Abstract
This work describes an autonomous condition monitoring framework to process and analyze data measured on wind turbine gearboxes. Industry 4.0 and the Industrial Internet of Things open the door for much more elaborate measurement and data analysis campaigns thanks to the reduction in cost of sensors and of processing power. This increase in data acquisition and handling potential is especially useful considering that most current state-of-the-art methods in signal processing often lead to large quantities of health indicators due to the multiple processing steps. Such large numbers of indicators become unfeasible to inspect manually when the data volume and the number of monitored turbines increases. Therefore, this paper illustrates a hybrid analysis approach that combines advanced signal processing methods with machine learning and anomaly detection. This approach is validated on an experimental wind turbine gearbox vibration data set.
Highlights
The rise of Industry 4.0 and correspondingly the Industrial Internet of Things (IIoT), will facilitate an increase in the amount of instrumentation in wind turbines, either directly mounted by the Original equipment manufacturer (OEM) or installed during its lifetime by owneroperators [1, 2, 3]
This paper illustrates a hybrid analysis approach that combines advanced signal processing methods with machine learning and anomaly detection. This approach is validated on an experimental wind turbine gearbox vibration data set
Advanced vibration-based signal processing methods are used to extract complex health indicators related to the gears and bearings in the turbine gearbox
Summary
The rise of Industry 4.0 and correspondingly the Industrial Internet of Things (IIoT), will facilitate an increase in the amount of instrumentation in wind turbines, either directly mounted by the Original equipment manufacturer (OEM) or installed during its lifetime by owneroperators [1, 2, 3]. The IIoT context in particular promotes sensors that are connected directly to the internet. A cloud data-center can continuously receive data coming from such sensors. This vast amount of sensor data can be employed for multiple goals. Continuous data streams have both longer signal length and a finer granularity compared to snapshot measurements which are often still common practice nowadays. These properties enable a more refined analysis and help to better understand the turbine characteristics and behavior changes a healthy turbine experiences
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