Multi-dimensional Fault Diagnosis for Complex Devices
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Solution Overview
Problem
Existing fault diagnosis methods for complex devices struggle to accurately identify valuable depth feature information, leading to low accuracy and high misjudgment rates in device fault diagnosis.
Innovation Solution
A complex device fault diagnosis method and system based on multi-dimensional features, which involves acquiring and preprocessing fault data, using a multi-head attention mechanism and multi-head dilated convolution series connection, and fusing extracted fault features to acquire a multi-dimensional feature vector for accurate fault diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional maintenance methods are used, then the operation and maintenance needs of complex devices cannot be met, but the complexity of the device increases with higher component composition and automation level
Solution Approach 1:
The patent segments the fault diagnosis process into multiple stages: data acquisition from multiple sensors, preprocessing of raw data, extraction of multi-dimensional features (temporal, spectral, spatial), and classification. This segmentation allows each stage to handle specific aspects of the complex data, making the overall diagnosis of complex devices more manageable and accurate.
Solution Approach 2:
The patent transforms fault diagnosis from traditional single-dimensional analysis to multi-dimensional feature space analysis. By extracting features across multiple dimensions (time domain, frequency domain, spatial domain) and combining them into a comprehensive feature vector, the system can accurately diagnose faults in complex devices with high component composition without being overwhelmed by complexity.
2Measurement precision
If existing intelligent models are used, then data-driven fault diagnosis can be applied, but the quality and depth of data mining is insufficient leading to low accuracy
Solution Approach 1:
The patent applies local quality by designing specialized processing for different types of features. Temporal features capture local time-domain characteristics, spectral features capture local frequency-domain characteristics, and spatial features capture local spatial relationships. This localized feature extraction ensures that subtle fault indicators are not missed, improving both measurement precision and diagnosis accuracy.
Solution Approach 2:
The patent creates a composite feature representation by integrating multiple types of features (temporal, spectral, spatial) into a unified multi-dimensional feature vector. This composite approach combines the strengths of different feature types, enabling deeper data mining and more accurate fault diagnosis than any single feature type could achieve alone.
3Loss of information
If more feature information is extracted, then valuable depth feature information can be identified, but the complexity of the diagnosis system increases
Solution Approach 1:
The patent designs a universal multi-dimensional feature extraction framework that can handle various types of sensor data (vibration, temperature, pressure, etc.) through a unified process. This multi-functional system extracts temporal, spectral, and spatial features from different data sources using consistent methods, reducing system complexity while maintaining complete feature information extraction.
Data Source
AI summary
Disclosed are a complex device fault diagnosis method and system based on multi-dimensional features, and the method comprises the following steps of: acquiring fault data of a target complex device, and carrying out input coding mapping and position coding; utilizing a multi-head attention mechanism and multi-head dilated convolution series connection, and acquiring fault features covering global and local deep information in combination with a feedforward neural network; fusing the extracted fault features to acquire a multi-dimensional feature vector; and acquiring a fault type probability score matrix by a classifier, and outputting a fault diagnosis result of the target complex device. The global modeling advantage of multi-head self-attention and the local multi-scale feature perception advantage of multi-head dilated convolution are fused, and a device fault diagnosis model in which a Transform model and the multi-head dilated convolution are mutually fused is established.