![]() ![]() Thus, faults related to planets may manifest themselves differently upon measurements not covering entire cycles. Secondly, in order to maximize even teeth wear, planetary gearboxes are designed with maximum length of a full cycle (i.e., exact repeatable position of planets and all teeth). Therefore, analysis of its data requires advanced signal selection algorithms as well as sophisticated signal resampling methods. Firstly, wind turbines inherently operate under nonstationary conditions, including variable speed and load. Unlike other typical monitored industrial objects, vibration analysis of such planetary gearboxes is generally more challenging. One of the possibilities for damage assessment of planetary gearboxes is vibration analysis. Figure 1 presents such high power gearbox with two parallel stages and single planetary stage. According to, a 2.5 MW wind turbine planetary gearbox replacement costs over £400,000, which significantly influences a total wind turbine revenue. It should be mentioned that, due to the sharing of load between several meshes during operation of a planetary gearbox, usually a single malfunction results in total gear damage requiring full gearbox replacement. ![]() ![]() In consequence, this initiated the pursuit for providing the methodology that could detect the damage at the early stage and therefore limit the downtime of the entire machine. This is one of the reasons why wind turbine gearboxes, and planetary ones in particular, received the utmost attention of researchers and maintenance engineers. On the other hand, as it was pointed out in a number of statistical summaries, gearboxes (with no special distinction for the planetary ones and others) are located on top of the list of most damage susceptible drive-train components in this group of rotating machinery. In recent years, large power planetary gearboxes became very popular elements of wind turbine’s drive-trains due to their ability to transmit a relatively high load from blades simultaneously providing a high gear ratio, which is required for efficient production of electrical energy by the generator. The proposed reasoning is presented on the real life example of ring gear fault in wind turbine’s planetary gearbox. To overcome this obstacle, it is proposed to detect a fault development with Artificial Neural Network (ANN) and further observation of linear regression parameters calculated on the estimation error between healthy and unknown condition. Thus, the sudden increase of a particular feature does not necessarily have to indicate the development of fault. Furthermore, for machinery working in highly changing operational conditions, like wind turbines, those estimators are strongly fluctuating, and this fluctuation is not linearly correlated to operation parameters. Although state-of-the-art CMS can usually provide advanced signal processing tools for extraction of diagnostic information, there are still many installations, where the diagnosis is based simply on the averaged wideband features like root-mean-square (RMS) or peak-peak (PP). They reveal that, on the one hand, gearboxes are one of the most fault susceptible elements in the drive-train and, on the other, the most expensive to replace. ![]() This conclusion is derived from numerous summary papers. In the monitoring process of wind turbines the utmost attention should be given to gearboxes. ![]()
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