Process Ontology Root Cause Analysis for Real-Time Industrial Diagnosis
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Solution Overview
Problem
Conventional root cause analysis in industrial processes is manual, time-consuming, and subjective, leading to inaccurate diagnosis and delays due to the reliance on human operators and document-based FMEA knowledge bases, resulting in productivity losses and quality issues.
Innovation Solution
A processor-implemented method for real-time root cause analysis that generates a process ontology using meta models, transforms root cause knowledge into machine instructions, and identifies root causes and interdependencies through detection models, enabling the creation of a root cause graph for efficient diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual root cause analysis using document-based FMEA is used, then human operators can identify possible root causes, but the process becomes time-consuming and subjective leading to inaccurate diagnosis and delays
Solution Approach 1:
The patent replaces the manual mechanical process of human operators reading and analyzing FMEA documents with an automated computer-based system. The system uses processors to execute machine instructions that automatically analyze sensor data, map it to the process ontology, and identify root causes, thereby eliminating the time-consuming and subjective nature of manual analysis while improving diagnosis accuracy through consistent algorithmic execution.
Solution Approach 2:
The patent creates a digital copy of the FMEA knowledge base in the form of a process ontology that can be automatically processed by computers. This ontology serves as a structured representation of failure modes, detections, and root causes that can be queried and analyzed by the automated system, replacing the need for human operators to manually interpret physical or digital documents.
2Reliability
If manual root cause analysis is performed by human operators, then corrective actions can be identified, but inconsistency in information utilization leads to unreliable diagnosis
Solution Approach 1:
The patent replaces human operators with an automated computer-based system that consistently applies the same analysis logic and algorithms. This substitution eliminates the variability and inconsistency inherent in manual analysis, ensuring that the same sensor data and FMEA knowledge base always produce the same root cause identification results, thereby improving reliability.
Solution Approach 2:
The patent transforms the FMEA knowledge base from a document format into a structured process ontology with defined parameters, relationships, and machine-executable instructions. This parameterization allows the system to consistently interpret sensor data and identify root causes without the subjectivity and inconsistency of manual analysis, while the automated execution simplifies the operational complexity for users.
3Loss of information
If FMEA documents are used for root cause analysis, then comprehensive failure information is available, but searching through large amounts of information is time-consuming
Solution Approach 1:
The patent creates a digital copy of the FMEA knowledge base in the form of a process ontology that can be automatically queried and processed by computers. This structured representation maintains all the comprehensive failure information from the original FMEA documents but organizes it in a machine-executable format that enables rapid automated analysis without manual searching.
Solution Approach 2:
The patent replaces the manual searching process with automated computer-based analysis. The system automatically maps sensor data to the process ontology, queries the relevant failure modes and root causes, and presents the analysis results, thereby eliminating the time-consuming manual search through large amounts of information while preserving information completeness.
4Productivity
If real-time root cause analysis is implemented using automated systems, then analysis speed and accuracy improve, but system complexity increases
Solution Approach 1:
The patent creates a universal process ontology that can be applied across different industrial processes and failure scenarios. This ontology serves multiple functions: it stores FMEA knowledge, maps sensor data, identifies root causes, and generates corrective actions. By consolidating these functions into a single structured framework, the system achieves high productivity without proportionally increasing complexity.
Solution Approach 2:
The process ontology acts as an intermediary layer between the sensor data and the root cause analysis logic. This intermediary structure standardizes the representation of process knowledge and enables automated analysis without requiring complex ad-hoc programming for each scenario, thereby improving productivity while managing system complexity through modular design.
Data Source
AI summary
Conventionally, root cause analysis and process documentation in process industries has been manually performed resulting in time consuming effort, cost, and human resources. Moreover, in the event of failure, looking at such document and searching for possible root causes is practically impossible in the interest of time and cost associated. Systems and methods of the present disclosure systematically curate knowledge of industrial process(es) from various sources and generate process ontology via meta model(s). Root cause graph (RCG) is created wherein the RCG corresponds to process and root cause and failure modes in the process. The RCG is then transformed to machine instructions which are executed for root cause analysis in real time. The created graphs/knowledge also help in identifying conflicting knowledge or redundant knowledge. Present disclosure enables root cause analysis as soon as a failure occurs or as the systems show or indicate a tendency towards failure.