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 use...
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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.
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 ...
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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.
The reliability of Heavy-Duty Vehicles (HDVs) is critical for continuous operations in sectors like transportation and logistics. However, the complexity of these vehicles' subsystems, including the Air Pressure S...
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ISBN:
(纸本)9798350384994;9798350384987
The reliability of Heavy-Duty Vehicles (HDVs) is critical for continuous operations in sectors like transportation and logistics. However, the complexity of these vehicles' subsystems, including the Air Pressure System (APS), poses significant challenges where failures lead to costly downtimes and safety risks. This paper introduces a novel semi-supervised anomaly detection approach based on a Long Short-Term Memory autoencoder (lstm-AE) model to identify APS failures in HDVs. Leveraging 30 days of operational time-series data from 140 vehicles, of which 30 experienced APS failures, our study presents a semi-supervised formulation of the problem bypassing the limitations of supervised classification and addressing the scarcity of labeled data in the real-world scenarios. After applying several essential preprocessing steps, the proposed model was rigorously trained and validated to ensure robustness. It achieved an F1 score of 0.75 with a corresponding accuracy of 91.4%. The proposed framework in this research promotes enhanced vehicle uptime and improved safety standards, providing practical implications for both HDV manufacturers and operators.
This paper proposes a deep-learning-based method for frequency-dependent grid impedance estimation. Through measurement of voltages and currents at a specific system bus, the estimate of the grid impedance was obtaine...
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This paper proposes a deep-learning-based method for frequency-dependent grid impedance estimation. Through measurement of voltages and currents at a specific system bus, the estimate of the grid impedance was obtained by first extracting the sequences of the time-dependent features for the measured data using a long short-term memory autoencoder (lstm-AE) followed by a random forest (RF) regression method to find the nonlinear map function between extracted features and the corresponding grid impedance for a wide range of frequencies. The method was trained via simulation by using time-series measurements (i.e., voltage and current) for different system parameters and verified through several case studies. The obtained results show that: (1) extracting the time-dependent features of the voltage/current data improves the performance of the RF regression method;(2) the RF regression method is robust and allows grid impedance estimation within 1.5 grid cycles;(3) the proposed method can effectively estimate the grid impedance both in steady state and in case of large transients like electrical faults.
Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications f...
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Anomaly occurrences in hydraulic machinery might lead to massive system shut down, jeopardizing the safety of the machinery and its surrounding human operator(s) and environment, and the severe economic implications following the faults and their associated damage. Hydraulics are mostly placed in ruthless environments, where they are consistently vulnerable to many faults. Hence, not only are the machines and their components prone to anomalies, but also the sensors attached to them, which monitor and report their health and behavioral changes. In this work, a comprehensive applicational analysis of anomalies in hydraulic systems extracted from a hydraulic test rig was thoroughly achieved. First, we provided a combination of a new architecture of lstm autoencoders and supervised machine and deep learning methodologies, to perform two separate stages of fault detection and diagnosis. The two phases were condensed by-the detection phase using the lstm autoencoder. Followed by the fault diagnosis phase represented by the classification schema. The previously mentioned framework was applied to both component and sensor faults in hydraulic systems, deployed in the form of two in-depth applicational experiments. Moreover, a thorough literature review of related work from the past decade, for autoencoders related fault detection and diagnosis in hydraulic systems, was successfully conducted in this study.
The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative so...
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The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applications in everyday life. However, data reliability is often compromised due to the vulnerability of sensors to faults arising from harsh operational conditions that can adversely affect the subsequent operations that depend on the collected data. Hence, the identification of anomalies within sensor-derived data holds significant importance in the IoT context. This article proposes a sensor fault detection method using a Long Short-Term Memory autoencoder (lstm-AE). The AE, trained on normal sensor data, predicts a 20-step window, generating three statistical features via SHapley Additive exPlanations from the estimated steps. These features aid in determining potential faults in the predicted steps using a machine learning classifier. A secondary classifier identifies the type of fault in the sensor signal. Experimentation on two sensor datasets showcases the method's functionality, achieving fault detection accuracies of approximately 93% and 97%. It is possible to attain a perfect fault classification performance by slightly modifying the feature calculation approach. In a univariate prediction scenario, our proposed approach demonstrates good fault detection and classification performance.
The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was ...
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The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal data. To emphasize these specific frequency bands, high-pass filters were applied to maximize the difference. Subsequently, the dimensions of the data were reduced through principal component analysis, giving unique characteristics to the data preprocessing process. Normal data collected from a wind farm located in northern Sweden was first preprocessed and trained using a long short-term memory (lstm) autoencoder to perform outlier detection. The lstm autoencoder is a model specialized for time-series data that learns the patterns of normal data and detects other data as outliers. Therefore, we propose a method for outlier detection through data preprocessing and unsupervised learning, utilizing the vibration signals from wind generators. This will facilitate the quick and accurate detection of wind power generator failures and provide alternatives to the problem of energy depletion.
One-class video classification (anomalous video detection) serves an important role when abnormal videos are absent during training, poorly sampled or not well defined. However, one-class video classification is chall...
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ISBN:
(纸本)9781728108582
One-class video classification (anomalous video detection) serves an important role when abnormal videos are absent during training, poorly sampled or not well defined. However, one-class video classification is challenging. Due to the unavailability of abnormal samples, it is a cumbersome task to train an end-to-end deep supervised learning model. Meanwhile, video data representation is challenging because of the unstructured scheme of video contents. To represent video data with temporal and spatial information, we propose multiple dynamic images in our task because dynamic image encodes the temporal evolution of video frames and represents video contents at the level of the image pixels. Multiple dynamic images are viewed as the input sequence with temporal and spatial information and achieve dimension reduction of original video data. In this paper, we propose a lstm-autoencoder-based adversarial learning model for one-class video classification ("VidAnomaly") without abnormal samples in the training stage. Our architecture is composed of three sub-networks. lstm-autoencoder network (R) learns the temporal dependence of the input sequence and reconstructs the input sequence for the discriminator network (D) to achieve adversarial learning. The novelty of the proposed model is that we add an additional lstm-encoder network (A) to obtain the latent representation of the reconstructed sequence. Minimizing the distance between the two latent representations from R and A benefits the model to further capture the training data distribution because it forces the lstm-autoencoder network to yield an essential representation of training samples in latent space. In the inference stage, for a given abnormal sample as the input, the model poorly reconstructs the input abnormal sample and the reconstruction error would be high because the proposed model is trained merely on normal samples and its parameters are only suitable for reconstructing normal samples. Based on this,
The neonatal period is a critical stage where physiological adaptations for extra-uterine life occur, and newborns are vulnerable to various diseases and disorders. Among these conditions, preterm neonates (PN) born b...
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ISBN:
(纸本)9783031349522;9783031349539
The neonatal period is a critical stage where physiological adaptations for extra-uterine life occur, and newborns are vulnerable to various diseases and disorders. Among these conditions, preterm neonates (PN) born before 37 weeks' gestation are at higher risk of developing intraventricular hemorrhage (IVH), a common complication that can result in severe neurological complications such as cerebral palsy, developmental delays, and cognitive impairments. Early detection and intervention are essential to prevent long-term consequences. Non-invasive cardiac output monitors (NICOM) have been widely accepted in the neonatal intensive care unit (NICU) for monitoring hemodynamic parameters and have provided vast amounts of data. However, further research is required to explore their predictive tendencies in relation to IVH. The present study aimed to evaluate the potential of deep learning models to enhance early detection and prevention of IVH in preterm neonates using NICOM parameters. From this study, it was shown that by the lstm autoencoders are able to predict IVH with moderate precision and accuracy but poor specificity. Nonetheless, this study represents a significant step towards developing a non-invasive, accurate, and timely method for monitoring and preventing IVH in preterm neonates, especially in low-resource settings.
Since creating labelled data in the field of remote sensing requires time and manpower, it has become important to use unlabelled data. In this paper we study a semi supervised long short term memory autocoder approac...
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ISBN:
(纸本)9781665436496
Since creating labelled data in the field of remote sensing requires time and manpower, it has become important to use unlabelled data. In this paper we study a semi supervised long short term memory autocoder approach for crop classification with multi-temporal remote sensing data. In this study, a long short-term memory autoencoder network was trained with unlabelled data and the learned weights were used for the initialization of a model trained for classification with labelled data. The challenging Breizhcrops time series dataset, and multitemporal images of the Sakarya region were used for validation. It has been observed that the network trained with unlabelled data outperformed the network trained with only labelled data. The results shed light on the potential for unlabelled data use in the crop classification field.
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