Purpose The paper presents reliability, maintainability and life cycle cost (LCC) analysis of a computerized numerical control (CNC) turning center which is manufactured and used in India. The purpose of this paper is to identify the critical components/subsystems from reliability and LCC perspective. The paper further aims at improving reliability and LCC by implementing reliability-improvement methods. Design/methodology/approach This paper uses a methodology for the reliability analysis based on the assessment of trends in maintenance data. The data required for reliability and LCC analysis are collected from the manufacturers and users of CNC turning center over a period of eight years. ReliaSoft’s Weibull++9 software has been used for verifying goodness of fit and estimating parameters of the distribution. The LCC of the system is estimated for five cost elements: acquisition cost, operation cost, failure cost, support cost and net salvage value. Findings The analysis shows that the spindle bearing, spindle belt, spindle drawbar, insert, tool holder, drive battery, hydraulic hose, lubricant hose, coolant hose and solenoid valve are the components with low reliability. With certain design changes and implementation of reliability-based maintenance policies, system reliability is improved, especially during warranty period. The reliability of the CNC turning center is improved by nearly 45 percent at the end of warranty period and system mean time between failure is increased from 15,000 to 17,000 hours. The LCC analysis reveals that the maintenance cost, operating cost and support costs dominate the LCC and contribute to the tune of 87 percent of the total LCC. Research limitations/implications The proposed methodology provides an excellent tool that can be utilized in industries, where safety, reliability, maintainability and availability of the system play a vital role. The approach may be improved by collecting data from more number of users of the CNC turning centers. Practical implications The approach presented in this paper is generic and can be applied to analyze the repairable systems. A real case study is presented to show the applicability of the approach. Originality/value The proposed methodology provides a practical approach for the analysis of time-to-failure and time-to-repair data based on the assessment of trends in the maintenance data. The methodology helps in selecting a proper approach of the analysis such as Bayesian method, parametric methods and nonparametric methods.

Purpose – Reliability analysis is required to identify the components or subsystems with low reliability for a given designed performance. Life cycle cost analysis helps understand the cost implications over the entire life span of a product. The purpose of this paper is to present a case study describing reliability analysis and life cycle cost optimization of a band saw cutting machine manufactured and used in India. Design/methodology/approach – The data required for reliability analysis is collected from the manufacturer and users of band saw cutting machine. The parameters of failure distribution have been estimated by using ReliaSoft’s Weibull++6 software. The life cycle cost is divided into various cost elements such as acquisition cost, operation cost, failure cost, support cost and net salvage value. Findings – The results of the analysis show that the components such as band wheel bearing, guide roller bearing, limit switch, carbide pad, hydraulic cylinder oil seal, control panel dial, control panel and solenoid valve are critical from reliability and life cycle cost analysis perspective. Originality/value – With certain design changes it is found that the reliability of the system is increased by 15.85 percent while the life cycle cost is reduced by 22.09 percent. The study also shows that the reliability analysis is useful for deciding maintenance intervals.

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This article provides a generalized framework for selection of time-to-failure model based on the assessment of trends in failure and repair time data. This framework is based on modifications of existing frameworks and can be applied for binary as well as multi-state systems. The proposed framework is applied for reliability analysis of a computerized numerical control turning center. For analysis purpose, the failure data are collected for 50 computerized numerical control turning center over a period of 7 years for three different working conditions, that is, when machining material is steel, aluminum and cast iron. The data collected are then processed using the proposed framework and the best-fit distribution is found for the time-to-failure data. Furthermore, the reliable life and reliabilities of the different sub-systems are estimated. From the analysis, it is found that spindle system, computerized numerical control system, electrical and electronic system, hydraulic system and cooling system are found to be critical from reliability and maintainability point of view. The analysis presented here is expected to help the users and manufacturers of computerized numerical control turning center to estimate the reliability in accurate manner.

Purpose Reliability, maintainability and availability of modern complex engineered systems are significantly affected by four basic systems or elements: hardware, software, organizational and human. Computerized Numerical Control Turning Center (CNCTC) is one of the complex machine tools used in manufacturing industries. Several research studies have shown that the reliability and maintainability is greatly influenced by human and organizational factors (HOFs). The purpose of this paper is to identify critical HOFs and their effects on the reliability and maintainability of the CNCTC. Design/methodology/approach In this paper, 12 human performance influencing factors (PIFs) and 10 organizational factors (OFs) which affect the reliability and maintainability of the CNCTC are identified and prioritized according to their criticality. The opinions of experts in the fields are used for prioritizing, whereas the field failure and repair data are used for reliability and maintainability modeling. Findings Experience, training, and behavior are the three most critical human PIFs, and safety culture, problem solving resources, corrective action program and training program are the four most critical OFs which significantly affect the reliability and maintainability of the CNCTC. The reliability and maintainability analysis reveals that the Weibull is the best-fit distribution for time-between-failure data, whereas log-normal is the best-fit distribution for Time-To-Repair data. The failure rate of the CNCTC is nearly constant. Nearly 66 percent of the total failures and repairs are typically due to the hardware system. The percentage of failures and repairs influenced by HOFs is nearly only 16 percent; however, the failure and repair impact of HOFs is significant. The HOFs can increase the mean-time-to-repair and mean-time-between-failure of the CNCTC by nearly 65 and 33 percent, respectively. Originality/value The paper uses the field failure data and expert opinions for the analysis. The critical sub-systems of the CNCTC are identified using the judgment of the experts, and the trend of the results is verified with published results.

PurposeThe demand of sewage treatment plants is increasing day by day, especially in the countries like India. Biological and chemical unit of such sewage treatment plants are critical and needs to be designed and developed to achieve desired level of reliability, maintainability and availability.Design/methodology/approachThis paper investigates and optimizes the availability of biological and chemical unit of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. A set of Chapman–Kolmogorov differential equations are derived for the model and a generalized solution is discovered using soft computing techniques namely genetic algorithm (GA) and particle swarm optimization (PSO).FindingsNature-inspired optimization techniques results of availability function depicted that PSO outperforms GA. The optimum value of the availability of biological and chemical processing unit is 0.9324 corresponding to population size 100, the number of evolutions 300, mutation 0.6 and crossover 0.85 achieved using GA while PSO results reflect that optimum achieved availability is 0.936240 after 45 iterations. Finally, it is revealed that PSO outperforms than GA.Research limitations/implicationsThis paper investigates and optimizes the availability of biological and chemical units of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process.Originality/valueAvailability model of biological and chemical units of a sewage treatment is developed using field failure data and judgments collected from the experts. Furthermore, availability of the system has been optimized to achieve desired level of reliability and maintainability.

The demand of steam in process industries is increasing rapidly, and this demand can be met by increasing the capacity utilization of steam boilers. Many of the process industries depend on industrial steam boilers as a vital component for their operation. The availability of the boiler can be improved by identifying critical mechanical sub-systems/components concerning failure frequency, reliability, and maintainability and minimizing their likelihood of occurrences. The selection of appropriate technique for data collection and reliability analysis is essential. The time between failure (TBF) and time to repair (TTR) of all components and sub-systems were collected to carry out Reliability, Availability and Maintainability (RAM) analysis. The best-fit distribution and distribution parameters were calculated using ReliaSoft software Weibull++10 after performing trend testing. The preventive maintenance intervals of all components and sub-systems and the availability of the system were evaluated. The analysis reveals that the combustion system, feed-water system, and blow-down system are the critical sub-systems from a reliability perspective and are still the biggest reasons for the boiler downtime. The research study also showed that TTR was longer for the combustion system than the other sub-systems, and thus, to enhance its availability, it is suggested that maintenance resources should be allocated at the appropriate moment to the combustion system. The study also shows the usage of RAM analysis in deciding the preventive maintenance intervals of components/sub-systems of the boiler. It also provides a reference for the preparation of the maintenance plan for the boiler system.

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