## 1 Introduction  
Electric machines are a vital component for major industries, such as manufacturing, mining, agriculture, energy and transport sectors. It is fair to say that all of these sectors are currently undergoing major growth and technological innovation. These machines are typically required to operate under harsh environmental conditions and demanding drive cycles, which gives rise to premature degradation and the occurrence of catastrophic failure modes [1, 2]. It is imperative for the future viability and sustainability of these sectors that we have efficient, robust and highly reliable electric machines. Sudden catastrophic machine breakdown results in acute manufacturing downtime, dramatic reductions in productivity and health and safety concerns. Moreover, performing critical maintenance is both labour-intensive and costly. Faults can be difficult to diagnose and troubleshoot for maintenance teams [3, 4, 5]. Broadly, electric machines can be broken down into the bearings, stator, rotor and other elements as shown in Figure 0(a). The statistics of failure for three classes, low voltage, medium voltage and high voltage, are presented in Figure 0(b). Bearings are the dominant failure mode at low and medium voltages, followed closely in the latter class by stator fault modes. The final class, high voltage, is dominated by stator fault modes, as seen in Figure 0(b). Research studies have shown bearings to be responsible for up to 75% of low-voltage electric machines breakdowns and up to 41% of all rotating machine failures [6, 7, 8, 9, 2, 6]. Condition-based monitoring has received considerable attention over the past years [2, 10, 11, 12]; hence, a rich literature exists at present. The areas of detection and diagnosis of fault modes has received the most attention with mature industrial technologies available. More recently, advanced methods of prognosis are being investigated, and these focus on the more challenging problem of predicting the Remaining Useful Life (RUL) of the machines or sub-components [13, 14, 15]. This is illustrated in Figure 1c. Knowing the RUL of a component ensures that the state of health of a machine is known and that suitable maintenance can be performed at the optimal times. The maximum usable life is achieved without the threat of total machine breakdown occurring [16, 17, 18, 19]. Typically, machine-learning (ML) methods for CbM focus on three key aspects: signal sensing modalities, feature engineering and supervised learning algorithms. Sensing modalities for CbM methods that have been explored in recent years include acoustic emission (noise) [5, 20, 21, 22], air gap torque monitoring [23, 24], motor current signal analysis (MCSA) [25, 26], oil and debris-monitoring [27, 28], thermography [29, 30], speed fluctuations monitoring [31], induced voltage, magnetic flux monitoring and vibration signals [4, 5, 11, 16]. The Short-Time Fourier Transform (STFT) feature extraction method has been extensively used to extract useful time-frequency features, which reported to achieve high levels of classification accuracy [32, 33, 34, 35]. Envelope Analysis (EA) has also been used extensively for prognostic and diagnostic purposes as the method is simple yet versatile, making it applicable to many different types of mechanical fault monitoring processes [21, 22, 36, 37, 38]. Other methods of time-frequency feature extraction, which have shown good promise for bearing prognostic use cases are Wavelet Transformation (WT) [39, 40, 41, 42] and Empirical Mode Decomposition (EMD) [43, 44, 45, 46, 47, 48], both of which were reported to achieve highly accurate performance scores. In the area of supervised learning, two of the most versatile, simple and effective ML techniques that have been extensively used for bearing condition monitoring are \(k\)-Nearest Neighbour (\(k\)-NN) [49, 40, 41] and Support Vector Machines (SVM) [52, 53, 54, 55].