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Machine Learning-Based alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance

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Journal of Computer and Communications 2023年第2期11卷 122-141页

作者： Deussom Djomadji Eric Michel Takembo Ntahkie Clovis Tchapga Tchito Christian Arabo Mamadou Michael Ekonde Sone Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Division of Information and Communications technology The National School of Posts and Telecommunications and Information and Communication Technologies University of Yaoundé I Yaoundé Cameroon

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

关键词： Optical Network alarms Log Files Root Cause Analysis Machine Learning

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New Method for Monitoring Tire Pressure of Cars Based on the Tire Radial Deformation

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Chinese Journal of Mechanical Engineering 2010年第2期23卷 180-184页

作者： HAN Zongqi WANG Liqiang LIU Quanyou JU Xuekun Vehicle and Energy College Yanshan University Qinhuangdao 066004 China State Key Laboratory of Automobile Safety and Energy Tsinghua University Beijing 100084 China

Monitoring tire pressure of cars and signaling abnormal conditions is an important means to prevent deadly accidents. Large achievements have been gained, especially in direct tire pressure monitoring system（TPMS）. But there has been rarely research on indirect TPMS in the world. In China, the research on indirect TPMS is almost lacking. The international research on the indirect monitoring tire pressure method is mainly based on measuring and comparing the rotating speed of wheels. But it is very difficult to measure wheel rotating speed accurately because of the influence of many random factors. In this paper, the authors propose a new method in which the tire pressure can be monitored indirectly. This method can be used for tire calibration, wheel speed frequency standardization, wheel speed frequency comparison, and abnormal tire pressure determination. The pulse frequencies from wheel speed sensors of ABS are used to indicate tire deformation. Because the frequency has a relationship with tire deformation, the tire deformation reflects the tire pressure. Small sample statistics is used in the new method to increase the accuracy, and the experimental samples using the principle of the new method have been made and tested. The result of vehicle tests on road demonstrates that the method is efficient and accurate to monitor tire pressure. The research has positive potential for developing products.

关键词： tire pressure monitoring system tire radial deformation pulse frequency alarms

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A practical approach to improve alarm system performance: Application to power plant

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Chinese Journal of Chemical Engineering 2019年第5期27卷 1094-1102页

作者： Jay Sompura Amit Joshi Babji Srinivasan Rajagopalan Srinivasan Department of Electrical Engineering Indian Institute of Technology GandhinagarIndia Department of Chemical Engineering Indian Institute ofTechnology GandhinagarIndia Department of Chemical Engineering Indian Institute ofTechnology MadrasIndia

Process safety in chemical industries is considered to be one of the important goals towards sustainable development. This is due to the fact that, major accidents still occur and continue to exert significant reputational and financial impacts on process industries. Alarm systems constitute an indispensable component of automation as they draw the attention of process operators to any abnormal conditi on in the plant. Therefore, if deployed properly, alarm systems can play a critical role in helping plant operators ensure process safety and profitability. How-ever, in practice, many process plants suffer from poor alarm system configuration which leads to nuisance alarms and alarm floods that compromise safety. A vast amount of research has primarily focused on developing sophisticated alarm management algorithms to address specific issues. In this article, we provide a simple, practical, systematic approach that can be applied by plant engineers (i.e., non-experts) to improve industrial alarm system performance. The proposed approach is demonstrated using an industrial power plant case study.

关键词： Chattering alarm Correspondence analysis Density based clustering Duplicate alarms Combined-cycle power plant Association rule mining

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Clinical predictors of abnormal chest CT scan findings following blunt chest trauma: A cross-sectional study

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Chinese Journal of Traumatology 2020年第1期23卷 51-55页

作者： Saeed Safari Melina Farbod Hamidreza Hatamabadi Mahmoud Yousefifard Navid Mokhtari Emergency Department Shohadaye Tajrish HospitalShahid Beheshti University of Medical SciencesTehranIran Emergency Department Imam Hossein HospitalShahid Beheshti University of Medical SciencesTehranIran Department of Physiology School of MedicineTehran University of Medical SciencesTehranIran

Purpose:Some surgeons believe that chest computed tomography(CT)scan should be used more prudently in management of blunt chest trauma *** study aimed to evaluate the clinical predictors of abnormal chest CT scan findings in trauma ***:This cross-sectional study was conducted on blunt chest trauma patients aged18 years who were referred to the emergency departments of two educational hospitals and underwent chest CT *** patients were enrolled in the study using a non-probability sampling *** exclusion criteria included:class III or IV hemodynamic shock,need for immediate surgical or neurosurgical interventions,penetrating trauma,lack of required information,and *** factors,accident details,trauma mechanism,vital signs,and level of consciousness in predicting abnormal chest CT scan findings were *** was performed using IBM SPSS statistics ***:A total of 977 patients(male 51.5%,female 48.5%)with the mean age of(41.71±14.24)years,range 18e88 years were studied;34.2% of them with high energy trauma *** 334(34.2%)patients had abnormal findings on chest X-ray(CXR)and 332(34.0%)cases had an abnormal findings on chest CT scan(agreement rate was 99.4%).There was a significant correlation between male gender(p20 breathes/min,decreased pulmonary sound,and chest wall crepitation were independent clinical predictors of abnormal c

关键词： Multiple injuries Decision support techniques Clinical alarms Thoracic injuries Computed tomography X-ray

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