Independent component analysis: algorithms and applications
Neural Networks
Variable selection using svm based criteria
The Journal of Machine Learning Research
Dynamic independent component analysis approach for fault detection and diagnosis
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A novel ant-based clustering algorithm using the kernel method
Information Sciences: an International Journal
Information Sciences: an International Journal
A fast fault-identification algorithm for bijective connection graphs using the PMC model
Information Sciences: an International Journal
Machine learning approach for face image retrieval
Neural Computing and Applications - Special Issue on ICONIP2010
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Multi-sensor data fusion using support vector machine for motor fault detection
Information Sciences: an International Journal
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
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This paper proposes a novel approach for dealing with fault detection of multivariate processes, which will be referred to as kernel dynamic independent component analysis (KDICA). The main idea of KDICA is to carry out an independent component analysis in the kernel space of an augmented measurement matrix to extract the dynamic and non-linear characteristics of a non-linear non-Gaussian dynamic process. Furthermore, as a new method of fault diagnosis, a non-linear contribution plot is developed for KDICA. A comparative study on the Tennessee Eastman process is carried out to illustrate the effectiveness of the proposed method. The experimental results show that the proposed method compares favorably with existing methods.