On the Use of a Single Piezoelectric Strain Sensor for Wind Turbine Planetary Gearbox Fault Diagnosis

Abstract

Planetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to breakdown of the whole drivetrain and major loss of wind turbines. Therefore, PGB fault diagnosis is important in reducing the downtime and maintenance cost and improving the reliability and lifespan of wind turbines. PGB fault diagnosis has been done mostly through vibration analysis over the past years. Vibration signals theoretically have an amplitude modulation (AM) effect caused by time-variant vibration transfer paths due to the rotation of planet carrier and sun gear, and therefore, their spectral structure is complex. Strain sensor signals, on the other hand, are closely correlated to torsional vibration, which is less sensitive to the AM effect caused by rotating vibration transfer path. Thus, it is potentially easy and effective to diagnose PGB faults via stain sensor signal analysis. In this paper, a new method using a single piezoelectric strain sensor for PGB fault diagnosis is presented. The method is validated on a set of seeded localized faults on all gears, namely, sun gear, planetary gear, and ring gear. The validation results have shown a satisfactory PGB fault diagnostic performance using strain sensor signal analysis.

Reasons for wind turbine generator failures: a multi-criteria approach for sustainable power production

“…The residual error between the actual and predicted power produced was then used for the condition monitoring of wind turbines. The condition monitoring, fault diagnosis and reliability of major wind turbine components and sub-systems, such as blades (Yang et al 2015), drive trains (Yang et al 2015), power converters (Zhou et al 2015), gearboxes (Yoon et al 2015;Du et al 2015) and bearings (Ming et al 2015;Gerber et al 2015), were well covered by the selected contributions. Moreover, it also uses sensors/signals for wind turbines fault diagnosis such as strain sensors (Yang et al 2015;Yoon et al 2015), vibration (Yang et al 2015a, b;Du et al 2015;Gerber et al 2015), acoustic emission (Ming et al 2015) and SCADA data (Long et al;Papatheou et al 2015).…”

Basic research on machinery fault diagnostics: Past, present, and future trends

“…Accelerometers are some of the most frequently used sensors and are often used to measure global information, such as frequency and mode shape [85]. Strain gauges not only provide localized measurement, they are also good at capturing static or dynamic measurands at a relatively low variation rate [86]. Fiber-optic sensors measure local strain, are immune to electromagnetic interference, and are suitable for long-distance data transportation [87].…”

Experimental application of stochastic resonance based on Wood–Saxon potential on fault diagnosis of bearing and planetary gearbox

“…Serious faults even lead to fatal casualties such as the LN-OJF helicopter accident at Norway in June 2016, which are caused by the main planetary gearbox fault. Therefore, mechanical fault diagnosis like fault mechanism and fault feature extraction has caused more and more attention [1][2][3][4][5][6][7][8][9].…”

“…Mishra et al [4] diagnosed bearing fault under slow speed operation by wavelet de-noising. Yoon et al [5] preprocessed the torsional Technical Editor: José Roberto de França Arruda. vibration signal of planet gearbox by time-domain synchronous average and extracted their characteristic parameters for fault diagnosis.…”