Multi-Modal Fault Resolution Step Identification
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Solution Overview
Problem
Existing fault detection mechanisms in equipment are less accurate due to limited inputs and sensor dependency, leading to incorrect fault resolution and neglect of cascading effects.
Innovation Solution
A method and system that capture multi-modal diagnosis data (visual, audio, and sensor data) to detect condition states and fault locations using trained deep learning models, and identify primary fault resolution steps based on historic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-modal diagnosis data is captured and processed using deep learning models, then fault detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the fault detection process into distinct functional modules: a data capture module that collects multi-modal diagnosis data, a feature extraction module that processes the data, and trained deep learning models (object fault detection model and fault location prediction model) that analyze features. This segmentation allows each module to be optimized independently, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary feature extraction process that transforms raw multi-modal diagnosis data into meaningful features before feeding them to the deep learning models. This intermediary layer simplifies the input complexity for the models while preserving critical fault-related information, thereby improving detection accuracy without proportionally increasing system complexity.
2Loss of information
If comprehensive multi-modal data is captured for accurate fault detection, then information completeness is improved, but data processing time increases
Solution Approach 1:
The system extracts only the relevant features from the comprehensive multi-modal diagnosis data using a dedicated feature extraction process. Instead of processing all raw data, the system identifies and extracts critical features that are most indicative of fault conditions, thereby maintaining information completeness for accurate detection while significantly reducing the data volume that requires intensive processing.
Solution Approach 2:
The patent applies partial action by focusing computational resources on extracting and processing only the most relevant features from multi-modal data rather than analyzing every piece of raw data in detail. This selective approach ensures that critical fault information is captured while avoiding unnecessary processing of redundant or less important data, thus reducing overall processing time.
3Device complexity
If traditional sensor-based fault detection is used, then device complexity is reduced, but detection accuracy deteriorates
Solution Approach 1:
The patent employs a composite approach by integrating multiple data modalities (visual, audio, and sensor data) into a unified diagnosis system. Instead of relying on a single sensor type, the system combines diverse data sources and processes them through feature extraction and deep learning models, creating a composite diagnostic system that achieves superior detection accuracy while managing complexity through structured integration.
Solution Approach 2:
The system replaces traditional mechanical sensor-based detection with an intelligent software-based approach using deep learning models. The trained object fault detection model and fault location prediction model substitute for conventional sensor interpretation methods, enabling more accurate fault detection by learning complex patterns from multi-modal data without requiring additional complex hardware.
Data Source
AI summary
The present disclosure discloses a method and a system for identifying fault resolution steps for an equipment. Method captures multi-modal diagnosis data associated with at least one primary part in the equipment. Method obtains multi-modal features of the at least one primary part from the multi-modal diagnosis data. Method detects a condition state of the at least one primary part using the multi-modal features and a trained object fault detection model. Method determines location of a fault on the at least one primary part using a trained fault location prediction model when the condition state is detected as a faulty state. Method identifies primary fault resolution steps for the at least one primary part based on historic data associated with the at least one primary part and the location of the fault. Method identifies secondary resolution steps for secondary parts based on the primary fault resolution steps before rendering.