Long distance wireless fault diagnosis for photovoltaic modules based on back propagation neural network

2020 ◽  
pp. 002072092094060
Author(s):  
Ling Chen ◽  
Wei Han ◽  
Hai-Tao Li ◽  
Zi-Kun Xu ◽  
Jing-Wei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Long Distance ◽  
Partial Shading ◽  
Bp Network ◽  
Depth Analysis ◽  
Pv Modules ◽  
Diagnosis Method ◽  
Diagnosis Model

Various faults of photovoltaic (PV) modules inevitably occur in the work process, since PV modules are installed in hostile situation. To obtain the types of failure, a novel fault diagnosis method based on back propagation (BP) neural network with Levenberg-Marquardt (L-M) algorithm for PV modules is proposed. Through the in-depth analysis the output of PV modules under normal and fault conditions, the input variables of the diagnosis model are acquired. The high-speed and real-time fault diagnosis model for PV modules is first designed based on TMS320VC5402 DSP and long-distance wireless fault diagnosis is realized by Zigbee technology. The simulation and experimental results show that the fault diagnosis method for PV modules based on BP network with L-M algorithm can effectively detect four types of fault for PV modules such as open circuit, short circuit, partial shading and abnormal degradation. The numerical results verify the effectiveness and correctness of the proposed method, which can provide a great educational benefit of PV operation technology.

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.

Research of Hard Fault Diagnosis Simulation Platform of Aero-Engine's Key Sensors Based on Neural Network

2013 ◽  
Vol 391 ◽  
pp. 150-154 ◽  
Author(s):  
Zhao Rong Sun ◽  
Yi Gang Sun ◽  
Chun Lin Sun Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Simulation Platform ◽  
Bp Network ◽  
Fault Type ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Engine Failure ◽  
Aero Engines ◽  
Hard Failure

The purpose of the research is to establish a fault diagnosis model of the aero-engines key sensors using the artificial neural networks to replace the engines mathematical model, so as to establish a hard fault diagnosis simulation platform to monitor the performances of the engine sensors on real-time, to judge the engine failure mode timely, and to locate the fault type of sensors accurately. By analyzing the correlations of the parameters that affect the conditions of the engine, a three-layer BP network model is established. The related QAR (Quick Access Recorder) data are used to simulate and analyze the models using the MATLAB. Combined with the characteristics of the hard failure of the critical engine sensors and the correlation of the parameters, the fault diagnosis simulation platform is established. Then, the parameters of the normal engine and the failure engine are used respectively to evaluate and validate the platform. The simulation results show that the platform can judge the critical sensors faults of the engine accurately, and can locate the type of sensors reliably.

Fault diagnosis method of mine hoist braking system based on deep convolution neural network

2021 ◽  
Author(s):  
Fangyuan Yan ◽  
Juanli Li ◽  
Dong Miao ◽  
Qi Cao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Descent Method ◽  
Convolution Neural Network ◽  
Monitoring Data ◽  
Gradient Descent Method ◽  
Braking System ◽  
Diagnosis Method ◽  
Mine Hoist

Abstract A reliable braking system is an important guarantee for safe operation of mine hoist. In order to make full use of the monitoring data in the operation process of mine hoist, identify the operation status of the hoist, and further carry out fault diagnosis on it, the deep learning method was introduced into the fault diagnosis of the hoist, and a fault diagnosis method of hoist braking system based on convolution neural network has been proposed. Firstly, the working principle and fault mechanism of disc brake and its hydraulic station in hoist braking system are analyzed, and the monitoring parameters of this study are determined; then, based on massive monitoring data, the convolutional neural networks (CNN) is established, the one-dimensional signal collected by the sensor is transformed into two-dimensional image for coding, the neural network is trained by gradient descent method, and the network structure parameters are modified according to the training results. Finally, the fault diagnosis model is compared and verified by using the sample set based on the traditional back propagation neural network (BP) and CNN. The results show that the accuracy of CNN is higher than that of BP, and the accuracy rate can reach 99.375% after reducing the involvement between samples. This method can make full use of the monitoring data for diagnosis, without subjective intervention of experts, and improve the accuracy of diagnosis.

scholarly journals Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kuo-Nan Yu ◽  
Her-Terng Yau ◽  
Jian-Yu Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Photovoltaic System ◽  
Photovoltaic Systems ◽  
Solar Photovoltaic ◽  
Error Back Propagation ◽  
Point Tracking ◽  
Diagnosis Method

At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

scholarly journals Research on Intelligent Diagnosis Method for Large-Scale Ship Engine Fault in Non-Deterministic Environment

2017 ◽  
Vol 24 (s3) ◽  
pp. 200-206 ◽  
Author(s):  
Donghua Feng ◽  
Yahong Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Use Value ◽  
Intelligent Diagnosis ◽  
High Diagnostic Accuracy ◽  
Diagnosis Method ◽  
Main Fault ◽  
Diagnosis Model ◽  
Fault Mode

Abstract Aiming at the problem of inaccurate and time-consuming of the fault diagnosis method for large-scale ship engine, an intelligent diagnosis method for large-scale ship engine fault in non-deterministic environment based on neural network is proposed. First, the possible fault of the engine was analyzed, and the downtime fault of large-scale ship engine and the main fault mode were identified. On this basis, the fault diagnosis model for large-scale ship engine based on neural network is established, and the intelligent diagnosis of engine fault is completed. The experiment proved that the proposed method has high diagnostic accuracy, engine fault diagnosis takes only about 3s, with a higher use value.

Fault Diagnosis of CNC Machine Using Hybrid Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 865-869
Author(s):  
Juan Du ◽  
Xian Guo Yan ◽  
Na Sha Wei
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
First Grade ◽  
Second Grade ◽  
Cnc Machine ◽  
Bp Network ◽  
Hybrid Neural Network ◽  
Neural Network System ◽  
Diagnosis Method

This paper proposed a technology of fault diagnosis for CNC machine based on hybrid neural network. Before the fault diagnosis was made, the fault patterns were firstly obtained from technical manuals and field experience to build a sample set, and then they were classified and coded according to the rule. In order to improve diagnosis speed, the fault diagnosis system was designed as a hybrid neural network system which consists of two-grade neural networks. When the fault pattern was input the system, the fault was first classified by the first-grade BP network, and according to the fault type, the corresponding second-grade ART network was activated to perform fault diagnosis. In this paper, the train algorithms of two kinds of neural networks were programmed by MATLAB. Comparing with the traditional diagnosis method, the presented technology possesses advantages of automatic fault diagnosis and ability for self-learning and self-organization.

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.

Fault Diagnosis of Some Equipment Based on BP Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 1193-1196
Author(s):  
Qing Li ◽  
Da Lin Sun ◽  
Li Zhang ◽  
Xue Qian Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Calculation Result ◽  
Qualitative Data ◽  
Front Line ◽  
Huge Amount ◽  
Bp Network ◽  
Diagnosis Method ◽  
Short Time

Neural network has obvious advantages on dealing with the uncertain problems with a huge amount of data. Some equipment’s faults performance of such characteristics that the date is abundant, and failure phenomenon is not explicit and uncertain. Then, it leads to being hard to diagnose the faults through the traditional diagnosis method in a short time. This paper will analysis the data feature and then build a model to deal with the qualitative data attributes in order that the BP network can use it smoothly. Calculation result shows that using this method, fault diagnosis can be simply and quickly. The paper also provides a new kind of composite way to figure out fault positions for the front-line operators based on experts’experience knowledge but not on measurement signals.

Fault Diagnosis Method for the Rolling Bearing Based on Information Fusion and BP Neural Network

2012 ◽  
Vol 538-541 ◽  
pp. 1956-1961 ◽  
Author(s):  
Jin Min Zhang ◽  
Yin Hua Huang ◽  
Si Ming Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Back Propagation Neural Network ◽  
Weighted Fusion ◽  
Diagnosis Method

Abstract. In order to diagnose the fault of rolling bearing by the vibration signal, a new method of fault diagnosis based on weighted fusion and BP (Back Propagation) neural network was put forward. At first, the vibration signal from the sensors was wave filtered through the method of correlation function, then the fused signal was obtained by the classical adaptive weighted fusion method, the multi-type characteristics parameters was to be as a neural network input. Finally, the fault diagnosis of rolling bearing was realized by the BP neural network, and the results show that the multi-sensor information fusion fault diagnosis method can be proved effectively to achieve the fault diagnosis of rolling bearing.

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