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Lubricating Oil Degradation Modeling and Prognostics using the Wiener Process

System degradation under random effects evolves as a continuous time process. Lubrication oil analysis has been a health indicator for degrading industrial systems ever since World War II. Advancement of sensors and analytics methods for oil debris monitoring has led to the development of applications using Prognostics and Health Management (PHM) for lube oil analysis so that the system health condition can be diagnosed and predicted more effectively in the field of manufacturing and transportation. Complexity in lube oil-based prediction arises with its dual role i.e. lubricating oil is just not an information source for system health, it also inherits the degradation nature like other components in a system. In contrast, other degradation indicators have a single mission which is to indicate the system state. Frequent top-up to maintain oil quantity in system and regular replenishments of degraded oil further add complexity to system state prediction. The inter-dependence of lube oil degradation and system degradation generates the need for an integrated methodology under PHM for system Remaining Useful Life (RUL) prediction based on lube oil degradation. To accomplish this, a degradation model is being developed here using a continuous time stochastic process i.e. the Wiener process for lubricating oil degradation tracking and RUL prediction under regular oil top-up effects. The impact of filtered / corrected degradation data on prediction is discussed in the case of frequent oil top-ups to the system. A perspective-based decision support taxonomy is presented for selection of lubrication oil analysis approach.