Fault Recognition Model Training for Automated Part and Type Detection
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Solution Overview
Problem
Existing methods for recognizing device faults rely heavily on manual recognition by technical personnel, which is time-consuming and labor-intensive, and there is a need for a more efficient and automated approach.
Innovation Solution
A training method for a fault recognition model that utilizes a first fault data set to generate a sample set with fault description texts, faulty parts, and corresponding fault types, and trains a model to output labels for faulty parts and fault types based on input texts, using a combination of cross entropy and L2 loss functions to ensure accurate two-level classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recognition by technical personnel is used, then fault recognition can be performed, but it is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the mechanical manual recognition system with an automated deep learning-based fault recognition system. The system uses a pre-trained language model to automatically process fault description texts and output faulty parts and fault types, eliminating the need for manual technical personnel intervention while maintaining high recognition accuracy.
Solution Approach 2:
The fault recognition system enables self-service by automatically analyzing fault descriptions and generating diagnostic results without human intervention. The model processes input texts and outputs fault information autonomously, allowing the system to serve itself in the fault recognition task.
2Extent of automation
If automated fault recognition is implemented, then labor intensity is reduced, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the language model on a large corpus of technical texts before fine-tuning it for fault recognition. This pre-training phase establishes foundational language understanding capabilities, simplifying the subsequent fault-specific training and reducing overall system complexity.
Solution Approach 2:
The patent merges the fault recognition task with a pre-trained language model, combining general language understanding capabilities with specific fault diagnostic functions. This integration allows the system to leverage existing model knowledge while adding fault-specific functionality, reducing the complexity of building a completely new system.
3Productivity
If deep learning models are used for fault recognition, then recognition speed improves, but training data requirements increase
Solution Approach 1:
The patent uses pre-training on large-scale technical corpora before fine-tuning on fault-specific data. This preliminary action allows the model to acquire general technical language understanding from abundant data, then adapt to specific fault recognition tasks with relatively smaller labeled datasets, reducing the overall training data burden.
Solution Approach 2:
The patent combines general language model pre-training with fault-specific fine-tuning, merging the benefits of large-scale data learning with targeted task optimization. This approach allows the system to leverage knowledge from vast amounts of general technical text while requiring only moderate amounts of labeled fault data for specific application.
Data Source
AI summary
Provided are a training method and apparatus for a fault recognition model, a fault recognition method and apparatus, an electronic device, and a medium. A training method for a fault recognition model includes: obtaining a first fault data set; generating a first sample set based on the first fault data set, where each sample in the first sample set includes a fault description text, a corresponding faulty part, and a corresponding fault type; and adjusting parameters of a fault recognition model by using the fault description text of the sample in the first sample set as an input to the fault recognition model, such that the fault recognition model outputs a first label and a second label, where the first label is the faulty part corresponding to the input fault description text, and the second label is the fault type corresponding to the faulty part.