A brief review and a first application of time-frequency-based analysis methods for monitoring of strip rolling mills. (November 2015)

• Record Type: Journal Article
• Authors: Rother, Astrid; Jelali, Mohieddine; Söffker, Dirk
• Is Part Of: Journal of process control. Volume 35(2015:Nov.)
• Page Start: 65
• Page End: 79
• Publication Date: 2015-11
• Subjects: VI vincent -- NA nandhan -- ARMA autoregressive moving average -- CWT continuous wavelet transform -- DWT discrete wavelet transform -- EMD empirical mode decomposition -- FFT fast Fourier transform -- FMEA failure mode and effect analysis -- FT Fourier transform -- FWT fast wavelet transform -- GRNN general regression neural network -- HHT Hilbert-Huang transform -- HT Hilbert transform -- IFFT inverse fast Fourier transform -- IMF intrinsic mode function -- ISOMAP isometric feature mapping -- LMD local mean decomposition -- MB model-based -- MCA morphological component analysis -- MW mother wavelet -- NN neural networks -- SALSA split augmented Lagrangian shrinkage algorithm -- STFT short time Fourier transform -- SVM support vector machine -- SWT stationary wavelet transform -- TQWT tunable Q-factor wavelet transform -- WPT wavelet Paket transform -- WT wavelet transform -- WVD Wigner–Ville distribution; Time-frequency analysis -- Fault detection -- Strip rolling mills -- Non-stationary signals -- Industrial application; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.08.010
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: To reduce downtimes and extend the lifetime of components, fault detection and identification become more important in production plants. Sensors and other information sources can be deployed for condition monitoring and fault diagnosis. In this contribution, first a review on the application of Short-time Fourier transform, continuous and discrete wavelet transform, Wigner–Ville distribution, and empirical mode decomposition to strip rolling mills is given. The suitability of these data analysis methods in the field of non-stationary signals is analyzed. Secondly, these methods are applied to hot strip mill data to detect deviations in strip travel that affect product quality or cause downtimes. Their capability to record characteristic fault features is evaluated. The results show that applying wavelet transform and empirical mode decomposition methods are able to detect process changes, here related to irregular production conditions and results.