Wind turbine database for intelligent operation and maintenance strategies.

With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors. The reported values for each sensor are minimum, maximum, mean, and standard deviation. The database also contains the alarm events, indicating the system and subsystem and a small description. Finally, a set of functions to download specific subsets of the whole database is freely available in Matlab, R, and Python. To demonstrate the usefulness of this database, an illustrative example is given. In this example, different gearbox variables are selected to estimate a target variable to detect whether or not the estimate differs from the actual value provided for the sensor. By using this normality modelling approach, it is possible to detect rotor malfunction when the estimate differs from the actual measured value.

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing.

A novel and innovative solution addressing wind turbines' main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets.

An mHealth intervention for the treatment of patients with an eating disorder: A multicenter randomized controlled trial.

OBJECTIVE: The current multicentre randomized controlled trial assessed the clinical efficacy of a combined mHealth intervention for eating disorders (EDs) based on cognitive behavioral therapy (CBT). METHOD: A total of 106 ED patients from eight different public and private mental health services in Spain were randomly assigned to two parallel groups. Patients of the experimental group (N = 53) received standard face-to-face CBT plus a mobile intervention through an application called "TCApp," which provides self-monitoring and an online chat with the therapist. The control group (N = 53) received standard face-to-face CBT only. Patients completed self-report questionnaires on ED symptomatology, anxiety, depression, and quality of life, before and after treatment. RESULTS: Significant reductions in primary and secondary outcomes were observed for participants of both groups, with no differences between groups. Results also suggested that the frequency with which patients attended their referral mental health institution after the intervention was lower for patients in the experimental group than for those in the control group. DISCUSSION: The current study showed that CBT can help to reduce symptoms relating to ED, regardless of whether its delivery includes online components in addition to traditional face-to-face treatment. Besides, the additional component offered by the TCApp does not appear to be promising from a purely therapeutic perspective but perhaps as a cost-effective tool, reducing thus the costs and time burden associated with weekly visits to health professionals.

Signal injection as a fault detection technique.

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.