Mean Time To Recovery Research Articles

Penjadwalan Pemeliharaan Mesin Produksi Menggunakan Reliability Centered Maintenance

CV. ABC is a furniture business that produces home equipment which includes all items, namely chairs, tables, cupboards, beds, nightstands, wooden stairs, etc. The aim of this research was to determine maintenance scheduling for production machines, namely wood cutting machines and drilling machines, and provide suggestions for improving the maintenance scheduling of production machines, namely wood cutting machines and drilling machines, which are more effective. The advantage of the Reliability Centered Maintenance (RCM) method is that it prioritizes the main action of preventive maintenance, namely preventing and minimizing the consequences of failures that arise, so that it can increase the reliability and safety of the equipment used. Failure Modes and Effects Analysis (FMEA) is a technique for evaluating the level of reliability of a system to determine the effects of failure of the system. Based on the results of data processing and research discussion regarding production machine maintenance scheduling using RCM at CV. ABC, a conclusion can be drawn, namely, the Cutting Machine has a breakdown frequency of 26 times with the Bearing component having the most breakdown frequency, namely 8 times, the highest Risk Priority Number (RPN) calculation is Solenoid component with a value of 105, Mean Time to Failure (MTTF) calculation with a value of 111.5385 and Mean Time to Recovery (MTTR) with a value of 0.769231. The drilling machine has a breakdown frequency of 24 times with the Drill Bit component having the highest breakdown frequency, namely 6 times, the highest RPN calculation is the Drill Bit component with a value of 105, MTTF calculation with a value of 120.8333 and MTTR with a value of 0.8333.

ОПТИМІЗАЦІЯ ПРОЦЕСІВ АВАРІЙНОГО ВІДНОВЛЕННЯ СЕРВІСІВ ІНФОРМАЦІЙНОЇ ІНФРАСТРУКТУРИ

The article describes the optimization of the process of disaster recovery of information infrastructure services by implementing the ability to restore service functionality without requiring a full recovery from backup storage. It outlines the criteria and parameters of the network that have a critical impact on recovery in the event of emergencies, allowing for the assessment of the solution's effectiveness in post-disaster recovery. A modification to the MTTR (Mean Time To Recovery) parameter is proposed for cases involving system element recovery from a backup or through service configuration restoration via infrastructure as code, with data necessary for service operation retrieved from backup storage, thereby accelerating the recovery process of a failed information infrastructure service. The article presents a scheme for infrastructure recovery organization by creating a backup location (Cold Site) for local infrastructure using dedicated cloud providers. The proposed solution utilizes Proxmox Backup Server capabilities for regular backups of critically important data center components. Following the development of a flowchart for the service recovery method from the Cold Site, research findings indicated that, for some services, reinstating configurations from code is more advantageous and speeds up the recovery process more than complete service restoration from backup storage.

ATA2022 Annual Conference & Expo May 1–3, 2022Boston, Massachusetts

ATA2022 Annual Conference & Expo May 1–3, 2022Boston, Massachusetts

Recurrent Laryngeal Nerve Paralysis Following Thyroidectomy: Analysis of Factors Affecting Nerve Recovery

Paralysis of the recurrent laryngeal nerves (RLNs), albeit decreased over the years, leaves the surgeon helpless as there is not much that can be done once it occurs. Nimodipine has been suggested as a remedy that could accelerate the recovery of the nerve. Our study aimed to examine the factors that affect the recovery rate (RR) and time to recovery (TTR) of post-thyroidectomy RLN paralysis, with an emphasis on the use of nimodipine. A total of 197 patients who had undergone thyroid and parathyroid surgeries were retrospectively reviewed from October 2016 to August 2019. Patients who had RLN paralysis following surgery were assessed. The medical records were retrospectively analyzed to look for possible factors that may influence RLN recovery. A total of 289 nerves were at risk. Temporary RLN paralysis rate was 7.9% while 1.7% was permanent. Age (odds ratio [OR]=4.8) and intra-operative extra-thyroid extension (OR=9.0) were independent risk factors for RLN paralysis. The rate of recovery was 82.1%. Loss of signal (LOS; P=.066) was a factor trending for an impact on RR but not nimodipine (P > .05). The mean TTR was 32 days. LOS, nimodipine, and steroid use, among others, were factors trending for an impact on the TTR. Although not reaching statistical significance, nimodipine and steroids might influence TTR but not the RR. Larger studies are warranted to address the effect of nimodipine on the outcome of RLN paralysis. 4 Laryngoscope, 132:1692-1696, 2022.

Modularisation of system architecture to improve system recoverability: a unique application of design structure matrix

This study addresses the problem of design of systems for recoverability and discusses that a modularisation of the system architecture can effectively facilitate the recovering operations for a damaged system. To modularise the system architecture, Design Structure Matrix (DSM) methodology along with Axiomatic Design (AD) theory are employed. The study discusses that preventing propagation of the failure(s) to the rest of the system architecture can significantly contribute to the recovery of the system and proposes a methodology and utilises the Mean Time to Recovery (MTTR) as a good indicator reflecting the stochastic behaviour of system recoverability. The present study mathematically shows that modularisation of the system architecture can considerably augment the probability of success in recovering and fulfilling the physical components following experiencing a failure in the system, significantly reduce the length of recovery time and improve the system recoverability. To verify the presented method, architecture of information flow in a real contract is addressed. Results from the application of the method indicated that modularisation of the architecture of the information flow in the contract significantly reduces the volume of time and effort required for adjusting content of the clauses and improves the system recoverability considerably.

Characterization and Prediction of Performance Loss and MTTR During Fault Recovery on Scale-Out Storage Using DOE & RSM: A Case Study with Ceph

Recognizing the impact from cluster recovery operations on performance and mean time to recovery (MTTR) is essential to maintain service and availability objectives. Testing the impact of recovery operation can reveal a cause-and-effect relationship between recovery parameters and responses of performance and MTTR during the recovery process. This study introduces a combination of systematic methodologies of design of experiments and response surface methodologies to effectively and efficiently find out main and factor-to-factor interaction effects toward the responses. Two Ceph clusters using different storage device technologies, HDD and SSD respectively, were used to characterize the impact of recovery operation on performance and MTTR. The combination of quadratic and linear effects from both Ceph clusters were determined and reported. With 28 tests, MTTR and performance models were developed for each Ceph cluster based on those quadratic and linear effects. These models demonstrate good prediction on performance and MTTR when recovery parameters are adjusted. Using design of experiment and response surface not only allow cause and effect analysis, but also provide the potential inefficient parameter that causes performance loss during recovery. This not only introduces a new method to study cause-and-effect in MTTR but serves as the indicator to areas for improvement for more efficient recovery operation.

Patient-reported outcomes from the investigational device exemption study of the Tablo hemodialysis system.

IntroductionWe recently completed an Investigational Device Exemption (IDE) study in which 30 patients were enrolled (13 patients previously on home hemodialysis (HHD) and 17 patients new to HHD) and treated with the Tablo Hemodialysis System (Outset Medical, Inc., San Jose, CA) for 8 weeks in‐center and 8 weeks in‐home with an interim 2–4 week transition period for home training.MethodsIn addition to assessments of urea kinetics, events related to safety, and operational issues (e.g., alarm resolution), we obtained data on several parameters of health‐related quality of life, including time to recovery (TTR), the EQ‐5D‐5L (a well‐validated measure of general health status), and the quality of sleep and related symptoms, to further assess the safety of HHD with Tablo. We compared results obtained during the in‐center and in‐home phases of the trial.ResultsTwenty‐eight of 30 patients (93%) completed all trial periods. Adherence to the prescribed four treatments per week schedule was 96% in‐center and 99% in‐home. Median TTR was 1.5 hours (10th, 90th percentile range 0.17 to 12, mean TTR 3.68 ± 5.88 hours) during the in‐center and 2 hours (10th, 90th percentile range 0 to 6.0, mean TTR 3.04 ± 5.14 hours) during the at‐home phase (Wilcoxon signed rank p = 0.57). Median index values on the EQ‐5D‐5L were similar during the in‐center (0.832, 10th, 90th percentile range 0.617 to 1, mean 0.817 ± 0.165) and in‐home (0.826, 10th, 90th percentile range 0.603 to 1, mean 0.821 ± 0.163) trial phases (Wilcoxon signed rank p = 0.36). Patients reported feeling alert or well‐rested with little difficulty falling or staying asleep or feeling tired and worn out when using Tablo in either environment.ConclusionWhen using Tablo in‐home, patients reported similar TTR, general health status, and sleep quality and related symptoms compared to using Tablo in‐center. (294 words).

A Highly Reliable Metadata Service for Large-Scale Distributed File Systems

Many massive data processing applications nowadays often need long, continuous, and uninterrupted data accesses. Distributed file systems are used as the back-end storage to provide the global namespace management and reliability guarantee. Due to increasing hardware failures and software issues with the growing system scale, metadata service reliability has become a critical issue as it has a direct impact on file and directory operations. Existing metadata management mechanisms can provide fault tolerance capability to some level but are inadequate. They often have limitations in system availability, state consistence, and performance overhead and lack an effective mechanism to offer metadata reliability. This paper introduces a novel highly reliable metadata service to address these issues in large-scale file systems. Different from traditional strategies, this proposed reliable metadata service adopts a new active-standby architecture for fault tolerance and uses a holistic approach to improve file system availability. A new shared storage pool (SSP) is designed for transparent metadata synchronization and replication between active and standby servers. Based on the SSP, a new policy called multiple actives multiple standbys (MAMS) is presented to perform metadata service recovery in case of failures. A new global state recovery strategy and a smart client fault tolerance mechanism are achieved to maintain the continuity of metadata service. We have implemented such highly reliable metadata service in a prototype file system CFS (Clover file system) and conducted extensive tests to evaluate it. Experimental results confirm that it can significantly improve file system reliability with fast failover under different failure scenarios while having negligible influence on performance. Compared with typical reliability designs in Hadoop Avatar, Hadoop HA, and Boom-FS file systems, the mean-time-to-recovery (MTTR) with the highly reliable metadata service was reduced by 80.23, 65.46 and 28.13 percent, respectively.

CauseInfer: Automated End-to-End Performance Diagnosis with Hierarchical Causality Graph in Cloud Environment

Modern computing systems especially cloud-based and cloud-centric systems always consist of a mass of components running in large distributed environments with complicated interactions. They are vulnerable to performance problems due to the highly dynamic runtime environment changes (e.g., overload and resource contention) or software bugs (e.g., memory leak). Unfortunately, it is notoriously difficult to diagnose the root causes of these performance problems in a fine granularity due to complicated interactions and a large cardinality of potential cause set. In this paper, we build an automated, black-box and end-to-end cause inference system named CauseInfer to pinpoint the root causes or at least provide some hints. CauseInfer can automatically map a distributed system to a two-layer hierarchical causality graph and infer the root causes along the causal paths in the causality graph. CauseInfer models the fault propagation paths in an explicit way and works without instrumentation to the running production system, which makes CauseInfer more effective and practical than previous approaches. The experimental evaluations in two benchmark systems show that CauseInfer can identify the root causes in a high accuracy. Compared to several state-of-the-art approaches, CauseInfer can achieve over 10 percent improvement. Moreover, CauseInfer is lightweight and flexible enough to readily scale out in large distributed systems. With CauseInfer, the mean time to recovery (MTTR) of the cloud systems can be significantly reduced.

Effect of Increased Blood Flow Rate on Hemodialysis Tolerability and Achieved Urea Reduction Ratio.

Long-term hemodialysis (HD) imposes a significant burden on the quality of life of end-stage kidney disease patients. Optimizing dialysis dose is an important consideration in this population; however, evidence exists that suggests that attainment of population dialysis targets is associated with increased intradialytic complications. In this prospective, before-after study, the blood flow rate (BFR) of 63 maintenance HD patients was increased by 100 mL/min to a maximum BFR of 400 mL/min to determine the impact on patient tolerability and urea reduction ratio (URR) of an increased BFR. Tolerability was assessed by time to recovery (TTR) after dialysis, a validated measure of patient tolerability, and intradialytic complications. Median pre-increase BFR was 252 mL/min compared to 349 mL/min post-increase. Mean TTR decreased from 4.67 h to 4.03 h (P = 0.688). No association was observed between percentage change in BFR (R2 = 0.0) or post-increase BFR (R2 = 0.0) and absolute change in TTR. A significant, positive association was observed between both the absolute and relative changes to BFR and the achieved URR. We found no evidence that increasing BFR by 100 mL/min diminishes patient tolerability.

Resource adequacy risks to the bulk power system in North America

To keep the electric power system reliable, grid operators procure reserve generation capacity to protect against generator failures and significant deviation from the load forecast. Current methods for determining reserve requirements use historical generator availability data (recorded as failure events) to compute the fraction of the time each unit in the power system was unavailable unexpectedly. These values are then combined using analytical or simulation methods to yield a distribution of available capacity. From this distribution, the reserve capacity needed to maintain a particular reliability target may be determined. Such an approach implicitly assumes that generator failures occur independently of one another and that generator availability is not seasonal.To test these assumptions, we process the more than two million event records reported to the Generating Availability Data System (GADS) database maintained by the North American Electric Reliability Corporation (NERC) between January 1, 2012 and December 31, 2015. This allows us to construct complete availability histories (hourly time series) for each of the approximately 8000 generating units reporting to GADS during this period. Using these time series, we find strong evidence of correlated failures in most regions, even when removing Hurricane Sandy and the exceptionally cold month of January 2014 from the data. We find that correlated failures occur in all seasons. We do not find evidence of seasonality but note that seasonal structure may emerge with more data.In addition we determine the distribution of unscheduled unavailable capacity, unscheduled derating magnitudes, event durations, event arrival probabilities, and mean time between failure (MTBF) and mean time to recovery (MTTR) values. In each case, we report fit parameters to facilitate use by practitioners. The distributions of unscheduled unavailable capacity in each region are reasonably well modeled by Weibull and lognormal distributions. We find statistically significant differences in mean time between failure for small and large units for three unit types when aggregating over regions. Finally we present time series of unavailable capacity from unscheduled, maintenance, and scheduled events. These may be used in conjunction with load data to directly study resource adequacy risks without assuming independent failures or constant availability. Our findings suggest that power system resource planners should consider correlated outages as they identify reliability and reserve capacity requirements.

Maintenance Optimization: Application of Remanufacturing and Repair Strategies

Power plant facilities located in remote and difficult to access regions often experience significant production losses due to failures, inaccurate remanufacturing, maintenance activities in addition to prolonged lead time delays while procuring critical spares. This is especially the case given unanticipated power plant failures which could portend significant power generation losses besides outages that could lead to significant societal disruptions, for instance, off grid hospital facilities. This study uses a discrete event simulation model to analyze the effect of remanufacturing and maintenance as part of circular economy strategies, on power plant availability and maintenance time. The proposed study is demonstrated through the use case of a thermal power plant located in a remote region, where findings suggest replacing components upon failure has the most significant impact and effect on system availability as well as maintenance time. Moreover, a reduction of mean time before Overhaul (MTBO) seems to lengthen the component's usage-life, hence reduces the maintenance time, and at the same time, increases the system availability. Reuse activity also yields a better mean time to recovery (MTTR), while repair has the highest amongst the evaluated recovery strategies. The study adds value by developing a framework, further applying a simulation modelling approach for evaluating the effects of remanufacturing and maintenance strategies on plant availability.

Reliability and quality of service of an off-grid wind powered roadside unit in a motorway vehicular environment

Wind-powered base stations and roadside units have been considered as a cost effective greening solution in windy countries which also have limited solar irradiation. The practicality of such a system increases significantly in sparse areas such as countryside and motorways. The deployment of standalone off-grid wind powered roadside units could alleviate the common issues related to grid connected renewable energy farms. Hence, there is need to study the feasibility of an off-grid wind powered roadside unit for seamless connectivity. Unlike the conventional usage of reliability analysis of fault-tolerant systems, in this paper, reliability is redefined in the context of availability of intermittent wind for powering a roadside unit (RSU) in a UK motorway vehicular environment. Transient analysis of energy consumption (energy demand) of the RSU and harnessed wind energy are carried out along with real measurements for developing respective generic energy models. Further, a generalised methodology is developed to determine the minimum battery size for achieving a certain reliability standard and quality of service. Several reliability indices such as loss of load probability (LOLP), loss of load expectation (LOLE), energy index of reliability (EIR), mean time between failures (MTBF), mean time to recovery (MTTR), forced outage rate (FOR), etc. are obtained for the RSU. The performance results reveal that with a standard micro-turbine and a reasonably small battery, an RSU achieves a good reliability of 99.9% with significant improvement in the quality of service.

The prediction of length of major depressive episodes: results from an epidemiological sample of female twins.

In order to examine factors that influence the time to recovery (TTR) from depressive episodes in women, we examined members of 1030 female-female twin pairs of known zygosity, ascertained from a population-based twin registry. We predicted, in a Cox model, TTR in 235 women with an onset of an episode of major depression (MD) in the last year meeting DSM-III-R criteria. The median and mean TTR for episodes of MD was 42 and 82 days, respectively; only 2.2% of women had not recovered by 1 year. Four variables predicted TTR: financial difficulties, obsessive-compulsive symptoms, severe life events (SLEs), and genetic risk. Dividing all depressive episodes meeting symptomatic DSM-III-R criteria into early (5-28 days) and late (> 28 days) phases, significant predictors of TTR early in the course of illness (income, parental protectiveness and separation, personality, lifetime traumas and SLEs) differed from those that predicted TTR later in the depressive episode (health, social support, obsessive-compulsive symptoms, SLEs and genetic risk). Including cases with chronic MD increased the strength of personality, financial problems and genetic risk as predictors of slow TTR. These exploratory analyses suggest that TTR from MD in women is influenced by multiple environmental, temperamental and genetic factors. Predictors of TTR early and later in the course of MD may differ qualitatively, suggesting different processes in recovery from brief versus prolonged depressions.