Xiaojian Li
ABSTRACT
Traditional statistical methods are currently used to analyze the feature of hard drives failure, but difficult to model the failure feature for random events with small probability of hard drives failure or datasets with unbalanced and missing values. Therefore, this paper presents a change-point wavelet transform (CPWT) method, which can extract the indicative SMART change-point features of failed hard drives. Because wavelet has the ability of denoting local signal characteristics in the time domain and frequency domain, we use the wavelet transform method to extract the sudden change features of the time series, to reveal the characteristics of the change-point wavelet. Then, we use convolution network and long short-term memory (CNN-LSTM) model, to verify the effectiveness of the change-point wavelet extracted for failure prediction. The experimental results show that the change-point wavelet evaluates the health of the hard drives, the accuracy and precision of prediction are 91.56% and 90.78% respectively, confirming that the change-point wavelet has a certain predictive ability for hard drives failures, which provides a basis for cloud storage failure judgment and prediction.
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