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lstm-autoencoder-Based Incremental Learning for Industrial Internet of Things

IEEE ACCESS 2023年 11卷 137929-137936页

作者： Takele, Atallo Kassaw Villanyi, Balazs Budapest Univ Technol & Econ Fac Elect Engn & Informat Dept Elect Technol H-1117 Budapest Hungary

Edge-based intelligent data analytics supports the Industrial Internet of Things (IIoT) to enable efficient manufacturing. Incremental learning in the edge-based data analytics has the potential to analyze continuously collected real-time data. However, additional efforts are needed to address performance, latency, resource utilization and storage of historical data challenges. This paper introduces an incremental learning approach based on Long-Short Term Memory (lstm) autoencoders, by sparsening the weight matrix and taking samples from previously trained sub-datasets. The aim is to minimize the resources utilized while training redundant knowledge for edge devices of IIoT. The degree of sparsity can be determined by the redundancy of patterns, and the inverse of the coefficient of variation has been utilized to recognize it. A higher value of the inverse of the coefficient of variation shows that the values of the weight matrix are close to each other, which indicates the redundancy of knowledge, and vice versa. In addition, the coefficient of variation has been applied for limiting the size of samples from the previously trained sub-datasets. The experiment conducted using the IIoT testbed dataset demonstrates substantial enhancements in resource optimization without compromising performance.

关键词： Training Industrial Internet of Things Real-time systems Performance evaluation Edge computing Task analysis Servers Encoding Weight measurement Industrial internet of things (IIoT) incremental learning lstm-autoencoder weight sparsification

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lstm-autoencoder-based Interpretable Predictive Maintenance Framework for Industrial Systems

LSTM-Autoencoder-based Interpretable Predictive Maintenance ...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Agrawal, Anmol Sinha, Aparna Das, Debanjan IIIT Naya Raipur Dept ECE Naya Raipur India

ISBN: (纸本)9798350380903;9798350380910

The overall efficiency of thermal power plants is largely dependent on the health and performance of its various systems, of which the air pre-heaters (APHs) are one of the most crucial. The existing APH fault diagnosis techniques require labeled data for training, which is challenging to obtain in real-life scenarios. To mitigate the existing drawbacks, this study presents an unsupervised fault detection method based on lstm-autoencoders, which gives 96% accuracy even in the absence of labeled data. Further, a Random Forest regressor has been utilized to predict the time until failure, with a Mean Absolute Error of 2 days. Explainable AI (XAI) is used to improve the interpretability of the prognosis model by shedding light on feature influences and decision-making. A root cause analysis was also performed to determine how the process parameters affect the fault occurrence in APH. The comprehensive approach to fault detection, prognosis, and root cause analysis contributes significantly to fault management and system optimization, ensuring the reliability and efficiency of industrial processes.

关键词： Unsupervised learning lstm-autoencoder Prognosis Root-cause analysis XAI

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An lstm-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第4期34卷 1815-1831页

作者： Shi, Zhangyue Al Mamun, Abdullah Kan, Chen Tian, Wenmeng Liu, Chenang Oklahoma State Univ Sch Ind Engn & Management Stillwater OK 74078 USA Mississippi State Univ Dept Ind & Syst Engn Mississippi State MS 39762 USA Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX 76019 USA

Additive manufacturing (AM) has gained increasing popularity in a large variety of mission-critical fields, such as aerospace, medical, and transportation. The layer-by-layer fabrication scheme of the AM significantly enhances fabrication flexibility, resulting in the expanded vulnerability space of cyber-physical AM systems. This potentially leads to altered AM parts with compromised mechanical properties and functionalities. Furthermore, those internal alterations in the AM builds are very challenging to detect using the traditional geometric dimensioning and tolerancing (GD&T) features. Therefore, how to effectively monitor and accurately detect cyber-physical attacks becomes a critical barrier for the broader adoption of AM technology. To address this issue, this paper proposes a machine learning-driven online side channel monitoring approach for AM process authentication. A data-driven feature extraction approach based on the lstm-autoencoder is developed to detect the unintended process/product alterations caused by cyber-physical attacks. Both supervised and unsupervised monitoring schemes are implemented based on the extracted features. To validate the effectiveness of the proposed method, real-world case studies were conducted using a fused filament fabrication (FFF) platform equipped with two accelerometers. In the case study, two different types of cyber-physical attacks are implemented to mimic the potential real-world process alterations. Experimental results demonstrate that the proposed method outperforms conventional process monitoring methods, and it can effectively detect part geometry and layer thickness alterations in a real-time manner.

关键词： Additive manufacturing Cyber-physical security lstm-autoencoder Online attack detection Process authentication Side channel

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A Study on Tool Breakage Detection During Milling Process Using lstm-autoencoder and Gaussian Mixture Model

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2022年第6期23卷 667-675页

作者： Nam, Jun Sik Kwon, Won Tae Univ Seoul Dept Mech & Informat Engn Seoul South Korea

In the milling process, a rotating cutting tool is used to cut the raw material into the desired shape. Since tool breakage adversely affects productivity, real-time tool breakage detection is required. In this study, a tool breakage monitoring system using AE signals and a deep learning model was investigated. First, lstm-autoencoder was constructed and trained using the AE signal, cutting speed, spindle speed, and depth of cut as input data. In order to distinguish between tool normality and anomalies, the largest value among the normal cutting data set was determined as the threshold to determine if the tool was broken. As the result of the experiment, we obtained the accuracy of 82.1% during normal cutting, but the accuracy was significantly reduced to 63.1% and 63.6% at the time of entry/exit. This is because the AE value that occurs during normal entry/exit is so large that it is mistaken for breakage. To overcome this problem, a combined model that uses both lstm-autoencoder and Gaussian Mixture Model was developed. First lstm-autoencoder was used to determine the breakage, and then Gaussian Mixture Model was used to determine the authenticity of the breakage. As a result of the experiment using the developed model, 52 out of 57 cuttings including entry/exit cutting were detected as failures, showing a high reliability of 91.2%, proving the superiority of the combined model.

关键词： Tool breakage detection Anomaly detection Deep learning lstm-autoencoder Gaussian mixture model

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Compression of EEG signals with the lstm-autoencoder via domain adaptation approach

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2024年 1-14页

作者： Liu, Yongfei Yang, Fan Wu, Binbin Qinghai Normal Univ Sch Comp Xining Peoples R China Qinghai Coll Architectural Technol Dept Math Xining Peoples R China Qinghai Univ Coll Civil & Hydraul Engn Xining Qinghai Peoples R China

The successful implementation of neural network-based EEG signal compression has led to significant cost reductions in data transmission. However, a major obstacle in this process arises from the decline in performance when compressing EEG signals from multiple subjects. This challenge arises due to the notable feature shift of EEG signals between subjects, which poses an impediment to the neural network's efficient concurrent acquisition of information from multiple subjects. To address this limitation and enable more effective utilization of data for improving the performance on target domain, we propose a Domain Adaptation (DA) framework based on lstm-autoencoder. Our experiments encompassed the following: (1) A comparison between lstm-autoencoder, GRU-autoencoder, and the commonly used convolutional autoencoder (CAE) in EEG compression. (2) A comparison between our proposed DA method and the MMD-based DA method, as well as Fine-tuning transfer learning. The results demonstrate the following: (1) lstm-autoencoder outperforms other models in both subject-specific and cross-subject scenarios. (2) Using transfer learning improves the performance of lstm-autoencoder on the target subject. (3) Our proposed method outperforms maximum mean discrepancy (MMD)-based domain adaptation and fine-tuning approaches, resulting in a more significant enhancement.

关键词： EEG compression lstm-autoencoder domain adaptation

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An Autocorrelation-based lstm-autoencoder for Anomaly Detection on Time-Series Data 8

An Autocorrelation-based LSTM-Autoencoder for Anomaly Detect...

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8th IEEE International Conference on Big Data (Big Data)

作者： Homayouni, Hajar Ghosh, Sudipto Ray, Indrakshi Gondalia, Shlok Duggan, Jerry Kahn, Michael G. Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Colorado State Univ Energy Inst Ft Collins CO 80523 USA Univ Colorado Anschutz Med Campus Boulder CO 80309 USA

ISBN: (纸本)9781728162515

Data quality significantly impacts the results of data analytics. Researchers have proposed machine learning based anomaly detection techniques to identify incorrect data. Existing approaches fail to (1) identify the underlying domain constraints violated by the anomalous data, and (2) generate explanations of these violations in a form comprehensible to domain experts. We propose IDEAL, which is an lstm-autoencoder based approach that detects anomalies in multivariate time-series data, generates domain constraints, and reports subsequences that violate the constraints as anomalies. We propose an automated autocorrelation-based windowing approach to adjust the network input size, thereby improving the correctness and performance of constraint discovery over manual and brute-force approaches. The anomalies are visualized in a manner comprehensible to domain experts in the form of decision trees extracted from a random forest classifier. D omain e xperts c an t hen provide feedback to retrain the learning model and improve the accuracy of the process. We evaluate the effectiveness of IDEAL using datasets from Yahoo servers, NASA Shuttle, and Colorado State University Energy Institute. We demonstrate that IDEAL can detect previously known anomalies from these datasets. Using mutation analysis, we show that IDEAL can detect different types of injected faults. We also demonstrate that the accuracy improves after incorporating domain expert feedback.

关键词： Anomaly detection Autocorrelation Data quality tests Explainability lstm-autoencoder Time series

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Active learning for lstm-autoencoder-based anomaly detection in electrocardiogram readings

Active learning for LSTM-autoencoder-based anomaly detection...

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2020 Workshop on Interactive Adaptive Learning, IAL 2020

作者： Šabata, Tomáš Holeňa, Martin Faculty of Information Technology Czech Technical University in Prague Prague Czech Republic Institute of Computer Science Czech Academy of Sciences Prague Czech Republic

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Deep learning for online AC False Data Injection Attack detection in smart grids: An approach using lstm-autoencoder

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2021年 193卷 103178-103178页

作者： Yang, Liqun Zhai, You Li, Zhoujun Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

The Power system is a crucial Cyber-Physical system and is prone to the False Data Injection Attack (FDIA). The existing FDIA detection mechanism focuses on DC state estimation. In this paper, we propose a phased AC FDIA targeting at generation rescheduling and load shedding. After injecting the false data into the measurements, the estimated states will be deviated from those in normal conditions. The proposed mechanism extracts the spatial and spectral features of the modes decomposed from the estimated states using variational mode decomposition (VMD). Then lstm-autoencoder is trained by learning the temporal correlations between the multi-dimensional feature vectors. The reconstruction error deviation vectors of the feature vectors are calculated and updated by lstm-autoencoder. Based on these error deviation vectors, the Logistic Regression (LR) classifier is trained to determine whether the error deviation vector is abnormal. We evaluate the performance of the proposed mechanism with comprehensive simulations on IEEE 14 and 118-bus systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy.

关键词： False data injection attack AC state Estimation Variational mode decomposition lstm-autoencoder Deviation vector Logistic regression

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Anomaly detection in groundwater monitoring data using lstm-autoencoder neural networks

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第8期196卷 692-692页

作者： Roukerd, Fatemeh Rezaiezadeh Rajabi, Mohammad Mahdi Tarbiat Modares Univ Civil & Environm Engn Fac POB 14115-397 Tehran Iran Univ Luxembourg Fac Sci Technol & Med FSTM Dept Engn Maison Nombre6 Ave Fonte L-4364 Esch Sur Alzette Luxembourg

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (lstm)-autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the lstm-autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed lstm-autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

关键词： Anomaly detection Groundwater lstm-autoencoder Uncertainty Monte Carlo SEAWAT Negative Log Likelihood

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电网数据流中的异常检测技术及应用

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微型计算机 2025年第4期 46-48页

作者：王奕张梓扬北京中电普华信息技术有限公司北京100192 新西兰奥克兰大学新西兰奥克兰1052

本研究通过分析大规模电网时间序列数据,提出了一种基于深度学习的异常检测模型。该模型结合了长短期记忆网络(lstm)和自编码器(autoencoder),能够有效捕捉电网数据的时间依赖性和非线性特征。实验结果表明,所提出的方法在检测准确率和... 详细信息

本研究通过分析大规模电网时间序列数据,提出了一种基于深度学习的异常检测模型。该模型结合了长短期记忆网络(lstm)和自编码器(autoencoder),能够有效捕捉电网数据的时间依赖性和非线性特征。实验结果表明,所提出的方法在检测准确率和实时性能上均优于传统方法。此外,研究还探讨了该技术在电网故障预警、负载预测和电力窃取检测等领域的具体应用,为提高电网运行的安全性和效率提供了新的思路。

关键词：异常检测电网数据流深度学习 lstm-autoencoder 实时监控

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