Wind Turbine Gearbox Early Fault Detection Using Mel‐Frequency Cepstral Coefficients of Vibration Data

Abstract

A methodology utilizing vibration data and Mel‐frequency cepstral coefficients (MFCCs) for wind turbine condition monitoring is developed to detect incipient faults in the wind turbine gearbox. This approach provides a more efficient and cost‐effective solution compared to traditional condition monitoring techniques relying on physical inspections, which can be time‐consuming and labor‐intensive. The use of vibration data enables the identification of subtle changes in a wind turbine’s operating condition, providing early warning signs of potential issues. When the vibration data are analyzed, changes in frequency and amplitude can be detected, indicating the presence of a developing fault. Vibration‐based condition monitoring systems (CMS) have already been widely used in the wind industry (mainly in new turbines). These systems utilize basic standard features, working in either the time or frequency domain, and are not optimized for nonstationary signals. In contrast, this work focuses on MFCCs, operating in both time and frequency domains, enabling the extraction of adequate information from nonstationary signals. The MFCCs are derived from vibration data signals, providing a compact representation for a more efficient analysis. These coefficients create a fingerprint of the wind turbine operating condition, compared to known healthy conditions, to identify anomalies. To underscore the practical value of this study, it is important to highlight the significant implications for the wind energy sector. The methodology developed offers an advanced, predictive tool for the early detection of gearbox faults, which is a critical aspect of optimizing the performance and longevity of wind turbines. By enabling earlier, more accurate fault detection, the proposed approach significantly reduces the likelihood of catastrophic failures and extensive downtime. This not only enhances the reliability and cost‐effectiveness of wind energy systems but also contributes to sustainable energy practices by optimizing resource use and minimizing maintenance costs. The results strongly suggest that the proposed methodology is highly effective in detecting incipient faults in the wind turbine gearbox. By providing early warnings of damage, operators can address issues before significant downtime or damage occurs. The use of MFCCs offers additional benefits since data can be collected remotely, eliminating physical inspections. Analysis can be performed faster, even in real time, allowing more frequent monitoring. This provides a more complete and accurate picture of the health of the system. The approach is tested in the EISLAB dataset concerning vibration signals from six wind turbines in northern Sweden, all with three‐stage gearboxes. All measurement data correspond to the axial direction of an accelerometer in the output shaft bearing housing of each turbine.