###### Abstract  
Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. Although numerous indicators can be extracted from vibration signals, selecting the most relevant ones remains challenging. This study proposes a methodology for selecting time-domain features to classify fault severity levels in spur gearboxes. Vibration signals are acquired using six accelerometers and processed to extract 64 statistical condition indicators (CIs). The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. Additionally, the effect of sensor position and inclination on signal quality and classification performance is analysed using factorial analysis of variance (ANOVA) and multiple comparison tests. The results confirm the robustness of the selected CIs and the minimal influence of sensor placement variability, supporting the practical applicability of the proposed approach in industrial settings. The methodology offers a structured framework for selecting condition indicators in vibration signals, experimentally validated using multiple sensors and fault severity levels, and it is both automated and straightforward to implement. fault severity; classification models; feature selection; gearbox fault diagnosis; monitoring condition; artificial intelligence; machine learning 2020 IEEEexample:BSTcontrol  
23 April 202518 May 202518 May 202519 May 2  
The monitoring process is typically divided into three stages: data acquisition, feature extraction, and failure identification. Data are captured via sensors placed at various locations on the system, providing accurate and reliable signals [5; 6]. Feature extraction is then performed by calculating condition indicators (CIs), which are statistical parameters that reflect the system's health condition [7; 8; 9]. CIs can be obtained in different domains. In the time domain, as shown in [10], in the frequency domain by transforming the signal from the time domain using the fast Fourier transform (FFT) [11], or in the time-frequency domain via wavelet transform [4; 12]. In the time domain, CIs can be classified as conventional--such as root mean square, mean, variance, kurtosis, and skewness--or non-conventional, including absolute mean value, waveform length, zero crossings, Wilson amplitude, and slope sign changes, among others [13]. Several studies have shown that an appropriate combination of CIs enhances the sensitivity of diagnostic systems. However, a poor combination may introduce redundancy or contradictions that degrade model performance [14]. Consequently, feature selection is a crucial step in optimising classification models. Feature selection is critical in developing machine learning models, particularly when dealing with high-dimensional data. The wrapper method is a feature selection technique that evaluates different subsets of variables based on the performance of a machine learning model, often resulting in improved predictive accuracy. This contrasts with filter methods, which assess variables independently of the model [15]. A significant advantage of the wrapper approach lies in its ability to identify combinations of features that may be uninformative in isolation but, when combined, enhance the model's performance [5; 16; 17]. In this study, the wrapper method uses the Random Forest (RF) algorithm to rank the importance of the condition indicators (CIs). To validate the proposed methodology, resampling techniques such as bootstrap and repeated hold-out are used to assess the model's stability and the variability of the results [18; 19].