AI Root Cause Analysis Engine for Real-Time Equipment Faults
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Solution Overview
Problem
Existing root cause analysis systems are complex, inaccessible to domain experts lacking programming skills, inefficient in real-time insights, and lack adaptability across diverse domains, necessitating extensive training and resource-intensive processes.
Innovation Solution
An AI-based system using a root cause analysis engine that identifies potential faults and causes, generates indicators and predictions, and provides responses through knowledge graphs, leveraging generative AI for adaptability and real-time insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional root cause analysis tools are used, then analysis depth and precision can be achieved, but the system complexity and technical proficiency requirements increase significantly
Solution Approach 1:
The patent introduces an AI-based intermediary layer that mediates between the complex analysis engine and the end user. This intermediary automatically processes streaming data, generates root cause hypotheses, and presents results in an accessible format, eliminating the need for users to directly interact with complex analytical tools while maintaining high precision analysis capabilities
2Reliability
If traditional root cause analysis tools are used, then comprehensive analysis can be performed, but accessibility to domain experts without programming skills deteriorates
Solution Approach 1:
The system implements self-service functionality where the AI automatically performs data collection, processing, analysis, and report generation without requiring user intervention in technical processes. Domain experts can simply input their domain knowledge and receive comprehensive analysis results, making the system as accessible as any web application while maintaining thorough analytical capabilities
3Productivity
If real-time analysis of streaming data is implemented, then operational efficiency improves, but the requirement for timely insights and reduced downtime increases system demands
Solution Approach 1:
The system performs preliminary actions by continuously analyzing streaming data in real-time to identify potential root causes before they manifest as actual equipment failures. The AI engine proactively generates hypotheses and alerts users to emerging issues, enabling preventive maintenance actions that avoid downtime entirely rather than merely reducing it
4Adaptability or versatility
If generative AI is integrated for automatic rule and algorithm generation, then adaptability to diverse domains improves, but the complexity of managing multiple domains increases
Solution Approach 1:
The patent implements a universal AI-based root cause analysis engine that can adapt to multiple domains through a single unified architecture. The system uses generative AI to automatically generate domain-specific rules and algorithms from streaming data patterns, allowing the same core system to serve diverse applications (manufacturing, healthcare, energy, etc.) without requiring separate specialized tools for each domain
Data Source
AI summary
An AI-based system and method for determining potential issues occurred in equipments by analyzing data using a root cause analysis engine, is disclosed. The AI-based method comprises: (a) obtaining the data associated with equipments from databases; (b) identifying potential faults in the equipments based on the data and historical knowledges of the equipments, stored in the databases; (c) identifying potential causes for the potential faults occurred in the equipments; (d) generating indicators based on the potential faults and the potential causes for the potential faults, using an AI model; (e) generating predictions on outcomes and future occurrences in the equipments based on a correlation between the indicators and potential causes, using the AI model; (f) generating responses based on the predictions, using the AI model; and (h) providing the potential faults, potential causes, indicators, predictions, and responses, as an output in form of knowledge graphs, to the users.