LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

Donghyun, Park; Kim, Seulgi; An, Yelin; Jung, Jae‐Yoon

doi:10.3390/s18072110

Cited by 128 publications

(67 citation statements)

References 49 publications

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“…The residual matrix is then used as the input for various time-domain feature functions, described in Table 1 [12]. The time-domain features used are as follows:…”

Section: Data Preprocessingmentioning

confidence: 99%

Fault isolation of reaction wheels onboard three-axis controlled in-orbit satellite using ensemble machine learning

Rahimi

Saadat

2020

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The primary objective of this study is to explore novel applications of data-driven machine learning methods for isolation of nonlinear systems with a case study for an in-orbit closed-loop controlled satellite with reaction wheels as actuators. Highfidelity models of the three-axis controlled satellite are developed to provide an abundance of data for both healthy and various faulty conditions of the satellite. These data are then used as input for the proposed data-driven fault isolation method. Once a fault is detected, the fault isolation module is activated, where it employs a machine learning technique that incorporates ensemble methods involving random forests, decision trees, and nearest neighbors. Results of the classified faulty condition are then cross-validated using k-fold and leave-one-out methods. Performance comparison among different combinations for the ensemble architecture shows promising fault isolation of the non-linear systems using ensemble methods.

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“…The residual matrix is then used as the input for various time-domain feature functions, described in Table 1 [12]. The time-domain features used are as follows:…”

Section: Data Preprocessingmentioning

confidence: 99%

Fault isolation of reaction wheels onboard three-axis controlled in-orbit satellite using ensemble machine learning

Rahimi

Saadat

2020

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“…Therefore, it has been used widely for text, videos, and time-series analysis. Park et al [15] proposed a lightweight as well as real-time system utilizing LSTM, a model of recurrent neural networks, for fault detection in smart factories. Next, Huang and Kuo [16] combined Convolutional Neural Network (CNN) and LSTM to particulate matter forecasting in smart cities.…”

Section: Introductionmentioning

confidence: 99%

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

et al. 2019

Applied Sciences

194

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The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

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“…Internet of Things (IoT) applications are processing growing amounts of sensor information to monitor everyday environments. Typical application examples include those supporting the elderly in smart homes [1], the ones monitoring environmental parameters in smart cities [2], smart health applications on wearable devices [3] and fault detection solutions for smart manufacturing [4].…”

Section: Introductionmentioning

confidence: 99%

Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments

Preuveneers

Tsingenopoulos

Joosen

2020

Sensors

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The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automate decisions based on the outcome of certain machine learning models. They collaborate at an ever increasing scale, ranging from smart homes and smart factories to smart cities. The best performing machine learning model, its architecture and parameters for a given task are ideally automatically determined through a hyperparameter tuning process. At the same time, edge computing is an emerging distributed computing paradigm that aims to bring computation and data storage closer to the location where they are needed to save network bandwidth or reduce the latency of requests. The challenge we address in this work is that hyperparameter tuning does not take into consideration resource trade-offs when selecting the best model for deployment in smart environments. The most accurate model might be prohibitively expensive to computationally evaluate on a resource constrained node at the edge of the network. We propose a multi-objective optimization solution to find acceptable trade-offs between model accuracy and resource consumption to enable the deployment of machine learning models in resource constrained smart environments. We demonstrate the feasibility of our approach by means of an anomaly detection use case. Additionally, we evaluate the extent that transfer learning techniques can be applied to reduce the amount of training required by reusing previous models, parameters and trade-off points from similar settings.

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LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks

Cited by 128 publications

References 49 publications

Fault isolation of reaction wheels onboard three-axis controlled in-orbit satellite using ensemble machine learning

Fault isolation of reaction wheels onboard three-axis controlled in-orbit satellite using ensemble machine learning

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments

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