improving equipment reliability and system maintenance and repair efficiency

improving equipment reliability and system maintenance and repair efficiency

mean time to failure of modern machinery and equipment, their individual parts and components can be calculated over the years. methods for determining the optimal frequency of maintenance and repair, based on the collection and processing of information about the reliability of industrial facilities, during their testing in laboratories and at special sites, as well as through long, operational tests require considerable time and become expensive. the purpose of this work is to develop methods for processing information about the reliability of equipment in automated systems for maintenance and repair, which will reduce the time to collect information on equipment failures and improve the cost-effectiveness of maintenance and repair. small, multiple-censored right-side samples of equipment operating time for failure are formed as a result of failure data collection in an automated system for equipment maintenance and repair. calculation of reliability indicators for such samples is performed using the maximum likelihood estimation method. the article presents experimental studies of the accuracy of the maximum likelihood estimates of the parameter of the exponential distribution law for small, multiple right-censored samples. the studies were carried out by computer modeling of censored samples, similar to samples that are formed when monitoring equipment during operation. methods of simulation modeling of random processes on a computer and methods of regression analysis were used. analysis show that most of the maximum likelihood estimates obtained from small, multiple-censored right-side samples have significant deviations from the true values. a technique for improving the accuracy of maximum likelihood estimates is proposed. the scientific novelty is regression models are constructed that establish the relationship between the deviation of the maximum likelihood estimate from the true value and the parameters characterizing the sample structure. these models calculate and introduce corrections to maximum likelihood estimates. the use of the developed regression models will reduce the time to collect information about the reliability of the equipment, while maintaining the reliability of the results.