Machine learning (ML) based bearing fault detection is an emerging application of Artificial Intelligence (AI) that has proven its utility in effectively classifying various faults for timely measures. There are myriad studies dedicated to the effective classification of bearing faults under different conditions and experimental settings. In this study, we proposed a weighted voting ensemble (WVE) of three low-computation custom-designed convolutional neural networks (CNNs) to classify bearing faults at 48KHz. Some of the recent studies have exploited 1-d time-series signals and time-frequency based 2-d transformations for bearing fault classification. However, 1-d signals lack contextual information and higher-dimensional interpretations whereas time-frequency based transformations provide a more appropriate, visually perceivable and explainable representation of the time and frequency changes. Therefore in this study, a scalogram based representation of the signals is leveraged for classification using the CNN. Furthermore, the class imbalance is a significant challenge that affects the modelling behavior and possibly create biases. This study provides a novel density and distance hybrid over-sampling approach namely Density-AwareSMOTE(DA-SMOTE) built upon theSMOTE methodology for a more refined representation of synthetic samples within the minority class distribution. The experimentation procedures were carried out before and after the oversampling and it was observed that the balanced dataset acquired much better accuracy then the imbalanced dataset. This is evident by the fact that the highest validation accuracy for the proposed ensemble method (WVCNN) reached at 0-HP and 1-HP reached 99.28% and 99.13% while for the over-sampled dataset the accuracy soared to 99.71% and 99.87% for 0 and 1-HP respectively. The performance was evaluated for other metrics apart from the accuracy to assess the model’s performance in terms of chance occurrences and the class wise performance.

Bearing faults are critical in machinery; their early detection is vital to prevent costly breakdowns and ensure operational safety. This study presents a pioneering take on addressing the challenges of imbalanced datasets in bearing fault diagnosis. By mitigating issues such as mode collapse and vanishing gradients, our novel method employs Conditional Generative Adversarial Networks (CGANs) with spectral normalization and adaptive adversarial noise injection to generate high-quality bearing fault samples. This enhances generalization and robustness against noisy data, significantly improving the stability of CGAN training. To extract meaningful features from grayscale bearing fault images, we introduce a novel combination of involution and convolution feature extraction method named I-C FFN. This innovative feature extraction method captures both local and global information, making it capable of handling various types of features, including channel-agnostic, spatial-specific, spatial-agnostic, and channel-specific characteristics. Our proposed oversampling methodology helped enhance the performance of proposed classification scheme as well as of benchmark transfer learning models. Having accuracy values between 99.40% to 99.61% for 0 and 1 HP imbalanced dataset respectively, our models outperformed standard transfer learning methodologies. Furthermore, by the inclusion of our proposed Adaptive Adversarial Class-Conditional GAN (AAC-cGAN), the samples quality and the robustness to noise was significantly increased as demonstrated by the quantitative assessment through various Evaluation Metrics employed in this paper. Lastly, the performance of each combination of both the up-sampled and under-sampled methodologies were assessed through multiple metrics to determine the effectiveness of our proposed approach in addressing class imbalance in bearing fault diagnosis.

The development and improvements in wind energy conversion systems (WECSs) are intensively focused these days because of its environment friendly nature. One of the attractive development is the maximum power extraction (MPE) subject to variations in wind speed. This paper has addressed the MPE in the presence of wind speed and parametric variation. This objective is met by designing a generalized global sliding mode control (GGSMC) for the tracking of wind turbine speed. The nonlinear drift terms and input channels, which generally evolves under uncertainties, are estimated using feed forward neural networks (FFNNs). The designed GGSMC algorithm enforced sliding mode from initial time with suppressed chattering. Therefore, the overall maximum power point tracking (MPPT) control is very robust from the start of the process which is always demanded in every practical scenario. The closed loop stability analysis, of the proposed design is rigorously presented and the simulations are carried out to authenticate the robust MPE.

A robust control algorithm is always needed to harvest maximum power from a Wind Energy Conversion System (WECS) by operating it consistently at a Maximum Power Point (MPP) in the presence of wind speed variations. In this work, a Maximum Power Point Tracking (MPPT) control algorithm is designed via Conventional Sliding Mode Control (CSMC), the Super Twisting Algorithm (STA), and the Real Twisting Algorithm (RTA) and is applied to a Permanent Magnet Synchronous Generator (PMSG)-based WECS. CSMC is model-based whereas the STA and RTA are model-free controllers. In practice, the unavailability of nonlinear terms and aerodynamic forces deteriorates the performance of these controllers. Thus, an offline NeuroFuzzy algorithm is incorporated to estimate the nonlinear drift and control input channel to improve the robustness of these algorithms. In addition, the generator shaft speed and its missing derivative is recovered via a Uniform Robust Exact Differentiator (URED). In order to carry out a comprehensive comparative study among the three competitors, the overall system is simulated in a closed loop under the action of these controllers at three different operating conditions, i.e., nominal, varying load and inertia, and varying wind speed, using MATLAB/Simulink. The acquired results confirm the superiority of the RTA over the STA and CSMC in terms of robustness and chatter reduction.

An Integral Sliding Mode (ISM) based robustified Linear Parameter Varying (LPV) control is presented for a laboratory scale ball on a beam balancer system. The inherent input channel nonlinearity and under-actuated coupled dynamics make it a challenging control problem. The ISM control, being famous for reaching phase elimination, operates under the action of a control input which is usually an algebraic sum of a continuous and a discontinuous component. In the design of continuous component, the LPV form is used to linearize the otherwise non linearizable input channel nonlinearity. Hence, an LPV control is designed to cope with the varying dynamics. On the other hand, the discontinuous component diminishes the effect of norm bounded matched disturbances. In addition, the discontinuous control component is made smooth (chattering free) in order to generate a continuous control signal. The stability of the proposed algorithm is presented rigorously in terms of a theorem and is validated experimentally.