Unbalanced hard disk data fault diagnosis method based on deep learning

A technology of deep learning and fault diagnosis, applied in faulty hardware testing methods, machine learning, electrical digital data processing, etc., can solve problems such as unbalanced data sets, achieve high robustness, low environmental requirements, and strong noise resistance Effect

Active Publication Date: 2020-07-14
TONGJI UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0006] It is a challenging task to balance the data set by oversampling and use the deep learning model to predict. Although there have been a lot of research work to solve the problem of unbalanced data sets, they have not been able to achieve better accuracy. The accuracy of fault detection

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  • Unbalanced hard disk data fault diagnosis method based on deep learning
  • Unbalanced hard disk data fault diagnosis method based on deep learning
  • Unbalanced hard disk data fault diagnosis method based on deep learning

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Embodiment

[0043] Since the hard disk model is specified by the manufacturer, the present invention trains different models according to different models. Capacity_bytes is the capacity of each hard disk, and S.M.A.R.T. numbers are some self-monitoring analysis and reporting techniques, which can represent the characteristics of the hard disk. The hard drive data is distributed in the form of dates, and more importantly, each hard drive has nearly 120 features, and in each feature, the missing values ​​are half of the total. On this basis, the present invention adopts the three-dimensional reconstruction of the original data, and preprocesses the data features with the feature engineering Auto-encoder and the missing value processing method. The present invention adopts the sample division method for extremely unbalanced samples , the processed training set is processed by SMOTE to balance the number of unbalanced samples, and finally a model with higher accuracy is obtained by combining ...

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Abstract

The invention relates to an unbalanced hard disk data fault diagnosis method based on deep learning. The method comprises the following steps: 1) carrying out preprocessing and three-dimensional reconstruction on original hard disk data; 2) performing data category balance processing on the hard disk data after three-dimensional reconstruction, and performing division to obtain a training set anda test set of a deep learning network; and 3) constructing a deep learning network and performing training through the training set, and finally performing fault prediction by adopting the trained deep learning network to judge whether the hard disk data is faulty or not. Compared with the prior art, the method has the advantages of high accuracy, high application generalization, low environmentalrequirements, risk prediction and the like.

Description

technical field [0001] The invention relates to the field of computer fault diagnosis, in particular to a deep learning-based fault diagnosis method for unbalanced hard disk data. Background technique [0002] When the equipment is down, it may delay the production line of the whole factory. What's more, it will waste a lot of time if experts check the machine. Therefore, the machine needs to be equipped with sensors and log functions to collect a large amount of historical data of the machine over a period of time, and Use machine learning to predict downtime. [0003] Many industrial machines are now IoT enabled, which means that the sensors and log functions on each machine can be transmitted to a central hub for analysis, which enables the powerful use of machine learning algorithms Imaging technology and deep learning technology can achieve more than 80% early fault detection. [0004] The data imbalance problem is common in various fields, usually, when the ratio of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/22G06N20/00
CPCG06F11/2205G06F11/2263G06F11/2273G06N20/00
Inventor 李莉刘宇广林国义
Owner TONGJI UNIV
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