Dimension reduction method of independent component analysis for process monitoring based on minimum mean square error

Noise-resistant joint diagonalization independent component analysis based process fault detection

“…The cumulative percent variance (CPV) criterion is commonly applied in the ICA-based fault detection methods [4,40]. We construct a CPV criterion using the absolute values of the ICs' kurtosis estimates given in Eq.…”

“…More recently, fault detection based on independent component analysis (ICA) has become a hot topic [2][3][4][5][6]8,10,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. ICA was originally derived for solving the blind source separation Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom problem [41][42][43][44][45][46][47][48] and was introduced to fault detection by Kano et al.…”

A new process monitoring method based on noisy time structure independent component analysis

“…In the NoisyTSICA-based monitoring method, the number of the dominant ICs is selected as c = 7 so that the cumulative sum of the dominant ICs' kurtosis absolute values is also above 90% of the cumulative sum of all the extracted ICs' kurtosis absolute values. Both the choices are on the basis of the commonly used cumulative percent variance (CPV) criterion [11,33]. The parameter settings for Eq.…”

Application of Blind Source Separation Algorithms and Ambient Vibration Testing to the Health Monitoring of Concrete Dams

“…In our previous work [19], the kernel component analysis (KPCA) method is adopted to monitor the health state of concrete dams. Recently, some literature has reported on the good performance of independent component analysis (ICA) [20][21][22], which is a typical BSS algorithm and can be deemed an extension of PCA, in state monitoring of complex systems. PCA can impose independence only up to second-order statistical information (mean and variance), whereas ICA involves higher-order statistics; that is, it not only decorrelates the data (second-order statistics) but also reduces higher-order statistical dependencies.…”