Container Treatment Predictive Maintenance Using Quality Classes
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Solution Overview
Problem
Current maintenance practices in beverage bottling plants require expert knowledge and lead to unplanned downtimes, resource wastage, and increased costs due to reactive maintenance, where fault-free or usable components are replaced unnecessarily.
Innovation Solution
A predictive maintenance system that receives and analyzes process data from container treatment systems to classify treatment processes, derive component wear states, and provide recommendations for action, using an electronic computing device in communication with system controllers, enabling proactive maintenance and optimizing spare part procurement through digital services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If reactive maintenance is performed by replacing components after malfunction, then operational simplicity is maintained, but unplanned downtime increases and reliability decreases
Solution Approach 1:
The system performs preliminary analysis of process data to predict component wear and potential failures before they occur. By analyzing trends in process parameters and quality classes, the system identifies components that are likely to fail soon, allowing maintenance to be scheduled in advance rather than reacting to actual failures, thus reducing unplanned downtime while maintaining operational simplicity
Solution Approach 2:
The system continuously monitors process data and provides feedback about component wear conditions and predicted failures. This feedback loop enables operators to see the actual condition of components based on objective data analysis rather than subjective assessment, allowing them to make informed decisions about maintenance timing to optimize both reliability and operational simplicity
2Reliability
If systematic preventative maintenance is performed by replacing components before failure, then reliability is improved, but resource consumption increases and costs rise
Solution Approach 1:
The system applies local quality by focusing maintenance attention only on specific components that show signs of wear or predicted failure based on process data analysis. Rather than performing blanket preventative maintenance on all components, the system identifies and targets only those components that actually need attention, reducing unnecessary resource consumption while maintaining reliability for critical components
Solution Approach 2:
The system uses parameter changes in process data to determine maintenance needs. By monitoring changes in process parameters and quality classes over time, the system identifies when components are degrading and maintenance is needed, allowing for condition-based maintenance that replaces components based on actual wear indicators rather than arbitrary time intervals, thus reducing resource consumption while maintaining reliability
3Measurement precision
If manual analysis of process data is performed, then expertise utilization is optimized, but operator workload increases and time consumption rises
Solution Approach 1:
The system replaces manual mechanical analysis with automated electronic data processing. A computer automatically analyzes process data, assigns quality classes, and identifies wear conditions without human intervention, eliminating the time-consuming manual analysis while maintaining or improving measurement precision through consistent algorithmic evaluation of process parameters
Solution Approach 2:
The system performs self-service by automatically analyzing its own process data to identify maintenance needs. The computerized system independently evaluates process parameters, assigns quality classes, and generates maintenance recommendations without requiring operator expertise or manual intervention, reducing both time consumption and workload while maintaining high measurement precision through systematic data evaluation
Data Source
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AI summary
Device (400), system and method for predictive maintenance of a container treatment plant (1), preferably a beverage filling plant, wherein the device (400) is configured to receive process data of at least one treatment process of one or more containers (100) carried out by the container treatment plant (1); to assign the treatment process to at least one of several quality classes based on the process data; and to derive a wear condition of one or more components of the container treatment plant (1) from an analysis of the quality classes.