Rotary Die Scoring Stress Wave Signal Acquisition
Real-time acoustic stress wave monitoring on rotary die scoring lines detects paperboard matrix micro-cracking instantaneously, reducing convert scrap by up to 80 percent.
Wavelet Analysis
Signal Decomposition
Analyzing complex, non-stationary signals from paper machine sensors requires a mathematical tool that can capture both time and frequency information simultaneously. Applying wavelet analysis allows process engineers to locate localized defects, such as a single flat spot on a roller or a brief tension spike, in the data stream. This technique decomposes the sensor signal into different scales to reveal patterns that are hidden in standard frequency plots.
Defect Identification
Fourier analysis assumes that signals are composed of infinite sine waves, which makes it poorly suited for detecting sudden, short-lived events in the paper machine. Transient events, such as web slips or blade streaks on a coater, are easily overlooked when averaged across a long measurement period. Decomposing the signals using small, localized wave shapes allows the software to pin down the exact millisecond a defect occurs.
This precise timing lets engineers match the electrical glitch with a mechanical cause on the production line.
Process Application
Continuous monitoring systems use these mathematical transforms to analyze data from paper machine scanners in real time. The resulting maps of paper thickness or moisture let the controller adjust the actuators across the headbox quickly. This instant correction reduces variance in the paperboard before it is wound onto the shipping roll.