Reliability–Centered Maintenance Using Reliability Parameters on Gas Compressors

In this paper, Bayes estimators of the unknown shape and scale parameters of the Exponentiated Inverse Rayleigh Distribution (EIRD) have been derived using both the frequentist and bayesian methods. The Bayes theorem was adopted to obtain the posterior distribution of the shape and scale parameters of an Exponentiated Inverse Rayleigh Distribution (EIRD) using both conjugate and non-conjugate prior distribution under different loss functions (such as Entropy Loss Function, Linex Loss Function and Scale Invariant Squared Error Loss Function). The posterior distribution derived for both shape and scale parameters are intractable and a Lindley approximation was adopted to obtain the parameters of interest. The loss function were employed to obtain the estimates for both scale and shape parameters with an assumption that the both scale and shape parameters are unknown and independent. Also the Bayes estimate for the simulated datasets and real life datasets were obtained. The Bayes estimates obtained under dierent loss functions are close to the true parameter value of the shape and scale parameters. The estimators are then compared in terms of their Mean Square Error (MSE) using R programming language. We deduce that the MSE reduces as the sample size (n) increases.

Estimation of the stress–strength reliability for the generalized logistic distribution

Drilling and evaluation services require repair and maintenance (R&M) programs to ensure that assets are returned to the fit-for-purpose state before their next application. R&M costs constitute a large percentage of the cost of service for an oil field services company. Consequently, there is a continuous " tactical" push to reduce R&M costs by increasing intervals, inspections, etc. To optimize a maintenance strategy, the cost of an on-rig failure or cost of failure (CoF) must be included. These costs include unplanned maintenance, lost asset utilization, transportation costs, variable labor costs, etc. The CoF indirectly includes the impact on the operator in terms of non-productive time (NPT) or unplanned downtime on the rig that could result in concessions or loss of contract. A reliability-centered maintenance (RCM) strategy provides a cost model that allows the service provider to consider internal cost reduction and customer impact. This paper presents the RCM-based maintenance strategy currently being developed at Baker Hughes, and reviews the model and the tradeoffs that must be made between product costs (R&M costs) and the cost of a downhole failure (cost of failure) to the operator and the service company. As the applications become more difficult (deeper, hotter, higher pressures, etc.), and the cost of operations continues to rise, there is increasing pressure on the service companies to reduce the downhole failure rates and extend the useful lives of the rental assets to keep the R&M costs at a minimum. Not only is tracking individual part conditions and history important for implementing a traditional condition-based maintenance (CBM) strategy, but also the Cost of Failure for the service provider and operator is important. Introduction Repair and maintenance is essential for a product to perform to its expected life. The fundamental question regarding repair and maintenance is whether to perform maintenance or to scrap. In many instances, there exists a trade-off between the cost of repair and the cost of part replacement, considering the remaining life of the product. This paper presents a life cycle cost (LCC) model for drilling and evaluation tools with a RCM approach for optimizing the repair and maintenance cycles that minimize the LCC. Throughout the paper, the term repair is employed for any corrective activity triggered by a failure, and the terms maintenance or preventive maintenance as an activity(s) for preventive measures. For drilling and evaluation tools, Baker Hughes sets levels of maintenance in order to standardize workflow and logistics. For example, during Level I maintenance, the system is calibrated and measured for degradation and functionality. Level II maintenance replaces consumable parts such as seals. Level III maintenance rewires and replaces major parts such as the drive-shaft bearing based upon inspections or life predictions. To set the appropriate maintenance level, the Baker Hughes' preventive maintenance policy blends together time-based and environment-based maintenance. Scheduling maintenance is determined by a combination of job and waiting time, transportation, and environmental conditions (temperature, vibration, H2S, mud property, etc.). The policy takes into consideration factors that cause greater strains on the system and its components. The maintenance is conservatively scheduled in an attempt to discover failures before they occur in the field. After each maintenance activity the system may be assumed to be as good as new; however, maintenance is imperfect and does not truly restore the system to its original state (Ben-Daya & El-Ferik, 2008). Only specific components are replaced or rewired during maintenance, yet the whole system experiences deterioration throughout its lifetime. The entire system's degradation must be considered to fully assess the health of the system.

Previously, the Weibull process with an unknown scale parameter was examined as a model for Bayesian decision making. The analysis is extended by treating both the shape and scale parameters as unknown. It is not possible to find a family of continuous joint prior distributions on the two parameters that is closed under sampling, so a family of prior distributions is used that places continuous distributions on the scale parameter and discrete distributions on the shape parameter. Prior and posterior analyses are examined and seen to be no more difficult than for the case in which only the scale parameter is treated as unknown, but preposterior analysis and determination of optimal sampling plans are considerably more complicated in this case. To illustrate the use of the present model, an example is presented in which it is necessary to make probability statements about the mean life and reliability of a long-life component both before and after life testing.

The Weibull distribution is one of the most popular distributions in analyzing the lifetime data . In this study, we consider the Bayes estimators of the scale and shape parameters of Weibull distribution under the assumptions of gamma priors and squared error loss function. While computing the Bayes estimates for a Weibull distribution, the continuous conjugate joint prior distribution of the shape and scale parameters does not exist and the closed form expressions of the Bayes estimators cannot be obtained. In this study first we will consider the Bayesian inference of the scale parameter under the assumption that the shape parameter is known. We will assume that the scale parameter has a gamma prior. Under these assumptions Bayes estimate can be obtained in explicit form. When both the parameters are unknown, the Bayes estimates cannot be obtained in closed form. In this case, we will assume that the scale parameter has the gamma prior, and the shape parameter also has the gamma prior and they are independently distributed. We will use the Lindley approximation to obtain the approximate Bayes estimators. Under these assumptions, we will compute approximate Bayes estimators and compare with the maximum likelihood estimators by Monte Carlo simulations.

Reduction of Failure Risk for the Key System of a Petrochemical Plant through Reliability Centered Maintenance (RCM)

Equivalent distributions with application to rainfall as an upper bound to flood distributions

Equivalent distributions with application to rainfall as an upper bound to flood distributions

Reliability Centered Maintenance (RCM) is a well-known method in several industries.RCM is a structure decision process to cost-effective determines and optimum maintenance requirements.This paper proposes the application of the RCM concept to a petrochemical case study.The results indicate that preventive maintenance plans and available time of maintenance staff are improved significantly.

Maintenance is an important issue that needs to be addressed during the design and manufacturing or building of a system. Both Total Productive Maintenance (TPM) and Reliability Centered Maintenance (RCM) have its own concepts and principles as a maintenance strategy to give impact to the performance and reliability of the system. Therefore, in this research, the TPM will be studied with RCM to show their relationship. Based on the conceptual analysis, the author will analyze publications from journals, books, and articles to find all related information, such as genealogy, definition, concepts, and principle, and implementation of both strategy. The conceptual method used is Näsi’s four elements of conceptual analysis and a questionnaire survey method which has been answered electronically to study the current maintenance strategies in the real industries and to relate it to TPM and RCM. The result of the study shows that TPM had started in 1970 while RCM started in the 1960s, but the foundation of TPM and RCM was started in 1950s with Breakdown Maintenance (BM). TPM is basically about elimination of faults through day-today activities involving the entire force work while the concept of RCM is to improve equipment reliability. The objective of TPM is to achieve zero breakdowns, zero defects, and zero accidents while TPM is to preserve the functions. Although both are using different tools but both are linked together through the Lean Tool and can increase product quality, equipment reliability, increase safety, and increase profit. From the survey result, most of the respondent comes from the companies that implementing traditional maintenance (PM and CM) rather than TPM and RCM. TPM mostly can be implemented in big plant industries while RCM can be applied in small or medium size plant.

Maintenance strategy for tilting table of rolling mill based on reliability considerations

Extreme rainfalls often occur everywhere just in a moment, very difficult to be anticipated and produce very detrimental impact to the environment and human society. Floods and landslides are influenced by high variability of extreme rainfalls, especially in the watershed area for floods and the hills as well as mountains for landslides, such as in Malang Residence, East Java, Indonesia as a case study in this study. The prediction tools for determining location and time of the next extreme rainfalls event will occur are required. The behavior of extreme rainfalls measured on one or several stations rain gauge could be approximated by Generalized Pareto (GP) Distribution. The prediction tools must be able to identify and characterize parameters of the GP Distribution such as shape and scale parameters over the entire area. Shape parameter of GP distribution has associated with characteristics of extreme rainfalls distributions. To identify characteristics of shape parameter on each station and their similarity, an algorithm to make a partition of shape parameters into several spatial clusters and investigate the type of distribution was proposed. In order to determine threshold value, mean residual life plot and stability of modified scale and shape parameters at a range of thresholds were used, Maximum Likelihood method was utilized to estimate parameter value and k-means method combined by Silhouette values to make the cluster of extreme rainfalls distribution. By using rainfalls data on twenty eight different stations rain gauge, the results showed that the proposed algorithm well performed and extreme rainfalls were heterogeneous with three type of GP distribution. In general, shape parameter values were negative and positive except on nine stations which were close to zero and were well partitioned by six clusters.

This paper investigates each of the quantitative methods that support Reliability Centered Maintenance (RCM) operation. The integration of quantitative methods with RCM activities is an innovative approach since previous RCM methodologies only utilized qualitative methods. A thorough literature review identified ten essential activities that are necessary for the application of RCM along with the respective quantitative methods that support these activities. Furthermore, in-person interviews were conducted at Brazilian manufacturing firms to confirm the actual application of the methods from the literature. These interviews also helped us identify additional methods that these companies use in their manufacturing practices. As a result, a table associating essential RCM activities with their quantitative method counterpart was created, along with a brief description of what each respective quantitative method contributes specifically. Among the quantitative methods evaluated, probability theory was identified as the one that was most commonly associated with RCM activities. The methods of Economic engineering and Monte Carlo simulation were also identified as key contributors since they allowed for more sophisticated analysis related to the cost and performance of production systems subject to maintenance. The use of probability distributions in modeling is important in many RCM activities. Not only should the average values be used, but it is necessary to understand the effects of times to failure and times to repair on time. The Economic engineering methods allowed the analysis of the life cycle costs of machines and equipment. Monte Carlo simulation is capable of analyzing real systems which are essentially stochastic. A more complete view of the equipment and their critical components is provided through the use of blocks diagram and system reliability analysis techniques. The methods of production planning and controlling, such as Manufacturing Resource Planning II (MRPII), support the optimal planning of maintenance activities. The stochastic and deterministic models of stocks management optimize storage costs in relation to the cost of lacking spare parts. Indicators specific to maintenance, such as Overall Equipment Effectiveness (OEE) and Total Effective Equipment Performance (TEEP), allow the monitoring of essential variables in the production lines and the calculation of global efficiency. Costing systems such as ABC quantify the productive system costs, which include the maintenance activities. These methods can have an expressive contribution to the RCM operation. The quantitative methods listed improve both the planning and activities control while also reducing costs.

Reliability centered substation equipment maintenance is developed on the basis of condition-based maintenance or predictive maintenance based on equipment condition monitoring and fault diagnosis. The maintenance scheme and resource allocation are determined by distinguishing different operation states and different influence degrees of equipment to achieve the optimal balance between maintenance cost and power supply reliability. Through a series of decision-making bases, joint maintenance strategies including condition-based maintenance, periodic maintenance, hidden danger detection and post maintenance, as well as the determination of corresponding maintenance cycle, are adopted to improve the power supply reliability and reduce the equipment maintenance cost. This paper summarizes and expounds RCM(Reliability Centered Maintenance) step by step from the selection of reliability indicators, determination of target equipment for implementing RCM, definition of equipment failure mode and its failure diagnosis, maintenance mode decision to system maintenance optimization strategy.

Life cycle costing considerations in reliability centered maintenance: An application to maritime equipment