A survey of the application of gamma processes in maintenance

By Jan M. van Noortwijk. Reliability Engineering & System Safety, 2009

1,369 citations recorded by the source corpus.

Subfields: Mechanics of Materials, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c3acd53715c90f35c0f95c2e729c03e377d7568d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanics of Materials

1. Philip A. Scarf
2. Benoît Iung
3. Bram de Jonge
4. Donghua Zhou
5. Isabel M. Ribeiro
6. Nan Chen
7. J. Zeng
8. Bo Guo

In Safety, Risk, Reliability and Quality

1. Bo Guo
2. Xiaolin Wang
3. Z. Ye
4. Philip A. Scarf
5. Benoît Iung
6. Bram de Jonge
7. Donghua Zhou
8. Isabel M. Ribeiro

In Statistics and Probability

1. Bo Guo
2. Xiaolin Wang
3. Z. Ye
4. Philip A. Scarf
5. Benoît Iung
6. Bram de Jonge
7. Donghua Zhou
8. Isabel M. Ribeiro

Citations in the corpus

• Residual life estimation based on bivariate non-stationary gamma degradation process (2015, methodology): “According to Noortwijk,[10] for the Gamma process-based degradation model (one dimension), we can obtain the estimates of parameters by solving the corresponding likelihood equations.”
• Residual life estimation based on bivariate Wiener degradation process with time-scale transformations (2014, methodology): “For the direct CM data, more representative models are regression-based models [4], Wiener process models [5], Gamma process models [6], and Markovian-based models [7].”
• Reliability Modeling for Systems with Multiple Degradation Processes Using Inverse Gaussian Process and Copulas (2014, positive): “Wiener process and the Gamma process are two classes of stochastic processes that have beenwidely used in degradationmodeling [1].”
• A review on maintenance optimization (2020, background): “Van Der Weide, Pandey & Van Noortwijk (2010) assume that shocks occur at random moments in time and that each shock causes a random amount of damage.”
• Predictive Maintenance and Intelligent Sensors in Smart Factory: Review (2021, background): “In engineering and production, maintenance is associated with predicting machines’ status [81] or deterioration of processes [82].”
• Remaining useful life estimation - A review on the statistical data driven approaches (2011, methodology): “van Noortwijk (2009) presented an excellent review on the Gamma process in the context of maintenance.”
• A framework for quantifying the value of vibration-based structural health monitoring (2022, background): “The second part of Equation (17) describes a homogeneous compound Poisson process (CPP) [46, 47], and is used to model deterioration due to sporadic shocks, which is typical of, e.”
• Railway condition-based maintenance model with stochastic deterioration (2014, background): “…timetables, assigning locomotive at a minimal operational costs, optimizing networks, assigning extra trains on a railway network, etc. (Higgins et al. 1996; Rackwitz 2001; Lingaya et al. 2002; Kallen, Noortwijk 2005; Guan et al. 2006; Rouillon et al. 2006; Noortwijk 2009; Cacchiani et al. 2010).”
• A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions (2015, methodology): “Various CBM policies have been proposed and applied for many industrial systems, see for example [9, 10, 21, 27, 28].”
• Deterioration state space partitioning method for opportunistic maintenance modelling of identical multi-unit systems (2015, background): “Another interesting aspect of these processes is the existence of an explicit probability distribution function that permits feasible mathematical developments (Van Noortwijk 2009).”