Abstract
Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.
Highlights
The Artificial Neural Networks (ANNs) [1,2] have an established position among the data-driven techniques which are applied to identify non-linear dynamic systems [3]
Related Works: Active and Passive Approaches to Fault Detection. As it was already mentioned, the main objective of this paper is to develop a technique which makes it possible to enhance performance of a neural network-based fault detection scheme
In the light of the above discussion, the objective of this paper is to propose a neural network-based fault detection scheme and to enhance its performance by minimizing the size of the uncertainty interval
Summary
The Artificial Neural Networks (ANNs) [1,2] have an established position among the data-driven techniques which are applied to identify non-linear dynamic systems [3]. Quasi-OBE Algorithm (MNQOA) with optimal input sequence is used to gradually decrease its uncertainty After this process, an adaptive threshold is obtained for the resulting neural network. Recently Deep Neural Networks (DNN) have received a considerable research attention Apart from their incontestable modeling appeal they usually contain a large number of parameters which increase the overall uncertainty of the resulting neural model. Its wide popularity in numerous engineering applications results from its possibility of modeling any non-linear dynamic system [3] Another advantage is that the several mature and effective training algorithms for such networks were developed. The description of the MLP uncertainty calculated with the application of the developed approach is employed in Section 6 to design the adaptive thresholds. The last section of this paper is devoted to conclusions and future research direction
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