OPC UA (Open Platform Communications Unified Architecture) is key for multi-heterogeneous software and hardware information integration. Some studies have proposed to apply OPC UA to smart factory information integrat...
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ISBN:
(纸本)9781538694084
OPC UA (Open Platform Communications Unified Architecture) is key for multi-heterogeneous software and hardware information integration. Some studies have proposed to apply OPC UA to smart factory information integration, but most tend to propose concepts and lack application research. While, lstm encoder-decoder (Long Short-Term Memory based encoder-decoder) is an unsupervised learning method not requiring labeled data. It has good performance in time series data analysis, but few researches relate it with smart factory predictive analysis. This paper proposed a conceptual framework of smart factory based on OPC UA and lstm encoder-decoder technology. Specifically, first, the theories are discussed including the general architecture of smart factory, information integration architecture based on OPC UA and lstm encoder-decoder model for smart factory predictive analysis. Then, we introduced the simulation result analysis. Finally, the conclusion was given.
The inherent characteristics of cloud systems often lead to anomalies, which pose challenges for high availability, reliability, and high performance. Detecting anomalies in cloud key performance indicators (KPI) is a...
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The inherent characteristics of cloud systems often lead to anomalies, which pose challenges for high availability, reliability, and high performance. Detecting anomalies in cloud key performance indicators (KPI) is a critical step towards building a secure and trustworthy system with early mitigation features. This work is motivated by (i) the efficacy of recent reconstruction-based anomaly detection (AD), (ii) the misrepresentation of the accuracy of time series anomaly detection because point-based Precision and Recall are used to evaluate the efficacy for range-based anomalies, and (iii) detects performance and security anomalies when distributions shift and overlaps. In this paper, we propose a novel semi-supervised dynamic density-based detection rule that uses the reconstruction error vectors in order to detect anomalies. We use long short-term memory networks based on encoder-decoder (lstm-ED) architecture to reconstruct the normal KPI time series. We experiment with both testbed and a diverse set of real-world datasets. The experimental results show that the dynamic density approach exhibits better performance compared to other detection rules using both standard and range-based evaluation metrics. We also compare the performance of our approach with state-of-the-art methods, outperforms in detecting both performance and security anomalies.
The proper operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial to reduce energy consumption because they are the major consumers of energy in buildings. Prognostic and Health Management S...
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The proper operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial to reduce energy consumption because they are the major consumers of energy in buildings. Prognostic and Health Management Systems (PHMS) can assist both operators and managers of Smart Buildings, anticipating potential problems that can reduce the energy efficiency of the building. However, PHMS usually lack the capability of predicting changes on Key Performance Indicators (KPIs) related to energy consumption. Therefore, having a reliable method to predict the energy demand of a building's HVAC system is an important complement to a building's PHMS. This paper proposes a data-driven methodology to create predictive models of energy demand taking into account the effect of weather conditions on energy consumption. The methodology consists of two stages. The first considers the environmental variables affecting the building to identify clusters with similar conditions. For the second stage, Deep Learning Long-short Term Memory (lstm) models in an encoder-decoder architecture were used. Our proposal is to build two different types of models, named reference cluster and recent models, both being able to predict the total heating demand of a smart building in the short and medium term, one day and one week in advance. The reference cluster model is built with historical data from the distant past for each cluster. The recent model is built with historical data from the immediate past. In this paper we have applied this methodology to a smart educational building: the Alice Perry School of Engineering at the National University of Ireland in Galway. This case study illustrates the methodology and successfully tests its feasibility. Both reference cluster and recent models provide predictions achieving the acceptance criteria in this domain.
The challenge faced by industrial control systems is that they are vulnerable to adversarial sample attacks. In the ICS field, the challenge with adversarial sample attacks is that the adversarial samples generated by...
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The challenge faced by industrial control systems is that they are vulnerable to adversarial sample attacks. In the ICS field, the challenge with adversarial sample attacks is that the adversarial samples generated by the attack do not conform to protocol specifications. The challenge of adversarial sample defense is that it is difficult to design a defense model without information about the adversarial samples. To tackle these challenges, we propose an adversarial sample attack and defense method based on Long Short -Term Memory Networks based encoder -decoder (lstm-ED). The objectives are to address challenges of adversarial samples not conforming to protocol specifications and physical meaning, inefficient generation of adversarial samples, and the difficulty of designing a defense model without information about adversarial samples. Our adversarial sample attack efficiently generates samples conforming to protocol specifications and physical meaning by adding perturbation values to sensors and actuators, while complying with feature constraints. Subsequently, we introduce an lstmED Feature Weight defense method (lstm-FWED) designed without explicit adversarial sample information. In lstm-FWED, we normalize reconstruction errors across different features to prevent anomaly scores from being influenced by poorly predicted features, thereby ensuring robust defense results. We validate the effectiveness of our approach on a real -world critical infrastructure testbed. The proposed adversarial sample attack reduces the precision of the lstm-ED model by an average of 66.26%, with a maximum adversarial sample generation time of 18 seconds, significantly improving attack efficiency. Furthermore, in comprehensive experiments, lstmFWED demonstrates an average AUC improvement of 21.83% compared to state-of-the-art anomaly detection baseline methods.
For detecting anomalies which are unexpected behaviors in complex systems, deep learning-based anomaly detection algorithms for multivariate time series have gained a lot of attention recently. While many anomaly dete...
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ISBN:
(纸本)9783030922313;9783030922306
For detecting anomalies which are unexpected behaviors in complex systems, deep learning-based anomaly detection algorithms for multivariate time series have gained a lot of attention recently. While many anomaly detection algorithms have been widely proposed, there has been no work on how to perform these detection algorithms for multivariate data streams with a stream processing framework. To address this issue, we present a real-time deep learning-based anomaly detection approach for multivariate data streams with Apache Flink. We train a lstm encoder-decoder model to reconstruct a multivariate input sequence and develop a detection algorithm that uses reconstruction error between the input sequence and the reconstructed sequence. We show that our anomaly detection algorithm can provide promising performance on a real-world dataset. Then, we develop a Flink program by implementing three operators which process and transform multivariate data streams in a specific order. The Flink program outputs anomaly detection results in real time, making system experts can easily receive notices of critical issues and resolve the issues by appropriate actions to maintain the health of the systems.
Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the...
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Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are: center dot Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling. center dot Advanced Predictive Modeling: Employs hybrid autoencoder-lstm and encoder-decoderlstm models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score. center dot Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.
The deployment of Internet of Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. The new security and reliability challenges require a holistic solution due to the cross-d...
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ISBN:
(纸本)9781665423243
The deployment of Internet of Things (IoT) devices in cyber-physical applications has introduced a new set of vulnerabilities. The new security and reliability challenges require a holistic solution due to the cross-domain, cross-layer, and interdisciplinary nature of IoT systems. However, the majority of works presented in the literature primarily focus on the cyber aspect, including the network and application layers, and the physical layer is often overlooked. In this paper, we utilize IoT sensors that capture the physical properties of the system to ensure the integrity of IoT sensors data and identify anomalous incidents in the environment. We propose an adaptive context-aware anomaly detection method that is optimized to run on a fog computing platform. In this approach, we devise a novel sensor association algorithm that generates fingerprints of sensors, clusters them, and extracts the context of the system. Based on the contextual information, our predictor model, which comprises an Long-Short Term Memory (lstm) neural network and Gaussian estimator, detects anomalies, and a consensus algorithm identifies the source of the anomaly. Furthermore, our model updates itself to adapt to the variation in the environment and system. The results demonstrate that our model detects the anomaly with 92.0% precision in 532ms, which meets the real-time constraint of the system under test.
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