Vibration-Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis

Abstract

Wind industry experiences a tremendous growth during the last few decades. As of the end of 2016, the worldwide total installed electricity generation capacity from wind power amounted to 486,790 MW, presenting an increase of 12.5% compared to the previous year. Nowadays wind turbine manufacturers tend to adopt new business models proposing total health monitoring services and solutions, using regular inspections or even embedding sensors and health monitoring systems within each unit. Regularly planned or permanent monitoring ensures a continuous power generation and reduces maintenance costs, prompting specific actions when necessary. The core of wind turbine drivetrain is usually a complicated planetary gearbox. One of the main gearbox components which are commonly responsible for the machinery breakdowns are rolling element bearings. The failure signs of an early bearing damage are usually weak compared to other sources of excitation (e.g., gears). Focusing toward the accurate and early bearing fault detection, a plethora of signal processing methods have been proposed including spectral analysis, synchronous averaging and enveloping. Envelope analysis is based on the extraction of the envelope of the signal, after filtering around a frequency band excited by impacts due to the bearing faults. Kurtogram has been proposed and widely used as an automatic methodology for the selection of the filtering band, being on the other hand sensible in outliers. Recently, an emerging interest has been focused on modeling rotating machinery signals as cyclostationary, which is a particular class of nonstationary stochastic processes. Cyclic spectral correlation and cyclic spectral coherence (CSC) have been presented as powerful tools for condition monitoring of rolling element bearings, exploiting their cyclostationary behavior. In this work, a new diagnostic tool is introduced based on the integration of the cyclic spectral coherence (CSC) along a frequency band that contains the diagnostic information. A special procedure is proposed in order to automatically select the filtering band, maximizing the corresponding fault indicators. The effectiveness of the methodology is validated using the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking dataset which includes various faults with different levels of diagnostic complexity.

A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions

“…Detection and quantification of noise in a gearbox caused due to vibro-impacts between teeth of unloaded gears was also experimentally studied [153]. Studies on cyclostationarity analysis fundamentally use the cyclic statistics to analyze, detect, and estimate signals with hidden periodicities of the occurring fault, modulated by nonstationary speed/load variations observed on wind turbine [154][155][156][157].…”

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

“…On consideration of the complexity of CSC and CSCoh analysis, an improved method called the enhanced envelope spectrum was employed for the fault diagnosis of wind turbine gearbox [101]. It was demonstrated that the method could effectively identify the characteristic frequencies of a wind turbine gearbox in comparison with other processing approaches (Figure 5).…”

Vibration-Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis

“…The development of HUMS on helicopter health monitoring contributed to the reduction of the worldwide helicopter accident rate, showing the advantages 3 GTP-19-1314 Gryllias of implementation of such systems. However, MGB of helicopters are complex rotating mechanisms with several rotating components and reduction modules, resulting in challenging diagnostics tasks [1][2][3][4].…”

“…Recently the IESFOgram has been proposed as a novel tool for the automatic optimal frequency band selection leading to remarkable results in the field of bearing diagnostics [4]. The experimental setup with the MGB used to acquire the data is described in Section 3.…”

“…this band selection in an automated manner. The IESFOgram is built on a series of frequency band integrations, following the same 1/3 binary tree that is used in the Fast Kurtogram[4]. For each integration of frequency band, with center frequency c f and bandwidth bw, a feature value based on the corresponding IES is extracted.…”