General Feature Selection for Failure Prediction in Large-scale SSD Deployment

Xu, Fan; Han, Shujie; Lee, Patrick P. C.; Liu, Yi; He, Cheng; Liu, Jiongzhou

doi:10.1109/dsn48987.2021.00039

Cited by 13 publications

(2 citation statements)

References 20 publications

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“…The effectiveness and superiority of the proposed method are experimentally verified by three processing equipment datasets: the SKAB dataset (Iurii D, et al) [28] ; and the Tennessee Eastman process dataset (Plakias, S, et al) [29] , edge server data comes from Alibaba cloud data center (Xu, et al 2021) [32] .…”

Section: Simulation Comparison Experimentalmentioning

confidence: 94%

Equipment anomaly detection method under cloud-edge collaboration model based bi-directional long short-term memory and variational autoencoder

Yin,

Liu,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Aiming at the problems of large amount of heterogeneous Industrial data, high fault concealment, complex feature engineering of traditional methods, an anomaly detection method combined with Bi-directional long-short term memory, variational autoencoder and whale optimization algorithm based on cloud-edge collaboration. By integrating the output of each detection models with different dimensions through the residual weight matrix, to obtain the comprehensive residual value, and compare with the residual threshold for anomaly detection. Through the experiments on SKAB and TEP datasets, the results verify the effectiveness and general adaptability of the proposed method, and the anomaly detection accuracy is higher than a single detection model and existing anomaly algorithms such as CNN and LSTM-AE.

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Section: Simulation Comparison Experimentalmentioning

confidence: 94%

Equipment anomaly detection method under cloud-edge collaboration model based bi-directional long short-term memory and variational autoencoder

Yin,

Liu,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

show abstract

“…In order to improve the predictive performance of the model, BP (back propagation) neural network model with strong adaptive ability was used to predict hard disk faults. In addition, Xu et al studied the selection method of SMART attributes for hard disk fault prediction based on Alibaba's SSD hard disk SMART data, so as to improve the accuracy of prediction [8]. Based on the above research work, this article selects eight attributes of SMART for research on hard disk fault prediction.…”

Section: Introductionmentioning

confidence: 99%