Gearbox faults feature selection and severity classification using machine learning

Condition monitoring systems (CMS) for gearboxes have received considerable attention in the last decade, primarily due to increased number of wind turbines installations and due to the fact that gearbox failure represents a significant part of wind turbines’ downtime. CMS helps to ensure the stability, extends the design life of drivetrain element especially gearboxes as a very important part and prevents complete failure, which could be very expensive. Thus, if applied properly, it allows significant savings. In addition to wind turbines, gearboxes are widely known to be used in other industries (energy, mining, petrochemical, automotive, etc.), as an element of high importance responsible for the smooth operation of many productions systems. Also, the availability of the entire system is almost always dependent on the usability of the gearbox. Gearbox fault can be identified while the defective component is still in operation and repair or replacement can be planned to reduce downtime, increase reliability, maximize availability with the ultimate goal of improving profitability. Gearbox condition monitoring often refers to gearbox diagnostics, which essentially process data with the evaluation of the functionality of a gearbox and detection or identification of faults using condition indicators. Different indicators could be used for gearbox condition monitoring process depending on the interested fault of the type we want to monitor – [3, 4, 13, 15, 25]. Establishing a reliable health detection system, especially for gear fitting faults is the key to ensuring smooth operation of industrial equipment [14]. In literature, fault detection and diagnostics of gears can be obtained by using the time domain analysis. In this case, statistical features, such as Root Mean Square (RMS), Standard Deviation (StD), Kurtosis (KUR), Skewness (SKE), etc., are extracted from vibration signals to perform condition monitoring [16]. On the other hand, the authors in [18] and [30] propose the use of the frequency analysis to identify the characteristic frequencies of the gear defects. Authors in [30] used time synchronous resample (TSR) to pre-process the raw signal to eliminate the interference of asynchronous shaft signal in process of in gearbox fault detection and then the adaptive variational mode decomposition is employed to process the fault synchronous shaft signals obtained by TSR to extract fault features. Many of these diagnostic methods are based on vibration spectrum analysis or demodulation techniques. The time-domain analysis is primarily performed to track changes during the gear meshing process, while time-domain analysis using the Cepstrum function is used for periodicity monitoring and modulation phenomena in the frequency domain from the sideband components. All of these methods and procedures have found application in the industry and provide relatively reliable diagnostics and often provide gear fault location ability for gearboxes with operation parameters at constant or fairly constant speed and load. However, this approach requires experienced users who will evaluate the status of the gearbox elements by looking at Keywords