Fault diagnosis on material handling system using feature selection and data mining techniques

Abstract The material handling systems are one of the key components of the most modern manufacturing systems. The sensory signals of material handling systems are nonlinear and have unique characteristics. It is very difficult to encode and classify these signals by using multipurpose methods. In this study, performances of multiple generic methods were studied for the diagnostic of the pneumatic systems of the material handling systems. Diffusion Map (DM), Local Linear Embedding (LLE) and AutoEncoder (AE) algorithms were used for future extraction. Encoded signals were classified by using the Gustafson–Kessel (GK) and k-medoids algorithms. The accuracy of the estimations was better than 90% when the LLE was used with GK algorithm.

[1]  Yoshihide Yokoi,et al.  Failure diagnosis system on pneumatic control valves by neural network , 1993, IEEE International Conference on Neural Networks.

[2]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[3]  K. Zehl,et al.  Fuzzy divisive hierarchical clustering of soil data using Gustafson–Kessel algorithm , 2007 .

[4]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[5]  C. Yoo,et al.  Overall statistical monitoring of static and dynamic patterns , 2003 .

[6]  Ibrahim N. Tansel,et al.  Fault diagnosis on bottle filling plant using genetic-based neural network , 2011, Adv. Eng. Softw..

[7]  Qiaoping Zhang,et al.  A New and Efficient K-Medoid Algorithm for Spatial Clustering , 2005, ICCSA.

[8]  Ann B. Lee,et al.  Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  N. Sepehri,et al.  Adaptive fuzzy-neural-based multiple models for fault diagnosis of a pneumatic actuator , 2004, Proceedings of the 2004 American Control Conference.

[10]  João Miguel da Costa Sousa,et al.  An architecture for fault detection and isolation based on fuzzy methods , 2009, Expert Syst. Appl..

[11]  James M. Keller,et al.  Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , 1999 .

[12]  Pekka Teppola,et al.  Possibilistic and fuzzy C‐means clustering for process monitoring in an activated sludge waste‐water treatment plant , 1999 .

[13]  Z. Nakutis,et al.  Pneumatic Cylinder Diagnostics using Classification Methods , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[14]  Wenxian Yang,et al.  Establishment of the mathematical model for diagnosing the engine valve faults by genetic programming , 2006 .

[15]  Arun K. Samantaray,et al.  Fault Detection and Isolation of Smart Actuators Using Bond Graphs and External Models , 2005 .

[16]  Yu Zhang,et al.  Simulating Wrinkles in Facial Expressions on an Anatomy-Based Face , 2005, International Conference on Computational Science.

[17]  L. J. P. van der Maaten,et al.  An Introduction to Dimensionality Reduction Using Matlab , 2007 .

[18]  Ronald J. Patton,et al.  Fault diagnosis of an electro-pneumatic valve actuator using neural networks with fuzzy capabilities , 2002, ESANN.

[19]  Garrison W. Cottrell,et al.  Non-Linear Dimensionality Reduction , 1992, NIPS.

[20]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[21]  Nariman Sepehri,et al.  Diagnosis of process valve actuator faults using a multilayer neural network , 2003 .

[22]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[23]  Ibrahim N. Tansel,et al.  Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms , 2011 .

[24]  J. Wang,et al.  Identification of pneumatic cylinder friction parameters using genetic algorithms , 2004, IEEE/ASME Transactions on Mechatronics.

[25]  Vasile Palade,et al.  NEURO-FUZZY BASED FAULT DIAGNOSIS APPLIED TO AN ELECTRO-PNEUMATIC VALVE , 2002 .

[26]  Gibaek Lee,et al.  Process Monitoring of an Electro-Pneumatic Valve Actuator Using Kernel Principal Component Analysis , 2004 .

[27]  Ann B. Lee,et al.  Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Ibrahim N. Tansel,et al.  Fault diagnosis of pneumatic systems with artificial neural network algorithms , 2009, Expert Syst. Appl..

[29]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[30]  Sheng-Fa Yuan,et al.  Fault diagnostics based on particle swarm optimisation and support vector machines , 2007 .

[31]  Farrokh Sassani,et al.  On-line fault diagnosis of hydraulic systems using Unscented Kalman Filter , 2010 .

[32]  S.Y. Kung,et al.  Adaptive Principal component EXtraction (APEX) and applications , 1994, IEEE Trans. Signal Process..

[33]  Q. Peter He,et al.  A New Fault Diagnosis Method Using Fault Directions in Fisher Discriminant Analysis , 2005 .

[34]  Ronald R. Coifman,et al.  Data Fusion and Multicue Data Matching by Diffusion Maps , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.