Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

The traditional intelligent identification method requires a complex feature extraction process and much diagnosis experience, considering the characteristics of one dimension of bearing vibration signals, a new method of intelligent fault diagnosis based on 1-dimensional convolutional neural network is presented. This method automatically extracts features from frequency domain signals and avoids artificial feature selection and feature extraction. The proposed method is validated on bearing benchmark datasets, these datasets are collected in different fault location, different health conditions and different operating conditions. The result shows that the proposed method can not only adaptively obtain representative fault features from the datasets, but also achieve higher diagnosis accuracy than the existing methods.

Analysis of Total Harmonic Distortion and implementation of Inverter Fault Diagnosis using Artificial Neural Network

As power electronics devices dependability is very significant to guarantee Multi Level Inverter (MLI) systems stable functioning, it is imperative to identify and position faults as promptly as possible. Due to the fault occurrences, the Total Harmonic Distortion (THD) on the system gets a hit. In this perspective, to improve fault diagnosis accuracy and efficient working of a Cascaded Multi level Inverter System (CHMLIS), a quick and accurate fault diagnosis strategy with an optimized training algorithm using Artificial Neural Network (ANN) is presented. Also, Total Harmonic Distortion (THD) is analyzed for each switch Fault simulated using MATLAB/Simulink and the results are presented. Results shows the efficacy of Algorithm in identifying the fault. The auxiliary cell is replaced while the fault occurs in the main cell thus making the uninterrupted working of the Multi-Level Inverter (MLI) in the Induction Motor Drive (IMD).

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

Diagnosis and Analysis of Transabdominal and Intracavitary Ultrasound in Gynecological Acute Abdomen

In order to explore the effective diagnosis method of gynecological acute abdomen, this paper takes hospital gynecological acute abdomen patients as samples and selects gynecological acute abdomen patients admitted to the hospital to be included in this study. They are divided into transabdominal ultrasound group, intracavitary ultrasound group, and combined group. Moreover, this paper uses mathematical statistics to carry out sample statistics. The statistical data mainly include ectopic pregnancy, torsion of ovarian tumor pedicle, acute suppurative salpingitis, torsion of fallopian tube, hemorrhagic salpingitis, acute pelvic inflammatory disease, rupture of corpus luteum cyst, and diagnosis accuracy rate. In addition, this paper compares the diagnostic accuracy of the abdominal ultrasound group, the intracavitary ultrasound group, and the combined group. The experimental research shows that the combined ultrasound diagnosis method can effectively improve the accuracy of the diagnosis of gynecological acute abdomen.

Fault Frequency Identification of Rolling Bearing Using Reinforced Ensemble Local Mean Decomposition

The vibration signal of rolling bearing exhibits the characteristics of energy attenuation and complex time-varying modulation caused by the transmission with multiple interfaces and complex paths. In view of this, strong ambient noise easily masks faulty signs of rolling bearings, resulting in inaccurate identification or even totally missing the real fault frequencies. To overcome this problem, we propose a reinforced ensemble local mean decomposition method to capture and screen the essential faulty frequencies of rolling bearing, further boosting fault diagnosis accuracy. Firstly, the vibration signal is decomposed into a series of preliminary features through ensemble local mean decomposition, and then the frequency components above the average level are energy-enhanced. In this way, principal frequency components related to rolling bearing failure can be identified with the fast spectral kurtosis algorithm. Finally, the efficacy of the proposed approach is verified through both a benchmark case and a practical platform. The results show that the selected fault characteristic components are accurate, and the identification and diagnosis of rolling bearing status are improved. Especially for the signals with strong noise, the proposed method still could accurately diagnose fault frequency.

A multiscale kernel-based network with improved attention mechanism for rotating machinery fault diagnosis under noisy environment

Considering the vibration signals are easily contaminated by the strong and highly non-stationary noise, extracting more sensitive and effective features from the noised vibration signals is still a great challenge for intelligent fault diagnosis of rotating machinery. This paper proposed a multiscale kernel-based network with improved attention mechanism (IA-MKNet) to overcome this challenge. In the proposed method, an improved attention mechanism (IAM) for multiscale convolution is firstly developed to adaptively extract the meaningful fault features and automatically suppress noise. Then, due to the inherent multiple time characteristics of vibration signals, an adaptive multiscale kernel-based residual block (AMKRB) with IAM is designed to capture fault features in multi-time scales of vibration signals. Finally, a combination strategy based on an adaptive ensemble learner is proposed to increase the diversity of features by fusing the outputs of multiple IA-MKNets, which further improves diagnosis accuracy and stability. The experimental results, verified by two bearing datasets with noise interference, confirm that the proposed method improves the fault diagnosis accuracy of rotating machinery under noisy environment, which performance is superior to the other five benchmark methods.

An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

Medical prediagnosis systems are now available online to give users quick and preliminary diagnosis information. The need for such a system has become particularly evident in areas with insufficient health professionals. Due to the privacy of patient medical information and the sensitivity of cloud diagnosis models, it is necessary to protect the security of data, models, and communications. These existing diagnosis systems can hardly provide a satisfied diagnosis accuracy while ensuring comprehensive security and high efficiency. In order to solve these problems, we proposed Relief- k minimum Wasserstein distance (Relief- k MW) classification method, which combined data encryption and BLS signature to form a privacy-preserving efficient online multiparty interactive medical prediagnostic scheme (OMPD). Theoretical analysis shows our OMPD effectively provides high-precision prediagnosis services. Extensive experimental results demonstrate that OMPD not only greatly improves the diagnostic accuracy but also reduces the computational and communication overhead.

Artificial Intelligence in Diagnosis of Oral Potentially Malignant Lesions- Need of the Hour

Artificial intelligence (AI) is a technological breakthrough that is rapidly progressing and has captivated the minds of researchers all over the world. AI can be used to make a diagnosis of oral cavity lesions, detect and identify suspicious changed oral mucosa undergoing premalignant and malignant transformations. The purpose of this review is to give a comprehensive summary of developing optical imaging technologies, innovative artificial intelligence-based techniques. The concepts of image-based techniques for identifying oral cancer are defined in terms of clinical requirements and features. Although artificial intelligence (AI) is beginning to have a significant impact on increasing diagnosis accuracy in a variety of fields of medicine, there has been limited research on oral cancer to date. These results suggest that combining artificial intelligence with imaging can improve oral cancer outcomes, applications ranging from very low-cost oral cancer screening with Smartphone-based probes to algorithm-guided identification premalignant lesion heterogeneity and margins using optical coherence tomography. Oral cancer outcomes can be improved by combining imaging and artificial intelligence technologies for better detection and diagnosis.

Fault Diagnosis of Permanent Magnet Synchronous Motor Inter Turn Short Circuit Based on Deep Reinforcement Learning

With the increasing market share of Permanent Magnet Synchronous Motor(PMSM), the fault diagnosis and prediction technology for PMSM is becoming increasingly important. Firstly, in order to solve the problem of insufficient fault sample data consisting of negative sequence current, electromagnetic torque and other inter turn short circuit fault feature terms, the Conditional Generation Adversarial Network(CGAN) is used to expand the data set. Then, with sufficient data, Dueling_DQN algorithm of deep reinforcement learning is used to train and optimize the extended data set. Finally, the effectiveness of the algorithm in the field of PMSM fault diagnosis is verified by simulation training. The results show that the fault diagnosis accuracy of the algorithm can be reached 97.5%, while improved the convergence speed and saved the time cost of fault diagnosis.