Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features

• Published in: IEEE access 2017, Vol.5, p.8682-8690
• Main Authors: Jan, Sana Ullah, Young-Doo Lee, Jungpil Shin, Insoo Koo
• Format: Article
• Language: English
• Subjects: Accuracy; Basis functions; Classification; Classifiers; Data acquisition; drift fault; erratic fault; Fault detection; Fault diagnosis; Feature extraction; hard-over fault; Kernel; Kernel functions; Mathematical analysis; Microcontrollers; Neural networks; Polynomials; Samples; Sensor faults; spike fault; Statistical methods; stuck fault; support vector machine; Support vector machines; Time domain analysis; Training; Voltage converters
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2017.2705644

This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above mentioned fault signals was acquired as follows: normal data signals were obtained from a temperature-to-voltage converter by using an Arduino Uno microcontroller board and MATLAB. Then, faults were simulated in normal data to get 100 samples of each fault, in which one sample is composed of 1000 data elements. A support vector machine (SVM) was used for data classification in a one-versus-rest manner. The statistical time-domain features, extracted from a sample, were used as a single observation for training and testing SVM. The number of features varied from 5 to 10 to examine the effect on accuracy of SVM. Three different kernel functions used to train SVM include linear, polynomial, and radial-basis function kernels. The fault occurrence event in fault samples was chosen randomly in some cases to replicate a practical scenario in industrial systems. The results show that an increase in the number of features from 5 to 10 hardly increases the total accuracy of the classifier. However, using ten features gives the highest accuracy for fault classification in an SVM. An increase in the number of training samples from 40 to 60 caused an overfitting problem. The k-fold cross-validation technique was adopted to overcome this issue. The increase in number of data elements per sample to 2500 increases the efficiency of the classifier. However, an increase in the number of training samples to 400 reduces the capability of SVM to classify stuck fault. The receiver operating characteristics curve comparison shows the efficiency of SVM over a neural network.