Failures in the main bearings of wind turbines are critical in terms of downtime and replacement cost. Early diagnosis of their faults would lower the levelized cost of wind energy. Thus, this work discusses a gated recurrent unit (GRU) neural network, which detects faults in the main bearing some months ahead (when the event that initiates/develops the failure releases heat) the actual fatal fault materializes. GRUs feature internal gates that govern information flow and are utilized in this study for their capacity to understand whether data in a time series is crucial enough to preserve or forget. It is noteworthy that the proposed methodology only requires healthy supervisory control and data acquisition (SCADA) data. Thus, it can be deployed to old wind parks (nearing the end of their lifespan) where specific high-frequency condition monitoring sensors are not installed and to new wind parks where faulty historical data do not exist yet. The strategy is trained, validated, and finally tested using SCADA data from an in-production wind park composed of nine wind turbines.

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

Implementing impedance control in Cartesian task space or directly at the joint level is a popular option for achieving desired compliance behavior for robotic manipulators performing tasks. The damping ratio is an important control criterion for modulating the dynamic response; however, tuning or selecting this parameter is not easy, and can be even more complicated in cases where the system cannot be directly solved at the joint space level. Our study proposes a novel methodology for calculating the local optimal damping ratio value and supports it with results obtained from five different scenarios. We carried out 162 different experiments and obtained the values of the inertia, stiffness, and damping matrices for each experiment. Then, data preprocessing was carried out to select the most significant variables using different criteria, reducing the seventeen initial variables to only three. Finally, the damping ratio values were calculated (predicted) using automatic regression tools. In particular, five-fold cross-validation was used to obtain a more generalized model and to assess the forecasting performance. The results show a promising methodology capable of calculating and predicting control parameters for robotic manipulation tasks.

Wind energy maintenance and operation costs can total millions of dollars each year in an average industrial‐size wind park. Therefore, moving from preventive and corrective maintenance to predictive maintenance is imperative in the wind energy sector. This paper contributes to this challenge by providing a main bearing early damage detection technique that exclusively uses standard supervisory control and data acquisition (SCADA) data (10‐min average) and a convolutional autoencoder with the following contributions. (i) Entirely semisupervised (not requiring the labeling of data through work order logs and avoiding the problem of data imbalance between classes) based only on healthy data, thus expanding its range of application (even when the failure of interest has never occurred in the park before). (ii) Validated using real‐world SCADA data and shown to be resistant to seasonality, and operational and environmental conditions. (iii) Reliable predictions with minimum false alarms thanks to specially designed fault prognosis indicators based on the image mean square error metric. (vi) The early warning is achieved months in advance, thus providing adequate time for plant operators to plan properly. (v) The main use of exogenous variables in the model (variables that are not affected by other variables, e.g., wind speed, wind turbulence, and ambient temperature) guarantees the detection of damage directly related only to the low‐speed shaft temperature (the only nonexogenous variable used by the stated model). (vi) Finally, the proposed strategy is validated in a wind park made up of 12 wind turbines.

The rapid growth of the energy capacity generated by wind turbines, as well as their size, creates new challenges in terms of operation and maintenance. Main bearing failures have been identified as one of the main and most critical issues in terms of increasing the availability and reliability of wind turbines. Therefore, predicting wind turbine main bearing failures has become a technical challenge and an economically important issue. In this manuscript it is established a fault prognosis procedure to forecast main bearing failures several months beforehand to help wind turbine operators plan maintenance in advance. Reducing downtime is one of the main goals in wind energy industry since it lets addressing its energy loss impact. The main contributions of this work are described below: i) the prognosis is carried out by using only SCADA data, which is accessible in all commercial wind turbines; therefore, no additional sensors designed for a determined purpose need to be installed; ii) it is a semi-supervised approach, thus it only requires healthy data to be collected; iii) it is based only on external variables and one representative temperature close to the subsystem under diagnosis, thus avoiding data contamination; iv) it accomplishes the prognosis (several months beforehand) of the main bearing fault; and v) the performance and validity of the proposed strategy are demonstrated on a real underproduction wind turbine.

A polymer electrolyte fuel cell (PEFC) is an electrochemical device that converts chemical energy into electrical energy and heat. The energy conversion is simple; however, the multiphysics phenomena involved in the energy conversion process must be analyzed in detail. The gas diffusion layer (GDL) provides a diffusion media for reactant gases and gives mechanical support to the fuel cell. It is a complex medium whose properties impact the fuel cell’s efficiency. Therefore, an in-depth analysis is required to improve its mechanical and physical properties. In the current study, several transport phenomena through three-dimensional digitally created GDLs have been analyzed. Once the porous microstructure is generated and the transport phenomena are mimicked, transport parameters related to the fluid flow and mass diffusion are computed. The GDLs are approximated to the carbon paper represented as a grouped package of carbon fibers. Several correlations, based on the fiber diameter, to predict their transport properties are proposed. The digitally created GDLs and the transport phenomena have been modeled using the open-source library named Open Pore Network Modeling (OpenPNM). The proposed correlations show a good fit with the obtained data with an R-square of approximately 0.98.