The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform

Abdalla S A Mohamed - One of the best experts on this subject based on the ideXlab platform.

  • medical Equipment Failure Rate analysis using supervised machine learning
    International Conference on Advanced Machine Learning Technologies and Applications, 2018
    Co-Authors: Rasha S Aboulyazeed, Ahmed M Elbialy, Abdalla S A Mohamed
    Abstract:

    Machine learning is widely used to identify patterns in data and to assemble models that anticipate future outcomes based on historical data. One of the critical components required for efficient healthcare services provision is medical Equipment. Applying machine learning for Failure Rate modeling and prediction is of great importance. Therefore, two different stochastic models, ARMA and GARCH models, were utilized to analyze Failure Rate data. The outcome of each model was compared with previous work so that to achieve the best model that represent the Failure Rate data.

  • AMLTA - Medical Equipment Failure Rate Analysis Using Supervised Machine Learning
    The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), 2018
    Co-Authors: Rasha S. Aboul-yazeed, Ahmed M. El-bialy, Abdalla S A Mohamed
    Abstract:

    Machine learning is widely used to identify patterns in data and to assemble models that anticipate future outcomes based on historical data. One of the critical components required for efficient healthcare services provision is medical Equipment. Applying machine learning for Failure Rate modeling and prediction is of great importance. Therefore, two different stochastic models, ARMA and GARCH models, were utilized to analyze Failure Rate data. The outcome of each model was compared with previous work so that to achieve the best model that represent the Failure Rate data.

  • prediction of medical Equipment Failure Rate a case study
    International Conference on Advanced Intelligent Systems and Informatics, 2016
    Co-Authors: Rasha S Aboulyazeed, Ahmed M Elbialy, Abdalla S A Mohamed
    Abstract:

    Medical Equipment is one of the important inputs required for the provision of efficient healthcare services. Following maintenance programs will make the Equipment last longer, work more efficiently and reduces the likelihood of Equipment Failure during critical processing operations. Prediction of these Failures affects the efficiency and enlarges the uptime of medical Equipment, minimizes sudden Failures and even can prevent it. Therefore, time series analysis using autoregressive model (AR) has been used to analyze Failure Rate data. AR model uses the past behavior of the system output to predict its behavior in the future. The mean squared error (MSE) between model output and real-life data was less than 0.1 %. Moreover, it succeeded to predict duration of next Failures.

  • AISI - Prediction of Medical Equipment Failure Rate: A Case Study
    Advances in Intelligent Systems and Computing, 2016
    Co-Authors: Rasha S. Aboul-yazeed, Ahmed M. El-bialy, Abdalla S A Mohamed
    Abstract:

    Medical Equipment is one of the important inputs required for the provision of efficient healthcare services. Following maintenance programs will make the Equipment last longer, work more efficiently and reduces the likelihood of Equipment Failure during critical processing operations. Prediction of these Failures affects the efficiency and enlarges the uptime of medical Equipment, minimizes sudden Failures and even can prevent it. Therefore, time series analysis using autoregressive model (AR) has been used to analyze Failure Rate data. AR model uses the past behavior of the system output to predict its behavior in the future. The mean squared error (MSE) between model output and real-life data was less than 0.1 %. Moreover, it succeeded to predict duration of next Failures.

Daqing Gong - One of the best experts on this subject based on the ideXlab platform.

  • Environmental Intelligent Control of Underground IntegRated Pipe Gallery Based on Equipment Failure Rate
    IEEE Access, 2019
    Co-Authors: Daqing Gong
    Abstract:

    As a comprehensive infrastructure, urban integRated pipe gallery has numerous risk factors and various types, and once an accident occurs, it will have a major impact on urban public safety. At present, the comprehensive management of the underground integRated pipe gallery in China is still in the stage of regular inspection of the appointed personnel. Risks cannot be foreseen in advance, and they cannot be prevented. Based on the environmental data and Equipment maintenance data in the pipe gallery, this paper proposes an intelligent regulation algorithm for underground integRated pipe gallery based on Equipment Failure Rate. The fusion of heterogeneous data is included in the specific modules, meanwhile, the feature of device Failure Rate is extracted based on machine learning, and the multi-objective optimization environment is intelligently regulated by energy consumption and Equipment Failure Rate. The algorithm can effectively reduce the probability of Equipment Failure by intelligent regulation of the environment, and improve the safety and economy of the underground integRated pipe gallery.

Michael Moosemiller - One of the best experts on this subject based on the ideXlab platform.

  • avoiding pitfalls in assembling an Equipment Failure Rate database for risk assessments
    Journal of Hazardous Materials, 2006
    Co-Authors: Michael Moosemiller
    Abstract:

    Abstract As companies move progressively toward quantifying the risks of releases of hazardous materials, there becomes a greater need for developing the data necessary to populate the risk analysis. Sophisticated mathematical models have been developed to predict the consequences of a hazardous material release. But the effort devoted to the frequency side of the “risk equation” has been very disorganized by comparison, with inconsistent or non-existent definitions of “Failure”, mixing of incompatible data, application of data from one industry to a completely different industry, and a host of other problems. Nonetheless, through judicious assembly and analysis of a variety of data sources, a useful Failure Rate database can be developed. Many seminal sources of data are described, with an emphasis on loss of containment Failure Rates. Pitfalls in interpreting Failure Rate data are also illustRated.

Ahmad Shafaghi - One of the best experts on this subject based on the ideXlab platform.

  • Equipment Failure Rate updating bayesian estimation
    Journal of Hazardous Materials, 2008
    Co-Authors: Ahmad Shafaghi
    Abstract:

    Abstract The paper presents a Bayes’ method for augmenting generic Equipment Failure data with a prior distribution – predicated on the evidence, e.g., plant data – resulting in a posterior distribution. The depth of the evidence is significant in shaping the characteristics of the posterior distribution. In conditions of insufficient data about the prior distribution or great uncertainty in the generic data sources, we may use “constrained non-informative priors”. This representation of the prior preserves the mean value of the Failure Rate estimate and maintains a broad uncertainty range to accommodate the site-specific event data. Although the methodology and the case study presented in this paper focus on the calculation of a time-based (i.e., Failures per unit time) Failure Rate, based on a Poisson likelihood function and the conjugate gamma distribution, a similar method applies to the calculation of demand Failure Rates utilizing the binomial likelihood function and its conjugate beta distribution.

Gladys Strain - One of the best experts on this subject based on the ideXlab platform.

  • Cost Comparison of Reusable and Single-Use Ultrasonic Shears for Laparoscopic Bariatric Surgery
    Obesity Surgery, 2010
    Co-Authors: Elliot Yung, Michel Gagner, Alfons Pomp, Gregory Dakin, Luca Milone, Gladys Strain
    Abstract:

    Background Use of ultrasonic shears is currently the standard for advanced laparoscopic digestive surgery. The costs of medical care continue to increase, yet the amount of evidence-based information on cost differences in reusable and single-use Equipment is scarce. Methods All bariatric laparoscopic cases in our division that required the use of ultrasonic shears were observed during a 7-month period. The reusable and single-use scalpels were alternated weekly. Associated expenses (replacements, cleaning, sterilization), blood loss, complications, and ease-of-use were assessed. The total cost and cost per case for the two types of scalpels were calculated and compared. Results Eighty-five cases with both the single-use and reusable scalpels were evaluated. Both groups of cases were comparable in type of surgeries and patient demographics. No significant difference in operation time (reusable, 156 ± 15 min; single-use, 174 ± 15 min; p  = 0.34) or ease-of-use was noted. The Equipment Failure Rate (one replacement each), complications, and estimated blood loss (reusable, 63 ± 11 mL; single-use, 83 ± 12 mL; p  = 0.06) were similar. A total cost saving of $15,163 resulted from the use and processing of the reusable Equipment. Using the reusable shears for 85 cases, the cost-per-case saving was $196.40. Conclusions The reusable scalpel had a cost saving over single-use scalpel that increased with the number of cases. The reusable scalpel resulted in significant cost savings without impact on complication Rate and ease-of-use.