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The aim of this paper is to explore the usefulness of repairable parts simple historical databases in assisting the human factors experts to identify candidate areas for applying human factors methods. Therefore, also contributing to the search for maintenance quality improvement.

The study was based on the failure history of part fleets installed on the same type of jet engines, and used mean time between failures (MTBF) and failure rates plots, the Laplace trend test, the AMSAA‐Crow‐Duane model and serial correlations.

Increasing and decreasing trends in failure rates indicated factors that cause deflection from the literature assumptions of constant failure mode and “as good as new” maintenance philosophy. Further statistical calculations revealed patterns between MTBF and frequency of maintenance, specific serial numbers (SN) vulnerability to replacement and depot maintenance tasks, correlations between MTBF and number of both installations and maintenances, and influence of the maintenance month on the maintenance‐failure hours' interval.

The literature refers to the relation between the parts reliability and the human factors in the maintenance domain. The research confirmed the literature references in data collection problems coming from human factors interferences; the patterns found were attributed to system deficiencies related to workload management, parts configuration management, supervision and manufacturing problems.

The application of this research in combination with methods such as field observations and interviews of personnel involved in the maintenance domain can uncover specific maintenance working environment weaknesses and lead to suitable remedies.

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

The purpose of this paper is to compute reliability, availability and maintainability (RAM) indices to measure and improve the performance of an automated croissant production line under real working conditions. Based on this study, the authors demonstrate how RAM analysis is very useful for deciding maintenance intervals and for planning and organizing the adequate maintenance strategy.

The present work is carried out by analyzing failure and repair data based on statistical techniques. Descriptive statistics of the failure and repair data at workstation and line level were carried out. Trend and serial correlation tests validated the assumption of independence and identical distribution of the failure data were conducted. Moreover, the reliability and maintainability of both the croissant production line and its workstations have been estimated at different mission times with their best fit distribution.

The main objectives of the applied method are to understand the nature of the failure patterns, and to estimate the reliability and maintainability characteristics of the croissant production system in precise quantitative terms. The analysis identifies the critical points of the production line that require further improvement through effective maintenance strategy.

This study is anticipated to serve as an illuminating effort in conducting a complete RAM analysis and its effect on the performance of the system that works under real conditions. The advantage of the methodology is the continuous monitoring of the production process through appropriate indices, the utilization of which leads to a continuous cycle of improvement.

Load haul dumper (LHD) is one of the main ore transporting machineries used in underground mining industry. Reliability of LHD is very significant to achieve the expected targets of production. The performance of the equipment should be maintained at its highest level to fulfill the targets. This can be accomplished only by reducing the sudden breakdowns of component/subsystems in a complex system. The identification of defective component/subsystems can be possible by performing the downtime analysis. Hence, it is very important to develop the proper maintenance strategies for replacement or repair actions of the defective ones. Suitable maintenance management actions improve the performance of the equipment. This paper aims to discuss this issue.

Reliability analysis (renewal approach) has been used to analyze the performance of LHD machine. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the maximum likelihood estimate (MLE) method.

Independent and identical distribution (IID) assumption of data sets was validated through trend and serial correlation tests. On the basis of test results, the data sets are in accordance with IID assumption. Therefore, renewal process approach has been utilized for further investigation. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the MLE method. Reliability of each individual subsystem has been computed according to the best-fit distribution. In respect of obtained reliability results, the reliability-based preventive maintenance (PM) time schedules were calculated for the expected 90 percent reliability level.

As the reliability analysis is one of the complex techniques, it requires strategic decision making knowledge for the selection of methodology to be used. As the present case study was from a public sector company, operating under financial constraints the conclusions/findings may not be universally applicable.

The present study throws light on this equipment that need a tailored maintenance schedule, partly due to the peculiar mining conditions, under which they operate. This study mainly focuses on estimating the performance of four numbers of well-mechanized LHD systems with reliability, availability and maintainability (RAM) modeling. Based on the drawn results, reasons for performance drop of each machine were identified. Suitable recommendations were suggested for the enhancement of performance of capital intensive production equipment. As the maintenance management is only the means for performance improvement of the machinery, PM time intervals were estimated with respect to the expected rate of reliability level.

The purpose of this paper is to implement the six sigma (SS) strategy in a bag sector under actual operating circumstances based on defining-measure-analyze-improve and control (DMAIC). During the project, several statistical tools and methods have been used efficiently to create inferences. Thus, to measure and enhance system efficiency, the author calculate reliability, availability and maintainability (RAM) indices. Based on this research, the author show how the SS method and RAM analysis are very helpful in determining maintenance intervals, as well as in planning and organizing the appropriate maintenance strategy.

This study introduces the step-by-step application of the DMAIC methodology for the identification and reduction of bag production line downtime and examines the present operations management. Thus, statistical techniques are used to analyze the failure and repair database. Pareto analysis, histograms and descriptive statistics at the machine and line-level of the historical data were conducted. Trend and serial correlation testing validated the hypothesis of independence and identical distribution of database was performed. In addition, with their best fit allocation, the RAM of both the bag production line and its machines was estimated at separate mission times.

The main goals of the applied method are to understand the nature of the downtime patterns and to accurately and quantitatively estimate the RAM characteristics of the bag production system. The assessment defines the production line's critical points, requiring further enhancement through an efficient maintenance approach. Therefore, by improving plant efficiency and safety, the author can decrease unplanned downtime and equipment failures.

This research is expected to serve as an attempt to conduct SS DMAIC methodology through RAM assessment and its impact on system efficiency under actual circumstances. The benefit of the methodology is that the manufacturing process is continuously monitored by suitable indicators, the use of which leads to a continuous improvement cycle.

The purpose of this research is to calculate and enhance the cheese cheddar manufacturing plant efficiency under actual workplace conditions by measuring reliability, availability and maintainability (RAM) indices. The authors highlight how RAM analysis is important in determining periodic maintenance and in scheduling and managing the appropriate maintenance policy.

The current work is conducted using statistical approaches to evaluate failure and repair statistics. The RAM estimation was calculated on the basis of quantitative data obtained over a span of 32 months. Descriptive statistics, Pareto analysis, as well as the presumption of independence were ensured through trend and serial correlation tests. In addition, the reliability and maintainability of the cheddar cheese processing plant and its machines were calculated at various mission periods.

The primary goal of the implementation approach is to understand the fault patterns and the accurate quantitative assessment of the reliability and maintainability of the cheddar production plant. The findings revealed the essential aspects of the line, which need improvement by an appropriate maintenance program.

This study is intended to serve to highlight the RAM assessment and its impact on the performance of the real-time system. The benefit of the technique is the continual control of the manufacturing process by means of acceptable indexes, whose use corresponds to a continuous improvement process.

The demand of cement in India is expected to increase rapidly as the government has been giving immense boost to various housing facilities, infrastructure projects, road networks and railway corridors. One of the ways to meet this rise in the demand of cement is to increase the capacity utilization of the existing cement plants by improving their availability. The availability of a cement plant can be improved by avoiding failures and reducing maintenance time through reliability, availability and maintainability (RAM) analysis of its subsystems. The paper aims to discuss this issue.

The data related to time between failure (TBF) and time to repair (TTR) of all the critical subsystems of a cement plant were collected over a period of two years for carrying out RAM analysis. Trend test and serial correlation test were performed on TBF and TTR data to verify whether these data are independent and identically distributed or not. Afterwards, the authors use EasyFit 5.6 professional software to find best-fit distribution of TBF and TTR data and their parameters. The effectiveness of a preventive maintenance policy was evaluated by simulating the real and proposed systems.

The results of the analysis show that the raw mill and the coal mill are critical subsystems of a cement plant from a reliability point of view, whereas the kiln is a critical subsystem from an availability point of view. The analysis shows that the repair time of the cement mill should be reduced for improving the availability of the cement plant. The RAM analysis showed that the capacity of the case study company is 17 percent underutilized due to maintenance-related problems and 15 percent underutilized because of management-related problems.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Thus, it would serve as a reference for reliability and maintenance managers in deciding maintenance strategies of cement plants as well as in improving their capacity utilization.

The study exhibits the usage of RAM analysis in deciding preventive maintenance programs of several cement plant subsystems. Even more, using a simulation study, the authors show that preventive maintenance of the cement plant beyond a certain level can be disadvantageous as it leads to an increase in downtime and decrease in availability.

The purpose of this paper is to provide results for a complete reliability, availability, and maintainability (RAM) analysis utilizing data sets from a production system in a wine packaging line. Through the illustrated case study, the author demonstrates how RAM analysis is very useful for deciding maintenance intervals, and for planning and organizing the adequate maintenance strategy.

RAM analysis has been done for each machine by using failures data. The parameters of some common probability distributions, such as Weibull, exponential, lognormal, and normal distributions, have been estimated by using the Minitab software package. An investigation to determine which of these distributions provide the best fit for characterizing the failure pattern at machine and line level has been made. Reliability and maintainability of both wine packaging and its machines has been estimated at different mission times with their best fit distribution. High maintainability issues and potential factors with their potential failure modes were presented, through failure mode and effect analysis process.

Analysis of the total downtime, breakdown frequency, reliability, and maintainability characteristics of different machines shows that: first, the availability for the wine packaging line was 91.80 percent, and for the remaining 8.2 percent the line is under repair. Second, about two failures per shift are displayed on the line, whereas for the mean time-to-repair (TTR) a failure is 24 minutes. Third, there is no correlation between the time-between-failures and the TTRs for the wine packaging line. Fourth, the main three factors affecting the maintainability process in the production line are: resources availability, manpower management, and maintenance planning procedures.

This study is anticipated to serve as an illuminating effort in conducting a complete RAM analysis in the much advertised field of wine packaging production line which on the other hand so little has been published on operational availability and equipment effectiveness. It can also be useful to serve as a valid data source for winery product manufacturers, who wish to improve the design and operation of their production lines.

The purpose of this paper is to provide results for a complete maintainability analysis utilizing data sets from a production system in a shaving blades division of a large high-tech razor manufacturer. Through the illustrated case study, the authors demonstrate how to spot improvement points for enhancing availability by carrying out an equipment effectiveness analysis.

Descriptive statistics of the repair data and the best fitness index parameters were computed. Repair data were collected from departmental logs, and a preliminary screening analysis was conducted to validate their usefulness for the indicated period of 11 months. Next, the maintainability and repair rate modes for all the machines of the production system were calculated. Maintainability and availability estimations for different time periods that took in account the overall system were obtained by trying out and selecting an optimum statistical model after considering of several popular distributions.

Out of the five considered machines in the system, two particular units received about half of the repairs (M2 and M3). The time to repair follows a loglogistic distribution and subsequently the mean time to repair is estimated at 25 minutes at the machine level. Repair time performance is approximated at 55 minutes if the availability of the system is to attain a 90 percent maintainability.

This study is anticipated to serve as an illuminating effort in conducting a complete maintainability analysis in the much advertised field of shavers, for which on the contrary so little has been published on operational availability and equipment effectiveness. The case study augments the available pool of sources where executing maintainability studies is highlighted usually under the direction of combined total quality management and total productive maintenance programs.