Reverse Bump Test for CD Controller Alignment
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Solution Overview
Problem
Current sheetmaking systems face challenges in maintaining cross-directional alignment due to difficulties in determining which measurement zones are associated with which actuator zones, exacerbated by uneven paper shrinkage and the limitations of conventional alignment methods like dye tests and bump tests, especially in closed-loop control systems.
Innovation Solution
A method that involves inserting a step signal into the measurement profile while the system remains in closed-loop control, recording the actuator responses, and refining a model to determine alignment information, allowing for continuous alignment monitoring without disrupting the production process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional alignment methods like dye tests and bump tests are used, then alignment information can be obtained, but the production process is disrupted and the systems are difficult to implement in closed-loop control
Solution Approach 1:
The system uses its own operational data (measurement profiles and actuator positions) to perform alignment identification, eliminating the need for external tests. The closed-loop control system continuously monitors and processes its own performance data to self-diagnose alignment status without requiring production stoppage or special test procedures.
Solution Approach 2:
The alignment identification process operates continuously during normal production without interrupting the manufacturing process. The system continuously collects measurement data, processes it through the identification algorithm, and updates alignment information in real-time, maintaining both production continuity and measurement capability.
2Measurement precision
If open-loop bump tests are performed to identify alignment, then alignment information can be extracted, but the control system stability is compromised and implementation becomes complex
Solution Approach 1:
The system utilizes feedback from the closed-loop control process itself, where measurement profiles and actuator positions are continuously monitored and fed back into the alignment identification algorithm. This feedback mechanism allows the system to extract alignment information from normal operational data without requiring external perturbations that could destabilize the control system.
Solution Approach 2:
The invention introduces an intermediary alignment identification algorithm that processes the relationship between measurement profiles and actuator positions. This intermediary computational layer extracts alignment information indirectly from operational data, avoiding the need for direct physical tests that could interfere with control system stability.
3Measurement precision
If traditional alignment determination methods are used, then alignment can be assessed, but the process is destructive and cannot be performed during production
Solution Approach 1:
The invention replaces physical/mechanical alignment test methods (like dye tests that require physical marking or bump tests that require mechanical actuator movement) with a computational approach. The system uses signal processing and mathematical algorithms to analyze operational data and determine alignment, eliminating the need for physical interventions that could damage or disrupt the production process.
Solution Approach 2:
The system creates a computational model or representation of the alignment state by processing measurement profile data and actuator position data. Instead of physically testing alignment, the system generates a digital copy or model of the alignment condition through data analysis, allowing non-invasive assessment during normal production operations.
Data Source
AI summary
A reverse bump test, for identifying the alignment of a sheetmaking system while the system remains in closed-loop control, includes the following steps: (a) leaving the control system in closed-loop, (b) artificially inserting a step signal on top of the measurement (or setpoint) profile from the scanner, (c) recording the data as the control system moves the actuators to remove the perceived disturbance (or setpoint change), and (d) refining or developing a model from the artificial measurement disturbance (or setpoint change) to the actuator profile. The technique supplies the probing/perturbation signal to the scanner measurement, which is equivalent to supplying the probing/perturbation signal to the setpoint target) rather than inserting bumps via the actuator set points as has been practiced traditionally.