AI Diagnostic System for Industrial Root Cause Analysis
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Solution Overview
Problem
Organizations face challenges in efficiently diagnosing and addressing root causes of performance issues across various operational areas due to the complexity and scale of industrial operations, leading to compounded problems that negatively impact overall operations.
Innovation Solution
A system that utilizes a multilayered data model and industry language conversation services to provide real-time guidance by mapping entity information to relevant areas of operation, identifying potential problems, and generating scores to gauge their significance, comparing them to benchmarks, and recommending digital transformation solutions through AI, machine learning, and blockchain applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual diagnosis methods are used to identify performance issues, then organizations can understand problems in detail, but the process is too slow and cannot keep pace with real-time operational needs
Solution Approach 1:
The patent replaces manual mechanical diagnosis processes with automated AI/ML-based systems. The system automatically collects operational data, applies machine learning models to identify root causes, and generates diagnostic reports without human intervention, thereby maintaining high diagnostic accuracy while dramatically reducing diagnosis time from days to minutes or seconds.
Solution Approach 2:
The patent introduces an intermediary AI/ML system that acts as a mediator between operational data and human decision-makers. This intermediary automatically processes raw data, identifies patterns and root causes, and presents actionable insights, eliminating the need for manual analysis while preserving diagnostic quality through algorithmic precision.
2Reliability
If comprehensive data collection across all operational areas is performed, then complete problem identification is achieved, but system complexity and resource requirements increase significantly
Solution Approach 1:
The patent extracts and isolates only the most critical data elements and operational parameters needed for effective diagnosis. Rather than processing all available data, the system identifies and focuses on key performance indicators and root cause indicators, reducing system complexity while maintaining complete problem identification capability through targeted data selection.
Solution Approach 2:
The patent segments the complex diagnostic system into modular components: data collection modules, processing modules, analysis modules, and reporting modules. Each module handles specific functions independently, reducing overall system complexity while maintaining comprehensive problem identification through coordinated operation of specialized segments.
3Measurement precision
If detailed analysis of all operational areas is conducted, then root causes are accurately identified, but the process becomes too slow for timely problem resolution
Solution Approach 1:
The patent performs preliminary actions by pre-configuring AI/ML models with historical data and known failure patterns. The system pre-processes data pipelines and establishes diagnostic frameworks in advance, enabling rapid root cause identification when issues occur without requiring detailed real-time analysis of all operational areas, thus maintaining accuracy while improving resolution speed.
Solution Approach 2:
The patent replaces slow manual detailed analysis with automated AI/ML systems that can rapidly process and analyze operational data. The machine learning models automatically identify root causes by comparing current operational patterns against trained historical data, achieving both high accuracy and fast resolution speeds that manual processes cannot match.
Data Source
AI summary
Techniques are described for diagnosing, characterizing, and addressing problems across a variety of industry sectors. In some embodiments, a system receives information about a company or other entity and maps the information to different areas of operation that are relevant to the entity. The system may identify potential problems and root causes that degrade operations relevant to the entity. The system may further use a model to gauge how significant various sector-specific and/or sector-generic problems are for the entity. Additionally or alternatively, the system may compare the scores to benchmark models to determine how an entity is performing and progressing relative to other entities in the same sector and/or across different sectors. The techniques allow users to quickly assess the performance of an entity across several different areas of operation, isolate underperforming areas, identify the root causes, and deploy technical solutions to address underlying problems.