The major challenge in predictive maintenance (PdM) is an insufficiency of failure data used to train the model and the high complexity of industrial plants, where operational conditions changed over time hence the pr...
详细信息
ISBN:
(数字)9781665485593
ISBN:
(纸本)9781665485593
The major challenge in predictive maintenance (PdM) is an insufficiency of failure data used to train the model and the high complexity of industrial plants, where operational conditions changed over time hence the predetermined threshold will change frequently. Novelty detection offers a solution to this problem by detecting anomalies through learning only the normal data. This study implements a novelty detection method as a one-class classification to detect early signs of failure in a rolling bearing using long short-term memory (lstm) autoencoder. lstm autoencoder is a deep learning algorithm combining an autoencoder and lstm network to reconstruct the normal data to learn nonlinear relationships and temporal nature. The model was tested on historical multivariate time-series fault data provided by Case Western Reserve University. The dropout layer is implemented to reduce overfitting by creating an ensemble model of a neural network. The results suggested that the lstm autoencoder can effectively differentiate between normal and fault patterns of the bearing with up to 92 % accuracy.
Personalised or intelligent tutoring systems are being rapidly adopted because they enable tailored learner choices in, for example, exercise materials, study time, and intensity (i.e., the number of chosen exercises)...
详细信息
ISBN:
(纸本)9783031362712;9783031362729
Personalised or intelligent tutoring systems are being rapidly adopted because they enable tailored learner choices in, for example, exercise materials, study time, and intensity (i.e., the number of chosen exercises) over extended periods of time. This, however, poses significant challenges for profiling the characteristics of learner behaviors, mostly due to the great diversity in each individual's learning path, the timing of exercise accomplishments, and varying degrees of engagement over time. To address this problem, this paper proposes an innovative approach that uses self-supervised deep learning to consolidate learner behaviors and performance into compact representations via irregular multivariate time series modeling. These representations can be used to highlight learners' multi-dimensional behavioral characteristics on a massive scale for selfdirected learners who can freely pick exercises and study at their own pace. With experiments on a large-scale real-world dataset, we empirically show that our approach can effectively reveal learner individuality as well as commonality in characteristics.
Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requi...
详细信息
Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (lstm-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The lstm-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution,
The prompt detection and diagnosis of anomalies in oil wells are fundamental to reduce production losses, maintenance costs and to avoid environmental damage. In this paper, a proposal for detecting such anomalies usi...
详细信息
The prompt detection and diagnosis of anomalies in oil wells are fundamental to reduce production losses, maintenance costs and to avoid environmental damage. In this paper, a proposal for detecting such anomalies using Long Short-Term Memory (lstm) autoencoder and one-class Support Vector Machine (SVM) is presented. A public dataset with instances of eight types of common anomalies characterized by eight process variables is used for training and performance evaluation. A methodology is proposed for training and performance evaluation. Faulty data are used as the target class for one-class classifiers. A time-shift of labels is proposed to improve the discrimination of normal and faulty data during the training phase. Criteria for selecting the time-shifting of the labels are proposed as well. A performance metric is proposed by computing the average of Specificity, F1, and instance identification performance. The detection time compared to the transient time of the fault was also computed. The analyses are performed with emphasis on two classes of anomalies, with slow and fast dynamics. For the fault with fast dynamic, performance was increased by 14% for lstm with no improvement for SVM. For the slow fault, the performance was increased by 41% for lstm and 21% for SVM. A noticeable reduction of detection time was observed for the slow fault, from 100% to 59.34% for lstm and 87.7% to 64.51% for SVM (100% corresponds to the transient time of the fault). A comparison with random forest and decision tree classifiers presented in the literature and applied to the same classes of anomalies shows the superiority of the proposed approach and methods. The special attention given to the metrics used for evaluating classifier performance and the time to detect the anomalies in the instances show that apparently good results measured by F1 and accuracy sometimes hide some weakness.
Though hyperspectral remote sensing images contain rich spatial and spectral information, they pose challenges in terms of feature extraction and mining. This paper describes the integration of a dimensionality reduct...
详细信息
Though hyperspectral remote sensing images contain rich spatial and spectral information, they pose challenges in terms of feature extraction and mining. This paper describes the integration of a dimensionality reduction technique that employs spectral attention and Hybrid Spectral Networks (HybridSN) with spatial attention for hyperspectral image classification. The goal of this approach is to improve the ability to classify hyperspectral images by increasing the capabilities of spectral-spatial feature fusion. Experiments on three hyperspectral datasets (Indian Pines, University of Pavia, and Houston University) demonstrate that our method's overall accuracy is 99.66%, 99.97%, and 99.17% under 20% of the training samples, respectively, which is superior to several well-known approaches.
In a world where energy efficiency and system reliability play a vital role, safeguarding these systems from various anomalies is of utmost significance. This task is demanding and requires a substantial amount of eff...
详细信息
A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently,...
详细信息
A graph is a data structure consisting of nodes and edges between these nodes. Graph embedding is to generate a low dimensional vector for a given graph that best represents the characteristics of the graph. Recently, there have been studies on graph embedding, especially using deep learning techniques. However, until now, most deep learning-based graph embedding techniques have focused on unweighted graphs. Therefore, in this paper, we propose a graph embedding technique for weighted graphs based on long short-term memory (lstm) autoencoders. Given weighted graphs, we traverse each graph to extract node-weight sequences from the graph. Each node-weight sequence represents a path in the graph consisting of nodes and the weights between these nodes. We then train an lstm autoencoder on the extracted node-weight sequences and encode each node-weight sequence into a fixed-length vector using the trained lstm autoencoder. Finally, for each graph, we collect the encoding vectors obtained from the graph and combine them to generate the final embedding vector for the graph. These embedding vectors can be used to classify weighted graphs or to search for similar weighted graphs. The experiments on synthetic and real datasets show that the proposed method is effective in measuring the similarity between weighted graphs.
In recent times, Cyber Physical Systems (CPSs) have been extensively deployed in vital infrastructures to provide crucial services to society. In the smart factory scenario, the Industrial Control Systems (ICSs) are C...
详细信息
In recent times, Cyber Physical Systems (CPSs) have been extensively deployed in vital infrastructures to provide crucial services to society. In the smart factory scenario, the Industrial Control Systems (ICSs) are CPSs that autonomously regulate the manufacturing process through sensors and actuators. Protection of these industrial control systems against cyber threats is essential to prevent any malfunction in the production procedure. In this article, a Fuzzy Controller-enabled autoencoder Framework (FCAF) is proposed. The proposed framework can detect anomalies that may arise due to cyberattacks in the smart factory environment. The results show evidence that the model can sense any rapid deviation from normal behaviour once the attack commences and also detects unforeseen anomalies. The model further reduces the bias arising from the machine learning algorithm by introducing a fuzzy controller. The highest specificity and sensitivity reported by the proposed technique are 93% and 49.9%, respectively.
Performance isolation is an essential property that network virtualization must provide for clouds. This study addresses the performance isolation of the control plane in virtualized software-defined networking (SDN),...
详细信息
ISBN:
(纸本)9798350301199
Performance isolation is an essential property that network virtualization must provide for clouds. This study addresses the performance isolation of the control plane in virtualized software-defined networking (SDN), which we call control channel isolation. First, we report that the control channel isolation is seriously broken in the existing network hypervisor in that the end-to-end control latency grows by up to 15x as the number of virtual switches increases. This jeopardizes the key network operations, such as routing, in datacenters. To address this issue, we take a machine learning approach that learns from the past control traffic as time-series data. We propose a new network hypervisor, Meteor, that designs an lstm autoencoder to predict the control traffic per virtual switch. Our evaluation results show that Meteor improves the processing latency per control message by up to 12.7x. Furthermore, Meteor reduces the end-to-end control latency by up to 73.7%, which makes it comparable to the non-virtualized SDN.
Infectious diseases epidemics such as the current COVID-19 pandemic have an immense impact on all facets of life. Consequently, the current dearth of effective and timely public health surveillance methods, especially...
详细信息
ISBN:
(纸本)9781728162515
Infectious diseases epidemics such as the current COVID-19 pandemic have an immense impact on all facets of life. Consequently, the current dearth of effective and timely public health surveillance methods, especially at the individual level, have been accentuated, prompting research into supplementary methods. Sensor-rich, ubiquitously owned smartphones can now gather large volumes of data that has been utilized for passive and continuous physical and mental health assessment. In this paper, we propose a Deep learning based Smartphone Early Ailment Sensing (DeepSEAS) framework that predicts a smart-phone user's future manifestation of influenza-like biological symptoms (e.g. coughing and sneezing) a day early while they are still asymptomatic. DeepSEAS works by analyzing a subject's historical one-day smartphone sensor and mobility data. First, we utilize the mean shift clustering algorithm to create clusters of users with similar social and behavioral traits such as their socialization levels, social media presence, eating and working out habits. Then, DeepSEAS employs an end-to-end trainable lstm autoencoder (lstm AE) coupled with a Feed Forward Neural network classifier, achieving a sensitivity of 78% in correctly identifying users who will manifest biological symptoms a day later. DeepSEAS facilitates up-to-date influenza surveillance at the individual level, which could transform the current healthcare system. Early detection can enable asymptomatic users to be alerted, notified and isolated, which could reduce disease transmission.
暂无评论