scholarly journals Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhenwei Zhao ◽  
Weining Jiang ◽  
Weidong Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Medical Imaging ◽  
Network Model ◽  
Rough Set ◽  
Bp Neural Network ◽  
Medical Equipment ◽  
Large Scale ◽  
Hybrid Network ◽  
Mechanical Equipment

In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnose the health status of medical imaging equipment. Based on the vibration signal of mechanical equipment, a PLSR-DNN hybrid network model for health prediction of medical equipment is proposed by using partial least squares regression (PLSR) algorithm and deep neural networks (DNNs). At the same time, in the diagnosis of medical imaging equipment fault, the paper proposes to use rough set to screen the fault factors and then use BP neural network to classify and identify the fault and analyzes the practical application effect of the two technologies. The results show that the PLSR-DNN hybrid network model for health prediction of medical imaging equipment is basically consistent with the actual health value of medical equipment; medical imaging equipment fault diagnosis technology is based on rough set and BP neural network. In the test set, the sensitivity, specificity, and accuracy of medical imaging equipment fault identification are 75.0%, 83.3%, and 85.0%. The above results show that the proposed health prediction method and fault diagnosis method of medical imaging equipment have good performance in health prediction and fault diagnosis of medical equipment.

A novel adaptive and fast deep convolutional neural network for bearing fault diagnosis under different working conditions

2019 ◽  
Vol 234 (4) ◽  
pp. 1167-1182 ◽  
Author(s):  
Kun Xu ◽  
Shunming Li ◽  
Jinrui Wang ◽  
Zenghui An ◽  
Yu Xin
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Receptive Field ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Neural Network Model ◽  
Network Architecture ◽  
Deep Convolutional Neural Network ◽  
Mechanical Equipment

Deep learning method is gradually applied in the field of mechanical equipment fault diagnosis because it can learn complex and useful features automatically from the vibration signals. Among the many intelligent diagnostic models, convolutional neural network has been gradually applied to intelligent fault diagnosis of bearings due to its advantages of local connection and weight sharing. However, there are still some drawbacks. (1) The training process of convolutional neural network is slow and unstable. It has more training parameters. (2) It cannot perform well under different working conditions, such as noisy environment and different workloads. In this paper, a novel model named adaptive and fast convolutional neural network with wide receptive field is presented to overcome the aforementioned deficiencies. The prime innovations include the following. First, a deep convolutional neural network architecture is constructed using the scaled exponential linear unit activation function and global average pooling. The model has fewer training parameters and can converge rapidly and stably. Second, the model has a wide receptive field with two medium and three small length convolutional kernels. It also has high diagnostic accuracy and robustness when the environment is noisy and workloads are changed compared with other models. Furthermore, to demonstrate how the wide receptive field convolutional neural network model works, the reasons for high model performance are analyzed and the learned features are also visualized. Finally, the wide receptive field convolutional neural network model is verified by the vibration dataset collected in the background of high noise, and the results indicate that it has high diagnostic performance.

Intelligent Fault Diagnosis Method Based on Operating Parameters in Nuclear Power Plant

2017 ◽  
Author(s):  
Zhiwu Ke ◽  
Xu Hu ◽  
Dawei Teng ◽  
Mo Tao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Real Time ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Operating Parameters ◽  
Feed Water ◽  
Mechanical Equipment ◽  
Single Fault ◽  
Diagnosis Method

The safety of mechanical equipment is more important, it directly determines the safety of nuclear power plant operation, and even nuclear safety. So it is necessary to monitor the operating state of NPP system and mechanical equipment in real time by inspecting operating parameters. However, the key technology is real-time fault diagnosis of the mechanical equipment in NPP. Traditional fault diagnosis method based on analytic model is difficult to diagnose relevant and superimposed fault because of model error, disturbance and noise. This paper studies the application of fault diagnosis method based on BP neural network in NPP, and proposes an improved method for neural BP network method. For the feed-water system in the variable load operation process, we select the normal operation, the single feed-water valve fault, feed-water pump and feed-water valve superimposed fault as the analysis objects. One hundred points of data are extracted as BP algorithm training elements in these three processes averagely. The normal and abnormal conditions (including single fault and superimposed fault) can be accurately judged, but the single fault and superimposed failure would produce miscarriage of justice, about 2.4% of the single fault is diagnosed as superimposed fault, the diagnosis time delay is less than 1 second. These results meet the accuracy and real-time requirements. Then we study the application of support vector machine (SVM), which can make up for the deficiency of BP neural network. The results of this paper are useful for the real-time and reliable fault diagnosis of NPP.

scholarly journals Neural Network Optimization and Data Fusion Recognition Method for Intelligent Mechanical Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ying Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Bp Neural Network ◽  
Hot Spot ◽  
Diagnostic Errors ◽  
Mechanical Equipment ◽  
Bp Network ◽  
Vibration Signals ◽  
Rbf Network

With the improvement of mechanical equipment complexity and automation level, the importance of mechanical equipment fault diagnosis is more and more prominent, and the choice of appropriate diagnosis method is crucial to the accuracy of the diagnosis results. Wavelet analysis and neural network technology, as the hot spot and frontier of research, are also important research contents in the development of intelligent diagnosis of mechanical fault. Data fusion can process multisource information to obtain more accurate and reliable methods. At the same time, because of its good nonlinearity, adaptability, and fault tolerance, neural network has become the preferred method of mechanical fault diagnosis. This paper first describes the research content and significance of fault diagnosis technology and introduces the main methods and steps of fault diagnosis, and through the introduction of mechanical fault vibration signals, vibration signals were analyzed in time domain and frequency domain. Secondly, the definition and classification of data I fusion and RBF neural network are introduced in detail and compared with BP neural network. Because the prediction accuracy of the RBF network is higher than that of the BP neural network and the training time of the RBF network is obviously shorter than that of the BP network, the RBF network has significant advantages over diagnostic errors. In this paper, six valve signals were collected under normal conditions and errors, and by analyzing and comparing different theoretical foundations, the 4-second network crisis time was effectively reduced, which provided the basis for teaching monitoring.

Fault Diagnosis Method for Automotive Panel Processing Equipment Hydraulic System Based on Rough Sets - Neural Networks

2012 ◽  
Vol 522 ◽  
pp. 546-551
Author(s):  
Jiang Han ◽  
Hai Jin Huang ◽  
Lian Xia ◽  
Hua Zhai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Deep Drawing ◽  
Rough Sets ◽  
Hydraulic System ◽  
High Speed ◽  
Hydraulic Press ◽  
Mechanical Equipment ◽  
Rough Sets Theory

The application, which the intelligence is used in the fault diagnosis, is the main direction of research currently, especially in the fault diagnosis of large mechanical equipment. In order to improve hydraulic system failure diagnosis of high-speed deep drawing hydraulic press, reduce the efficiency and accuracy of difficulty diagnostic staff. By using rough sets theory combining neural network and the method of large NC hydraulic press, hydraulic system fault diagnosis of diagnosis. This paper established based on rough set - neural network fault diagnosis model, and the following hydraulic cushion hydraulic system as an example, the diagnosis in establishing the fault table based on the rough set theory to fault table attribute reduction and generating rules, will rule input to the BP neural network was trained learning. Get in neural network after the test data repository and simulation. Test results show that the method for the diagnosis of high-speed deep drawing hydraulic press hydraulic system fault is effective.

Fault Diagnosis Based on Rough Set and BP Neural Network (RS-BP) for Gas Turbine Engine

2013 ◽  
Vol 732-733 ◽  
pp. 397-401 ◽  
Author(s):  
Ning Bo Zhao ◽  
Shu Ying Li ◽  
Shuang Yi ◽  
Yun Peng Cao ◽  
Zhi Tao Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Rough Set ◽  
Bp Neural Network ◽  
Rough Set Theory ◽  
Original Data ◽  
Optimal Decision ◽  
Identification Rate ◽  
Diagnosis Model

This paper presents a new fusion diagnosis based on rough set and BP neural network for the fault diagnosis of gas turbine. The frame is designed to fusion fault diagnosis, which is composed by three parts: the rough set data pre-processor, rough set diagnosis model and BP neural network diagnosis model. Aiming at the difficulty in getting adequate fault samples in fault diagnosis, rough set theory is first used to process the original data, establish the decision table and generate rules, which can eliminate the redundant information and build the rough set diagnosis model. After that, according to the optimal decision attribute pre-treated by rough set, BP neural network is designed for fault diagnosis, which can reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole fusion diagnosis system. Finally, an example of gas turbine generator sets fuel system is taken as a case study to demonstrate the feasibility and validity of the proposed method in this paper.

scholarly journals Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Neng-Sheng Pai ◽  
Her-Terng Yau ◽  
Tzu-Hsiang Hung ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Solar Energy ◽  
Bp Neural Network ◽  
Large Scale ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Training Time ◽  
Cmac Neural Network ◽  
The Individual

Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC) neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF) neural network and back propagation (BP) neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields.

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