Cost Effective On-Site Fault Diagnosis Home Appliance Using a Smart Phone and Support Vector Machine

2019 ◽  
Author(s):  
Ji Min Baek ◽  
Kyeong Ha Lee ◽  
Seung Ho Lee ◽  
Ja Choon Koo
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Smart Phone ◽  
Poor Performance ◽  
Cost Effective ◽  
Consumer Electronics ◽  
Support Vector ◽  
Rotating Machines ◽  
Pass Filter ◽  
Rotating Machine

Abstract One of the common rotating machines of the consumer electronics might be a washing machine. The rotating machinery normally suffers mechanical failures even during daily operations that results in poor performance or shortening lifetime of the machine. Therefore, engineers have been interested in the earliest fault diagnosis of the rotating machine. Existing fault diagnosis methods for rotating machines have used fast fourier transform (FFT) method in frequency domain to detect abnormal frequency. However, it is difficult to diagnose using the FFT method if the normal frequency components of the rotating machines overlaps with the fault frequencies. In this paper, sets of acoustic signals generated by the washing machines are collected by using a smart phone in which an inexpensive microphone is equipped, and collected data are analyzed using a new algorithm, which combining the skewness, kurtosis, A-weighting filter, high-pass filter (HPF), and FFT. The analyzed data is applied to support vector machine (SVM) to determine defect existence. The proposed algorithm solves the disadvantages of the existing method and is accurate enough to discriminate the data collected by the cheap microphone of the smart phone.

Experimental Frequency-Domain Vibration Based Fault Diagnosis of Roller Element Bearings Using Support Vector Machine

2020 ◽  
Author(s):  
Vishal G Salunkhe ◽  
Ramchandra Ganapati Desavale ◽  
Jagadeesha T.
Keyword(s):  
Mathematical Model ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Experimental Results ◽  
Life Extension ◽  
Support Vector ◽  
Bearing Failure ◽  
Rotating Machines ◽  
Experimental Frequency ◽  
Model Based

Abstract In heavy rotating machines and assembly lines, bearing failure in any one of the rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems avoids breakdown and saves preventive and corrective maintenance time and cost. This research paper proposes advanced strategies in early fault detection of taper rolling bearings. In view of this, a mathematical model based- fault diagnosis and support vector machine (SVM) is proposed in this work. The mathematical model using dimension analysis by matrix method (DAMM) and SVM is developed to predict the vibration characteristic of the rotor bearing system. Various types of defects created using an electric discharge machine (EDM) are analyzed by correlating dependent and independent parameters. Experiments were performed to classify the rotor dynamic characteristic of healthy and unhealthy bearing. Experimental results are used to validate the model obtained by DAMM and SVM. Experimental results showed that vibration characteristics are evaluated by using a theoretical model and SVM. This contribution to the service life extension and efficiency improvement, so as to reduce bearing failure. Thus, the automatic online diagnosis of bearing faults is possible with a developed model-based by DAMM and SVM.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

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