## 1 Introduction  
In electromechanical engineering, motion is usually determined by mechanical device structures (e.g., rotating machines or induction motors), which leads to satisfactory records of nearly 70% of the gross energy ingestion in modern manufacturing economics [1; 2]. Rotating machineries with induction motors use bearings to moderate friction, which preserves otherwise wasted energy and increases the useful life of a machine. Nevertheless, inimical operating environments and cyclic stuffing can lead to substantial wear in bearings, exhibiting in the form of exterior cracks [3]. If these surface cracks go undetected, it can lead to unexpected shutdowns, resulting in financial inefficiency, as well as human injuries [4; 5; 6; 7]. Bearings play an important role in condition monitoring because, in more than 50% of the cases involving induction motor failures, they are the root cause of the failure [2; 8]. Health state diagnosis under variable operating conditions can be employed to improve smooth operability of the machines. In real time, bearing fault diagnosis is performed through the collected data (i.e., vibration acceleration signals, acoustic emission signals, and motor currents), which has been an important area of study over the last few decades [6; 9; 10; 11]. These fault diagnosis studies have shown that diagnosis of bearing wear can reduce maintenance expenses and enhance machine reliability [6; 12; 13; 14; 15; 16]. Bearing fault diagnosis studies have extensively investigated vibration signals and motor current analysis [17; 18]. Multiple signature analysis of vibrations and motor currents has also been researched to improve reliability [19]. Primarily, bearing faults occur due to localized imperfections, i.e., cracks or spalls. These flaws create shocks and stimulate high-occurrence reverberations of the bearings and machine assembly due to the recurring effect on spinning parts [17]. Time--frequency signal analysis approaches have been employed to solve these issues, such as fast Fourier transform (FFT) and short-term-Fourier transform (STFT), which convert time-domain signals into frequency-domain signals for further analysis [20]. However, inappropriate time-window adjustments downgrade the performance of these methods. Another powerful signal processing approach that has been considered is wavelet analysis [21]. However, wavelet analysis creates good time but poor frequency resolutions at high frequencies, and good frequency but poor time resolutions at low frequencies. Adjusting the window size is often a solution to gather important information from vibration signals [22]. Enveloping with energy kurtosis [23] is another approach. However, detecting the envelopes of real signals does not always rely on analytic signals [24]. Existing signal processing techniques have employed feature analysis approaches to detect statistical features for further classification employing classical machine learning algorithms, such as SVM (support vector machine) and PSVM (proximal support vector machine) [25; 26]. In addition to these classical approaches, several deep learning techniques have also been explored to extract automated features through the hidden layer architecture for bearing fault classification. Examples include deep autoencoders [27; 28], ANNs (artificial neural networks), and hierarchical convolution network [29]. These methods established several dimensions for fault classification without considering the handcrafted features. Due to the limitation of experimental data, however, these methods are not compatible with dynamic invariant scenarios. In the present work, the feature extraction process is automated by using deep neural network techniques. In contrast to existing methods, this study focuses on a signal processing technique to create an invariant scenario under different load and rpm levels to extract automated features by employing an advanced neural network mechanism. To address this issue, the Stockwell transform (S-transform) is employed in this paper [30].