Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks

Xu, Chang; Wang, Gang; Liu, Xiaoguang; Guo, Dongdong; Liu, Tie-Yan

doi:10.1109/tc.2016.2538237

Cited by 127 publications

(45 citation statements)

References 11 publications

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“…An RNN has incomparable advantages in terms of prediction [119,120], which has attracted significant attention in the field of deep learning-based fault diagnosis in recent years. Under the background of the large-scale and complex development of a system, an RNN fault diagnosis method will play an increasingly important role.…”

Section: Research Status Of Rnn-based Fault Diagnosismentioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge. To provide accurate diagnosis results automatically, numerous studies have been carried out. Among them, signal-based methods are commonly used, which employ signal processing techniques based on the state signal used for extracting features, and further input the features into the classifier for fault recognition. However, their main deficiencies include the following: (1) The features are manually designed and thus may have a lack of objectivity. (2) For signal processing, feature extraction and pattern recognition are conducted using independent models, which cannot be jointly optimized globally. (3) The machine learning algorithms adopted by these methods have a shallow architecture, which limits their capacity to deeply mine the essential features of a fault. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome such deficiencies. Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional data. In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized. The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed, and research ideas for applying DNNs for hydraulic fault diagnosis are proposed. Meanwhile, the research advantages and development trend of DNNs (both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed. Furthermore, the fault characteristics of a complex hydraulic system are summarized and discussed, and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.

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Section: Research Status Of Rnn-based Fault Diagnosismentioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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“…We follow the previous works [6], [2], [19] to select distinctive SMART features. As the values of different SMART attributes vary widely, we rescale the values of each selected SMART attributes by the following formula to avoid bias to SMART attributes with large values: Train Valid Test Unlabeled ST-1 17137 4285 5356 10326 ST-2 755 -236 489 where v is the original value of a SMART attribute, and v min and v max are the minimum value and the maximum value of a SMART attribute.…”

Section: A Data Preparationmentioning

confidence: 99%

“…As the values of different SMART attributes vary widely, we rescale the values of each selected SMART attributes by the following formula to avoid bias to SMART attributes with large values: Train Valid Test Unlabeled ST-1 17137 4285 5356 10326 ST-2 755 -236 489 where v is the original value of a SMART attribute, and v min and v max are the minimum value and the maximum value of a SMART attribute. Similar to the definitions used in [20], [19], we predict the hard drives based on their residual life. Label '0' is "red alert", which means the residual life is less than 5 days.…”

Section: A Data Preparationmentioning

confidence: 99%

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Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction

Zhang

Wang

et al. 2018

2018 IEEE International Conference on Data Mining (ICDM)

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With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning algorithms based on SMART attributes are effective methods to predict hard drive failures. In this paper, we use SMART attributes to predict hard drive health degrees which are helpful for taking different fault tolerant actions in advance. Given the highly imbalanced SMART datasets, it is a nontrivial work to predict the health degree precisely. The proposed model would encounter overfitting and biased fitting problems if it is trained by the traditional methods. In order to resolve this problem, we propose two strategies to better utilize imbalanced data and improve performance. Firstly, we design a layerwise perturbation-based adversarial training method which can add perturbations to any layers of a neural network to improve the generalization of the network. Secondly, we extend the training method to the semi-supervised settings. Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model. Our extensive experiments on two real-world hard drive datasets demonstrate the superiority of the proposed schemes for both supervised and semi-supervised classification. The model trained by the proposed method can correctly predict the hard drive health status 5 and 15 days in advance. Finally, we verify the generality of the proposed training method in other similar anomaly detection tasks where the dataset is imbalanced. The results argue that the proposed methods are applicable to other domains.Index Terms-Hard drive, SMART, deep neural network, adversarial training arXiv:1809.04188v4 [cs.LG]

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“…Recurrent neural networks (RNNs) [17] have been proven as an effective tool to model temporal dependency in various applications. Xu et al [18] introduced a novel method based on the RNN to assess the health status of hard drives via the sequence of their attributes. Experimental results show that the RNN method can effectively evaluate the health status of the hard drives and play the role of fault prediction.…”

Section: Introductionmentioning

confidence: 99%

Power Transformer Operating State Prediction Method Based on an LSTM Network

et al. 2018

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Abstract:The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM) network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.

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Health Status Assessment and Failure Prediction for Hard Drives with Recurrent Neural Networks

Cited by 127 publications

References 11 publications

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction

Power Transformer Operating State Prediction Method Based on an LSTM Network

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