A COMBINATION OF SUPPORT VECTOR MACHINE AND k-NEAREST NEIGHBORS FOR MACHINE FAULT DETECTION

This article presents a combination of support vector machine (SVM) and k-nearest neighbor (k-NN) to monitor rotational machines using vibrational data. The system is used as triage for human analysis and, thus, a very low false negative rate is more important than high accuracy. Data are classified using a standard SVM, but for data within the SVM margin, where misclassifications are more like, a k-NN is used to reduce the false negative rate. Using data from a month of operations of a predictive maintenance company, the system achieved a zero false negative rate and accuracy ranging from 75% to 84% for different machine types such as induction motors, gears, and rolling-element bearings.

[1]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[2]  R. J. Kuo.  Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic , 1995 .

[3]  Bo-Suk Yang,et al.  Condition classification of small reciprocating compressor for refrigeration using artificial neural networks and support vector machines , 2005 .

[4]  Bo-Suk Yang,et al.  Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors , 2007, Expert Syst. Appl..

[5]  Asoke K. Nandi,et al.  Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines , 2006 .

[6]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[7]  R. Keith Mobley,et al.  An introduction to predictive maintenance , 1989 .

[8]  B. Samanta,et al.  Gear fault detection using artificial neural networks and support vector machines with genetic algorithms , 2004 .

[9]  Guy Clerc,et al.  The use of features selection and nearest neighbors rule for faults diagnostic in induction motors , 2006, Eng. Appl. Artif. Intell..

[10]  Robert Randall,et al.  Vibration signature analysis - Techniques and instrument systems , 1975 .

[11]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[12]  Chandrasekhar Nataraj,et al.  Use of particle swarm optimization for machinery fault detection , 2009, Eng. Appl. Artif. Intell..

[13]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.