Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering

Abstract

In order to reduce the costs of wind energy, it is necessary to improve the wind turbine availability and reduce the operational and maintenance costs. The reliability and availability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as the gearbox and means for lubrication oil condition monitoring and degradation detection. The wind industry currently uses lubrication oil analysis for detecting gearbox and bearing wear but cannot detect the functional failures of the lubrication oils. The main purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oils have deteriorated to such a degree that they no longer fulfill their functions. This paper describes a research on developing online lubrication oil condition monitoring and remaining useful life prediction using particle filtering technique and commercially available online sensors. It first introduces the lubrication oil condition monitoring and degradation detection for wind turbines. Viscosity and dielectric constant are selected as the performance parameters to model the degradation of lubricants. In particular, the lubricant performance evaluation and remaining useful life prediction of degraded lubrication oil with viscosity and dielectric constant data using particle filtering are presented. A simulation study based on lab verified models is provided to demonstrate the effectiveness of the developed technique.

A review on lubricant condition monitoring information analysis for maintenance decision support

“…In static sampling, often referred to as off-line analysis, the lubricant sample is drawn out from the machine and analyzed in the laboratory which can be on-site or out of the site. Recent reviews on the sampling methods are given in [24][25][26][27]. For accurate maintenance decision making, the integrity of the lubricant sample which is a legitimate representative must be guaranteed.…”

“…Particle filtering is a filtering technique which can handle statistic prediction data, unlike Kalman technique. This approach was used for predicting RUL [27].…”

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“…Therefore, the knowledge of dielectric properties, such as the relative electrical permittivity is important for the adequate performance of ICE engines. The monitoring of oil condition and its degradation in the online system and forecasting its replacement periods forms ones of the goals of the current automotive research [18]. Monitoring the oil condition and its degradation using an online system involving the analysis of its dielectric constant is associated with the need to get to know about its changes in the function of temperature.…”

Bench Testing of Sensors Utilized for In-Line Monitoring of Lubricants and Hydraulic Fluids Properties

“…Therefore, in both phases, the continuous monitoring of fluid conditions would be desirable. In this picture, several manufacturers are considering techniques for real time, continuous monitoring of fluids quality, for predictive maintenance purposes, by using suitable measurement sensors inserted along the flow lines [7][8][9]. These techniques help to provide early warning of machine failure and to minimize downtime, reducing maintenance costs, and to avoid bad lubrication conditions in the transmission and hydraulic circuits [10].…”

“…Albeit less precise than laboratory analyses, the sensors seem effective to show early any variation in the key characteristic of the fluid allowing the selection of the most appropriate oil maintenance (refill) and replacement programs [26] and, ultimately, to prevent damage to machines, particularly in the case of the testing of new fluids like the bio-based ones. The sensors could also be part of an integrated system for monitoring the conditions of oil-plant systems, in which other techniques are adopted, such as, e.g., thermography, vibration analysis [27], or used to develop models for the prediction of the remaining oil life [8].…”