Online Fault Classification in HPC Systems through Machine Learning

As High-Performance Computing (HPC) systems strive towards the exascale goal, studies suggest that they will experience excessive failure rates. For this reason, detecting and classifying faults in HPC systems as they occur and initiating corrective actions before they can transform into failures will be essential for continued operation. In this paper, we propose a fault classification method for HPC systems based on machine learning that has been designed specifically to operate with live streamed data. We cast the problem and its solution within realistic operating constraints of online use. Our results show that almost perfect classification accuracy can be reached for different fault types with low computational overhead and minimal delay. We have based our study on a local dataset, which we make publicly available, that was acquired by injecting faults to an in-house experimental HPC system.Comment: Accepted for publication at the Euro-Par 2019 conferenc

Distributed Intermittent Fault Diagnosis in Wireless Sensor Network Using Likelihood Ratio Test

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%

Predicting Electrical Faults in Power Distribution Network

Electricity is becoming increasingly important in modern civilization, and as a result, the emphasis on and use of power infrastructure is gradually expanding. Simultaneously, investment and distribution modes are shifting from the large-scale centralized generation of electricity and sheer consumption to decentralized generators and extremely sophisticated clients. This transformation puts further strain on old infrastructure, necessitating significant expenditures in future years to ensure a consistent supply. Subsequent technical and prediction technologies can help to maximize the use of the current grid while lowering the probability of faults. This study discusses some of the local grid difficulties as well as a prospective maintenance and failure probabilistic model. To provide an effective and convenient power source to consumers, a high Volta protects and maintains under fault conditions. Most of the fault identification and localization approaches rely on real and reactive power converter observations of electronic values. This can be seen in metrics and ground evaluations derived via internet traffic. This paper provides a thorough examination of the mechanisms for error detection, diagnosis, and localization in overhead lines. The proposal is then able to make suggestions about the ways that can be incorporated to predict foreseen faults in the electrical network. The three classifiers, Random Forest, XGBoost and Decision tree are producing high accuracies, while Logistic Regression and SVM are producing realistic accuracy results

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems

An early Little Ice Age brackish water invasion along the south coast of the Caspian Sea (sediment of Langarud wetland) and its wider impacts on environment and people

Caspian Sea level has undergone significant changes through time with major impacts not only on the surrounding coasts, but also offshore. This study reports a brackish water invasion on the southern coast of the Caspian Sea constructed from a multi-proxy analysis of sediment retrieved from the Langarud wetland. The ground surface level of wetland is >6 m higher than the current Caspian Sea level (at -27.41 m in 2014) and located >11 km far from the coast. A sequence covering the last millennium was dated by three radiocarbon dates. The results from this new study suggest that Caspian Sea level rose up to at least -21.44 m (i.e. >6 m above the present water level) during the early Little Ice Age. Although previous studies in the southern coast of the Caspian Sea have detected a high-stand during the Little Ice Age period, this study presents the first evidence that this high-stand reached so far inland and at such a high altitude. Moreover, it confirms one of the very few earlier estimates of a high-stand at -21 m for the second half of the 14th century. The effects of this large-scale brackish water invasion on soil properties would have caused severe disruption to regional agriculture, thereby destabilizing local dynasties and facilitating a rapid Turko-Mongol expansion of Tamerlane’s armies from the east.N Ghasemi (INIOAS), V Jahani (Gilan Province Cultural Heritage and Tourism Organisation) and A Naqinezhad (University of Mazandaran), INQUA QuickLakeH project (no. 1227) and to the European project Marie Curie, CLIMSEAS-PIRSES-GA-2009-24751

Data Mining Pipeline for Performing Decision Tree Analysis On Mortality Dataset With ICD-10 Codes

Modernization of the healthcare sector has led to the introduction of wider and newer varieties of medical devices in hospitals. Consequently, there are increasing numbers of infectious complications related to medical devices. However, managing and monitoring the risk of medical devices are difficult and costly. The hospitals and the healthcare device service providers require effective means to manage the healthcare device maintenance to provide better patient care. To address this issue, we propose a data mining pipeline to classify medical devices based on mortality rates and ICD-10 codes. We utilize the decision tree grouping method to build a connection between the mortality dataset and ICD-10 codes. We anticipate that the results of this study will assist with healthcare providers identify risks associated with medical devices based on how many deaths are caused due to the improper use or use of faulty medical instruments during the treatment

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen