Knowledge Mapping Software for Asset Issue Diagnosis
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Solution Overview
Problem
The challenge lies in the time-consuming process of diagnosing and resolving issues with complex machine and equipment assets, as subject matter experts often spend hours analyzing various sources of information to determine the cause of failures, and their expertise is not readily available when they leave an organization or are unavailable.
Innovation Solution
A knowledge mapping software system that captures and applies the expertise of subject matter experts by clustering historical issues and resolutions, allowing new issues to be automatically diagnosed and potential solutions to be ranked, reducing the need for extensive expert analysis and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject matter experts manually analyze historical issues and resolutions to diagnose new issues, then diagnostic accuracy is improved, but work order resolution time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing historical issue data, extracting keywords and characteristics, and organizing them into structured clusters before they are needed for diagnosis. This preparation work is done in advance so that when a new issue arises, the diagnostic engine can quickly compare it against pre-organized historical patterns rather than manually analyzing raw historical data from scratch.
Solution Approach 2:
The system creates simplified copies of complex historical issue data by extracting essential keywords and characteristics, then storing these as structured diagnostic patterns. When diagnosing new issues, the system compares against these copied patterns rather than the full original historical records, maintaining diagnostic accuracy while reducing analysis time and complexity.
2Reliability
If subject matter experts are trained to handle complex asset issues, then diagnostic capability is improved, but the organization becomes dependent on retaining highly skilled personnel
Solution Approach 1:
The diagnostic system enables self-service by automating the diagnostic process that previously required expert human analysis. The system independently processes new issues by comparing them against historical patterns, generating diagnostic recommendations without requiring subject matter expert intervention. This captures and codifies expert knowledge into an autonomous system that maintains diagnostic capability regardless of personnel availability.
Solution Approach 2:
The system acts as an intermediary between historical expert knowledge and current diagnostic needs. It mediates by translating unstructured historical issue data into structured diagnostic patterns, then using these patterns to bridge the gap between past expert insights and present diagnostic challenges, making expert knowledge accessible without requiring the original experts to be present.
3Loss of information
If extensive historical issue data is collected for analysis, then diagnostic completeness is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential keywords and critical characteristics from extensive historical issue data, separating the vital diagnostic information from the voluminous but less relevant details. This extraction process maintains diagnostic completeness by capturing the key elements needed for accurate diagnosis while eliminating unnecessary data complexity.
Solution Approach 2:
The system segments historical issue data into distinct structured patterns based on extracted keywords and characteristics. By dividing the large body of historical data into organized, reusable diagnostic patterns, the system maintains comprehensive diagnostic coverage while reducing the complexity of processing and comparing the data during actual diagnostic operations.
Data Source
AI summary
The example embodiments are directed to a system and method that applies knowledge developed by a subject matter expert with respect to a physical asset. In one example, the method includes receiving knowledge and issue resolution information developed of subject matter experts in association with historical issues for an asset, generating a plurality of data clusters for the asset based on the knowledge, wherein each historical issue of the asset is mapped to a cluster and includes a plurality of resolutions for the issue, receiving a new issue and new issue information, and processing the new issue by extracting keywords from the new issue information and assigning the new issue to a data cluster from among the plurality of data clusters based on the extracted keywords, and outputting, to a display, a cause of the new issue and potential solutions for the new issue.