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.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Application Overview of Neural Network in Fault Diagnosis

2014 ◽  
Vol 697 ◽  
pp. 419-424
Author(s):  
Ze Fan Cai ◽  
Dao Ping Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Development Trend ◽  
System Structure ◽  
Bp Network ◽  
Rbf Network ◽  
The Third ◽  
The Neural Network ◽  
The Development Trend

This paper introduces the system structure of neural network in fault diagnosis, and summarizes some applications of neural network in fault diagnosis. The most commonly used neural network in fault diagnosis is BP network. The second is RBF network and the third is ART. For each neural network, the paper will discuss the neural network, and the introduce some applications. It also introduces the combination of neural networks and other techniques. In the last part, this paper points out the development trend of the neural network in fault diagnosis.

Fault Diagnosis of Engine Ignition System Based on the Optimized BP Neural Networks by Improved Particle Swarm Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 366-370
Author(s):  
Zhi Mei Duan ◽  
Xiao Jin Yuan ◽  
Yan Jie Zhou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Particle Swarm Algorithm ◽  
Classification Result ◽  
Bp Network ◽  
Ignition System ◽  
Good Classification

In order to improve the accuracy of fault diagnosis of engine ignition system, in this paper, adaptive mutation particle swarm optimization (AMPSO) algorithm is used to optimize the weight of BP neural network. According to the fault feature of engine ignition system, the fault diagnosis is accomplished by the optimized BP neural network. The algorithm overcomes disadvantages that slowly convergence and easy to fall into local minima of standard PSO and BP network. The simulation results show that the method gains good classification result and has a certain practicality.

Application of Data Fusion Based on Genetic Algorithm and BP Neural Network in WSN

2012 ◽  
Vol 532-533 ◽  
pp. 1606-1610 ◽  
Author(s):  
Yi Xu ◽  
Zhao Xiang Li ◽  
Jiao Jiao Liu
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Bp Neural Network ◽  
Mean Squared Error ◽  
Rates Of Convergence ◽  
Sensor Nodes ◽  
Bp Network ◽  
Bp Artificial Neural Network ◽  
Learning Rates ◽  
Individual Measure

Energy-saving is one of the inevitable problems of the routing design in WSN, while Data Fusion technology is widely utilized in energy constraint WSN to reduce the amount of messages exchanged between sensor nodes. This paper proposes a new algorithm based on Integrated Genetic and BP Neural Network(IGBP), IGBP uses the global search capability of GA to remedy the deficiency of BP artificial neural network. First, IGBP generates the best individuals in different networks by GA algorithm. Then it chooses the most optimize individual measure by Mean Squared Error to construct the BP network which was supplied to train of the WSN. Using the optimize individual nodes as initialization value training the BP network, it will enhance the learning rates of convergence and avoid falling into the local minimums .The simulation results show that the IGBP algorithm has made great progress in balancing the consumption of energy so as to prolong the network lifetime.

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.

Application of the Wavelet Fuzzy BP Network in the Automotive Fault Diagnosis

2014 ◽  
Vol 668-669 ◽  
pp. 458-461
Author(s):  
Bing Yan Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Diagnostic Method ◽  
Fuzzy Theory ◽  
Convergence Speed ◽  
Bp Network ◽  
The Common ◽  
Fault Diagnostic

Combined with the wavelet analysis, the fuzzy theory and the BP neural network this paper constructs the fault diagnostic method of the wavelet fuzzy BP network. In the process of the automotive fault diagnosis, through comparing the common BP neural network with the wavelet fuzzy BP network, the result indicates that the wavelet fuzzy BP network has the characteristics of higher precision and faster convergence speed etc.

The Fault Diagnosis Method of Diesel Based on Wavelet Pocket and BP Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 910-913 ◽  
Author(s):  
Hong Hui Sun ◽  
Jun Xu ◽  
Qing Hua Zhang ◽  
Hong Xia Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Vibration Signals ◽  
Signal Energy ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Neural Network Algorithm

Because of the well time-frequency spectrum disposal capability of wavelet packet, the wavelet packet algorithm is used to analyze the time - frequency characteristics of diesel vibration signals. The signal energy distributing characteristics based on wavelet packet transform. are extracted and taken as diagnostic characteristic vector, then improved BP neural network algorithm that connects additional momentum with self-adaptive learning rate was used to classify and recognize faults of diesel valves. The experimental results show the fault diagnosis method of diesel based on wavelet pocket and BP neural network is effective and feasible.

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.

Fault Diagnosis of Analog Circuits Based on Evidence Theory

2014 ◽  
Vol 556-562 ◽  
pp. 2149-2152
Author(s):  
Cheng Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Analog Circuits ◽  
Bp Neural Network ◽  
Analog Circuit ◽  
Evidence Theory ◽  
Fusion Technology ◽  
Diagnosis Method ◽  
Simulation Results

BP neural network and evidence theory data fusion technology can be used in troubleshooting electronic equipment, from the simulation results show that the fault diagnosis method based on evidence theory and BP neural network can effectively diagnose faults in analog circuit, and it has automated intelligent characteristics.

Analog Circuit Fault Diagnosis Based on Levenberg-Marquardt Learning Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 979-982
Author(s):  
Huang Guo ◽  
Bao Ru Han ◽  
Guo Fang Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Convergence Speed ◽  
Bp Network ◽  
Descent Algorithm ◽  
Newton Algorithm ◽  
Levenberg Marquardt ◽  
Diagnosis Method

This paper presents a fault diagnosis method of BP neural network based on Levenberg-Marquardt learning algorithm. First, the use of principal component analysis to reduce the dimension of the fault sample reduced BP neural network input variables. Then use the Levenberg-Marquardt learning algorithm to adjust the network weights. Levenberg-Marquardt learning algorithm is combination of the Gauss - Newton algorithm and steepest descent algorithm. It has Gauss - Newton algorithm of local convergence and gradient descent algorithm of the global characteristic. So it has higher convergence speed, reduces the training time, to a certain extent, overcomes the problem of traditional BP network convergence speed slow and easy to fall into local minimum point. Simulation results demonstrate the correctness and accuracy of this fault diagnosis method.

Sign in / Sign up

Export Citation Format

Share Document