A framework for automated vibration-based fault diagnostics of rotating machinery using supervised machine learning algorithms

This study proposes a comprehensive framework for automated diagnostics of rotating machinery using vibration signals. The methodology integrates the stages of dataset selection and preparation, signal processing, feature extraction, dimensionality reduction and training and testing of supervised machine learning algorithms. The preparation of the dataset involves proposing a technique for addressing data imbalance, normalisation of the data amplitude, adjusting the sampling frequency, and calculating the actual rotational frequency. Then, appropriate signal filters are designed and applied, and a wide range of statistical and spectral features is extracted in the time and frequency domain. To address the challenges of high-dimensional feature spaces and allow transparency in identifying features with the highest level of importance in recognising machinery faults, a group-wise dimensionality reduction technique, Sequential Selection Algorithm (SSA), is proposed. The applicability of the proposed SSA technique is validated by comparing its performance with that of commonly used dimensionality reduction techniques, by testing its compatibility with supervised machine learning algorithms, and by analyzing its efficiency. In that sense, a set of models combining four dimensionality reduction techniques and four supervised machine learning algorithms is trained and tested, and their accuracy is compared.