Equipment Predictive Maintenance via Energy Peak Distribution
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Solution Overview
Problem
Current predictive maintenance methods for equipment in large-scale production facilities are inadequate in detecting abnormal symptoms in a timely manner, leading to significant monetary losses due to equipment failures and downtime.
Innovation Solution
A method using a distribution chart to extract peak values based on energy changes during normal operations, construct distribution charts, and set detection sections with low probability and high risk, allowing for predictive detection of equipment abnormalities and timely maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to track equipment status, then the monitoring system is simple and easy to implement, but the detection of abnormal symptoms is delayed and不够 precise
Solution Approach 1:
The monitoring system is segmented into multiple functional modules: energy consumption data acquisition module, peak value extraction module, distribution chart construction module, detection section identification module, and abnormal symptom detection module. Each module performs a specific function, allowing the complex detection task to be divided into manageable steps that improve precision without overwhelming system complexity
Solution Approach 2:
The patent transitions from traditional time-series monitoring to a distribution-based approach by constructing histograms of energy consumption values. This dimensional transformation allows the system to detect abnormalities based on distribution patterns rather than simple threshold comparisons, significantly improving detection precision
2Loss of time
If equipment is monitored continuously to detect abnormalities early, then the detection timeliness is improved, but the energy consumption and computational resources increase
Solution Approach 1:
The system extracts only the peak values from energy consumption data rather than processing the entire continuous signal. By focusing on peak values which represent critical operational states, the system achieves timely abnormal detection while significantly reducing computational load and energy consumption compared to continuous full-signal processing
Solution Approach 2:
The patent applies partial action by monitoring only specific detection sections (low probability, high risk regions) of the distribution chart rather than the entire distribution. This selective monitoring approach maintains detection responsiveness while minimizing computational resources and energy consumption
3Reliability
If maintenance is performed frequently to prevent equipment failure, then the equipment reliability is improved, but the productivity and operational time are reduced
Solution Approach 1:
The system performs preliminary detection by identifying detection sections in the distribution chart that indicate potential abnormalities before they lead to actual equipment failure. By detecting early signs of problems through distribution pattern changes, maintenance can be scheduled proactively rather than reactively, maintaining high reliability while minimizing unplanned downtime and productivity loss
Data Source
AI summary
A method for predictive maintenance of equipment via a distribution chart is disclosed. Peak values are extracted based on a change in an amount of energy required for performing a work process by the equipment in a normal state, a distribution chart of the extracted peak values is constructed, and an abnormal symptom of the equipment is predictively detected in advance based on a change in distribution probability of a detection section having a low distribution probability and somewhat high risk in the constructed distribution chart thereof such that maintenance and replacement of the equipment are induced to be carried out at an appropriate time. Thus, an enormous monetary loss caused by a failure in the equipment may be prevented in advance.