Fault Diagnostics of Industrial Robots Using Support Vector Machines and Discrete Wavelet Transforms

2006 ◽  
Author(s):  
A. Datta ◽  
S. Patel ◽  
C. Mavroidis ◽  
I. Antoniadis ◽  
J. Krishnasamy ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Wavelet Transforms ◽  
Industrial Robot ◽  
Operating Conditions ◽  
Industrial Robots ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Fault Diagnostics ◽  
Large Variability ◽  
Vector Machines

In this paper we address the problem of fault diagnostics in industrial robots. The goal was to develop a method that automatically, accurately and in a generic way could identify and classify faults once they occur for any type of industrial robot used. Although a large number of diagnosis methods and relevant applications for industrial equipment already exist, the current research in the area of fault diagnosis of industrial robotic manipulators is rather poor, due to the large variability of faults, the unsteady and non-uniform operating conditions, the small amount of sensors used in industrial manipulators and the rather limited time records of the equipment. These restrictions present key challenges of the current research to be undertaken. In this paper we present a novel approach to perform fault diagnostics of industrial robotic systems using Support Vector Machines (SVM) and Discrete Wavelet Transform based feature extraction. Experimental results are obtained from an industrial manipulator used in the semi-conductor industry.

Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction

2011 ◽  
Vol 13 (1) ◽  
pp. 28-40 ◽  
Author(s):  
P-K Wong ◽  
H-C Wong ◽  
C-M Vong
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Fuel Efficiency ◽  
Time Sequence ◽  
Operating Conditions ◽  
Incremental Algorithm ◽  
Training Dataset ◽  
Support Vector ◽  
Fuel Ratio ◽  
Vector Machines

Fuel efficiency and pollution reduction relate closely to air-ratio (i.e. lambda) control among all the engine control variables. Lambda indicates the amount that the actual available air-fuel ratio mixture differs from the stoichiometric air-fuel ratio of the fuel being used. Accurate lambda prediction is essential for effective lambda control. This paper employs an emerging online time-sequence incremental algorithm and proposes one novel online time-sequence decremental algorithm based on least squares support vector machines (LS-SVMs) to continually update the built LS-SVM lambda function whenever a sample is added to, or removed from, the training dataset. Moreover, the online time-sequence algorithm can also significantly shorten the function updating time as compared with function retraining from scratch. In order to evaluate the effectiveness of this pair of online time-sequence algorithms, three lambda time series obtained from experiments under different operating conditions are employed. The prediction results of the online time-sequence algorithms over unseen cases are compared with those under classical LS-SVMs, typical decremental LS-SVMs, and neural networks. Experimental results show that the online time-sequence incremental and decremental LS-SVMs are superior to the other three typical methods.

scholarly journals Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qiang Liu ◽  
Songyong Liu ◽  
Qianjin Dai ◽  
Xiao Yu ◽  
Daoxiang Teng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Back Propagation ◽  
Operating Conditions ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Fault Detection And Identification ◽  
Vector Machines

Incipient fault detection and identification (IFDI) of cutting arms is a crucial guarantee for the smooth operation of a roadheader. However, the shortage of fault samples restricts the application of the fault diagnosis technique, and the data analysis tools should be optimized efficiently. In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. The commonly measured current and vibration data cutting arms are used in the IFDI. The experimental results show that the support vector machines based on dynamic cuckoo outperform the other methods. Besides, the performance of the four methods under different operating conditions is compared. The fault cause of cutting arms of the roadheader is analyzed and the design improvement scheme for cutting arms is provided. This study provides a reference for improving the fault diagnosis of the roadheader.

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