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In recent years, acoustic emission (AE) sensors and AE-based techniques have been developed and tested for gearbox fault diagnosis. In general, AE-based techniques require much higher sampling rates than vibration analysis-based techniques for gearbox fault diagnosis. Therefore, it is questionable whether an AE-based technique would give a better or at least the same performance as the vibration analysis-based techniques using the same sampling rate. To answer the question, this paper presents a comparative study for gearbox tooth damage level diagnostics using AE and vibration measurements, the first known attempt to compare the gearbox fault diagnostic performance of AE- and vibration analysis-based approaches using the same sampling rate. Partial tooth cut faults are seeded in a gearbox test rig and experimentally tested in a laboratory. Results have shown that the AE-based approach has the potential to differentiate gear tooth damage levels in comparison with the vibration-based approach. While vibration signals are easily affected by mechanical resonance, the AE signals show more stable performance.

The widespread deployment of industrial wind projects will require a more proactive maintenance strategy in order to be more cost competitive. This paper describes an ongoing research project on developing online lubrication oil condition monitoring and degradation detection tools using commercially available online sensors. In particular, an investigation on particle contamination of lubrication oil is reported. Methods are presented for online lubrication oil condition monitoring and remaining useful life prediction using viscosity and dielectric constant sensors along with particle filtering technique. Physical models are derived in order to establish the mathematical relationship between lubrication oil degradation and particle contamination level. Laboratory experiments are performed to validate the accuracy of the developed models by comparing viscosity and dielectric constant sensor outputs of different particle concentration levels with those simulated by the lubricant deterioration physical models. A case study on lubrication oil degradation detection and remaining useful life prediction is provided. Discussions on the potential for extrapolating the presented methods to typical wind turbine gearbox oil and the practical implementation of particle filter‐based approach for online wind turbine gearbox oil remaining useful life prediction are also included. Copyright © 2014 John Wiley & Sons, Ltd.

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In this paper, we develop an integrated prognostics method considering a time-varying operating condition, which integrates physical gear models and sensor data. By taking advantage of stress analysis in finite element modeling (FEM), the degradation process governed by Paris' law can adjust itself immediately to respond to the changes of the operating condition. The capability to directly relate the load to the damage propagation is a key advantage of the proposed integrated prognostics approach over the existing data-driven methods for dealing with time-varying operating conditions. In the proposed method, uncertainties in material parameters are considered as sources responsible for randomness in the predicted failure life. The joint distribution of material parameters is updated as sensor data become available. The updated distribution better characterizes the material parameters, and reduces the uncertainty in life prediction for the specific individual unit under condition monitoring. The update process is realized via Bayesian inference. To reduce the computational effort, a polynomial chaos expansion (PCE) collocation method is applied in computing the likelihood function in the Bayesian inference and the predicted failure time distribution. Examples based on crack propagation in a spur gear tooth are given to demonstrate the effectiveness of the proposed method. In addition, the example also shows that the proposed approach is effective even when the current loading profile is different from the loading profile under which historical data were collected.Index Terms-Bayesian update, integrated prognostics, polynomial chaos expansion, time-varying operating condition, uncertainty quantification. ACRONYMS AND ABBREVIATIONS: FEM finite element modeling PCE polynomial chaos expansion RUL remaining useful life PHM proportional hazard model FE finite element SIF stress intensity factor Manuscript

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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.