Factory Machine Diagnostics Using Time-Linked History and Live Data
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Solution Overview
Problem
Conventional diagnostic service systems struggle to comprehensively monitor and efficiently diagnose maintenance needs for machines with diverse specifications, as they separate past and current data, making it difficult to provide effective preventative maintenance and quick fault resolution.
Innovation Solution
A diagnostic service system that integrates a factory monitoring system to acquire and store data from machines, including time information, with a data acquisition unit and storage management unit, allowing for comprehensive analysis of past history and current state data to estimate abnormality occurrence and provide preventative maintenance information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If past history data and current state data are treated separately, then data management becomes simpler, but comprehensive diagnosis and preventative maintenance capability deteriorates
Solution Approach 1:
The patent merges past alarm history data and current alarm data into a unified diagnostic framework. The diagnosis system integrates historical alarm records with real-time machine state data, enabling comprehensive analysis that combines temporal evolution with current status to improve preventative maintenance capability while maintaining manageable system complexity through standardized data structures.
Solution Approach 2:
The patent adds a temporal dimension to data analysis by incorporating time-stamped alarm history alongside current state data. This dimensional expansion allows the system to analyze alarm patterns over time while maintaining the current state view, enabling more reliable preventative maintenance decisions without significantly increasing management complexity.
2Measurement precision
If comprehensive analysis of all machine data is performed, then diagnostic accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the diagnostic process into distinct stages: data acquisition from multiple sources, alarm history retrieval, current state monitoring, and integrated analysis. This segmentation allows parallel processing of different data types and enables the system to maintain high diagnostic accuracy while reducing overall processing time through efficient task distribution.
Solution Approach 2:
The patent performs preliminary actions by pre-collecting and organizing alarm history data and machine parameter thresholds before actual diagnostic needs arise. This advance preparation ensures that when comprehensive analysis is required, the system can quickly access pre-processed information, maintaining high diagnostic accuracy while minimizing real-time processing time.
3Adaptability or versatility
If the system monitors and analyzes data from multiple machines with different specifications, then comprehensive factory monitoring capability improves, but system complexity increases
Solution Approach 1:
The patent implements a universal diagnostic framework that can handle multiple machine types with different specifications through standardized data interfaces and configurable parameter sets. The system uses a common alarm data structure and diagnostic algorithm framework that adapts to various machine specifications, enabling comprehensive multi-machine monitoring while keeping system complexity manageable through standardization.
Data Source
AI summary
To provide a diagnostic service system and diagnostic method using a network. A factory monitoring system (100) comprises a factory monitoring system (100) which includes: a data acquisition unit (1011) that acquires data related to at least one machine, including time information; and a stored data management unit (1012) that stores data related to each machine acquired by the data acquisition unit in a storage unit (1002) together with identification information of each machine, wherein, based on past history data related to the machine and current data related to the machine, the diagnostic service system (1) predicts a possibility of abnormality occurrence in the machine, and provides preventative maintenance information related to the machine.