We review basic modeling approaches for failure and mainte- nance data from repairable systems. In particular we consider imperfect re- pair models, defined in terms of virtual age processes, and the trend-renewal process which extends the nonhomogeneous Poisson process and the renewal process. In the case where several systems of the same kind are observed, we show how observed covariates and unobserved heterogeneity can be included in the models. We also consider various approaches to trend testing. Modern reliability data bases usually contain information on the type of failure, the type of maintenance and so forth in addition to the failure times themselves. Basing our work on recent literature we present a framework where the ob- served events are modeled as marked point processes, with marks labeling the types of events. Throughout the paper the emphasis is more on modeling than on statistical inference.

On the statistical modeling and analysis of repairable systems

This paper presents a survey of the applications of prognostics and health management maintenance strategy to machine tools. A complete perspective on this Industry 4.0 cutting-edge maintenance policy, through the analysis of all its preliminary phases, is given as an introduction. Then, attention is given to prognostics, whose different approaches are briefly classified and explained, pointing out their advantages and shortcomings. After that, all the works on prognostics of machine tools and their main subsystem are reviewed, highlighting current open research areas for improvement.

A review of prognostics and health management of machine tools

The decision-making processes have become crucial for organizations immersed on an intensified competitiveness situation. Because of current industrial reality, a continuous improvement of the ability for adding value and enhancing profitability of decisions is needed by firms (Pascual et al, 2009). Companies are frequently required for reducing production costs and increasing asset utilization; consequently, stress on machines is generated. This situation affects reliability and, most important, system throughput. Due to the demanding use of equipment, the stressful effect tends to be critical for the performance of asset intensive industries, e.g.: mining operations, aeronautic, nuclear industry, or defence. One way to improve the system performance is to allocate inventory resources. Stocks can represent about one third of all assets of a typical company (Dfaz & Fu, 1997). Of these assets, spare parts have special relevance for asset intensive industries since extensive system downtime could be produced by the insufficiency of spare part stocks (Louit 2006). As an example, in the airlines business, spares sum up above US$50 billion (Kilpi & Vepsalainen, 2004). The management of spare part stocks requires a balance between shortage costs (costs of shutting down the operations) and overstock costs (financial costs associated with holding a safety stock) (Sarker & Haque, 2000) and ordering costs. Insufficient stocks affect overall performance of physical assets; on the other hand, oversized stocks lead to an inefficient use of resources and can imply high investment costs. Therefore, decisions about spare-stocking policies can become essential in the cost structure of companies. In order to provide an efficient spare management performance, a suitable ordering strategy can be relevant. This situation provides an opportunity to improve decision making methods. A spare part classification scheme becomes necessary in order to set optimal policies for those spares that may affect the system the most and at the least effort. We proposed an ordering decision-aid method, in order to secure the spare management performance into an operational environment that needs continuity to compete into a demanding business context.

Critical spare parts ordering decisions using conditional reliability and stochastic lead time

Purpose




Maintenance plans are programmes, which follow maintenance appraisals, contain information of what to do and the time approximates for accomplishments. They also deal with how to carry out maintenance jobs. In contemporary period, curiosity has proliferated about how sustainability affects manufacturing plans. The purpose of this paper is to offer a comprehensive notion of maintenance sustainability in maintenance planning. The literature has downplayed maintenance sustainability but may support in understanding how to crack the present company-community conflicts about the negative influence of manufacturing on the environment.




Design/methodology/approach




This study develops the idea of selecting the proper maintenance strategy based on integrated fuzzy axiomatic design (FAD) principle and fuzzy-TOPSIS. This work suggests that the maintenance function is an uncertain, activity-oriented system. To fully appreciate the proposed framework, the work employs data from a cement manufacturing plant to test the structure. This study offers 20 influential factors on which it build the fundamental structure of maintenance system sustainability for manufacturing concerns. A novel literature contribution that departs from existing conceptions is the classical determination of weights of each sustainability factor, employing fuzzy entropy weighting approach. Furthermore, work innovatively determines the ranking of some important tenets of sustainability in maintenance and optimises the maintenance consumables employing the FAD principle.




Findings




Interestingly, the output of the investigation revealed differences as the work adopts fuzzy-TOPSIS in comparison with FAD principle.




Originality/value




Case examination of a real-life manufacturing venture validated the claims, showing maintenance workforce training as a top-echelon strategy for maintenance system sustainability.

Ranking maintenance strategies for sustainable maintenance plan in manufacturing systems using fuzzy axiomatic design principle and fuzzy-TOPSIS

Do life-cycle costing and assessment integration support decision-making towards sustainable development?

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 p...

Reliability analysis and life cycle cost optimization: a case study from Indian industry

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.

Reliability analysis of CNC turning center based on the assessment of trends in maintenance data: A case study

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 e...

Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data

The intensity growth of manufacturing industry in India is increasing the demand of CNC turning centers. The producers of CNC turning centers are trying to fulfil this demand. The quality and reliability of such CNC turning centers is critical for manufacturers and users to meet the market demand. In this context, it is required to identify the potential failures and minimize their occurrence probability. Failure modes and effects analysis (FMEA) is a robust methodology used to study the risk and reliability of various systems. In this paper, FMEA methodology is used to analyze the risk involved in the operation of CNC turning centers. The objective of the analysis was to identify critical failure modes, components and sub-systems. The study uses field failure data from more than 50 CNC turning centers and more than five industrial experts. FMEA results are further compared to the failure rates obtained from the field failure data. The results of the FMEA analysis are useful for system reliability improvement and optimizing maintenance. Electrical and electronic components have higher failure rates compared to mechanical components. CNCS, MT, XZAS, HS, ChS and SS are found to be critical sub-systems.

Failure Modes and Effects Analysis (FMEA) of Computerized Numerical Control (CNC) Turning Center

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.

Integrated reliability and maintainability analysis of Computerized Numerical Control Turning Center considering the effects of human and organizational factors

Rajkumar Bhimgonda Patil

Papers

Reliability analysis and life cycle cost optimization: a case study from Indian industry

Reliability analysis of CNC turning center based on the assessment of trends in maintenance data: A case study

Selection of time-to-failure model for computerized numerical control turning center based on the assessment of trends in maintenance data

Failure Modes and Effects Analysis (FMEA) of Computerized Numerical Control (CNC) Turning Center

Integrated reliability and maintainability analysis of Computerized Numerical Control Turning Center considering the effects of human and organizational factors