Many vibration based fault diagnostic methods count on the fact that the speed and load are constant during data collection. Most of the frequency domain methods used in vibration based CMS today are based on the fast Fourier transform (FFT) techniques.
However, reflection of component degradation will not be efficient under nonstationary operating conditions and will lead to fault misdiagnosis. Features extracted from time domain signals usually reflect component degradation under constant operating conditions. Features are used to reflect the condition of the gearbox components and in literature are referred to as condition indicator (CI). One of the approaches in gearbox fault diagnostics is direct feature extraction from the vibration signal.
For gearboxes operating at constant speed and load, traditional vibration based fault diagnostic techniques are reliable and allow end-users to accurately track fault progression over time. Operators of wind turbines pay attention to gearbox reliability due to the following reasons : the gearbox cost which is about 13% of overall wind turbine costs, high cost of replacement and installation, complex repair procedure, and high revenue losses due to long downtime. Standard method of gearbox fault diagnostics is data collection by accelerometers located on gearbox housing with the main purpose to collect vibration signals associated with transmission components, such as gears, shafts, and bearings. Gearbox repair is complex and contributes to the long wind turbine downtime, especially when turbines are deployed offshore with requirement of special maintenance support vessel, crane ship, and good weather conditions for safe operation. Despite technical challenges, vibration based CMS is the most popular diagnosis approach for wind turbine gearboxes. Most wind turbines are designed to operate for at least 20 years and in addition to current market share of gearbox based wind turbines there is no doubt about the importance and necessity of CMS implementation. The major goal of wind turbine condition monitoring system (CMS) is to provide predictive, condition based maintenance that will improve safety, decrease maintenance costs, and increase system availability. In order to maintain the high availability of multistage gearbox wind turbine, condition monitoring of gearbox is essential. Direct drive turbines are still under development and they have some disadvantages, like large volume which leads to difficult transportation and manipulation.
Market share of direct drive turbines installations grew by 30% in 2014 and took 27% of the global market, according to World Wind Energy Market Update 2015, a slight decline in market share compared to 2013 despite good overall performance. Wind turbine manufacturers have been exploring different drive train topologies ranging from multistage gearbox and induction generators to direct drive systems. At the same time, there is constant effort to reduce wind turbine downtime and increase availability. Wind turbines experience component failures which lead to increased operation and maintenance costs and, at the end, high cost of energy. By the end of 2014, the global total installed wind capacity had reached more than 369 GW with about 133 GW of wind energy installed in Europe alone.
Wind energy is currently the fastest growing renewable energy source in the world. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal.
First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. On the other side, gearbox is one of the key components of wind turbine drivetrain. Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox.