Hard Disk Vibration Classification for Low Efficiency Diagnosis
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Solution Overview
Problem
Existing methods fail to effectively identify the main causes of low hard disk efficiency, leading to server inefficiencies that affect data transmission and disrupt services like gaming and video conferencing.
Innovation Solution
A classification model is created using k-means and decision tree algorithms to analyze vibration parameters of hard disks, enabling accurate identification of factors affecting efficiency by discretizing measurement data and constructing a decision tree for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If engineers use gradual manual analysis to find reasons for low hard disk efficiency, then the analysis process is simple to implement, but the ability to accurately identify the main reason is insufficient
Solution Approach 1:
The patent segments the analysis process into distinct stages: data collection from multiple sensors, data preprocessing, feature extraction, and classification. This segmentation allows the system to handle complex analysis tasks through modular components, improving identification accuracy while managing system complexity through structured organization
Solution Approach 2:
The patent introduces an intermediary classification model that acts as a mediator between raw sensor data and diagnostic conclusions. This model processes and interprets multi-source data, enabling accurate identification of main reasons without requiring engineers to directly analyze complex raw data, thus improving accuracy while keeping the user interface simple
2Measurement precision
If multiple vibration parameters are measured to improve analysis accuracy, then the identification accuracy of problem factors is improved, but the complexity of data processing increases
Solution Approach 1:
The patent extracts only the most relevant features from the multi-dimensional vibration data using feature extraction techniques. By taking out and focusing on key discriminative features rather than processing all raw data, the system maintains high identification accuracy while significantly reducing data processing time and computational resources
Solution Approach 2:
The patent transforms raw vibration parameters into meaningful feature representations through parameter changes. This transformation converts complex multi-parameter data into a reduced set of informative features that capture essential characteristics, enabling accurate classification without the computational burden of processing all original parameters
Data Source
AI summary
A creating method of a classification model about a hard disk efficiency problem comprising: by an analyzing device, performing: obtaining a plurality of pieces of measurement data of a plurality of hard disk devices each of which comprises a plurality of values of a plurality of vibration parameters; discretizing the plurality of pieces of measurement data based on a k-means algorithm; and obtaining the classification model about the hard disk efficiency problem based on the plurality of pieces of discretized measurement data and a decision tree algorithm.