Fibrous Web Break Detection Using Cross-Section Anomaly Analysis
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Solution Overview
Problem
Web breaks in fibrous material webs during production lead to costly shutdowns of industrial plants, particularly in the dry section, and existing methods only inspect the second part after a break occurs, failing to identify underlying causes in the first part.
Innovation Solution
Implement a method using sensors and encoders to collect and analyze time-series data of first and second parameters from both the wet and dry sections, employing artificial intelligence to detect anomalies and adapt control settings to prevent web breaks by addressing malfunctions in either part.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If web breaks are detected only after occurrence in the dry section, then the detection method is simple, but shutdowns occur and productivity is lost
Solution Approach 1:
The system performs preliminary analysis of operational parameters from both the wet part and dry section to identify anomalies that precede web breaks. By analyzing parameters such as web tension, drive motor current, and operational patterns before a break occurs, the system enables preventive action to avoid shutdowns and maintain productivity.
Solution Approach 2:
The system implements continuous feedback by monitoring operational parameters in real-time from both sections of the paper machine. When anomalies are detected in the wet part or dry section, the system provides feedback signals that enable corrective actions to prevent web breaks, thereby improving reliability without sacrificing productivity.
2Loss of information
If only second part parameters are inspected after web break, then the inspection process is simple, but the root cause in the first part cannot be identified
Solution Approach 1:
The inspection system is segmented into two independent but coordinated analysis modules: one for the wet part and one for the dry section. Each module collects and analyzes relevant parameters independently, allowing comprehensive cause identification without requiring a single complex inspection system. This segmentation enables complete loss of information prevention while managing device complexity through modular design.
Solution Approach 2:
The system merges the analysis of parameters from both the wet part and dry section into a unified diagnostic framework. By combining data from both sections and analyzing their interrelationships, the system can identify root causes that may originate in the wet part but manifest in the dry section, thereby achieving complete cause identification without excessive complexity.
3Reliability
If malfunctions in the wet part are not monitored, then the monitoring system is simpler, but web breaks cannot be prevented
Solution Approach 1:
The system applies preliminary anti-action by detecting anomalies in the wet part parameters (such as web tension variations, drive motor anomalies, or operational irregularities) before they lead to web breaks. By identifying these preliminary signs of malfunction, the system can take preventive measures to avoid web breaks, improving reliability while adding only necessary monitoring complexity.
Data Source
AI summary
A method for monitoring an industrial plant. In a first part of the industrial plant, first parameters are provided. The industrial plant is used to produce and/or process a fibrous material web. Second parameters are provided in a second part. The parameters are stored, preferentially as time series. In the case of a web break in the second part, the second parameters are first analyzed for a second anomaly. If no second anomaly can be detected, the first parameters are analyzed for a first anomaly. During the analysis, the parameters which were stored in a time range before the web break are preferably examined. If a first or second anomaly is detected, these, and optionally measures to avoid such web breaks, are displayed to the user. Optionally, the first parameters and/or the second parameters can be set so as to avoid future web breaks.