Year: 2025
Type: article
Abstract: This study investigates the application of Detectivity, a composite metric derived from Hjorth’s parameters, for the condition monitoring of wind turbines. These parameters were originally introduced to describe the morphology of biomedical signals, and they consist of three scalar descriptors: Activity, Mobility, and Complexity, capturing, respectively, signal variance, frequency content, and waveform shape. Detectivity, proposed in a previous work by the authors as a condensation of Hjorth’s parameters, can be interpreted as the total gain in these parameters with respect to a reference condition corresponding to a healthy component. The analysis is conducted on two distinct datasets. The first, publicly available from the Luleå University website, contains vibration data from six wind turbines in a Swedish wind farm, one of which is affected by a bearing fault. A robust methodology was developed to manage the strong variability in rotational speed. The second dataset includes vibration signals from a 2 MW commercial turbine, acquired over 50 consecutive days during which an inner race fault progressively developed. The use of the Detectivity cumulant proved particularly effective: in the first case, it clearly identified the faulty machine; in the second, it enabled the detection of the time at which the probable onset of the fault occurred.
Source: Machines
Institution: University of Modena and Reggio Emilia
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415499162