This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines. The Part II of this survey focuses on the signals and signal processing methods used for wind turbine condition monitoring and fault diagnosis.

/pdf/a-survey-on-wind-turbine-condition-monitoring-and-fault-3150r9pj27.pdf

A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods

Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

Wind turbines have been developed fast in the recent years and at the same time have brought some problems. It is important to maintain the healthy condition of the running turbine because the consequences after faults are miserable for both the company and owner. There is a constant need to reduce the costs of operating and maintaining the turbines. Therefore, it is very important to detect the faults/failures early so as to minimize downtime and maximize productivity. This paper reviewed the structure of wind turbines and analyzed the different components of wind turbines in order to detect the faults that may happen. Meanwhile, this paper mainly reviewed fault diagnosis methods of wind turbines in the last three years. The main purpose of this paper is to supply some information on structure healthy condition monitoring (SHCM) and fault diagnosis in wind turbines for related researchers.

The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

Wind energy is contributing to more and more portions in the world energy market However, one deterrent to even greater investment in wind energy is the considerable failure rate of turbines In particular, large wind turbines are expensive, with less tolerance for system performance degradations, unscheduled system shut downs, and even system damages caused by various malfunctions or faults occurring in system components such as rotor blades, hydraulic systems, generator, electronic control units, electric systems, sensors, and so forth As a result, there is a high demand to improve the operation reliability, availability, and productivity of wind turbine systems It is thus paramount to detect and identify any kinds of abnormalities as early as possible, predict potential faults and the remaining useful life of the components, and implement resilient control and management for minimizing performance degradation and economic cost, and avoiding dangerous situations During the last 20 years, interesting and intensive research results were reported on fault diagnosis, prognosis, and resilient control techniques for wind turbine systems This paper aims to provide a state-of-the-art overview on the existing fault diagnosis, prognosis, and resilient control methods and techniques for wind turbine systems, with particular attention on the results reported during the last decade Finally, an overlook on the future development of the fault diagnosis, prognosis, and resilient control techniques for wind turbine systems is presented

/pdf/an-overview-on-fault-diagnosis-prognosis-and-resilient-2ent1ags0i.pdf

An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA

Expert System of Fault Diagnosis for Gear Box in Wind Turbine

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Expert System of Fault Diagnosis for Gear Box in Wind Turbine