Synthetic Fibre Machinery Log Analysis for Predictive Monitoring
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Solution Overview
Problem
Current methods for monitoring machine systems in synthetic fiber production and treatment are overwhelmed by the sheer volume of system messages, including warnings and errors, making it difficult for operators to manage and utilize all information effectively for predictive control.
Innovation Solution
A method and device that continuously record and analyze system messages using a data recorder, log memory, and data analysis unit with machine learning algorithms to identify sequences and anomalies, reducing information complexity and enabling operators to interpret and respond to potential events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all system messages are continuously recorded and analyzed, then the comprehensiveness of monitoring is improved, but the complexity of data processing increases
Solution Approach 1:
The patent segments the overwhelming stream of system messages into meaningful sequences by identifying patterns and groupings. The data analysis unit divides messages into sequences based on temporal relationships and contextual connections, making the data manageable and analyzable without losing comprehensiveness.
Solution Approach 2:
The patent introduces an intermediary data analysis unit that acts as a mediator between the raw system messages and the control decisions. This intermediary layer processes, sequences, and interprets messages using machine learning algorithms, reducing the complexity burden on both the recording system and the final control actions.
2Extent of automation
If machine learning algorithms are used to analyze message sequences, then predictive control capability is improved, but the computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and sequencing system messages before applying complex machine learning algorithms. The data analysis unit prepares the data by organizing messages into sequences and identifying patterns in advance, which reduces the computational burden during actual predictive analysis and control decisions.
3Ease of operation
If system messages are reduced to human-interpretable information, then ease of operation is improved, but information loss increases
Solution Approach 1:
The patent applies local quality by providing different levels of information processing for different users or purposes. The system maintains detailed message sequences for comprehensive analysis while simultaneously generating simplified, human-interpretable summaries for operators. This allows detailed information to be preserved where needed while providing ease of operation where required.
Data Source
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AI summary
The invention relates to a method and device for monitoring machinery for the production or treatment of synthetic fibres. In said method, system messages are continuously generated and captured by machinery components and control components in the machinery. The system messages are stored in a log memory as log data, then preprocessed and read and analysed by algorithms on the basis of statistical methods and machine learning methods for identifying frequently occurring sequences of system messages. The objective, inter alia, is the identification of frequently occurring patterns in sequences of system messages. This permits the compression of data, the detection of anomalies and forecasting of incidents. The results of the analysis can be better interpreted and continuously improved and made more accurate through combination with the expert knowledge of the operators. To this end, a data recorder for the continuous acquisition of the system messages, a protocol memory linked to the data recorder for storing the system messages in log data and a data analysis unit are assigned to the machine control system. The data analysis unit is linked to the log memory and has at least one data analysis program with algorithms based on statistical methods and machine learning methods.