Automated Fault Detection of Wind Turbine Gearbox using Data-Driven Approach

Wind turbine manufacturers have adopted condition monitoring systems to monitor and report a turbine's health and operating parameters to ensure that the system operates within its design specifications. While the present systems use specialized condition monitoring hardware to detect abnormal acoustic or vibration signals, it is not capable of pinpointing the exact location of the fault apart from isolating the system from which the signal originated. This drawback can be attributed to the requirement of powerful signal processors in order to decode the signal and efforts to train a system to identify the signal emitted by a faulty component. In the light of recent advancement of data-driven approaches and signal processing, these drawbacks can be overcome with increased computation power and sophisticated algorithms that foray into every integrated system. This paper reports such an investigation conducted on a miniature wind turbine planetary gearbox subjected to multi-component failures. The vibration signals were acquired using two accelerometers placed inside the gearbox. The speed of the gearbox was varied according to a simulated wind flow pattern. The primary goal of the study was to investigate the practicality of implementing data-driven approaches to categorise multi-component faults from a composite non-stationary signal. Short time Fourier transforms (STFT) coefficients were used as attributes by a set of data-driven algorithms to build machine learning models. Each model built was tested with a randomised set of instances which was reserved from the main dataset and tested multiple times by means of cross validation. The novelty in the paper entails a methodology which has been devised to classify faults using a randomised vibration dataset with little human intervention by means of machine learning algorithms. The authors propose that this methodology can also be used for real-time fault detection and classification for various machinery and components.