Production Machine Evaluation Device for Predictive Maintenance
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Solution Overview
Problem
Existing production machines face challenges in predicting when and which components need replacement or servicing, relying heavily on technician experience due to limited and indirect raw data quality, often leading to premature replacements and unpredictable shutdowns.
Innovation Solution
An evaluation device that receives actual component states from sensors, compares them with pre-defined combinations, selects relevant data, and stores information for further analysis, enabling more accurate failure predictions and diagnosis by determining preconditions, pre-evaluating trends, and comparing results with reference data to provide actionable insights to operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If component replacements are carried out regularly based on technician experience, then unpredictable shutdown times are avoided, but component replacements are performed prematurely and servicing intervals are shorter than technically necessary
Solution Approach 1:
The evaluation device performs preliminary evaluation of component states by continuously monitoring operating parameters and comparing them with pre-defined state combinations. This allows the system to predict component failures before they actually occur, enabling maintenance to be scheduled at the optimal time rather than prematurely or reactively.
Solution Approach 2:
The system establishes a feedback loop where sensor data from the production machine is continuously fed to the evaluation device, which processes the data and provides feedback about component health status. This feedback mechanism enables dynamic adjustment of maintenance schedules based on actual component conditions rather than fixed intervals.
2Loss of information
If raw data evaluation is performed using available sensor data, then component state information is obtained, but the evaluation quality is insufficient due to limited discriminatory information and indirect suitability of raw data
Solution Approach 1:
The evaluation device acts as an intermediary between the raw sensor data and the component state assessment. It processes and interprets the raw data by comparing it with pre-defined state combinations and stored reference data, transforming indirect and limited sensor information into reliable component health evaluations.
Solution Approach 2:
The system performs preliminary evaluation by pre-processing sensor data and comparing it with pre-defined state combinations before final assessment. This preliminary processing enhances the quality of evaluation by preparing the data in advance and identifying relevant patterns that indicate component wear or failure conditions.
3Loss of information
If the evaluation device stores all detected states and information, then complete data availability is achieved, but data processing complexity and storage requirements increase
Solution Approach 1:
The evaluation device extracts and stores only the relevant information from the continuous stream of sensor data. By comparing detected states with pre-defined state combinations, the system identifies and stores only those data points that are significant for component health assessment, rather than storing all raw data indiscriminately.
Solution Approach 2:
The system performs preliminary filtering and selection of data before storage by comparing detected states with pre-defined state combinations. This preliminary action ensures that only relevant and meaningful data are stored and processed further, reducing overall data complexity while maintaining information availability.
Data Source
AI summary
During the production of a product by means of a production machine, an evaluation device receives actual states of components of the production machine, detected by sensors at detection times, and/or operating states of a control device of the production machine at the defined detection times. The evaluation device compares the states of each detection time transmitted thereto with pre-defined state combinations and thus determines fulfilled state combinations. For each fulfilled state combination, the evaluation device selects relevant states from the states of the respective detection time transmitted thereto, and corresponding information and the respective detection time are stored with the relevant states such that they are available for other evaluations.