Hermetic Compressor Vibration Fusion for Real-Time Defect Classification
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Solution Overview
Problem
Current methods for detecting defects in hermetic refrigeration compressors rely on manual sensory experience, making it difficult to identify and classify defects in real-time during production, and subsequent dissection is required for defect determination.
Innovation Solution
A product defect online detection apparatus and method using a mechanical system with a triaxial acceleration sensor and a measurement and control system, employing a multi-channel time-frequency fusion deep learning algorithm to analyze vibration signals and classify defects automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sensory experience methods are used for defect detection, then the detection process is simple to implement, but the detection accuracy and ability to identify defect types is insufficient
Solution Approach 1:
The patent replaces manual sensory detection (human listening and touching) with automated vibration signal acquisition using triaxial acceleration sensors. This substitution enables objective, quantifiable measurement of compressor vibrations, significantly improving detection accuracy while eliminating the subjectivity and limitations of manual methods.
Solution Approach 2:
The patent introduces vibration signal acquisition, processing, and analysis systems as intermediaries between the compressor and the defect detection process. These systems capture vibration signals, process them through signal processing algorithms, and provide objective defect identification, serving as a bridge between physical compressor operation and defect classification.
2Productivity
If automated vibration detection is implemented, then real-time defect identification is achieved, but the detection system becomes more complex
Solution Approach 1:
The patent performs preliminary actions by pre-positioning triaxial acceleration sensors on the compressor housing and establishing signal processing algorithms before actual defect detection occurs. This preparation enables immediate real-time monitoring without requiring complex setup during production, as the detection system is already configured and calibrated.
Solution Approach 2:
The patent employs triaxial acceleration sensors that can detect vibrations in multiple directions simultaneously, and signal processing algorithms that can identify various defect types (unbalanced, misalignment, bearing defects, etc.) using the same hardware platform. This multi-functionality reduces the need for multiple specialized detection devices, thereby limiting the increase in system complexity.
3Measurement precision
If comprehensive vibration analysis is performed to classify defect types, then defect classification accuracy improves, but the signal processing complexity increases
Solution Approach 1:
The patent segments the vibration analysis process into distinct components: signal acquisition from three orthogonal directions, frequency domain transformation using Fast Fourier Transform, spectral analysis for feature extraction, and pattern recognition for defect classification. This segmentation allows each processing stage to be optimized independently, managing overall complexity while maintaining high classification accuracy.
Solution Approach 2:
The patent transforms time-domain vibration signals into the frequency domain through Fast Fourier Transform, adding a frequency dimension to the analysis. This dimensional transformation enables the identification of characteristic frequencies associated with different defect types, significantly improving classification accuracy by revealing patterns not visible in the time domain.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time identification and classification of defects in hermetic compressors, establishing an intelligent closed loop for manufacturing, improving detection accuracy and automating the defect detection process.
Implementation Method 1
a triaxial acceleration sensor is mounted below the telescopic sensor chain
Data Source
AI summary
A product defect online detection apparatus and method in hermetic compressor manufacturing. The online detection apparatus uses a multi-channel time-frequency-space feature fusion deep learning algorithm to perform information fusion on time-frequency features of vibration signals in three directions of a housing of a complete hermetic compressor; time-frequency features and spatial features are learned and extracted to solve the identification and classification of a manufacturing defect of the hermetic compressor; and a complete compressor defect is fed back to front-end part machining and assembling stages in real time during intelligent compressor manufacturing, so as to establish an intelligent closed loop for hermetic compressor manufacturing.