The Dynamic Evolution Modeling of the Bearings State Change
Journal Title: RECENT - Year 2012, Vol 13, Issue 36
Abstract
Predictive maintenance is used to improve the availability of industrial bodies while minimizing maintenance costs. Monitoring the evolution of defects and early detection are based on periodic controls to avoid failures and to plan maintenance operations considered relevant which aims to guarantee normal operation later. So talking about the future is knowing the present. In this context, a stochastic model is discussed based on a type of Markov chain (Xn, n = 1, 2, 3). That is, if Xn = i then the process is said to state i at time n with the existence of a probability Pij that this process will be in State j at time (n + 1). Thus, the conditional distribution of a future state of Xn+1 is independent of the past but only the Xn present state-dependent States. In this study, is presented the methodology to model and simulate the evolution of the degradation occurring in mechanical parts, the cases studied its double row bearings installed on centrifugal fan of average power. Each bearing, we propose to associate a Markov chain describing the transition from one State (i) to another (j) State following the evolution of the overall level of vibration, knowing that according to standards (NF, ISO, VDI, API...) the degradation process of imports it what mechanical body passes by States such as: 'Good', 'Eligible', 'Yet eligible' and 'Ineligible' or 'Disentitled'. The proposed model to calculate the probability of having a bearing faded along life. In addition, it is possible to use the transition matrix associated with Markov chain analysis of operation for the determination of the mechanical system reliability.
Authors and Affiliations
Azzedine BOUZAOUIT, Lakhdar KHOCHMANE, Mounir GAHGAH
Keywords
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