Text-Guided Fault Audio Generation for ML Diagnosis Training
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Solution Overview
Problem
Existing machine learning-based fault diagnosis systems face challenges in training due to the scarcity of fault data, particularly in industrial applications where faults occur infrequently and briefly, leading to a high ratio of healthy to fault data, which complicates the effective use of generative adversarial networks (GANs) and other data generation techniques.
Innovation Solution
Implement data generation techniques that incorporate text-guided audio manipulation, leveraging deep learning models like CLAP and LLMs, to generate synthetic fault data by using textual descriptions and contextual information, enabling the creation of coherent and context-sensitive audio signals that simulate various conditions and scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning training is used with limited fault data, then the system can be trained with available data, but the training effectiveness and model accuracy deteriorate due to the high ratio of healthy to fault data
Solution Approach 1:
The patent creates synthetic copies of fault data by training a generator network to produce artificial fault audio samples that mimic real fault characteristics. The generator learns from limited real fault data and generates additional synthetic fault data copies, enabling the ML model to train with sufficient fault examples without requiring extensive real fault data collection.
Solution Approach 2:
The patent performs preliminary data preparation by pre-training the generator network on available fault data before the main training process. This preliminary action creates a reservoir of synthetic fault data that can be used during subsequent training iterations, ensuring the fault diagnosis model receives adequate fault examples from the outset rather than waiting for rare real fault occurrences.
2Quantity of substance
If more fault data is collected from actual device operation, then the training data quantity increases, but the time and resources required for data collection and the inability to capture all fault scenarios worsen
Solution Approach 1:
Instead of spending time collecting real fault data from actual device operations, the patent creates synthetic fault data copies through the generator network. This copying approach generates unlimited fault data scenarios instantaneously, eliminating the time-consuming data collection process while providing diverse fault examples for comprehensive model training.
Solution Approach 2:
The patent performs preliminary training of the generator network using any available fault data, establishing a synthetic data generation capability before deployment. This preliminary action ensures that when the system operates, fault data is generated on-demand through the trained generator rather than requiring time-consuming real-time data collection during actual fault events.
3Quantity of substance
If generative adversarial networks are used to generate fault data, then the available training data increases, but the complexity of implementing and training GANs worsens
Solution Approach 1:
The patent implements a simplified copying mechanism where the generator network directly learns to replicate fault data characteristics from real examples. This copying approach avoids the complex adversarial training dynamics of traditional GANs, reducing implementation complexity while still generating sufficient synthetic fault data for effective model training.
Solution Approach 2:
The patent extracts and isolates the essential fault data generation function from the complex GAN framework. By focusing only on the generator component and training it directly on fault data without the full adversarial setup, the system removes unnecessary complexity while retaining the core benefit of synthetic data generation for fault diagnosis training.
4Productivity
If the ML model is trained with insufficient fault data, then the training process is faster and simpler, but the model's ability to accurately diagnose faults and distinguish faulty from healthy states deteriorates
Solution Approach 1:
The patent creates synthetic fault data copies through the generator network to supplement limited real fault data. This copying approach provides sufficient training examples for accurate fault classification without requiring extensive real fault data collection, maintaining training efficiency while improving model precision through increased data availability from synthetic sources.
Solution Approach 2:
The patent performs preliminary generation of synthetic fault data before the main training process, preparing a comprehensive training dataset in advance. This preliminary action ensures that when training begins, the model has adequate fault examples ready, enabling both efficient training execution and accurate fault diagnosis performance without data scarcity limitations.
Data Source
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AI summary
A method of generating audio to obtain manipulated audio data includes receiving textual descriptions of audio associated with operation of a device, receiving audio data associated with the operation of the device, generating, based on the textual descriptions, descriptive text inputs of audio features associated with the operation of the device, generating the manipulated audio data based on the descriptive text inputs and the audio data, the manipulated audio data including the one or more audio features indicative of faults associated with the descriptive text inputs, training a machine learning (ML) model to diagnose the faults using the manipulated audio data, the ML model being trained to generate an output indicative of the faults based on audio data obtained during the operation of the device, and, based on convergence during the training, outputting a trained ML model configured to generate the output indicative of the faults.