Machine Failure Diagnosis Using Deep Temporal Clustering
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Solution Overview
Problem
Current methods for diagnosing machine failures in industrial settings rely heavily on empirical analysis and lack accuracy, particularly in identifying the cause of mechanical equipment failures, leading to inefficiencies and increased costs due to prolonged equipment usage and potential accidents.
Innovation Solution
A system utilizing a pre-processing module to generate Mel spectrum images, a feature extraction module with an autoencoder to output Euclidean distances between latent variables and centroids, and a failure diagnosis module employing K-means clustering and SVM algorithms to classify machine states and determine failure presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirical analysis by experts is used to diagnose machine failures, then diagnostic experience can be leveraged, but diagnostic precision and accuracy are insufficient
Solution Approach 1:
The patent replaces empirical expert analysis with an automated deep learning system consisting of autoencoders, K-means clustering, and SVM algorithms. This substitution transforms the diagnostic process from manual expert judgment to an automated computational system that processes vibration signals, extracts features, and classifies failure modes with higher precision and objectivity.
2Measurement precision
If simple analysis methods are used for machine failure diagnosis, then the system remains simple, but the ability to accurately identify failure causes is limited
Solution Approach 1:
The patent segments the diagnostic process into distinct functional modules: signal preprocessing, feature extraction using autoencoders, clustering analysis with K-means, and classification with SVM. This segmentation allows each component to specialize in a specific task, improving overall diagnostic accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces latent variables as intermediaries between raw vibration signals and final failure classification. The autoencoder transforms high-dimensional signal data into compressed latent representations, which then serve as input for K-means clustering and SVM classification. These intermediary representations enable more accurate failure cause identification by capturing essential features while reducing noise and redundancy.
3Measurement precision
If advanced deep learning models are implemented for accurate failure diagnosis, then diagnostic accuracy improves, but computational requirements and system complexity increase
Solution Approach 1:
The patent extracts only the most essential features from raw vibration signals through the autoencoder's latent space representation, rather than processing the entire high-dimensional signal dataset. By taking out and focusing on the most discriminative features captured in the latent variables, the system achieves high diagnostic accuracy while reducing the computational burden required for subsequent clustering and classification operations.
Data Source
AI summary
A system for diagnosing a machine failure according to an embodiment of the inventive concept includes a pre-processing module that receives a primitive signal in a time-amplitude domain to generate a Mel spectrum image in a time-frequency domain, a feature extraction module that learns features of the Mel spectrum image to output an Euclidean distance between a latent variable and a centroid, a failure diagnosis module that performs classification into a preset number of classes on the basis of the Euclidean distance between the latent variable and the centroid, and a result output module that outputs whether a failure has occurred according to a class classified by the failure diagnosis module.