As one of the most important artificial intelligence-enabled industrial applications, fault diagnosis is vital in the safe, stable and reliable operation of the equipment. Many existing deep learning-based fault diagnosis methods assume that the distribution of training data is the same as that of testing data, which is almost impossible in practical industrial applications. In addition, most of these fault diagnosis methods are generally memory intensive and computationally expensive. A compressed unsupervised deep domain adaption model-based fault diagnosis method is proposed to overcome the above-mentioned two issues. Firstly, a standard unsupervised domain adaption model is designed to extract the features of training data and testing data, respectively. Then, the maximum mean discrepancy term is introduced to minimize the discrepancy between the extracted features of them. Next, the standard model is compressed through iteratively pruning the redundant convolutional channels. Finally, the obtained compressed model is applied to diagnose faults. The performance of the proposed method is verified on the Case Western Reserve University bearing dataset. Experimental results show that the compressed model can significantly reduce the memory occupation, computational cost and inference time compared with the standard model, but still achieve comparable or even better accuracy on ten transfer diagnostic tasks.

Atrial fibrillation (AF) is a serious medical condition of the heart potentially leading to stroke, which can be diagnosed by analyzing electrocardiograms (ECG). Technologies of Artificial Intelligence of Things (AIoT) enable smart abnormality detection by analyzing streaming healthcare data from the sensor end of users. Analyzing streaming data in the cloud leads to challenges of response latency and privacy issues, and local inference by a model deployed on the user end brings difficulties in model update and customization. Therefore, we propose an AIoT Platform with AF recognition neural networks on the sensor edge with model retraining ability on a resource-constrained embedded system. To this aim, we proposed to combine simple but effective neural networks and an ECG feature selection strategy to reduce computing complexity while maintaining recognition performance. Based on the platform, we evaluated and discussed the performance, response time, and requirements for model retraining in the scenario of AF detection from ECG recordings. The proposed lightweight solution was validated with two public datasets and an ECG data stream simulation on an ATmega2560 processor, proving the feasibility of analysis and training on edge.

Breast cancer is the type of cancer with the highest incidence and global mortality of female cancers. Thus, the adaptation of modern technologies that assist in medical diagnosis in order to accelerate, automate and reduce the subjectivity of this process are of paramount importance for an efficient treatment. Therefore, this work aims to propose a robust platform to compare and evaluate the proposed strategies for improving breast ultrasound images and compare them with state-of-the-art techniques by classifying them as benign, malignant and normal. Investigations were performed on a dataset containing a total of 780 images of tumor-affected persons, divided into benign, malignant and normal. A data augmentation technique was used to scale up the corpus of images available in the chosen dataset. For this, novel image enhancement techniques were used and the Multilayer Perceptrons, k-Nearest Neighbor and Support Vector Machines algorithms were used for classification. From the promising outcomes of the conducted experiments, it was observed that the bilateral algorithm together with the SVM classifier achieved the best result for the classification of breast cancer, with an overall accuracy of 96.69% and an accuracy for the detection of malignant nodules of 95.11%. Therefore, it was found that the application of image enhancement methods can help in the detection of breast cancer at a much earlier stage with better accuracy in detection.