Industrial Troubleshooting Knowledge Engine for Faster Equipment Resolution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial workforces face challenges in quickly resolving equipment issues due to reliance on manual references and in-house expertise, which can lead to prolonged downtime and varied effectiveness across different industrial environments.
Innovation Solution
A system and method utilizing an autodidact engine with a knowledge base and deep learning framework to process structured and unstructured data from various sources, providing immediate recommendations through an interactive interface for equipment troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If skilled technicians refer to log books or manuals to assess corrective actions, then they can find resolutions to equipment issues, but the resolution time increases significantly
Solution Approach 1:
The system enables self-service by allowing technicians to independently query the knowledge base and receive automated recommendations without needing to contact in-house expertise or manually search through extensive manuals. The autodidact engine processes queries and provides tailored resolutions autonomously.
Solution Approach 2:
The patent replaces the mechanical process of manually searching through log books and manuals with an automated electronic system. The autodidact engine uses natural language processing and deep learning to automatically retrieve and present relevant information, substituting human manual search efforts with machine-based information retrieval.
2Reliability
If in-house expertise teams are approached for equipment resolutions, then comprehensive solutions can be obtained, but the time required for resolution increases
Solution Approach 1:
The system empowers frontline workers to independently resolve equipment issues by providing them with access to a knowledge base that contains resolutions from expert teams. This eliminates the need to actually contact expert teams while maintaining access to their knowledge.
Solution Approach 2:
The knowledge base is pre-populated with equipment resolutions and tribal knowledge collected from expert teams during previous incidents. This preliminary collection and organization of expert knowledge allows instant retrieval without needing to contact experts during actual incidents.
3Reliability
If exhaustive manuals are used for troubleshooting, then comprehensive coverage of potential issues is achieved, but the time required to review and assess solutions increases
Solution Approach 1:
The system extracts only the relevant information needed for specific equipment issues from the comprehensive knowledge base. The autodidact engine processes queries and retrieves only the specific resolutions applicable to the current problem, eliminating the need to review entire manuals.
Solution Approach 2:
The autodidact engine acts as an intermediary between the technician's query and the knowledge base. It processes the query using natural language understanding, identifies relevant information, and presents filtered results, serving as a smart mediator that reduces information overload.
4Adaptability or versatility
If tribal knowledge from multiple sources is integrated, then the system adapts to different industrial environments, but the data processing complexity increases
Solution Approach 1:
The knowledge base is designed to store and process multiple types of data from diverse sources including equipment manuals, tribal knowledge, and industry-specific information. The system provides universal functionality across different industrial environments through a single platform that handles various data formats and query types.
Solution Approach 2:
The patent uses natural language processing and deep learning algorithms to automatically process and integrate data from multiple sources, replacing manual data consolidation efforts. The autodidact engine handles the complexity of integrating diverse data sources through machine learning rather than manual processes.
Data Source
AI summary
The present invention relates to a method for providing aid to the industrial workforce based on intelligent learning, comprising receiving a query via an input acceptor, routing the query to an autodidact engine, said autodidact engine comprising a knowledge base and a native accumulator. The method further comprises receiving, by a data segregator, data associated with a product or services from one or more data sources, said data sources including at least a product data source, a customer data source and a tribal knowledge source and segregating, by the data segregator, the received data from said data sources into structured and unstructured data. Further, the method comprises providing, by the data segregator, the structured and the unstructured data to the native accumulator and performing, by the native accumulator, one or more operations to process the received data from the segregator. Further, the method includes storing the processed data from the native accumulator into the knowledge base. Further, the deep learning framework is executed to process the data from the native accumulator and provides the responses to the customer corresponding to the query received through the user input acceptor.