In engineering applications, the bearing faults diagnosis is essential for maintaining reliability and extending the lifespan of rotating machinery, thereby preventing unexpected industrial production downtime. Prompt fault diagnosis using vibration signals is vital to ensure seamless operation of industrial system avert catastrophic breakdowns, reduce maintenance costs, and ensure continuous productivity. As industries evolve and machines operate under diverse conditions, traditional fault detection methods often fall short. In spite of significant research in recent years, there remains a pressing need for improve existing methods of fault diagnosis. To fill this research gap, this research work aims to propose an efficient and robust system for diagnosing bearing faults, using deep and Shallow features. Through the evaluated experiments, our proposed model Multi-Block Histograms of Local Phase Quantization (MBH-LPQ) showed excellent performance in classification accuracy, and the audio-trained VGGish model showed the best performance in all tasks. Contributions of this work include: Combine the proposed Shallow descriptor, derived from a novel hand-crafted discriminative features MBH-LPQ, with deep features obtained from VGGish pre-trained of Convolutional Neural Network (CNN) using audio spectrograms, by merging at the score level using Weighted Sum (WS). This approach is designed to take advantage of the complementary strengths of both feature models, thus enhancing overall bearing fault diagnostic performance. Furthermore, experiments conducted to verify the approach’s performance is assessed based on fault classification accuracy demonstrated a significant accuracy rate on two different noisy datasets, with an accuracy rate of 98.95% and 100% being reached on the CWRU and PU datasets benchmark, respectively.

In bearing fault diagnosis, ensemble empirical mode decomposition (EEMD) is a reliable technique for treating rolling bearing vibration signals by dividing them into intrinsic mode functions (IMFs). Traditional methods used in EEMD consist of identifying IMFs containing the fault information and reconstructing them. However, an incorrect selection can result in the loss of useful IMFs or the addition of unnecessary ones. To overcome this drawback, this paper presents a novel method called combined modes ensemble empirical mode decomposition (CMEEMD) to directly obtain a combination of useful IMFs containing fault information. This is without needing to pass through the processes of IMF selection and reconstruction, as well as guaranteeing that no defect information is lost. Owing to the small signal-to-noise ratio, this makes it difficult to determine the fault information of a rolling bearing at the early stage. Therefore, improving noise reduction is an essential procedure for detecting defects. The paper introduces a robust process for extracting rolling bearings defect information based on CMEEMD and an enhanced deconvolution technique. Firstly, the proposed CMEEMD extracts all combined modes (CMs) from adjoining IMFs decomposed from the raw fault signal by EEMD. Then, a selection indicator known as kurtosis median absolute deviation (KMAD) is created in this research to identify the combination of the appropriate IMFs. Finally, the enhanced deconvolution process minimizes noise and improves defect identification in the identified CM. Analyzing real and simulated bearing signals demonstrates that the developed method shows excellent performance in extracting defect information. Compared results between selecting the sensitive IMF using kurtosis and selecting the sensitive CM using the proposed KMAD show that the identified CM contains rich fault information in many cases. Furthermore, our comparisons revealed that the enhanced deconvolution approach proposed here outperformed the minimum entropy deconvolution (MED) approach for improving fault pulses and the wavelet de-noising method for noise suppression.

Diagnosing and classifying breast cancer tumors is a rather complex activity for pathologists due to the heterogeneous nature of the tumor cells. The wide use of artificial intelligence (AI) and the rise of Deep Learning (DL) have led to promising results in terms of breast histopathology images classification. The outcomes depend largely on two main factors, namely, the number and quality of images. BreaKhis dataset shows an imbalance in the image classes distribution, thus generating the performance degradation of the classifier model due to a biased classification towards the majority class. In this paper, a Deep Convolution Generative Adversial Network (DCGAN) is applied to give the number of images consistence in the minority (benign) class with that of the majority (malignant) class. Data augmentation is a technique used later to create more data from the limited ones. The DenseNet201 pre-trained model is chosen and used with the concatenation of features from various DensNet blocks. Instead of considering all the layers of the pre-trained network, the features are extracted from the lower layers of DensNet201, via a global average pooling (GAP). These features are passed to the softmax classifier to classify breast cancer. The model is evaluated using a two-class BreaKhis, provided at four magnification levels 40x, 100x, 200x, and 400x. The proposed method yielded test accuracies of 96%, 95%, 88%, and 92% respectively for each magnification factor. As indicated in the results, the proposed method based on data augmentation by DCGAN and feature concatenation using DenseNet201 pre-trained models could produce an efficient prediction for breast cancer image classification.