Corpus ID: 125665501

Approach for Improved Signal-Based Fault Diagnosis of Hot Rolling Mills

  title={Approach for Improved Signal-Based Fault Diagnosis of Hot Rolling Mills},
  author={A. Rother},
Der hier vorgestellte Ansatz ist in der Lage, zwei spezifische schwere Fehler zu erkennen, sie zu identifizieren, zwischen vier verschiedenen Systemzustanden zu unterscheiden und eine Prognose bezuglich des Systemverhaltens zu geben. Die vorliegende Arbeit untersucht die Zustandsuberwachung des komplexen Herstellungsprozesses eines Warmbandwalzwerks. Eine signalbasierte Fehlerdiagnose und ein Fehlerprognoseansatz fur den Bandlauf werden entwickelt. Eine Literaturubersicht gibt einen… Expand
1 Citations
Fault diagnosis using kernel principal component analysis for hot strip mill
It is shown in this work that the exploitation of the measurements in the form of KPCA can effectively improve the detection results. Expand


Fourier-Analyse versus Wavelet-Analyse
Sollen aus Zeitsignalen y (t ) spezifische Informationen gewonnen werden, so kann dies mit Hilfe geeigneter Analysen, also Transformationen geschehen. Die wohl bekannteste Transformation ist dieExpand
Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox
Abstract Localized faults in gearboxes tend to result in periodic shocks and thus arouse periodic responses in vibration signals. Feature extraction has always been a key problem for localized faultExpand
Contribution rate plot for nonlinear quality-related fault diagnosis with application to the hot strip mill process
Abstract In this paper, a nonlinear fault diagnosis scheme is established for the hot strip mill process (HSMP). In HSMP, the faults affecting quality index are denoted as quality-related faults,Expand
A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing
Abstract For rolling bearing fault detection, it is expected that a desired time–frequency analysis method should have good computation efficiency, and have good resolution in both time domain andExpand
Signal-based Fault Prognosis Approach Based on Time-frequency Analysis Applied to Industrial Data
Increasing customer demands in product quality and increasing complexity in production plants found the need of profound system knowledge to reduce downtimes, to extend the lifetime of components,Expand
Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform
Abstract A number of techniques for detection of faults in rolling element bearing using frequency domain approach exist today. For analysing non-stationary signals arising out of defective rollingExpand
A brief review and a first application of time-frequency-based analysis methods for monitoring of strip rolling mills
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 beExpand
Fault prognostics using dynamic wavelet neural networks
  • P. Wang, G. Vachtsevanos
  • Engineering, Computer Science
  • Artificial Intelligence for Engineering Design, Analysis and Manufacturing
  • 2001
This paper attempts to address the most difficult of the three issues leading to condition-based maintenance (CBM), prognosis, with intelligence-oriented techniques, specifically dynamic wavelet neural networks (DWNNs). Expand
Empirical mode decomposition, an adaptive approach for interpreting shaft vibratory signals of large rotating machinery
Abstract The Fourier transform (FT) has been the most popular method for analyzing large rotating machine shaft vibration problems, but it assumes that these vibration signals are linear andExpand
A review on empirical mode decomposition in fault diagnosis of rotating machinery
Abstract Rotating machinery covers a broad range of mechanical equipment and plays a significant role in industrial applications. It generally operates under tough working environment and isExpand