Alarm Information Research Articles

Power grid fault classification method of alarm information based on bigru-attention.

With the continuous expansion of the power grid, the number of alarm information collected by the dispatching center is also increasing. How to filter out key information from massive alarm information, delete irrelevant data, classify the importance of alarm information, and make preparations for power grid fault diagnosis based on alarm information has become an urgent problem to be solved in online fault diagnosis. Based on this, this paper proposes an importance classification method of alarm information based on bigru attention to screen the data with the strongest correlation with fault, avoid the impact of irrelevant data on fault diagnosis, and meet the needs of intelligence. Firstly, the two-way gated loop (bigru) neural network layer is used to preprocess the alarm information text and extract the features of the deep-seated information; Secondly, the attention mechanism layer is used to assign corresponding weights to the extracted text deep-seated information; Finally, the text feature information of alarm information with different weights is put into the softmax function layer for alarm information text classification. Finally, an actual fault case in an area is tested to verify the effectiveness and practicability of the proposed method.

Optimized Fault Diagnosis Algorithm under GAN and CNN Hybrid Model

The network fault diagnosis algorithm is one of the most important algorithms to solve network transmission problems. In traditional network fault diagnosis, it mainly analyzes and troubleshoots the fault manually by comparing the alarm information of the network performance index with the expert experience database. Diagnosis methods based on manpower analysis will take up much material resources and manpower and increase maintenance costs. Therefore, there is an urgent need for a more efficient and intelligent fault diagnosis technology. In addition, as the current network is becoming more and more complex, and based on this consideration, a semi-supervised fault diagnosis algorithm is studied in this paper. It is a combination of the GAN and CNN models. Meanwhile, the method of combining relief and mutual information is applied to reducing the dimensionality of network feature parameters, and the optimal feature combination is selected. The fluctuations in the convergence of the generated confrontation network model are stabilized. Moreover, the simulation software is used to build the heterogeneous wireless network scenario studied. Meanwhile, an improved fault diagnosis model is constructed to verify that in the case of both GAN and CNN models, the accuracy of fault diagnosis algorithm can reach 98.6%, which is significantly higher than other comparative analysis methods. It has contributed to ensuring the user’s service experience and reducing the cost of network maintenance and operation.

Research on fault detection and identification methods of nonlinear dynamic process based on ICA

Abstract In the present study, nonlinear dynamic process data are mapped into the kernel state space by kernel gauge variable analysis method to obtain decorrelated state data. The time-lapse covariance matrix of the state data is weighted and summed to obtain the time-lapse structure matrix of the state data, and then supervised kernel independent component analysis (SKICA) is established, the independent component feature data is extracted from the status data and the monitoring statistics are constructed to detect the process faults. The data show that kernel independent component analysis (ICA) method (KICA) method can detect slow fault faster than the ICA method, except that the statistical detection ability of F3 and FS is reduced, and the KICA method can significantly improve the detection performance of other faults and statistics. By analyzing the detection results of SKICA method, it is obvious that in the detection process of all five kinds of slow faults, the fault detection capability of SKICA is better than that of ICA and KICA. The results of continuous stirred reactor simulation system show that, compared with the basic linear process, the slow fault detection has a good monitoring performance, it can detect the small deviation in the process sensitively and give alarm information to the slow fault in time, to improve the fault detection rate.

A knowledge representation based recognition method on power grid alarm monitoring data

The identification of power grid monitoring and alarm information is an essential guarantee for the stable operation of the power grid. Identifying current power grid alarms is based on an expert rule system. This method has the problems of cumbersome rule matching, difficulty in maintaining the rule base, and low matching efficiency. In order to solve the above problems, this paper proposes a method of identifying power grid monitoring and warning information based on knowledge representation. The method first uses grid topology information to construct a knowledge graph. It then uses knowledge representation learning algorithms to represent the power knowledge graph which digitally represents the textual warning information, and finally uses a series of deep learning models to identify ”alarm-event.” This paper uses six algorithms, such as TransE for knowledge representation, combined with three deep learning models such as ConvLSTM for identification. The verification shows that this method has specific ”alarm-event” identification capabilities based on actual power grid monitoring data.

Analysis of failure causes and countermeasures of automatic coagulation analyzer detection of thrombin time

To study the failure alarm information displayed on the automatic coagulation analyzer (coagulation method) of thrombin time (TT), and formulate the coping strategies combined with clinical information. Methods: A total of 233 failed TT blood samples [132 males, 101 females, with a median age of 73 (66, 79) years] were selected from 21 359 inpatients in Peking University First Hospital from January to June 2021. The statistical analysis was made and the failure causes and solutions were summarized according to the coagulation curve and the error codes displayed on the coagulation instrument, in combination with the clinical information, sample characteristics, medication status and other reasons. Meanwhile, a total of 96 TT detection failed lipid blood samples [56 males, 40 females, with a median age of 72 (65, 79) years] were analyzed from the inpatients in Peking University First Hospital from July to November 2021. TT results were obtained by artificial coagulation curve interpretation method, magnetic bead method and high-speed centrifugal re-detection method, respectively. The TT results of the three methods were compared. Results: The proportion of 233 failed TT tests from the total number of samples was 1.1% (233/21 359). There were 41.2% (96/233) samples with lipids, 23.2% (54/233) samples with heparin interference, 22.3% (52/233) samples with oral anticoagulant, and 13.3% (31/233) samples with micro-coagulation or insufficient plasma volume among these test failure samples. The classifications for these alarm information of coagulation curves showed on the instrument were as follows: 32.6% (76/233) of samples with higher changes in absorbance at baseline (SD>2 mAbs), 30.5% (71/233) of samples without peak values of second derivative, 25.8% (60/233) of samples with absorbance difference<35 mAbs between baseline and plateau period, 8.6% (20/233) samples with too low starting point or no starting point, and 2.6% (6/233) samples without coagulation curves. Among these 233 samples, there were 55.8% (130/233) samples that could be manually judged according to the reaction principle and standard coagulation curve pattern. Among the 96 samples that failed in coagulation method due to lipemia, there were 78 samples with sufficient blood volume tested by magnetic bead method. The TT results of the high-speed centrifugal redetection method, artificial coagulation curve interpretation method and magnetic bead method were 14.10 (14.80, 13.38) s, 14.30 (14.99, 13.60) s, and 15.65 (17.25, 14.65) s, respectively, but the difference was not statistically significant (P=0.055). For 78 lipid samples, there was a correlation between the results of the artificial coagulation curve interpretation method and the results of magnetic bead method (r=0.99,P=0.001). Conclusions: For those samples failed in TT detection by coagulation method on automatic coagulation instrument, the cause of failure can be analyzed through coagulation curve and alarm information. For the lipid samples, TT results can be obtained by manual interpretation method, high-speed centrifugation method and magnetic bead method.

Power Inspection Design by Internet of Things and RFID Technology in Smart City

As the basic element and module unit in the construction architecture of smart city, the Internet of things (IoT) is also the key foundation and important support for realizing "automatic perception, rapid response, and scientific decision-making" of smart city. The study aims to improve the efficiency of power inspection in power enterprises and provide intelligent technology for power inspection. After the theoretical analysis of IoT and RFID technology under the background of smart city, the study discusses the information acquisition principle of RFID technology. Second, based on IoT and smart grid, the study takes the power equipment of power company A as an example, and puts forward the power inspection scheme, including the information required for inspection and the inspection of primary and secondary equipment. The specific contents are as follows: health assessment is carried out according to the real-time alarm of power equipment, and the control command is sent to the downstream control equipment to confirm the alarm information. Meantime, based on the alarm information of power equipment and related equipment, the expert analysis system is used for reasoning and analysis, and the RFID tag is used to supervise the inspectors to arrive at the site and inspect the situation according to the predetermined route. The temperature of the cable of power company A is measured under normal and hidden dangers, and the body insulation test is conducted on No. 1 main transformer. The results show that under normal operation, the cable temperature at each point is slightly different, which is basically about 40 °C. The temperature before the fourth point of the cable rises obviously, indicating that the fourth point has faulty. Over time, the more chemical molecules are detected in phase A. When phase B and phase C are tested on the same day, the difference of chemical molecular content is relatively small. It indicates that the chemical molecular content detected by chromatographic test of bulk insulating oil is normal. To sum up, smart power, as the objective need of urban intelligent development, is an important foundation of smart city. The power inspection scheme based on IoT and RFID technology proposed has high detection efficiency.

Interaction Model of the Cabin of Combined Sugarcane Harvesters

Owing to visual blind spot areas and occasional negligence, combined sugarcane harvester drivers often make mistakes in field operation, some of which evolve into major accidents. To improve drivers’ perception of and response to warning information, this paper explores the optimal interaction mode of warning information for the cabin of combined sugarcane harvesters. A series of experiments were carried out on a stationary driving simulator to verify the driver experience and alarm efficiency of three modes of warning information, namely, text, audio, and image, as well as their dual-channel modes. The physiological data, such as electrodermal activity (EDA), photoplethysmography (PPG), and electroencephalogram (EEG), of eight subjects were collected through the experiments. On this basis, the cognitive load of drivers was analyzed under different modes of warning information. The motion feedback time was recorded to parse the driver’s recognition rate and reaction speed to the warning information, and the eye movement was captured to analyze the driver’s attention distribution. The results show that the recognition rate under the dual-channel mode of visual and audio is higher than that of the single-channel mode of text or image. The addition of the visual warning information (text or image) to the audio information reduces the attention distribution time, and the best reduction effect is achieved in the image plus voice mode. The EDA indices of latency, amp sum, and mean half decay time fully reflect the effect of alarm information modes on the subjects’ reaction speed and emotional stimulation. The image plus voice mode has the fastest response speed, smallest response to stimuli, and the best ability for emotional recovery than the other modes. The eye movement, some EDA indices, and EEG are more sensitive to stress reaction, while the HRV is not sensitive for analyzing drivers’ stress to the stimuli of warning information in a short time. The research results lay the basis for designing a more efficient and accurate reminder mode of warning information for combined sugarcane harvesters.

Broadcasting capability analysis of WSNs in ship information collection system

In the Internet of things environment, the information collection system designed with wireless sensor networks (WSNs) equipment as the transmission medium can realize the collection of marine environmental information. The WSN nodes can work in a harsh environment without the costly and complicate wiring in the ships. It is necessary to distribute monitoring or alarm information through broadcast operation in ship WSNs. Therefore, in order to improve the efficient application of WSNs in ship information system, the broadcast characteristics of WSNs are analysed and the existing broadcast algorithms are summarized and classified. Finally, some problems faced by broadcast technology are pointed out through simulation results.

Algorithm analysis based on machine learning in alarm information of metering system

Due to the huge scale of the metering warning system and the wide variety of warning information, manual identification can no longer meet the real-time monitoring needs of modern power grids. Through the use of machine learning, data mining technology, linear regression method, naive Bayes method, automatic classification analysis of alarm information from the massive alarm information. Construct an SVM prediction model to improve the accuracy of alarm information analysis and improve the effectiveness of power grid monitoring and management. The experimental results show that it can provide support for the normal operation of the grid metering system.

Metrics for objectively assessing operator training using eye gaze patterns

Process plant operators rely on their knowledge of process cause-and-effect relationships during abnormal situation management. Novice operators develop such process knowledge during training. Hence, holistic assessment of operators’ training is essential to ensure process safety. Currently, during training, operators’ process understanding is evaluated using criteria such as successful completion, task based measures, and operator actions that ignore their cognitive behavior. In this work, we propose an eye-tracking-based approach that uses the operator’s attention allocation during different pre-specified training scenarios along with process data, alarm information, and operator actions. Our approach is based on the precept that an operator would focus their attention on those variables on the human-machine interface that they believe have a direct causal relationship to the situation at hand. Also, expert operators seek time-based information for proactive monitoring. Accordingly, to quantify the progress of a novice operator’s learning, we develop two metrics — association metric and salience metric — using correspondence analysis of operators' eye gaze. To evaluate the applicability of the metrics, we conducted experiments with ten participants who performed 486 tasks. Statistical studies reveal that the proposed metrics can quantify operators’ learning and thus can be used to objectively evaluate training effectiveness.

Design and Realization of Remote Infusion Monitoring System

In order to reduce the working intensity of medical staff in inspecting patients during traditional infusion, a remote monitoring system for intravenous infusion is designed for solving the problem of delay in handling treatment during infusion process and to reduce the incidence of medical accidents. The system uses Visual Basic.NET language to develop the upper computer platform for infusion monitoring. It uses the Arduino control board and infrared photoelectric sensor to form a monitoring device to detect relevant information. At the same time, it uses Zigbee wireless sensing technology to transmit data and upload it to the software platform. The results show that the system can receive data from multiple monitoring terminal devices in the upper computer platform application interface at the same time. It can display the data in the nurse station in a graphical way, and perform alarm warning and information storage during the infusion process. The infusion monitoring system can observe the monitoring situation in real time, reduce the workload of medical staff, and further improve the operating efficiency and safety of the hospital.

A Power Grid Fault Diagnosis Method in the Case of Alarm Information Loss Based on the Top-k Skyline Query Algorithm

After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.

A gas pipeline anti-damage early warning monitoring system

This paper introduces a gas pipeline anti-damage early warning monitoring system, which involves the field of pipeline detection technology. Its basic principle is to use vibration detection and sound detection technology, combined with Internet of Things sensing technology, to provide vibration amplitude monitoring and accurate positioning function for pipeline leakage or third-party sabotage occurring in underground pipeline network, and finally transmit alarm information to pipeline maintenance personnel through data analysis platform, so as to achieve the role of pipeline real-time early warning monitoring.

Virtual Network Security Control Mechanism based on Multi-level Alerting and Linkage Defense

The intrusion detection system can effectively detect network attacks dynamically by analyzing network traffic or data packets. However, for complex multi-step attacks with high concealment and persistence, traditional intrusion detection systems generally have the problems of large redundancy of alert data and poor data readability, which seriously affects the security administrators to quickly identify attack behaviors and intentions. To address the above problems, this paper proposes a multi-level alert aggregation method based on software-defined security and linked correction defense based on the Markov chain model. This method designs the reporting message format between the data plane security component and the control plane security controller, and automatically extends the time window by using the temporal proximity relationship of alarms. Multi-level alert aggregation is performed based on the attack attribute matching method, resulting in the effective aggregation of similar alarms. Further, the control plane uses the Markov chain model to generate alert association graphs to obtain the transfer probability between attack types, which is sent down to the security components for linkage defense. The experiments show that this mechanism can effectively achieve multi-level alarm aggregation and alarm information association linkage.

The utility of competency-oriented clinical laboratory teaching combined with case-based learning (CBL).

This study aimed to evaluate the effectiveness and efficiency of competency-oriented clinical laboratory teaching combined with case-based learning (CBL) and improve the examination of students' competence of laboratory medicine. A total of 107 medical laboratory medicine interns at the Affiliated Hospital of Xuzhou Medical University from June 2017 to July 2019 volunteered to participate in the study and were randomly assigned into a control group with training of the traditional teacher-centered method, and an experimental group under a CBL teaching program. Student basic theory tests and skill assessment were designed to evaluate what the students gained from their internship when they completed their studies at the Affiliated Hospital of Xuzhou Medical University. Compared to students in the control group taught with the teacher-centered method, those in the CBL teaching program had significantly higher theory test scores and skill assessment scores on average. Competencies with particularly significant improvement included identification and processing of instrument alarm information, analysis of test results, identification and solution of the problem, as well as identification and reporting of the critical value and clinical communication. The competency-oriented teaching method combined with CBL is an effective method for improving students' professional knowledge, increasing language expression, and enhancing interpersonal relationship and teamwork, which is worthy of being promoted in laboratory medicine teaching.

Fault Diagnosis Method of Road Traffic Communication Network Based on SVM

The development of road traffic communication technology makes communication means more diverse. With the increase of the complexity of urban road traffic communication network, the fault diagnosis of road traffic communication network has been paid more and more attention. The alarm data in the communication network is the manifestation of network failure. Network operation and maintenance personnel can understand the current network operation status according to the alarm information displayed in the system, and then analyze, locate and handle the fault. Therefore, the identification of alarm information is very important for the timely treatment of network failure. In this paper, based on the existing communication network theory and network equipment alarm information, this paper proposes a method using SVM, which can effectively solve the fault identification of network communication input anomaly.

FAULT DIAGNOSIS OF COMPUTER NUMERICAL CONTROL (CNC) MACHINE USING CLOUD KNOWLEDGE BASE AND MONITORING SYSTEM

In the world of manufacturing, numerical control machine occupies a major role. Recent studies on CNC machines aim to rapidly evolve CNC Controller towards smart production and flexible production lines.As an essential part of system reliabilityanalysis, fault classification analysisiscritical in the aspects of reliability design improvement. Most original Equipmentmanufactures do not accept the effect of machine reactions on component qualitywhen the equipment fails. The errorscomingfrom servo parameters for the absolute system are indicated by newlyproduced CNC machines. Alarm information isusedonly for attention, warning, and not for safeguardingworkpiecesagainst direct harm to quality. This paperaims to develop a faultdiagnosis system of the CNC through monitoring the alarm panel of CNC drivers using a webcam, raspberry pi 3, Optical Character Recognition (OCR) and cloud knowledge base. By using the proposed diagnostic system, the efficiency, accuracy, speed of development, and maintenance of the CNC controllers are improved. في عالم التصنيع ، تحتل آلة التحکم الرقمى بالحاسب دورا رئيسيا. تهدف الدراسات الحديثة على آلات CNC إلى التطور السريع لوحدات التحکم CNC نحو الإنتاج الذکي وخطوط الإنتاج المرنة. کجزء أساسي من تحليل موثوقية النظام ، يعد تحليل تصنيف الأخطاء أمرا بالغ الأهمية في جوانب تحسين تصميم الموثوقية. لا تقبل معظم الشرکات المصنعة للمعدات الأصلية تأثير تفاعلات الماکينة على جودة المکونات عند تعطل الالة.يتم الإشارة إلى الأخطاء الناتجة عن معلمات المؤازرة للنظام المطلق بواسطة آلات CNC المنتجة حديثا. تستخدم معلومات الإنذار فقط للانتباه والتحذير وليس لحماية قطع العمل من الضرر المباشر بالجودة. تهدف هذه الورقة إلى تطوير نظام تشخيص الأعطال في CNC من خلال مراقبة لوحة إنذار محرکات ماکينة التحکم الرقمى بالحاسب باستخدام کاميرا الويب ، و Raspberry pi 3 ، والتعرف البصري على الأحرف (OCR) ، وقاعدة المعرفة السحابية.وبالتالى من خلال نظام التشخيص المقترح ، يتم تحسين الکفاءة والدقة وسرعة التطوير والصيانة لوحدات التحکم CNC.

Reliable Multicast Based on Congestion-Aware Cache in ICN

Reliable multicast distribution is essential for some applications such as Internet of Things (IoT) alarm information and important file distribution. Traditional IP reliable multicast usually relies on multicast source retransmission for recovery losses, causing huge recovery delay and redundancy. Moreover, feedback implosion tends to occur towards multicast source as the number of receivers grows. Information-Centric Networking (ICN) is an emerging network architecture that is efficient in content distribution by supporting multicast and in-network caching. Although ubiquitous in-network caching provides nearby retransmission, the design of cache strategy greatly affects the performance of loss recovery. Therefore, how to recover losses efficiently and quickly is an urgent problem to be solved in ICN reliable multicast. In this paper, we first propose an overview architecture of ICN-based reliable multicast and formulate a problem using recovery delay as the optimization target. Based on the architecture, we present a Congestion-Aware Probabilistic Cache (CAPC) strategy to reduce recovery delay by caching recently transmitted chunks during multicast transmission. Then, we propose NACK feedback aggregation and recovery isolation scheme to decrease recovery overhead. Finally, experimental results show that our proposal can achieve fully reliable multicast and outperforms other approaches in recovery delay, cache hit ratio, transmission completion time, and overhead.

Design of an Intelligent Fiber Bragg Grating Temperature Measurement System for Electrical Equipment

For the power system, the main reason for many fire accidents is that the temperature of electrical equipment is too high, and now the widely used fire detector can only detect the fire that has occurred. If the temperature change of electrical equipment can be tracked in real time and the alarm information can be sent in time when the operating temperature is too high, the fire of power system can be scientifically and effectively prevented. The use of fiber Bragg grating sensor can provide a basis for the realization of the theoretical conception. For the traditional temperature measurement system, fiber Bragg grating temperature measurement system has the advantages of convenient maintenance, fault detection, less affected by light source power fluctuation, information sharing and long service time. It can effectively measure the temperature of isolated contact of high-voltage switchgear, the temperature of wet steam inside steam turbine and the surface temperature of power cable, etc. This paper researches a design of fiber Bragg grating temperature measurement system.

Analysis of the Design and Performance of Microcomputer Fault Alarm System

The fault detection and alarm system designed in this paper, which can be used in home, including a series of structures such as fault detector, cooling plate, support plate, controller, voice chip, voice player, alarm lamp, cooling fin, etc, is convenient for users to obtain fault alarm information effectively and carry out maintenance.