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Detecting failed tethers in submerged floating tunnels using an lstm autoencoder and DNN algorithms

OCEAN ENGINEERING 2024年第Part1期312卷

作者： Min, Seongi Jeong, Kiwon Kim, Seungjun Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea

This study proposes a two-step approach for detecting damaged tethers in submerged floating tunnels. The proposed method employs two different artificial neural network algorithms. First, the long short-term memory (lstm) autoencoder model trained using response datasets under intact conditions was used to reconstruct the measured acceleration data of the target structure. Further, the data reconstruction error was used as the input for the deep neural network algorithm trained in advance using the reconstruction error pattern in various tether damage cases. The proposed method was verified by conducting a well-validated simulation based on hydrodynamics. The damage-detection accuracy of the proposed method was directly compared with that of a conventional supervised learning algorithm-based approach. In addition, the case study results confirmed that the proposed approach was applicable to other submerged floating tunnel (SFT) structures by retraining the lstm autoencoder and deep neural network algorithms with intact datasets only. Thus, this approach does not require a large amount of training data or simulation model updates for other SFT structures.

关键词： Submerged floating tunnel Damage detection and localization Tether lstm autoencoder Deep neural networks Measured motion response data

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B-Detection: Runtime Reliability Anomaly Detection for MEC Services With Boosting lstm autoencoder

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2599-2613页

作者： Wang, Lei Chen, Shuhan Chen, Feifei He, Qiang Liu, Jiyuan Nanjing Forestry Univ Dept Management Sci & Engn Nanjing 210037 Jiangsu Peoples R China Nanjing Forestry Univ Data Sci & Syst Sci DTripleS Lab Nanjing 210037 Jiangsu Peoples R China Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518055 Peoples R China Deakin Univ Sch Informat Technol Geelong 3217 Australia Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Serv Comp Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Syst Lab Cluster & Grid Comp Lab Wuhan 430074 Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne 3122 Australia

By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services' large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services' current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (lstm) autoencoder for detecting MEC services' runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named lstm autoencoder to characterize the MEC services' historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services' historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance lstm autoencoder with a weight-based reservoir sampling technique and an lstm boosting algorithm. The reconstruction loss of the trained lstm autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services' runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.

关键词： Reliability Runtime Anomaly detection Boosting Training Data models Servers boosting concept drift deep learning MEC service lstm autoencoder

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Anomaly Detection with a lstm autoencoder Using InfluxDB 32nd

Anomaly Detection with a LSTM Autoencoder Using InfluxDB

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32nd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Peixoto, Joao Sousa, Joao Carvalho, Ricardo Soares, Martinho Cardoso, Ricardo Reis, Ana INEGI Inst Sci & Innovat Mech & Ind Engn P-4200465 Porto Portugal Univ Porto Fac Engn P-4200465 Porto Portugal Parque Ind Vendas Novas Toolpresse P-7080341 Vendas Novas Portugal

ISBN: (纸本)9783031381645;9783031381652

In the manufacturing industry, anomalies are an unfortunate but inevitable reality. If left unaddressed, they can lead to costly production defects and halted production lines. However, with the rise of Industry 4.0, many industrial machines are now equipped with sensors that can be used to detect anomalous behaviors, allowing for early identification and prevention of defects. Therefore, this study presents a solution using a Long Short-Term Memory (lstm) autoencoder to detect abnormal behavior in an industrial machine temperature sensor dataset. The algorithm is compared with conventional methods, further demonstrating its capabilities in anomaly detection. Additionally, an implementation architecture is proposed using InfluxDB and Telegraf software, providing a simulated real-world application of the proposed solution.

关键词： anomaly detection deep learning lstm autoencoder

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Characterizing residential load patterns on multi-time scales utilizing lstm autoencoder and electricity consumption data

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SUSTAINABLE CITIES AND SOCIETY 2022年 84卷

作者： Yang, Wei Li, Xinhao Chen, Chao Hong, Jingke Chongqing Univ Sch Management Sci & Real Estate Chongqing 400045 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Load patterns represent a clear picture of electricity usage, reflecting the consumer's habits. Previous works mainly focused on load patterns discovery on a fixed scale, but limited to characterize load patterns on multi-time scales utilizing electricity consumption data (ECD). Therefore, we propose a novel framework to characterize residential load patterns on multi-time scales. The long-short-term memory autoencoder (lstm-AE) model is designed for dimensionality reduction and feature extraction. Furthermore, a two-level clustering method is proposed to discover and characterize typical load patterns (TLPs) and multifaceted load patterns (MLPs) on multi-time scales. The proposed framework is comprehensively evaluated via extensive experiments on three real ECD. Results show that: (1) Reconstruction errors of lstm-AE are lower than 6 benchmark models across different time scales, which validates the superiority of lstm-AE. (2) TLPs and MLPs on daily, weekly, monthly and yearly scale are discovered by the two-level clustering method. TLPs profile the resident's electricity usages from a global view. (3) MLPs present the consumer segmentation and characterize residential load patterns of individual and groups. Especially, customer groups and electricity usage habits or lifestyles are revealed thoroughly to customize personal demand response strategies. This study can provide new valuable insights for smart grid applications.

关键词： Multi -time scale Load patterns lstm autoencoder Electricity consumption data Clustering

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Anomaly Detection for the MIL-STD-1553B Multiplex Data Bus Using an lstm autoencoder

Anomaly Detection for the MIL-STD-1553B Multiplex Data Bus U...

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19th International Conference on Cyber Warfare and Security

作者： Harlow, Alec Lachine, Brian Roberge, Vincent Royal Mil Coll Canada Kingston ON Canada

ISBN: (纸本)9781914587962;9781914587979

Due to the modernization of commercial and military aircraft, real-time systems and their connectivity to ground based networks, including the Internet, that were thought to be "air-gapped", are becoming more susceptible to cyber-attack. Most real-time systems that communicate using the Military Standard 1553B Multiplex data bus (MIL-STD-1553B) protocol do not have the ability to detect cyber-attacks. These systems were originally developed with safety and redundancy in mind, not security. These two factors introduce attack vectors to MIL-STD-1553B communication buses and expose associated avionics systems to exploitation. Recent approaches to anomaly detection for the MIL-STD-1553B data bus have leveraged statistical analysis, Markov Chain modelling, remote terminal fingerprinting and signature-based detection. However, their comparative effectiveness is unknown. Regarding the statistical analysis technique, the lack of accuracy and precision in detecting the start and stop time of anomalous events are not ideal for conducting investigations due to the sheer volume of messages still required to be manually analysed. Deep learning techniques offer an effective means of anomaly detection and applying these techniques to the MIL-STD-1553B data bus could provide more accurate and precise detection times when anomalies or attacks are present, when compared to known statistical analysis, leading to more efficient forensic investigations of anomalous events. The aim of this research is to improve the time-related performance metrics when detecting attacks on the MIL-STD-1553B data bus traffic using a Long Short-Term Memory (lstm) autoencoder. In order to accomplish this aim, an lstm autoencoder detector was developed and tested on two separate datasets from different MIL-STD-1553B network architectures, totalling 15 threat instances over 5 scenarios. The detector was then compared to the MIL-STD-1553B Anomaly-Based Intrusion Detection System (MAIDENS) detector, a statisti

关键词： MIL-STD-1553B Anomaly Detection Deep Learning lstm autoencoder Aviation Cybersecurity

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Anomaly Detection in Robotic Welds - Investigation of lstm autoencoder Model Performance 11

Anomaly Detection in Robotic Welds - Investigation of LSTM A...

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11th International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Skar, Eirik Magnus Kloumann, Jon-Erick Robbersmyr, Kjell G. Lovasen, Torfinn Univ Agder Dept Engn Sci Grimstad Norway

ISBN: (纸本)9798350315684

Gas metal arc welding (GMAW) is commonly used for joining metals. Despite the widespread adoption of robotic GMAW, welding errors still occur frequently [1]. They can be costly and time consuming to discover and fix after welding has been completed. This paper presents a method for detecting welding anomalies using unsupervised machine learning on sound data. Earlier attempts have yielded unsatisfactory results, we propose using a long short-term memory (lstm) autoencoder model to detect welding anomalies in sound from the flux cored arc welding (FCAW) method. The main findings are that the model is well suited, and that it outperforms previous methods.

关键词： Robotic welding Anomaly detection Sound Unsupervised learning lstm autoencoder

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lstm autoencoder aided Estimation of Primary Activity Statistics under Imperfect Sensing

LSTM Autoencoder aided Estimation of Primary Activity Statis...

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International Conference on COMmunication Systems and NETworkS (COMSNETS)

作者： Patel, Bhargav Patel, Dhaval K. Soni, Brijesh Lopez-Benitez, Miguel Kavaiya, Sagar Ahmedabad Univ Sch Engn & Appl Sci Ahmadabad Gujarat India Univ Liverpool Dept Elect Engn & Elect Liverpool Merseyside England Antonio de Nebrija Univ ARIES Res Ctr Hoyo De Manzanares Spain

ISBN: (纸本)9781728191270

Primary activity statistics contribute (PAS) significantly in increasing the efficiency of the dynamic spectrum access/cognitive radio system. PAS can be estimated from the spectrum sensing observations. To achieve a precise estimation of PAS, accurate spectrum sensing is required. However, it is difficult to maintain perfect spectrum sensing in a realistic scenario because of various hardware and sensing errors (false alarms and miss detections). In this work, Long-Short Term Memory autoencoder based deep learning framework is proposed to detect the sensing errors in imperfect spectrum sensing scenarios. Moreover, to correct the sensing errors, we propose a simple single iteration reconstruction algorithm and further estimate the PAS. The error in the estimated PAS is quantified through the Kolmogorov Smirnov distance. Finding suggests that relative error of estimated mean decreases from 80% to 9%. The proposed framework doesn't require any prior knowledge of PU activity statistics to achieve this performance making it feasible in realistic scenarios.

关键词： lstm autoencoder Primary activity statistics Reconstruction algorithms Dynamic spectrum access Imperfect spectrum sensing

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MicroMILTS: Fault Location for Microservices Based Mutual Information and lstm autoencoder 23

MicroMILTS: Fault Location for Microservices Based Mutual In...

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23rd Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Yang, Linwei Li, Jing Shi, Kuanzhi Yang, Songlin Yang, Qingfu Sun, Jiangang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Coll Artificial Intelligence Nanjing Peoples R China State Grid Corp China Informat & Commun Branch Beijing Peoples R China

ISBN: (数字)9784885523397

ISBN: (纸本)9784885523397

Driven by the development of cloud computing and artificial intelligence, architecture has dramatically improved in terms of flexibility and scalability in software development. Therefore, it is increasingly being used to build large-scale applications for agile development. However, along with the technology heterogeneity, the dynamics of running instances, and the complexity of service dependencies, fault localization is extraordinarily difficult. In this paper, we present MicroMILTS, a microservice fault location method based on mutual information and an lstm autoencoder. MicroMILTS first uses BIRCH for anomaly detection based on the analysis of the performance metrics data correlated to microservice anomalies. Once anomalies are detected, a service dependency property graph is constructed based on the real-time microservice invocation relationships and the reconstructed deviations of performance metrics with the lstm autoencoder. Next, MicroMILTS dynamically updates the weight of each node in the service dependency property graph. Then, a PageRank-based random walk is applied for further ranking root causes. Finally, a Sock-shop microservice system is built on the Huawei Cloud to evaluate the performance of MicroMILTS. The experiment shows that MicroMILTS achieves a good root cause location result, with 90.4% in precision and 91.6% in mean average precision, outperforming state-of-the-art methods.

关键词： performance metrics microservices fault location lstm autoencoder

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Proactive Fault Diagnosis of a Radiator: A Combination of Gaussian Mixture Model and lstm autoencoder

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SENSORS 2023年第21期23卷 8688-8688页

作者： Lee, Jeong-Geun Kim, Deok-Hwan Lee, Jang Hyun INHA Univ Dept Smart Digital Engn Incheon 22212 South Korea Doosan Ind Vehicle Co Ltd Incheon 22503 South Korea INHA Univ Dept Elect Engn Incheon 22212 South Korea INHA Univ Dept Naval Architecture & Ocean Engn Incheon 22212 South Korea

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states-two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model.

关键词： PHM radiator vibration anomaly detection machine learning PCA deep learning lstm autoencoder GMM

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Multistep Forecasting of Power Flow Based on lstm autoencoder: A Study Case in Regional Grid Cluster Proposal

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ENERGIES 2023年第13期16卷 5014页

作者： Aksan, Fachrizal Li, Yang Suresh, Vishnu Janik, Przemyslaw Wroclaw Univ Sci & Technol Fac Elect Engn PL-50370 Wroclaw Poland Brandenburg Univ Technol Cottbus Senftenberg Dept Energy Distribut & High Voltage Engn D-03046 Cottbus Germany

A regional grid cluster proposal is required to tackle power grid complexities and evaluate the impact of decentralized renewable energy generation. However, implementing regional grid clusters poses challenges in power flow forecasting owing to the inherent variability of renewable power generation and diverse power load behavior. Accurate forecasting is vital for monitoring the imported power during peak regional load periods and surplus power generation exported from the studied region. This study addressed the challenge of multistep bidirectional power flow forecasting by proposing an lstm autoencoder model. During the training stage, the proposed model and baseline models were developed using autotune hyperparameters to fine-tune the models and maximize their performance. The model utilized the last 6 h leading up to the current time (24 steps of 15 min intervals) to predict the power flow 1 h ahead (4 steps of 15 min intervals) from the current time. In the model evaluation stage, the proposed model achieved the lowest RMSE and MAE scores with values of 32.243 MW and 24.154 MW, respectively. In addition, it achieved a good R-2 score of 0.93. The evaluation metrics demonstrated that the lstm autoencoder outperformed the other models for multistep forecasting task in a regional grid cluster proposal.

关键词： multistep power flow forecast lstm autoencoder regional grid cluster proposal

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