Fault Code Prediction System for Manufacturing Maintenance
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Solution Overview
Problem
In large-scale manufacturing plants, the overwhelming number of fault codes generated by machines leads to inefficient maintenance and repair processes, as existing technologies lack effective methods for predicting and managing machine failures dynamically, resulting in resource overload and production disruptions.
Innovation Solution
A system and method for processing historical fault code data to generate reports, alerts, and predictions by filtering, classifying, and analyzing the data using a plurality of analyzers, which improves maintenance efficiency and prevents production line disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time sensing data from machine key parts or subsystems is processed using many sensors, then machine performance estimation is improved, but data traffic and system complexity increase significantly
Solution Approach 1:
The patent extracts only the necessary fault code data from machine controllers rather than processing all real-time sensing data from multiple sensors. By taking out only the relevant fault information and processing it offline, the system achieves performance estimation without the complexity of real-time multi-sensor data processing
Solution Approach 2:
The patent replaces the mechanical/sensor-based real-time monitoring system with an information-processing system that uses fault code data. Instead of using many physical sensors to monitor machine parts, the system substitutes this with electronic fault code collection and offline analysis, reducing physical system complexity
2Reliability
If historical fault code data is collected and stored for analysis, then prediction accuracy is improved, but data storage requirements and processing time increase
Solution Approach 1:
The patent performs preliminary filtering and classification of fault code data as it is collected, organizing it into structured formats with defined schemas. This preliminary action reduces the complexity of subsequent analysis and enables faster processing when predictions are needed, as the data is already organized and ready for analysis
Solution Approach 2:
The patent applies selective filtering to capture only the most relevant fault codes for prediction, rather than processing all possible fault code data. By focusing on partial but critical data subsets, the system achieves adequate prediction accuracy while reducing processing time and computational requirements
3Ease of operation
If fault codes are processed and analyzed in real-time, then immediate alerts are generated, but maintenance resource overload occurs due to the volume of fault codes
Solution Approach 1:
The patent segments the large volume of fault codes into categorized groups based on machine components, fault types, and severity levels. By dividing the fault code stream into manageable segments with defined classifications, the system enables targeted analysis and alerting, preventing maintenance resource overload while maintaining operational efficiency
Solution Approach 2:
The patent applies different processing and analysis methods to different segments of fault code data based on their specific characteristics and importance. Critical fault codes receive immediate attention with detailed analysis, while less critical codes are processed differently or aggregated, optimizing maintenance resource allocation across the entire system
Data Source
AI summary
Disclosed herein are a system, method and apparatus for reporting, making alerts and predicting fault codes generated by machines in a line. Historical fault code data is received and filtered according to particular criteria to generate filtered fault code data. Classification of the filtered fault code data into physical groups and into logical groups is followed by sorting the groups to produce fault trend data. Processing the fault trend data with a plurality of analyzers generates output including reports, alerts, and predictions of future fault code occurrences.