scholarly journals A Self-Diagnostic Method for Automobile Faults in Multiple Working Conditions Based on SOM-BPNN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhou Zhou ◽  
Xin Cheng ◽  
Hui Chang ◽  
Jingmei Zhou ◽  
Xiangmo Zhao
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Pid Control ◽  
Control Method ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Variable Speed ◽  
Chassis Dynamometer ◽  
Diagnosis Model ◽  
Vehicle Chassis

Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle chassis dynamometer is proposed. 3σ method and data normalization were used to pretreat tail gas data. BPNN-RNN (Back Propagation Neural Networks-Recurrent Neural Networks) variable speed integral PID control method was used to achieve high-precision vehicle chassis dynamometer control. Accurate tail gas data were obtained. The simulation and test results of BPNN-RNN variable speed integral PID control were verified and analyzed. The PID control method can quickly adjust PID parameters (within 10 control cycles), control overshoot within 2% of the target value, eliminate the static error, and improve the control performance of the vehicle chassis dynamometer. Combined with BPNN (Back Propagation Neural Network) and SOM (Self-organizing Maps) network, a BPNN-SOM fault diagnosis model is proposed in this paper. By comparing and analyzing the fault diagnosis performance of various neural networks and SOM-BPNN algorithm, it is found that the SOM-BPNN model has the best comprehensive result, the prediction accuracy is 98.75%, the time is 0.45 seconds, and it has good real-time stability. The proposed model can effectively diagnose the vehicle fault, provide a certain direction for maintenance personnel to judge the vehicle state, and provide certain help to alleviate traffic pollution problem.

Compressor fault diagnosis system based on PCA-PSO-LSSVM algorithm

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110261
Author(s):  
Kun Zhang ◽  
Jinpeng Su ◽  
Shaoan Sun ◽  
Zhixiang Liu ◽  
Jinrui Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Recognition Rate ◽  
Back Propagation ◽  
Test Sample ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Diagnosis Model ◽  
Pca Algorithm

On the basis of the principal components analysis-particle swarm optimization-least squares support vector machine (PCA-PSO-LSSVM) algorithm, a fault diagnosis system is proposed for the compressor system. The relationship between the working principle of a compressor system, the fault phenomenon, and the root cause is analyzed. A fault diagnosis model is established based on the LSSVM optimized using PSO, the compressor fault diagnosis test experimental platform is used to obtain the fault signal of various fault occurrence states, and the PCA algorithm is employed to extract the characteristic data in the fault signal as input to the fault diagnosis model. The back-propagation neural network, the LSSVM algorithm, and the PSO-LSSVM algorithm are analyzed and compared with the proposed fault diagnosis model. Results show that the PCA-PSO-LSSVM fault diagnosis model has a maximum fault recognition efficiency that is 10.4% higher than the other three models, the test sample classification time is reduced by 0.025 s, the PCA algorithm can effectively reduce the input dimension, and the PSO-LSSVM fault diagnosis model based on the PCA algorithm for extracting features has a high recognition rate and accuracy. Therefore, the proposed fault diagnosis system can effectively identify the compressor fault and improve the efficiency of the compressor.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

Multisensor information-based adaptive control method for cutting head speed of roadheader

2020 ◽  
pp. 095440622095249
Author(s):  
Pengjiang Wang ◽  
Yang Shen ◽  
Rui Li ◽  
Kai Zong ◽  
Shichen Fu ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fuzzy Logic Controllers ◽  
Control Effect ◽  
Cutting Efficiency ◽  
Cutting Head ◽  
Pi Controllers ◽  
Particle Swarm Optimisation Algorithm

An adaptive control method to improve the cutting head speed of roadheaders using multisensor information is proposed, so as to solve the problems of low cutting efficiency and low intelligence of roadheaders during underground tunnelling. The operation of a roadheader is analysed, and a control strategy for its cutting head speed is proposed. In addition, the cutting head speed is categorised into five gears according to the multisensor information of different cutting states. The controller for speed estimation is designed using a back propagation neural network optimised using an improved particle swarm optimisation algorithm. A control system is established in MATLAB to analyse the effectiveness of the method. The simulation results show that an IPSO-BP controller has the best control effect and can attain the target speed. The response time was lower than those of fuzzy logic controllers and traditional PI controllers by 46% and 68%, respectively, and the overshoot decreased by 4.69% and 12.19%, respectively. Furthermore, experimental research verified the effectiveness of this method. This method can adaptively adjust the cutting head speed of a roadheader using multisensor information and is important (both theoretical and practically) for extending the service life of roadheaders and improving tunnelling efficiency.

Constructing Expert System to Automatic Translation for Software development

2018 ◽  
Vol 2 (2) ◽  
pp. 231-247
Author(s):  
Safwan Hasoon ◽  
Fatima Younis
Keyword(s):  
Neural Networks ◽  
Expert System ◽  
Radial Basis Function Network ◽  
Back Propagation ◽  
Transition Process ◽  
Back Propagation Neural Network ◽  
Knowledge Bases ◽  
Translation Process ◽  
Automatic Translation ◽  
The Cost

the development in computer fields, especially in the software engineering, emerged the need to construct intelligence tool for automatic translation from design phase to coding phase, for producing the source code from the algorithm model represented in pseudo code, and execute it depending on the constructing expert system which reduces the cost, time and errors that may occur during the translation process, which has been built the knowledge base, inference engine, and the user interface. The knowledge bases consist of the facts and the rules for the automatic transition. The results are compared with a set of neural networks, which are Back propagation neural network, Cascade-Forward network, and Radial Basis Function network. The results showed the superiority of the expert system in automatic transition process speed, as well as easy to add, delete or modify process for rules or data of the pseudo code compared with previously mentioned neural networks.

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

2021 ◽  
Author(s):  
Hao DeChen ◽  
HuaLing Li ◽  
JinYing Huang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Network Structure ◽  
Rotating Machinery ◽  
Variable Speed ◽  
Data Set ◽  
Public Data ◽  
Diagnosis Method ◽  
Diagnosis Model

Abstract Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

scholarly journals Real-Time Stripe Width Computation Using Back Propagation Neural Network for Adaptive Control of Line Structured Light Sensors

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2618 ◽  
Author(s):  
Jingbo Zhou ◽  
Laisheng Pan ◽  
Yuehua Li ◽  
Peng Liu ◽  
Lijian Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Real Time ◽  
Exposure Time ◽  
Control Method ◽  
Structured Light ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stripe Width ◽  
Accuracy Result

A line structured light sensor (LSLS) is generally constituted of a laser line projector and a camera. With the advantages of simple construction, non-contact, and high measuring speed, it is of great perspective in 3D measurement. For traditional LSLSs, the camera exposure time is usually fixed while the surface properties can be varied for different measurement tasks. This would lead to under/over exposure of the stripe images or even failure of the measurement. To avoid these undesired situations, an adaptive control method was proposed to modulate the average stripe width (ASW) within a favorite range. The ASW is first computed based on the back propagation neural network (BPNN), which can reach a high accuracy result and reduce the runtime dramatically. Then, the approximate linear relationship between the ASW and the exposure time was demonstrated via a series of experiments. Thus, a linear iteration procedure was proposed to compute the optimal camera exposure time. When the optimized exposure time is real-time adjusted, stripe images with the favorite ASW can be obtained during the whole scanning process. The smoothness of the stripe center lines and the surface integrity can be improved. A small proportion of the invalid stripe images further proves the effectiveness of the control method.

scholarly journals Automatical Knowledge Representation of Logical Relations by Dynamical Neural Network

2017 ◽  
Vol 26 (4) ◽  
pp. 625-639 ◽  
Author(s):  
Gang Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Knowledge Representation ◽  
Network Structure ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ann Model ◽  
Functional Approximation ◽  
Dynamical Neural Network

AbstractCurrently, most artificial neural networks (ANNs) represent relations, such as back-propagation neural network, in the manner of functional approximation. This kind of ANN is good at representing the numeric relations or ratios between things. However, for representing logical relations, these ANNs have disadvantages because their representation is in the form of ratio. Therefore, to represent logical relations directly, we propose a novel ANN model called probabilistic logical dynamical neural network (PLDNN). Inhibitory links are introduced to connect exciting links rather than neurons so as to inhibit the connected exciting links conditionally to make them represent logical relations correctly. The probabilities are assigned to the weights of links to indicate the belief degree in logical relations under uncertain situations. Moreover, the network structure of PLDNN is less limited in topology than traditional ANNs, and it is dynamically built completely according to the data to make it adaptive. PLDNN uses both the weights of links and the interconnection structure to memorize more information. The model could be applied to represent logical relations as the complement to numeric ANNs.

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