Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs, and, therefore, it is necessary to monitor the condition of each wind turbine on the farm and identify the different states of alarm. Common alarms are raised based on data acquired by a supervisory control and data acquisition (SCADA) system; however, this system generates a large number of false positive alerts, which must be handled to minimize inspection costs and perform preventive maintenance before actual critical or catastrophic failures occur. To this end, a fault detection methodology is proposed in this paper; in the proposed method, different data analysis and data processing techniques are applied to real SCADA data (imbalanced data) for improving the detection of alarms related to the temperature of the main gearbox of a wind turbine. An imbalanced dataset is a classification data set that contains skewed class proportions (more observations from one class than the other) which can cause a potential bias if it is not handled with caution. Furthermore, the dataset is time dependent introducing an additional variable to deal with when processing and splitting the data. These methods are aimed to reduce false positives and false negatives, and to demonstrate the effectiveness of well-applied preprocessing techniques for improving the performance of different machine learning algorithms.

A methodology utilizing vibration data and Mel‐frequency cepstral coefficients (MFCCs) for wind turbine condition monitoring is developed to detect incipient faults in the wind turbine gearbox. This approach provides a more efficient and cost‐effective solution compared to traditional condition monitoring techniques relying on physical inspections, which can be time‐consuming and labor‐intensive. The use of vibration data enables the identification of subtle changes in a wind turbine’s operating condition, providing early warning signs of potential issues. When the vibration data are analyzed, changes in frequency and amplitude can be detected, indicating the presence of a developing fault. Vibration‐based condition monitoring systems (CMS) have already been widely used in the wind industry (mainly in new turbines). These systems utilize basic standard features, working in either the time or frequency domain, and are not optimized for nonstationary signals. In contrast, this work focuses on MFCCs, operating in both time and frequency domains, enabling the extraction of adequate information from nonstationary signals. The MFCCs are derived from vibration data signals, providing a compact representation for a more efficient analysis. These coefficients create a fingerprint of the wind turbine operating condition, compared to known healthy conditions, to identify anomalies. To underscore the practical value of this study, it is important to highlight the significant implications for the wind energy sector. The methodology developed offers an advanced, predictive tool for the early detection of gearbox faults, which is a critical aspect of optimizing the performance and longevity of wind turbines. By enabling earlier, more accurate fault detection, the proposed approach significantly reduces the likelihood of catastrophic failures and extensive downtime. This not only enhances the reliability and cost‐effectiveness of wind energy systems but also contributes to sustainable energy practices by optimizing resource use and minimizing maintenance costs. The results strongly suggest that the proposed methodology is highly effective in detecting incipient faults in the wind turbine gearbox. By providing early warnings of damage, operators can address issues before significant downtime or damage occurs. The use of MFCCs offers additional benefits since data can be collected remotely, eliminating physical inspections. Analysis can be performed faster, even in real time, allowing more frequent monitoring. This provides a more complete and accurate picture of the health of the system. The approach is tested in the EISLAB dataset concerning vibration signals from six wind turbines in northern Sweden, all with three‐stage gearboxes. All measurement data correspond to the axial direction of an accelerometer in the output shaft bearing housing of each turbine.

Nowadays, engineering is working side by side with medical sciences to design and create devices which could help to improve medical processes. Physiotherapy is one of the areas of medicine in which engineering is working. There, several devices aimed to enhance and assist therapies are being studied and developed. Mechanics and electronics engineering together with physiotherapy are developing exoskeletons, which are electromechanical devices attached to limbs which could help the user to move or correct the movement of the given limbs, providing automatic therapies with flexible and configurable programs to improve the autonomy and fit the needs of each patient. Exoskeletons can enhance the effectiveness of physiotherapy and reduce patient rehabilitation time. As a contribution, this paper proposes a dynamic model for two degrees of freedom (2 DOF) leg exoskeleton acting over the knee and ankle to treat people with partial disability in lower limbs. This model has the advantage that it can be adapted for any person using the variables of mass and height, converting it into a flexible alternative for calculating the exoskeleton dynamics very quickly and adapting them easily for a child’s or young adult’s body. In addition, this paper includes the linearization of the model and an analysis of its respective observability and controllability, as preliminary study for control strategies applications.

According to statistical data provided by the National Administrative Department of Statistics (DANE), during 2005, 29.32% of Colombias disabled population had problems with their legs related to moving or walking. In order to contribute and help these people, medical science and engineering have been working together to provide solutions that can improve the quality of life of injured people. In this sense, it is possible to design and implement electromechanical devices to assist and facilitate movements in rehabilitation processes. Such devices can keep detailed records of movements performed, patient history, speed, force, muscle interaction, among others; also allows physiotherapists to have complete control of rehabilitation processes, improving it and offering a lot more possible treatments by analyzing the summarized collected data. Rehabilitation focused exoskeletons work as guide and support in physical therapy, those make sure that the treated patient performs correctly all its exercises, in case that the patient cannot perform the movement by himself such devices helps the patient to finish the exercise, improving the effectiveness of the therapy and reducing the time it takes to recover lost faculties. As a contribution to rehabilitation processes, this paper proposes the design of an exoskeleton by considering biome-chanical models involving the most possible characteristics of the human body to reduce the differences between the mathematical model and the real behavior of the body segments; That proposed model is used to constraint and design a controller in master-slave configuration to assist and ensure soft movements improving rehabilitation processes involving flection and extension movements in the sagittal plane of the lower limbs.

This paper presents a strategy for computing model predictive control of linear Gaussian noise systems with probability constraints. As usual, constraints are taken on the system state and control input. The novelty relies on setting bounds on the underlying cumulative probability distribution, and showing that the model predictive control can be computed in an efficient manner through these novel bounds— an application confirms this assertion. Indeed real-time experiments were carried out to control a direct current (DC) motor. The corresponding data show the effectiveness and usefulness of the approach.

Wind energy plays a pivotal role in the transition to sustainable power generation. However, maintaining the reliability and efficiency of wind turbine (WT) remains a significant challenge due to complex operational conditions and the high cost associated with unexpected failures. Effective condition monitoring (CM) and predictive maintenance (PM) strategies are critical to mitigate these risks. This study presents a data‐driven fault detection framework that fuses supervisory control and data acquisition (SCADA) data with high‐frequency vibration signals using deep learning techniques to enhance diagnostic performance. Unlike conventional normal behavior models that rely exclusively on healthy data for training, the proposed framework incorporates limited labeled fault data when available. As only a few types of faults and a few samples are typically available in real‐world scenarios, the approach does not assume a complete representation of all possible fault conditions. Instead, it is designed to generalize beyond the specific faults seen during training. This is demonstrated by training the model on healthy conditions and only two known fault types (with labeled data available) and testing it on a third, previously unseen fault type. In particular, Siamese networks with contrastive and reconstruction learning are employed to improve feature representation and anomaly detection. Two distinct methodologies are compared: the first utilizes a binary cross‐entropy (BCE) loss function to classify the healthy or faulty status of the WT, while the second uses a triplet loss function for multiclass representation learning. Both methodologies generate low‐dimensional representations of the input features, also known as embeddings. The resulting feature embeddings are passed through a k ‐means clustering algorithm to improve fault separation and identification. Statistical features are extracted from SCADA data to capture key trends or event information, while the linear prediction coefficient (LPC) method, which models a signal by predicting future values based on its past samples, is applied to the vibration data for better fault characterization. The proposed approach is evaluated using the publicly available ETH Zurich dataset from an Aventa AV‐7 turbine. Experimental results indicate that the fusion of SCADA and vibration‐based diagnostics, in combination with contrastive and representation learning, substantially improves the predictive accuracy and generalization of fault detection models.