Maintenance models, system safety

Martorell S, Sanchez A, Serradell V. Age-dependent reliability model considering effects of maintenance and working conditions. Reliability Engineering and System Safety, 1999 64(1) 19-31. 

Wu S. Clements-Croome D. 2005. Preventive maintenance models with random maintenance quality. Reliability Engineering and System Safety. 90 99-105. 

Weide, J.A.M. van der, M.D. Pandey and J.M. van Noortwijk, 2009. Discounted Cost Model for Condition-based Maintenance Optimization, submitted to Reliability Engineering and System Safety. 

Jayabalan, V. Chaudhuri, D. (1992). Optimal maintenance-replacement policy under imperfect maintenance. Reliability Engineering and System Safety 36, 165-169. Kijima, M. Nakagawa, T. (1991). Accumulative damage shock model with imperfect preventive maintenance. Naval Research Logistics 38, 145-156. 

Marquez, A. Heguedas, A. (2002). Models for maintenance optimization a study for repairable systems and finite time periods. Reliability Engineering and System Safety 75, 367-377. 

Dekker, R. 1996. Applications of maintenance optimization models A review and analysis. Reliability Engineering System Safety 51(3) 229-240. 

Dekker, R. Scarf, P. A. 1998. Onthe impactof optimisation models in maintenance decision making the state of the art. Reliability Engineering System Safety 60(2) 111-119. 

Samrout, M., Chatelet, E., Kouta, R. Chebbo, N. 2009. Optimization of maintenance policy using the proportional hazard model. Reliability Engineering and System Safety 94, 44-52. 

Some questions cross-loaded onto more than one factor in factor analysis (e g. Teamwork and Communications), or loaded on different factors in different factor analyses, suggesting that the item may not fit consistently into a coherent model of safety culture. Many of these concerned issues that were better covered by other items statistical analysis was used to determine the best - most precise - questions. An engineering-related example was I sometimes have to do workarounds to compensate for lack of resources (equipment, manpower or time) . Wording was another reason for removal of certain questiotmaire items. For instance, controllers pointed out that they could not be sure what would constitute sufficient system checks by maintenance staff when asked whether Maintenance staff perform sufficient system checks . 

Within Reason s model, latent conditions and unsafe acts combine to produce organizational accidents and incidents. Latent conditions are inadequate conditions or failures residing throughout a system and include poor designs, inadequate supervision, manufacturing defects, maintenance failures, inadequate training, clumsy automation, inappropriate or ill-defined procedures, inadequate equipment, and procedural shortcuts, to name only a few. Unsafe acts, on the other hand, represent those errors that are made by human operators at the sharp end of system operation that have an immediate impact upon system safety. 

Sanchez A., Carlos S., Martorell S., Villanueva J.F. Addressing imperfect maintenance modelling uncertainty in unavailabihty and cost based optimization. Reliability Engineering and System Safety 2009 94 (1) 22-32. 

Keedy, E. Feng, Q. 2012. A physics-of-failure based reliability and maintenance modeling framework for stent deployment and operation. Reliability Engineering System Safety, 103, 94-101. 

Zio, E., Compare, M. 2013. Evaluating maintenance policies by quantitative modeling and analysis. Reliability Engineering and System Safety 109 53-65. 

Barata, X, Guedes Soares, C, Marseguerra, M. Zio, E. (2002). Simulation Modelling of Repairable Multi-Component Deteriorating Systems for On Condition Maintenance Optimization. Reliability Engineering and System Safety. 76 255-264. 

Jones, B., Jenkinson, L, Yang, Z., Wang, J. 2010, The Use of Bayesian network modelling for maintenance planning in a manufacturing industry . Reliability Engineering and System Safety, vol. 95, pp. 267-277. 

Wang, W. 2012. An overview of the recent advances in delay-time-based maintenance modelling. Reliability Engineering and System Safety 106 165-178. 

The MSF model (NUREG/CR-3837) is used principally to determine the level of dependence between safety systems introduced by maintenance, testing, and calibration activities. It is a mathematical model which modifies the independent failure probability of any single component by considering that a component with which it is redundant has already failed. This allows the conditional failure probabilities of redundant components to be calculated to determine the overall system failure probability. Documentation requirements are given in Table 4.5-6. 

II, Chapters 4, 5, and 6 give all the information regarding the theoretical aspects involved in designing a complete NMMS and ensuring its successful implementation and maintenance. A complete system to detect, describe, analyse and follow-up near misses is outlined (Chapter 4), with special emphasis on a model-based classification of system failure (Chapter 5) a number of key issues relating to organisational aspects like acceptance by employees, and safety cultures are discussed in Chapter 6,... 

Assurance of rehability and continued safety, and availability, requires a quantitative assessment of the system in its projected future state. For this assessment, appropriate quantitative models are needed for estimating the accumulation of damage (in size and distribution) over its projected period of operation. The outcome of this assessment then serves as the basis for decisions on its suitability for continued service as reflected in Fig. 10.1 by the labels Reliable, Conditioned Reliability, and Not Reliable. A system judged to be reliable would be accepted for unrestricted operation until the next scheduled maintenance, the one with conditioned reliability would be subjected to operational constraints, and the one deemed to be... 

Many of the equipment vendors have developed cost of ownership (COO) models, some traceable, at lease in part, to SEMATECH. These COO models may be used to account for all aspects of amortized costs and provide a user with a highly accurate anticipated cost schedule. At a minimum a COO model should include the cost of the system, utilities, facilitization, mean-time-between-failures, mean-time-to-repair, preventative maintenance, personnel, all consumable safety costs (including that of required support equipment), reactant, and substrate costs. Each of these parameters" should be well defined and guaranteed, and the user of such models should precisely understand how up-time, mean-time-to-repair, and other terms are defined. A 90% uptime schedule is useless if the system is routinely defined to be out of service, for maintenance, 25 % of the time. 

---