A parameter estimation method for machine tool reliability analysis using expert judgement

Lad, Bhupesh Kumar; Kulkarni, Makarand S.

doi:10.1504/ijdats.2010.032455

Cited by 39 publications

(18 citation statements)

References 10 publications

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“…The following data are required to determine the corrective and preventive maintenance costs: 1) Time to implement the maintenance actions and any logistical delays of materials, 2) costs of materials, labor, and downtime, 3) equipment restoration degree (i.e., good-as-new, badas-old, or imperfect) and restoration factor determined by expert judgement [36], and 4) probability of equipment failure captured from historical data. The expected total maintenance cost is the sum of the expected cost of preventive and corrective maintenances and is expressed as…”

Section: A Cost Model For Corrective and Preventive Maintenance Polimentioning

confidence: 99%

New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

et al. 2019

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Section: A Cost Model For Corrective and Preventive Maintenance Polimentioning

confidence: 99%

New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

et al. 2019

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“…[14][15][16][17] Several procedures have been proposed for the reliability modeling of repairable systems. [18][19][20][21][22][23][24][25][26][27][28][29][30] Sehgal et al 20 suggested a procedure for the evaluation and selection of components such as rolling contact bearings for a given application considering reliability aspect, which is based on graph theory and matrix method. Pulido et al 21 presented a comprehensive methodology using Bayesian approach for obtaining prior distribution in reliability and require only prior intervals for the mean and the standard deviations of TTF of the product.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Patil

Kothavale

Waghmode³

2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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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.

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“…This is quantified in terms of a restoration factor. The restoration factor can be determined empirically or based on expert judgement as calculated in and Lad and Kulkarni (2010) respectively. .…”

Section: Optimisation Modelmentioning

confidence: 99%

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

Pandey

Kulkarni

Vrat

2012

International Journal of Production Research

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The performance of a production system depends on the breakdown-free operation of equipment and processes. Maintenance and quality control play an important role in achieving this goal. In addition to deteriorating with time, equipment may experience a quality shift (i.e. process moves to out-of-control state), which is characterised by a higher rejection rate and a higher tendency to fail. This paper develops an integrated model for joint optimisation of preventive maintenance interval and control parameters incorporating the Taguchi loss function. We consider two types of maintenance policies: minimal corrective maintenance that maintains the state of the equipment without affecting the age and imperfect preventive maintenance that upgrades the equipment in between 'as good as new' and 'as bad as old' condition. The proposed model enables the determination of the optimal value of each of the four decision variables, i.e. sample size (n), sample frequency (h), control limit coefficient (k), and preventive maintenance interval (t PM ) that minimises the expected total cost of the integration per unit time. A numerical example is presented to demonstrate the effect of the cost parameters on the joint economic design of preventive maintenance and process quality control policy. The sensitivity of the various parameters is also examined.

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A parameter estimation method for machine tool reliability analysis using expert judgement

Cited by 39 publications

References 10 publications

New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

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

A methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

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