Failure Detection Rate Research Articles

Prognostic Value of Cardiac Color Doppler Ultrasound in Patients with Chronic Heart Failure

Objective: To investigate the value of cardiac color Doppler ultrasound (CDUS) in determining the clinical characteristics and assessing the prognosis of patients with chronic heart failure (CHF). Methods: 200 patients with CHF admitted to our hospital from January 2021 to June 2023 were selected as the study subjects, and these 200 patients were followed up for 6 months, with 159 cases in the surviving group and 41 cases in the deceased group, and examined with a diagnostic CDUS instrument (model GEVividE95ACUSONSC2000). The detection rate of chronic heart failure and cardiac function indexes of the two groups were observed. Results: The deceased group showed a chronic heart failure detection rate of 92.68%, which was significantly higher than the surviving group (76.73%; P < 0.05). Moreover, the cardiac function indexes of both groups were significantly different (P < 0.05). Conclusion: By performing cardiac ultrasonography on patients with CHF, their cardiac function can be clearly understood and the changes can be timely detected during treatment, hence physicians can formulate a more reasonable treatment plan for patients and improve their quality of life.

Impact of obesity in the identification of the sentinel lymph node in endometrial cancer: a retrospective, monocentric study and literature review

PurposeTo evaluate the sentinel lymph node (SLN) protocol for staging endometrial carcinomas, assessing its impact on surgical management, and determining indications for adjuvant therapies. The study also examines factors that may influence SNL mapping, particularly focusing on the failure of the technique due to obesity.MethodsA retrospective analysis was conducted on the medical records of patients with a histological diagnosis of endometrial carcinoma, who underwent surgical staging with SLN biopsy. The lymph node status was compared between non-obese (group 1) and obese (group 2) patients.Results71 women were included in the study, of which 33 were non-obese (46.5%) and 38 were obese (53.5%). The failure detection rate was higher in obese patients (14, 36.8%) compared to non-obese patients (5, 15.2%) (p = 0.039). The risk of mapping failure increased by 1.6 times for every 5-unit increase in body mass index (BMI) (OR 1.672, 95% CI 1.024–2.730, p = 0.040). BMI was confirmed as an independent risk factor for mapping failure in both univariate (OR 3.267, 95% CI 1.027–10.395, p = 0.045) and multivariate analyses (OR 5.779, 95% CI 1.320–25.297, p = 0.020).ConclusionSLN detection in obese patients requires great care, as obesity may alter the sensitivity of the technique.

Cost aware LSTM model for predicting hard disk drive failures based on extremely imbalanced S.M.A.R.T. sensors data

Failure prediction in a population of hard drives remains challenging due to the extreme class imbalance. Many existing failure prediction methods rely on problematic resampling approaches. Besides, existing deep learning methods in the failure prediction domain typically employ the sigmoid cross-entropy loss function which is not capable of solving the class imbalance problem. To address these challenges, this study proposes long-short term memory networks that leverage the sequential time series self monitoring analysis and reporting technology sensors data provided by the Backblaze data center. These models employ a new, modified focal loss function combined with weighted cross-entropy loss to tackle the negative influence of the class imbalance during training the long-short term memory networks. The long-short term memory networks models are compared to two traditional machine learning algorithms: random forest and logistic regression. This study focuses on daily snapshot data of the Seagate ST4000DM000 hard drive model from the Backblaze data center to maintain the homogeneity of the SMART attributes. Long-short term memory networks with modified focal loss function model provides the highest geometric mean score of 0.786±0.011 while keeping the failure detection rate higher without compromising the false alarm rate. Hence, it demonstrates that the proposed focal loss function in long-short term memory networks leads to improved performance compared to other approaches under the class imbalance.

Modeling and monitoring multi-release software systems based on failure detection rate: a generalized approach

Modeling and monitoring multi-release software systems based on failure detection rate: a generalized approach

A Software Reliability Model with Dependent Failure and Optimal Release Time

In the past, because computer programs were restricted to perform only simple functions, the dependence on software was not large, resulting in relatively small losses after a failure. However, with the development of the software market, the dependence on software has increased considerably, and software failures can cause significant social and economic losses. Software reliability studies were previously conducted under the assumption that software failures occur independently. However, as software systems become more complex and extremely large, software failures are becoming frequently interdependent. Therefore, in this study, a software reliability model is developed under the assumption that software failures occur in a dependent manner. We derive the software reliability model through the number of software failure and fault detection rate assuming point symmetry. The proposed model proves good performance compared with 21 previously developed software reliability models using three datasets and 11 criteria. In addition, to find the optimal release time, a cost model using the developed software reliability model was presented. To determine this release time, four parameters constituting the software reliability model were changed by 10%. By comparing the change in the cost model and the optimal release time, it was found that parameter b had the greatest influence.

Multi-Objective Modified Particle Swarm Optimization for Test Suite Reduction (MOMPSO)

Software testing plays a pivotal role in entire software development lifecycle. It provides researchers with extensive opportunities to develop novel methods for the optimized and cost-effective test suite Although implementation of such a cost-effective test suite with regression testing is being under exploration still it contains lot of challenges and flaws while incorporating with any of the new regression testing algorithm due to irrelevant test cases in the test suite which are not required. These kinds of irrelevant test cases might create certain challenges such as code-coverage in the test suite, fault-tolerance, defects due to uncovered-statements and overall-performance at the time of execution. With this objective, the proposed a new Modified Particle Swarm optimization used for multi-objective test suite optimization. The experiment results involving six subject programs show that MOMPSO method can outer perform with respect to both reduction rate (90.78% to 100%) and failure detection rate (44.56% to 55.01%). Results proved MOMPSO outperformed the other stated algorithms.

A ground fault detection method of substation DC system based on particle filter

In order to reduce the false alarm rate and false alarm rate of grounding fault detection in substation DC system and improve the efficiency of fault detection, a method of grounding fault detection in substation DC system based on particle filter is proposed. The wavelet threshold denoising method is used to remove the noise of the grounding fault signal of the substation DC system. The wavelet packet decomposition method is used to decompose the grounding fault signal of the substation DC system and extract the characteristics of the grounding fault signal of the substation DC system. Based on the particle filter algorithm, the residual smoothing value is used to detect the grounding fault of the DC system in the substation. The experimental results show that the failure detection rate and false alarm rate of the proposed method are low, which has a good fault detection effect.

고장 발견율이 욕조모양으로 다중변환점을 가진 SRGM의 추정과 분석

Purpose: Considering the change point is very useful to industry and software experts for improving software reliability with respect to the software reliability growth model (SRGM). In this study, we evaluated the SRGM performance with multiple change points for a bath-shaped failure detection rate (FDR).<BR>Methods: We estimated the points of change using the PL/I applied program test data of Obha[21] and considered the mean value function (MVF) using the change points. We compared the performance by calculating the sums of squared errors from test data with and without considering the change points.<BR>Results: Considering the change points in estimating the mean value function of the SRGM yielded a smaller sums of squared errors than when it is not considered.<BR>Conclusions: In this paper, we consider the SRGM with multiple change points for a bath-shaped FDR. It was found that the performance was better when the change point was considered. Thus, an appropriate number of change points can improve the reliability of the model in many situations.

Explore deep auto-coder and big data learning to hard drive failure prediction: a two-level semi-supervised model

Predicting impending failure of hard disk drives (HDDs) is crucial to avoid losing essential data and service downtime. However, most HDD failure prediction is being challenged by using labelled data itself to evaluate failure rate, while the fact that HDDs deteriorate gradually cannot be described and exploited suitably. Most works on the Self-Monitoring and Reporting Technology (SMART) system attributes utilize simple and traditional methods from machine learning and statistics to achieve HDD failure prediction. So, we propose a novel two-level prediction model Dab, hard Drive failure prediction based on deep Auto-coder and Big data learning, to exploit SMART data for better online HDD failure prediction, constructing detection sub-models of anomaly and health degree. With better accuracy, better performance, better prediction earnings, and proactive fault tolerance, Dab has reduced false alarm rate (FAR) and maintenance cost, and improved failure detection rate (FDR), reliability and robustness of large-scale storage systems.

Anomaly Detection Model for Predicting Hard Disk Drive Failures

ABSTRACT The electromechanical design of the HDD (Hard Disk Drive) renders it more susceptible to failures than other components of the computer system. The failure of HDD leads to permanent data loss, which is typically more expensive than HDD itself. The SMART (Self-Monitoring, Analysis and Reporting Technology) system warns the user if any HDD parameter has exceeded the predefined threshold value needed for safe HDD operation. Machine learning methods take advantage of dependence between multiple SMART parameters in order to make failure prediction more precise. In this paper, we present a failure prediction model based on the anomaly detection method involving an adjustable decision boundary. SMART parameters are ranked by the importance and the 13 most significant ones are used as the initial feature set in our model. In the following stage, we optimized the feature set by removing those that have no major contribution to the anomaly detection model, forming the final feature set comprising seven features only. The proposed anomaly detection model achieved 96.11% failure detection rate on average, with 0% false detection rate in ten random tests. The proposed model predicted more than 80% of failures 24 hours before their actual occurrence, which enables timely data backup.

Comparison of Nutritional Status between Left-behind and Non-left-behind Middle School Students in Central China and Its Relation with Cardiopulmonary Endurance

This paper compares the nutritional status of left-behind and non-left-behind middle school students in central China, analyzes the association between nutritional status and cardiopulmonary endurance of middle school students.This paper adopts a randomized whole group stratified sampling method to test the height and weight of 3320 middle school students aged 13-17 years old in Jiangxi Province, using the 20m SRT for cardiopulmonary endurance level testing. The association between different nutritional status and 20m SRT performance was explored by one-way ANOVA and logistic regression analysis.The detection rate of boys in the normal nutritional status group was statistically significant (p < 0.01), as the detection rate of left-behind students was less than that of non-leave-behind students. The 20m SRT results of male and female students were all in the normal>emaciated>overweight and obese group. Logistic regression analysis showed that the 20m SRT performance failure detection rate was positively correlated with overweight and obesity, with the failure detection rate in the wasting and overweight and obesity groups being 1.06 (95% CI = 0.89-1.27) and 1.61 (95% CI = 1.16-2.22) times higher than that in the normal group. In the group of male middle school students with normal nutritional status in central China, the detection rate of left-behind middle school students was lower than that of non-left-behind middle school students. The level of cardiopulmonary endurance was higher in normal nutritional status middle school students than in emaciated, overweight and obese middle school students, and overweight and obesity were negatively correlated with cardiopulmonary endurance. Key words: Left-behind Middle School Students, Non-left-behind Middle School Students, Nutritional Status, Cardiopulmonary Endurance

Implementation of Memory in a System on a Chip with Built-In Self-Testing and Self-Healing

When manufacturing systems on a chip using modern technologies with highly integrated elements, problems arise in testing and repairing the internal memory. The original architecture of the fault-tolerant semiconductor memory with the given failure detection rate is proposed. At the same time, rather than the entire device being backed up only the elements that are most susceptible to failures are backed up, which reduces its weight and cost. The fault-tolerant memory design with automatic recovery is verified after quadruple failures. The fault-tolerant memory project is implemented in the M2S010-TQ144 chip of the system based on the SF2-Junior-KIT chip in the integrated development environment of Microsemi Libero SoC v11.8. The proposed architecture of fault-tolerant memory ensures the automatic recovery from the multiple failures of elements onboard spacecraft control systems using a built-in self-healing device without the use of fusible jumpers and the participation of maintenance personnel. In the semiconductor memory, when faults are detected, the data bits of the main array of storage cells in which failures occur are automatically replaced with the data emerging from the outputs of the spare array of storage cells. This improves reliability when performing multiple recovery cycles.

Developing a 3D decision-making grid based on failure modes and effects analysis with a case study in the steel industry

PurposeThe aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.Design/methodology/approachIn a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.FindingsFindings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.Research limitations/implicationsIn failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.Originality/valueIn conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.

오토인코더를 이용한 지하전력구 이상감지 시스템

With the advent of the era of the 4th industrial revolution, AI technique has been applied in various fields. KEPCO developed an anomaly detection system using AI technology to detect abnormal situation in the underground cable tunnel. This anomaly-detection system consists of a robot-based data acquisition, communication and analysis module which works with stacked autoencoder neural network model. This system utilizes the data from audio sensors and determines the condition of equipment in the underground cable tunnel which is normal or abnormal. Moreover, by adding the attention-module in autoencoder neural network model we increased the recognition accuracy by 4%. The performance of this system is over 90%. Also, we investigated the performance of adversarial autoencoder (AAE) and attention based AAE model, which showed worse failure detection rate than attention added autoencoder model.

Feature Selection for Improving Failure Detection in Hard Disk Drives Using a Genetic Algorithm and Significance Scores

Hard disk drives (HDD) are used for data storage in personal computing platforms as well as commercial datacenters. An abrupt failure of these devices may result in an irreversible loss of critical data. Most HDD use self-monitoring, analysis, and reporting technology (SMART), and record different performance parameters to assess their own health. However, not all SMART attributes are effective at detecting a failing HDD. In this paper, a two-tier approach is presented to select the most effective precursors for a failing HDD. In the first tier, a genetic algorithm (GA) is used to select a subset of SMART attributes that lead to easily distinguishable and well clustered feature vectors in the selected subset. The GA finds the optimal feature subset by evaluating only combinations of SMART attributes, while ignoring their individual fitness. A second tier is proposed to filter the features selected using the GA by evaluating each feature independently, using a significance score that measures the statistical contribution of a feature towards disk failures. The resultant subset of selected SMART attributes is used to train a generative classifier, the naïve Bayes classifier. The proposed method is tested on a SMART dataset from a commercial datacenter, and the results are compared with state-of-the-art methods, indicating that the proposed method has a better failure detection rate and a reasonable false alarm rate. It uses fewer SMART attributes, which reduces the required training time for the classifier and does not require tuning any parameters or thresholds.

Link Failure Detection and Classification in Wireless Sensor Networks using Classification Method

This paper develops a method to detect the failures of wireless links between one sensor nodes to another sensor node in WSN environment. Every node in WSN has certain properties which may vary time to time based on its ability to transfer or receive the packets on it. This property or features are obtained from every node and they are classified using Neural Networks (NN) classifier with predetermined feature set which are belonging to both weak link and good link between nodes in wireless networks. The proposed system performance is analyzed by computing Packet Delivery Ratio (PDR), Link Failure Detection Rate (LFDR) and latency report.

Efficient Tracing Methodology Using Automata Processor

Tracing or trace interface has been used in various ways to find system defects or bugs. As embedded systems are increasingly used in safety-critical applications, tracing can provide useful information during system execution at runtime. Non-intrusive tracing that does not affect system performance has become especially important, but unfortunately, the biggest obstacle to this approach was the vast amount of real-time trace data, making it challenging to address complex requirements with relatively limited hardware implementations. Automata processors can be programmed with a memory-like structure of automata and have a structure specific to streaming data, large capacity, and parallel processing functions. This paper promotes the idea of high-level system-on-chip monitoring using automata processors. We used a safety-critical pacemaker application in the experiments, described timed automata (TA)-based requirements, and tested intentionally injected 4,000 random failures. The TA model converted for Automata Processor to monitor system, correctness, and safety properties achieved 100% failure detection rate in the experiment, and the detected failure is reported as fast enough to allow enough extent for failure recovery.

A Systematic Study on Factors Impacting GUI Traversal-Based Test Case Generation Techniques for Android Applications

Many test case generation algorithms have been proposed to test Android apps through their graphical user interfaces. However, no systematic study on the impact of the core design elements in these algorithms on effectiveness and efficiency has been reported. This paper presents the first controlled experiment to examine three key design factors, each of which is popularly used in GUI traversal-based test case generation techniques. These three major factors are definition of GUI state equivalence, state search strategy, and waiting time strategy in between input events. The empirical results on 33 Android apps with real faults revealed interesting results. First, different choices of GUI state equivalence led to significant difference on failure detection rate and extent of code coverage. Second, searching the GUI state hierarchy randomly is as effective as searching it systematically. Last but not the least, the choices on when to fire the next input event to the app under test is immaterial so long as the length of the test session is practically long enough such as 1 h. We also found two new GUI state equivalence definitions that are statistically as effective as the existing best strategy for GUI state equivalence.

An efficient approach for the detection of link failures in WBAN system for health care applications

SummaryWireless body area network (WBAN) plays an important role in patient health care. The performance of this WBAN system is affected by link failures due to the presence of malicious sensor nodes. Hence, the detection and mitigation of this link failure is important for improving the efficiency of the WBAN system. This paper proposes a methodology for link failure detection using weight metric approach. The performance of the proposed methodology is analyzed in terms of packet delivery ratio (PDR), link failure detection latency, and link failure detection rate.

A Method of Failure Detection Rate Calculation for Setting up of Guided Missile Periodic Test and Application Case

Since guided missiles with the characteristics of the one-shot system remain stored throughout their entire life cycle, it is important to maintain their storage reliability until the launch. As part of maintaining storage reliability, period of preventive test is set up to perform preventive periodic test, in this case failure detection rate has a great effect on setting up period of preventive test to maintain storage reliability. The proposed method utilizes failure rate predicted by the software on the basis of MIL-HDBK-217F and failure mode analyzed through FMEA (Failure Mode and Effect Analysis) using data generated from the actual field. The failure detection rate of using the proposed method is applied to set periodic test of the actual guided missile. The proposed method in this paper has advantages in accuracy and objectivity because it utilizes a large amount of data generated in the actual field.