Feature Extraction and Classification of the Electric Current Signal of an Induction Motor for Condition Monitoring Purposes

2016 ◽  
Vol 856 ◽  
pp. 244-251
Author(s):  
Christian Gebbe ◽  
Christin Tran ◽  
Florian Lingenfelser ◽  
Johannes Glasschröder ◽  
Gunther Reinhart
Keyword(s):  
Feature Extraction ◽  
Induction Motor ◽  
Current Data ◽  
High Availability ◽  
Signal Acquisition ◽  
Classification Methods ◽  
Current Signal ◽  
Data Set ◽  
Motor Current

A high availability of machines has always been important in production. One way to increase it is to avoid unscheduled production stops by detecting the onset of machine faults and to conduct preventative repairs. The detection part consists of the three steps signal acquisition, feature extraction and classification. This paper focuses on the last two steps through the example of an induction motor. Based on a publicly available motor current data set, features were extracted using the continuous wavelet transform. In the subsequent classification step eight different classification methods were compared with each other. It was found, that the accuracy of the classifiers varied significantly in a range from 20.6 % to 92.8 %. Moreover, the supportive vector machine, scoring an accuracy of 92.8 %, was the only classifier with an accuracy above 55.0 %.

scholarly journals Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

2019 ◽  
Vol 11 (4) ◽  
pp. 405
Author(s):  
Xuan Feng ◽  
Haoqiu Zhou ◽  
Cai Liu ◽  
Yan Zhang ◽  
Wenjing Liang ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Decomposition Methods ◽  
Support Vector ◽  
Classification Methods ◽  
Target Classification ◽  
Data Set ◽  
Particle Center ◽  
Ground Penetrating ◽  
New Criterion

The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

scholarly journals Bispectrum Analysis of Motor Current Signals for Fault Diagnosis of Reciprocating Compressors

2009 ◽  
Vol 413-414 ◽  
pp. 505-511 ◽  
Author(s):  
Abdelhamid Naid ◽  
Feng Shou Gu ◽  
Yi Min Shao ◽  
Salem Al-Arbi ◽  
Andrew Ball
Keyword(s):  
Induction Motor ◽  
Current Data ◽  
Fault Classification ◽  
Diagnostic Features ◽  
Linear Classifiers ◽  
Motor Current ◽  
Working Cycle ◽  
Novel Approach ◽  
Valve Leakage ◽  
Feature Values

The induction motor is the most common driver in industry and has been previously proposed as a means of inferring the condition of an entire equipment train, predominantly through the measurement and processing of power supply parameters. This has obvious advantages in terms of being non-intrusive or remote, less costly to apply and improved safety. This paper describes the use of the induction motor current to identify and quantify a number of common faults seeded on a two-stage reciprocating compressor. An analysis of the compressor working cycle leads to current signal the components that are sensitive to the common faults seeded to compressor system, and second- and third-order signal processing tools are used to analyse the current signals. It is shown that the developed diagnostic features: the bispectral peak value from the amplitude modulation bispectrum and the kurtosis from the current gives rise to reliable fault classification results. The low feature values can differentiate the belt looseness from other fault cases and valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis stator current data for the diagnosis of motor drive faults.

scholarly journals On the Classification of ECG and EEG Signals with Various Degrees of Dimensionality Reduction

Biosensors ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 161
Author(s):  
Monica Fira ◽  
Hariton-Nicolae Costin ◽  
Liviu Goraș
Keyword(s):  
Dimensionality Reduction ◽  
Input Data ◽  
Random Projections ◽  
Signal Acquisition ◽  
Classification Methods ◽  
Eeg Signals ◽  
Laplacian Eigenmaps ◽  
The Third ◽  
Locality Preserving

Classification performances for some classes of electrocardiographic (ECG) and electroencephalographic (EEG) signals processed to dimensionality reduction with different degrees are investigated. Results got with various classification methods are given and discussed. So far we investigated three techniques for reducing dimensionality: Laplacian eigenmaps (LE), locality preserving projections (LPP) and compressed sensing (CS). The first two methods are related to manifold learning while the third addresses signal acquisition and reconstruction from random projections under the supposition of signal sparsity. Our aim is to evaluate the benefits and drawbacks of various methods and to find to what extent they can be considered remarkable. The assessment of the effect of dimensionality decrease was made by considering the classification rates for the processed biosignals in the new spaces. Besides, the classification accuracies of the initial input data were evaluated with respect to the corresponding accuracies in the new spaces using different classifiers.

scholarly journals Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach

2021 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Vijayakumar T ◽  
Vinothkanna R ◽  
Duraipandian M
Keyword(s):  
Feature Extraction ◽  
Research Work ◽  
Electrical Signal ◽  
The Body ◽  
Handheld Devices ◽  
Ecg Signal ◽  
Data Set ◽  
Raw Data ◽  
Feature Extraction Method

Our human heart is classified into four sections called the left side and right side of the atrium and ventricle accordingly. Monitoring and taking care of the heart of every human is the very essential part. Therefore, the early prediction is essential to save and give awareness to humans about diet plan, lifestyle schedule. Also, this is used to improve the clinical diagnosis and treatment of any patients. To predict or identifying any cardiovascular problems, Electro Cardio Gram (ECG) is used to record the electrical signal of the heart from the body surface of humans. The algorithm learns the dataset from before cluster is called supervised; The algorithm learns to train the data from the set of a dataset is called unsupervised. Then the classification of more amount of heartbeat for different category of normal, abnormal, irregular heartbeats to detect cardiovascular diseases. In this research article, a comparison of various methods to classify the dataset with a fusion-based feature extraction method. Besides, our research work consists of a de-noising filter to reconstruct the raw data from the original input. Our proposed framework performing preprocessing that consists of a filtering approach to remove noises from the raw data set. The signal is affected by thermal noise and instrumentation noise, calibration noise due to power line fluctuation. This interference is high in many handheld devices which can be eliminated by de-noising filters. The output of the de-noising filter is input for fusion-based feature extraction and prediction model construction. This workflow progress has given good results of classifier effectiveness and imbalance arrangement conditions. We achieved good accuracy 96.5% and minimum computation time for classification of ECG signal.

scholarly journals The Feature Extraction for Coronavirus Disease Classification Techniques

2021 ◽  
Author(s):  
Anis Fitri Nur Masruriyah ◽  
Hasan Basri ◽  
Hanny Hikmayanti Handayani ◽  
Ahmad Fauzi
Keyword(s):  
Feature Extraction ◽  
Disease Classification ◽  
Classification Methods ◽  
Extraction Techniques ◽  
Comparison Result ◽  
Early Symptoms ◽  
Number Of Patients ◽  
Improve Accuracy ◽  
Long Time

Abstract COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore confirmed by the swab and CXR methods. The process of swab and CXR takes a relatively long time since in CXR some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with feature extraction techniques and classification methods. The result of this study capable to identify CXR with COVID-19 and an accuracy of 96.5%. In addition, this study even compares the classification results without using feature extraction techniques. The comparison result showed that feature extraction was able to significantly improve accuracy.

scholarly journals CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

2016 ◽  
Vol XLI-B3 ◽  
pp. 441-445 ◽  
Author(s):  
N. Yastikli ◽  
Z. Cetin
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Classification Methods ◽  
Data Set ◽  
Cloud Data ◽  
Rule Sets

LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

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