Effective fault detection in structural health monitoring systems

Abstract

A new fault detection technique is considered in this article. It is based on kernel partial least squares, exponentially weighted moving average, and generalized likelihood ratio test. The developed approach aims to improve monitoring the structural systems. It consists of computing an optimal statistic that merges the current information and the previous one and gives more weight to the most recent information. To improve the performances of the developed kernel partial least squares model even further, multiscale representation of data will be used to develop a multiscale extension of this method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale kernel partial least squares method that combines the advantages of the kernel partial least squares method with those of multiscale representation will be developed to enhance the structural modeling performance. The effectiveness of the proposed approach is assessed using two examples: synthetic data and benchmark structure. The simulation study proves the efficiency of the developed technique over the classical detection approaches in terms of false alarm rate, missed detection rate, and detection speed.

A novel industrial process fault monitoring method based on kernel robust non-negative matrix factorization

“…Zhang et al [8] proposed a health monitoring scheme based on independent component difference (Diff-ICA), which was used to monitor the changes of latent variables in this process and significantly reduced the intermediate parameterization efforts and detection false alarm rates. Chaabane et al [9] proposed a multi-scale kernel partial least squares method, which was a powerful data analysis method that can effectively separate deterministic features from random noise. At the same time, Botre et al [10] used multi-scale representation to deal with the autocorrelation and noise in the data set.…”