Deterioration estimation for predicting and controlling RUL of a friction drive system.

This paper develops a method for estimating the state of deterioration of a friction drive system and presents its use for predicting and controlling the Remaining Useful Life (RUL) of such a system. The friction drive system is assumed to be affected by endogenous uncertainties and exogenous disturbances. The proposed method is intended for on-line estimation of the contact surface deterioration and it is based on a parameter-varying model that includes both the motion dynamics and the deterioration dynamics of the device. Since, in the presented setting, the control actions on the mechanical system play a role on the non-linear deterioration dynamics, an Extended Kalman Filter is developed for simultaneously estimating both the state of deterioration and its associated estimation error bounds. A numerical example is presented to illustrate the interest of such estimations for RUL prognosis and RUL control. The presented example considers the availability of angular speed measurements and the possibility of re-planning motor torques and/or re-planning desired angular speeds in order to control RUL based on RUL prognosis.

A combined mono- and multi-turbine approach for fault indicator synthesis and wind turbine monitoring using SCADA data.

The monitoring of wind turbines using SCADA data has received lately a growing interest from the fault diagnosis community because of the very low cost of these data, which are available in number without the need for any additional sensor. Yet, these data are highly variable due to the turbine constantly changing its operating conditions and to the rapid fluctuations of the environmental conditions (wind speed and direction, air density, turbulence, …). This makes the occurrence of a fault difficult to detect. To address this problem, we propose a multi-level (turbine and farm level) strategy combining a mono- and a multi-turbine approach to create fault indicators insensitive to both operating and environmental conditions. At the turbine level, mono-turbine residuals (i.e. a difference between an actual monitored value and the predicted one) obtained with a normal behavior model expressing the causal relations between variables from the same single turbine and learnt during a normal condition period are calculated for each turbine, so as to get rid of the influence of the operating conditions. At the farm level, the residuals are then compared to a wind farm reference in a multi-turbine approach to obtain fault indicators insensitive to environmental conditions. Indicators for the objective performance evaluation are also proposed to compare wind turbine fault detection methods, which aim at evaluating the cost/benefit of the methods from a production manager's point of view. The performance of the proposed combined mono- and multi-turbine method is evaluated and compared to more classical methods proposed in the literature on a large real data set made of SCADA data recorded on a French wind farm during four years : it is shown than it can improve the fault detection performance when compared to a residual analysis limited at the turbine level only.