Causal Map Fault Traversal for Manufacturing Root Cause Identification
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Solution Overview
Problem
Root cause identification in manufacturing and process industries is challenging due to complex multivariate data and high dependency on manual inputs and subject matter expertise.
Innovation Solution
A method and system for root cause identification using causal maps generated from multivariate time series data, employing a Fault Traversal and Root Cause Identification (FTRCI) technique to automate the identification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used for root cause identification, then subject matter expertise can be applied, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that uses machine learning models and algorithms to perform root cause identification, thereby reducing diagnosis time while maintaining or improving accuracy through consistent automated processing
Solution Approach 2:
The patent introduces intermediate processing layers including data pre-processing modules, feature extraction components, and machine learning models that act as mediators between raw sensor data and final root cause identification, enabling automated analysis without direct human intervention in the diagnostic process
2Measurement precision
If complex multivariate data is analyzed manually, then detailed fault signatures can be identified, but the complexity increases the difficulty of manual analysis
Solution Approach 1:
The patent employs automated computational systems with machine learning algorithms to handle complex multivariate data analysis, replacing manual analysis capabilities with automated processing that can efficiently manage high-dimensional data relationships and identify fault signatures without being constrained by human cognitive limitations
Solution Approach 2:
The patent segments the complex data analysis process into distinct modular components including data acquisition, pre-processing, feature extraction, fault detection, and root cause identification stages, with each module handling specific aspects of the analysis to reduce overall complexity while maintaining comprehensive analysis capabilities
3Productivity
If existing root cause identification techniques are used, then fault analysis can be performed, but dependency on manual inputs and expert knowledge remains high
Solution Approach 1:
The patent replaces manual expert knowledge input with automated machine learning models that learn from historical data, substituting human expertise with automated systems that can process and analyze data consistently without requiring continuous human intervention or specialized knowledge input
Solution Approach 2:
The patent implements self-service capabilities where the system automatically performs data collection, preprocessing, analysis, and root cause identification without requiring manual inputs or expert intervention, with the machine learning models autonomously learning from data and improving their performance over time through continuous operation
Data Source
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AI summary
The disclosure is a method and a system for root cause identification (RCI) of faults in manufacturing and process industries. With complex interrelated multivariate data in manufacturing and process industries, the process of root RCI of faults is challenging. Further, the existing techniques for RCI have significant dependency on manual inputs and subject matter knowledge/experts. The disclosure is method and a system for root cause identification of a fault based on causal maps. The root cause of fault is identified in several steps including: generation of casual maps using data received from a manufacturing and process industry and root cause identification from the causal maps based on a Fault Traversal and Root Cause Identification (FTRCI) technique. The FTRCI identifies root cause from the causal map by identifying a fault traversal pathway from a leaf node in the causal map, wherein the fault traversal pathway is identified for even cyclic paths.