Predicting Failures in Hard Drives with LSTM Networks

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

doi:10.1109/bracis.2017.72

Cited by 29 publications

(8 citation statements)

References 16 publications

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“…RNNs are offered to evaluate the vague in sequential patterns of the temporal and spatial sequential data [65]. Furthermore, the connections of the peephole also allow LSTMs to identify the timed patterns accurately and compute the internal states in the cost and weight matrices [66]. Fig.…”

Section: Time Series Fault Detectionmentioning

confidence: 99%

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

et al. 2022

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Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.INDEX TERMS Deep distributed energy, equilibrium optimizer algorithm (EOA), fault detection and classification, grid-connected photovoltaic systems, optimal feature selection, wavelet packet transform (WPT).

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Section: Time Series Fault Detectionmentioning

confidence: 99%

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

et al. 2022

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“…Moreover, forget gates add enhancements in the learning ability to forget (eliminate) information stored in the memory cell. Furthermore, connections of peepholes allow the LSTMs to realize accurately timed patterns and calculate the internal states within the matrices of cost and weight [34].…”

Section: B Fault Detection Using (Rnn-lstm) With Time Seriesmentioning

confidence: 99%

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

2021

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The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the process signals are useful to diagnose the status of the machine. Though, a common challenge in many applied applications is the practical knowledge about the risk of failure or historical records, which is totally unlabeled and difficult to be identified by traditional fault approaches. Hence, in the present study, a novel deep learning (DL) framework is proposed to fill the gap by balancing the three stages of fault feature extraction, fault detection, and parameter optimization based on the long short term memory-recurrent neural networks (RNN-LSTM), stacked autoencoders (SAE), and particle swarm optimization (PSO) techniques. The suggested framework focuses on failure detection through a sequence of numerous features for the unlabeled historical data and unknown anomaly. To validate the effectiveness of the proposed DL framework, an experiment for failure detection of the electrical generator was conducted for the data of risky environment at Yemen oil and gas plant. The experimental results compared with the earlier studies validate that, the DL framework can address the faults for vibration signals of the electrical generator in a well-diagnosis performance effectively. INDEX TERMSDeep learning (DL), Fault detection, Long short-term memory (LSTM), Oil and gas plant, Recurrent neural networks (RNN), Stacked autoencoders (SAE) This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“… LSTM [ 9 , 10 ]: LSTM is a deep learning model in which the cell state is added to the hidden state of the recurrent neural network (RNN). It is proposed in order to solve the vanishing gradient problem that occurs when the length of the input sequence in the RNN increases.…”

Section: ML Problem Formulation and Ml Modelsmentioning

confidence: 99%

“…Disk failure prediction using LSTM has been studied earlier [ 9 ]. Unlike the above models in which the extracted features of 1 h are inserted, the input of several hours is bundled using windowing and given as the time-series input of the LSTM to exploit the superiority of the time-series processing capability of LSTM.…”

Section: ML Problem Formulation and Ml Modelsmentioning

confidence: 99%

A Cost-Effective CNN-LSTM-Based Solution for Predicting Faulty Remote Water Meter Reading Devices in AMI Systems

Lee

Choi

Kim

2021

Sensors

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Automatic meter infrastructure (AMI) systems using remote metering are being widely used to utilize water resources efficiently and minimize non-revenue water. We propose a convolutional neural network-long short-term memory network (CNN-LSTM)-based solution that can predict faulty remote water meter reading (RWMR) devices by analyzing approximately 2,850,000 AMI data collected from 2762 customers over 360 days in a small-sized city in South Korea. The AMI data used in this study is a challenging, highly unbalanced real-world dataset with limited features. First, we perform extensive preprocessing steps and extract meaningful features for handling this challenging dataset with limited features. Next, we select important features that have a higher influence on the classifier using a recursive feature elimination method. Finally, we apply the CNN-LSTM model for predicting faulty RWMR devices. We also propose an efficient training method for ML models to learn the unbalanced real-world AMI dataset. A cost-effective threshold for evaluating the performance of ML models is proposed by considering the mispredictions of ML models as well as the cost. Our experimental results show that an F-measure of 0.82 and MCC of 0.83 are obtained when the CNN-LSTM model is used for prediction.

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Predicting Failures in Hard Drives with LSTM Networks

Cited by 29 publications

References 16 publications

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

A Cost-Effective CNN-LSTM-Based Solution for Predicting Faulty Remote Water Meter Reading Devices in AMI Systems

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