Research on Electronic Equipment Fault Diagnosis Expert System Based on Embedded Linux

2013 ◽  
Vol 683 ◽  
pp. 837-840
Author(s):  
Jian Hu Zhang ◽  
Lei Lei ◽  
Jia Feng Li ◽  
Xin You Cui ◽  
Yong Wu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Embedded Linux ◽  
Electronic Equipment ◽  
Accuracy Rate ◽  
Embedded Linux System ◽  
Knowledge Updating ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Circuit Fault Diagnosis

This paper elaborate one circuit fault diagnosis method about electronic equipment circuit detection combined with expert system on ARM9 and embedded Linux platform and design CLIPS expert system using DSP combined with CPLD data acquisition, making full use of DSP high for-speed data processing capability and then passing the data to the Embedded Linux system operation. Expert system implemente a real-time fault diagnosis according to the the predefined fault diagnosis Knowledge. Compared with traditional testing equipment, the expert system has the advantage of knowledge updating conveniently, high fault diagnosis accuracy rate, etc.

Application of SVM Classifier and Fractal Feature in Circuit Fault Diagnosis

2012 ◽  
Vol 490-495 ◽  
pp. 942-945
Author(s):  
Jing Kui Mao ◽  
Xian Bai Mao
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Fractal Theory ◽  
Svm Classifier ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Band Pass ◽  
Circuit Fault Diagnosis

Combining SVM and fractal theory, a novel fault diagnosis method for analog circuits based on SVM using fractal dimension is developed in this paper. Simulation results of diagnosing the Sallen-Key band pass filter circuit have confirmed that the proposed approach increases the fault diagnosis accuracy, thereby it may be considered as an alternative for the analog fault diagnosis.

scholarly journals Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

2018 ◽  
Vol 173 ◽  
pp. 03090
Author(s):  
WANG Ying-chen ◽  
DUAN Xiu-sheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Network Application ◽  
Circuit Fault Diagnosis

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huali Yang ◽  
Renying Wang ◽  
Liangchao Zhao ◽  
Jinhua Ye ◽  
Nengping Li ◽  
...  
Keyword(s):  
Acute Abdomen ◽  
Statistical Data ◽  
Abdominal Ultrasound ◽  
Accuracy Rate ◽  
Acute Pelvic Inflammatory Disease ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Effective Diagnosis ◽  
Ultrasound Group ◽  
Corpus Luteum Cyst

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.

scholarly journals Study on a Fault Identification Method of the Hydraulic Pump Based on a Combination of Voiceprint Characteristics and Extreme Learning Machine

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 894 ◽  
Author(s):  
Wanlu Jiang ◽  
Zhenbao Li ◽  
Jingjing Li ◽  
Yong Zhu ◽  
Peiyao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Recognition Accuracy ◽  
Test Sample ◽  
Training Sample ◽  
Accuracy Rate ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Hidden Layer ◽  
Axial Piston ◽  
Sample Set

Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.

Implementing an expert system for fault diagnosis of electronic equipment

1995 ◽  
Vol 8 (3) ◽  
pp. 355-364 ◽  
Author(s):  
T. Satyanarayana ◽  
G. Subramanyam ◽  
K.V. Rama Rao
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Electronic Equipment
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