Probabilistic Fault Diagnosis Using Semantic Model Integration
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Solution Overview
Problem
Current fault diagnosis systems in electrical appliances and medical domains face challenges in accurately diagnosing faults due to the inability to effectively incorporate unstructured historical data and provide 100% confidence in diagnoses, leading to potential misdiagnoses and increased costs.
Innovation Solution
A probabilistic fault diagnosis system that generates a statistical model by extracting structured and unstructured values from historical information, using a semantic model to represent the diagnosis domain, and trains the model with input data to output probabilities of possible faults, incorporating unstructured data to improve accuracy and reduce misdiagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only structured historical data is used for training, then the system is simpler to implement, but diagnostic accuracy is reduced due to inability to incorporate unstructured data
Solution Approach 1:
The system segments historical data into structured and unstructured components, processing each through dedicated modules. Structured data is extracted from databases while unstructured data is processed through NLP pipelines, allowing the system to handle multiple data types without overwhelming complexity
Solution Approach 2:
A semantic model acts as an intermediary layer that bridges structured and unstructured data. This semantic model translates unstructured text into meaningful concepts and relationships, enabling integration with structured data without direct complex interactions between raw data types
2Reliability
If probabilistic reasoning is used, then diagnosis confidence is improved, but the system cannot achieve 100% confidence leading to potential misdiagnoses
Solution Approach 1:
The system implements feedback loops where diagnosis results are continuously evaluated against new data. When misdiagnoses occur or new evidence emerges, the probabilistic model updates its parameters, allowing the system to learn from errors and improve reliability over time without claiming false 100% confidence
Solution Approach 2:
The diagnostic confidence levels are dynamic rather than static. The system continuously adjusts probability estimates based on new observations, historical patterns, and emerging evidence, allowing confidence to evolve as more information becomes available rather than remaining fixed
3Measurement precision
If more historical data is collected for training, then model performance improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of historical data during off-peak times, pre-extracting features and pre-training models with available data. This allows the system to have a ready-to-use baseline model that can be quickly updated with new data without always requiring extensive reprocessing
Solution Approach 2:
The system implements incremental learning where only portions of the data need to be reprocessed when new data arrives, rather than retraining on the entire dataset. This partial action approach maintains model performance while significantly reducing processing time compared to full retraining
Data Source
AI summary
A system for generating a statistical model for fault diagnosis comprising at least one hardware processor, adapted to: extract a plurality of structured values, each associated with at least one of a plurality of semantic entities of a semantic model or at least one of a plurality of semantic relationships of the semantic model, from structured historical information organized in an identified structure and related to at least some of a plurality of historical events, the semantic model represents an ontology of an identified diagnosis domain, each of the plurality of semantic entities relates to at least one of a plurality of domain entities existing in the identified diagnosis domain, and each of the plurality of semantic relationships connects two of the plurality of semantic entities and represents a parent-child relationship therebetween; extract a plurality of unstructured values, each associated with at least one of the plurality of semantic entities.