Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies.

Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

Purpose Despite the wide dissemination and application of current signature analysis (CSA) in general industry, CSA is not commonly used in the wind industry, where the use of vibration signals predominates. Therefore, the purpose of this paper is to review the use of generator CSA (GCSA) in the online fault detection and diagnosis of wind turbines (WTs). Design/methodology/approach This is a bibliographical investigation in which the use of GCSA for the maintenance of WTs is analyzed. A section is dedicated to each of the main components, including the theoretical foundations on which GCSA is based and the methodology, mathematical models and signal processing techniques used by the proposals that exist on this topic. Findings The lack of appropriate technology and mathematical models, as well as the difficulty involved in performing actual studies in the field and the lack of research projects, has prevented the expansion of the use of GCSA for fault detection of other WT components. This research area has yet to be explored, and the existing investigations mainly focus on the gearbox and the doubly fed induction generator; however, modern signal treatment and artificial intelligence techniques could offer new opportunities in this field. Originality/value Although literature on the use of GCSA for the detection and diagnosis of faults in WTs has been published, these papers address specific applications for each of the WT components, especially gearboxes and generators. For this reason, the main contribution of this study is providing a comprehensive vision for the use of GCSA in the maintenance of WTs.

In the wind industry (WI), a robust and effective maintenance system is essential. To minimize the maintenance cost, a large number of methodologies and mathematical models for predictive maintenance have been developed. Fault detection and diagnosis are carried out by processing and analyzing various types of signals, with the vibration signal predominating. In addition, most of the published proposals for wind turbine (WT) fault detection and diagnosis have used simulations and test benches. Based on previous work, this research report focuses on fault diagnosis, in this case using the electrical signal from an operating WT electric generator and applying various signal analysis and processing techniques to compare the effectiveness of each. The WT used for this research is 20 years old and works with a squirrel-cage induction generator (SCIG) which, according to the wind farm control systems, was fault-free. As a result, it has been possible to verify the feasibility of using the current signal to detect and diagnose faults through spectral analysis (SA) using a fast Fourier transform (FFT), periodogram, spectrogram, and scalogram.

Climatic change has taxed humanity in terms of the use of renewable energy in most countries around the world. In this context, installed wind power has recently increased at an almost exponential rate. Although there is still a large potential market that could be covered, minimizing costs has become a critical factor for a wind project to be financially justified and competitive. The present work is a bibliographical review of the primary strategies and methodologies used to reduce the cost of these activities while producing the maximum electricity from wind turbines. This review includes tabulated information on the types of faults, occurrence statistics, and associated costs and equipment suppliers for monitoring, detection, diagnosis and fault prediction and although most of it refers to onshore wind turbines, offshore wind turbines are also included in several parts. Research shows that in order to guarantee the reliability and economic feasibility of wind farms, the most used methodologies is CMS, which include vibration analysis, acoustic emission, ultrasonic testing techniques, thermography, oil analysis, structural health monitoring, optical fiber monitoring, strain measurement, radiographic inspection, power signal analysis. Currently, in addition to technical personnel, turbine operation and maintenance include, sensors, software, trucks, cranes, boats, helicopters, drones and smartphone applications. Keywords: wind turbines, maintenance strategies, cost, types of faults, condition monitoring system