Transfer Learning for Bayesian Networks with Application on Hard Disk Drives Failure Prediction

Pereira, Francisco; Lima, Fernando Dione dos Santos; Leite, Lucas G. M.; Gomes, João P. P.; Machado, Javam C.

doi:10.1109/bracis.2017.64

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…is approach eliminated 88% of triple disk errors. e Bayesian network failure prediction method has been used with transfer learning so that HDD models with an abundance of data can be used to build prediction models for drives with a lack of data [6]. e Bayesian network-based method for failure prediction in HDDs (BNFH) [7] was proposed to estimate the remaining life of HDDs.…”

Section: Prediction Of Soon-to-failmentioning

confidence: 99%

“…( 18) return health_degree End ALGORITHM 1: e algorithm for calculating the health degree of a soon-to-fail HDD. 6 Mobile Information Systems already normalized in the dataset from Baidu when it was publicized. For the dataset from Backblaze, the formula for data normalization that we used is given as follows:…”

Section: Inputmentioning

confidence: 99%

“…To improve the performance of HDD failures prediction, many machine-learning-based prediction approaches have been proposed, including Bayesian algorithms [6][7][8][9], support vector machine (SVM) [10], classification tree (CT) [11,12], random forest (RF) [13,14], artificial neural network (ANN) [15], convolution neural network (CNN) [16], and recurrent neural network (RNN) [17,18]. RNN-based prediction models achieve the highest FDRs, and RF-based models attain the lowest FARs.…”

Section: Introductionmentioning

confidence: 99%

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Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Shen

Ren

Wan

et al. 2021

Mobile Information Systems

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With the increase in intelligence applications and services, like real-time video surveillance systems, mobile edge computing, and Internet of things (IoT), technology is greatly involved in our daily life. However, the reliability of these systems cannot be always guaranteed due to the hard disk drive (HDD) failures of edge nodes. Specifically, a lot of read/write operations and hazard edge environments make the maintenance work even harder. HDD failure prediction is one of the scalable and low-overhead proactive fault tolerant approaches to improve device reliability. In this paper, we propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve prediction efficiency. In addition, we design a new health degree evaluation method, which stores current health details and deterioration. The comprehensive experiments on two real-world hard drive datasets demonstrate that the proposed approach achieves a good prediction accuracy with low overhead.

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Section: Prediction Of Soon-to-failmentioning

confidence: 99%

Section: Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Shen

Ren

Wan

et al. 2021

Mobile Information Systems

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“…For sample-based transfer learning, Botezatu [5] proposed a sample selection method, in which a classifier is trained that can rank the observations linked to a specific disk model based on their similarity to samples pertaining to the target disk model. FLF Pereira et al [6] proposed the multi-source domain transfer learning for the Bayesian network to solve the problem of fault prediction of a few disk data based on homogeneous transfer learning. Multi-source domain transfer learning can make full use of existing source domain data, reduce the probability of negative transfer learning, and improve the robustness of the transfer learning model.…”

Section: Introductionmentioning

confidence: 99%

Disk anomaly detection for remote maintenance control system of natural gas pipeline based on multi-source domain transfer and incremental learning

Xiao

Wang²,

Shi

et al. 2022

J. Phys.: Conf. Ser.

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Accurate disk anomaly detection is of great significance for maintaining the safe and stable operation of the remote control system of natural gas pipelines. Most disk transfer algorithms use only a single-source domain model for offline transfer learning, resulting in lower classifier performance. This paper proposes a disk online anomaly detection algorithm combining transfer learning and incremental learning. A transfer learning algorithm based on data distribution similarity and dynamic index weighted ensemble is proposed. According to multiple source domain models and target domain model negative data in hyperspace, k neighbour data of each target domain negative data is extracted, and then different test criteria are adopted for each sub-model according to the number of target domain positive data, and index reweighting is performed according to the model detection capability. An incremental learning algorithm based on online automatic annotation is proposed. The data popped up by the sliding window is automatically marked at the current disk state, and the update period of the prediction model is dynamically adjusted according to the false detection rate and the missed detection rate. By setting three different experiments for comparison, the effectiveness and advanced nature of the method proposed in this paper are illustrated.

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Hard Disk Failure Prediction via Transfer Learning

Zhao

Guan

Jin

et al. 2021

Communications in Computer and Information Science

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Transfer Learning for Bayesian Networks with Application on Hard Disk Drives Failure Prediction

Cited by 12 publications

References 17 publications

Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Disk anomaly detection for remote maintenance control system of natural gas pipeline based on multi-source domain transfer and incremental learning

Hard Disk Failure Prediction via Transfer Learning

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