference Record of the 1997 IEEE Industry Applications Conference Thirty-Second IAS Annual Meeting.

[3]  Narendra Kumar Dhar,et al.  Improved EMD Local Energy with SVM for Fault Diagnosis in Air Compressor , 2018, Advances in Intelligent Systems and Computing.

[4]  Jerry M. Mendel,et al.  Back-propagation fuzzy system as nonlinear dynamic system identifiers , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[5]  Yan Cui,et al.  Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[6]  Nishchal K. Verma,et al.  Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors , 2016, IEEE Transactions on Reliability.

[7]  Nishchal K. Verma,et al.  Additive and Nonadditive Fuzzy Hidden Markov Models , 2010, IEEE Transactions on Fuzzy Systems.

[8]  Girish Kumar Singh,et al.  Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine , 2004 .

[9]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[10]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[11]  D. Shanno.  Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .

[12]  Yongyong He,et al.  Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery , 2005 .

[13]  Jie Liu,et al.  Prognosis of Diesel Engine (MBT) Using Feature Extraction Techniques: A Comparative Study , 2018, 2018 IEEE International Conference on Prognostics and Health Management (ICPHM).

[14]  Nishchal K. Verma,et al.  From a Gaussian Mixture Model to Nonadditive Fuzzy Systems , 2007, IEEE Transactions on Fuzzy Systems.

[15]  Nishchal K. Verma,et al.  Developing deep fuzzy network with Takagi Sugeno fuzzy inference system , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[16]  Peter Tavner,et al.  Condition monitoring of electrical machines , 1987 .

[17]  Vikas Singh,et al.  A Type-2 Fuzzy Systems Approach for Clustering-Based Identification of a T-S Regression Model , 2019 .

[18]  Nalinaksh S. Vyas,et al.  Artificial neural network design for fault identification in a rotor-bearing system , 2001 .

[19]  Nishchal K. Verma,et al.  Fuzzy Inference Network with Mamdani Fuzzy Inference System , 2018, Computational Intelligence: Theories, Applications and Future Directions - Volume I.

[20]  Rahul Kumar Sevakula,et al.  Pattern Analysis Framework With Graphical Indices for Condition-Based Monitoring , 2017, IEEE Transactions on Reliability.

[21]  C. G. Broyden.  The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm , 1970 .

[22]  R. Fletcher,et al.  A New Approach to Variable Metric Algorithms , 1970, Comput. J..

[23]  Larry Nazareth,et al.  A family of variable metric updates , 1977, Math. Program..

[24]  Yan Cui,et al.  Optimal Feature Selection using Fuzzy Combination of Feature Subset for Transcriptome Data , 2018, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[25]  Nishchal K. Verma,et al.  Adaptive Type-2 Fuzzy Approach for Filtering Salt and Pepper Noise in Grayscale Images , 2018, IEEE Transactions on Fuzzy Systems.