Multilevel Pattern Monitoring for Faster Industrial Fault Detection
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Solution Overview
Problem
Conventional parameter control methods in industrial processes fail to guarantee product quality and meet production requirements due to the multi-factor characteristics of process parameter variables, leading to inconsistent product quality and delayed issue detection, resulting in economic and energy losses.
Innovation Solution
A multilevel pattern monitoring method that divides industrial processes into levels, selects key performance indices, acquires and analyzes relevant data using clustering analysis, and monitors real-time data to identify patterns, calculate economic indices, and provide fault predictions and alarms, utilizing techniques like principal component analysis and Mahalanobis distance calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional parameter control method is used to control process parameters within certain ranges, then individual parameter control is achieved, but product quality consistency cannot be guaranteed
Solution Approach 1:
The patent segments the complex multivariable control problem into multiple hierarchical levels (process level, device level, control level). Each level has its own pattern monitoring model and key performance indicators, allowing quality consistency to be achieved through coordinated control at different levels rather than attempting to control all parameters simultaneously at a single level.
Solution Approach 2:
The patent transforms the control approach from traditional single-dimension parameter control to multidimensional pattern control. By using principal component analysis and pattern recognition, the system monitors the overall pattern of multiple parameters together rather than individually, enabling quality consistency through holistic pattern management across multiple dimensions.
2Reliability
If comprehensive process monitoring is implemented to ensure quality, then product quality can be maintained, but the time to detect and respond to issues is delayed
Solution Approach 1:
The patent establishes pattern monitoring models and identifies key performance indicators in advance at each hierarchical level. By pre-defining normal and abnormal patterns along with their corresponding responses, the system can immediately detect and respond to deviations without delayed analysis, achieving both quality assurance and rapid response time.
Solution Approach 2:
The patent implements real-time feedback mechanisms at each level where pattern deviations are immediately detected and trigger corresponding control actions. The feedback loop operates continuously across all levels, allowing the system to maintain quality while rapidly responding to issues as they emerge rather than detecting them after delays.
3Measurement precision
If detailed pattern analysis is performed to identify process states, then accurate fault detection is achieved, but computational complexity increases
Solution Approach 1:
The patent extracts the essential features from complex process data by identifying and monitoring only the key performance indicators at each hierarchical level. Through pattern recognition and principal component analysis, the system extracts the most critical patterns that indicate process states, achieving accurate fault detection without analyzing every detail of the complex data set.
Data Source
AI summary
The present invention discloses a multilevel pattern monitoring method for a process industry process and belongs to the fields of industrial production and processing. The multilevel pattern monitoring method comprises the steps: dividing an industry process into a plurality of levels from the view of patterns, selecting a different key performance index for each level, acquiring operating data relevant to the key performance index, identifying the pattern of each level, and proposing a pattern monitoring method for each level based on a data driven method to realize pattern monitoring in the industry process. The effect of rapidly finding a fault is achieved by monitoring real-time data according to the pattern, identified in a clustering plane, of each level, the effect of removing the fault is achieved by the selected N variables generating the greatest influence to the current pattern in combination with an expert system and an inference engine, meanwhile, the energy consumption of the process is reduced, the operating cost is optimized, and the competitiveness of a product is improved.