Multifault Diagnosis of Combined Hydraulic and Mechanical Centrifugal Pump Faults Using Continuous Wavelet Transform and Support Vector Machines

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Janani Shruti Rapur ◽  
Rajiv Tiwari
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Strong Association ◽  
Principal Component ◽  
Fault Classification ◽  
Support Vector ◽  
Operating Speed ◽  
Continuous Wavelet ◽  
Centrifugal Pumps ◽  
Multiple Faults

Abstract Centrifugal pumps (CPs) fail due to anomalies in fluid flow patterns and/or due to failure of mechanical subsystems in them. In this work, a technique built on the multiclass support vector machine (MSVM) is developed to identify multiple faults in the CP. In addition, the complex problem of fault combinations and their classification is dealt with in this work. The combination of features from motor line current sensors and accelerometers is used to train the algorithm. To take into account the transient as well as harmonic components of fault signatures, continuous wavelet transform (CWT) analysis is used. Thereafter, the most important information from the CWT coefficients is selected using the two proposed novel methods CWT-based on energy (BE)-MSVM and CWT-principal component analysis (PCA)-MSVM, which are BE as well as PCA, respectively. It is experimentally observed that faults in the CPs have a very strong association with its operating speed. Thus, in order to make the CP versatile in operation, it is important that the fault diagnosis methodology is also efficient at large speed range of CP operation. This work attempts to develop a fault classification methodology, which is independent of the CP operating speed.

scholarly journals A Technique for Frequency Converter-Fed Asynchronous Motor Vibration Monitoring and Fault Classification, Applying Continuous Wavelet Transform and Convolutional Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3690 ◽  
Author(s):  
Tomas Zimnickas ◽  
Jonas Vanagas ◽  
Karolis Dambrauskas ◽  
Artūras Kalvaitis
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Short Circuit ◽  
Frequency Converter ◽  
Asynchronous Motor ◽  
Vibration Monitoring ◽  
Fault Classification ◽  
Continuous Wavelet ◽  
Simple Method ◽  
Measuring Device

In this article, a type of diagnostic tool for an asynchronous motor powered from a frequency converter is proposed. An all-purpose, effective, and simple method for asynchronous motor monitoring is used. This method includes a single vibration measuring device fixed on the motor’s housing to detect faults such as worn-out or broken bearings, shaft misalignment, defective motor support, lost phase to the stator, and short circuit in one of the phase windings in the stator. The gathered vibration data are then standardized and continuous wavelet transform (CWT) is applied for feature extraction. Using morl wavelets, the algorithm is applied to all the datasets in the research and resulting scalograms are then fed to a complex deep convolutional neural network (CNN). Training and testing are done using separate datasets. The resulting model could successfully classify all the defects at an excellent rate and even separate mechanical faults from electrical ones. The best performing model achieved 97.53% accuracy.

scholarly journals Microsaccade characterization using the continuous wavelet transform and principal component analysis

2010 ◽  
Vol 3 (5) ◽  
Author(s):  
Mario Bettenbühl ◽  
Claudia Paladini ◽  
Konstantin Mergenthaler ◽  
Reinhold Kliegl ◽  
Ralf Engbert ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Eye Movements ◽  
Continuous Wavelet Transform ◽  
Principal Component ◽  
Real Data ◽  
Component Analysis ◽  
Continuous Wavelet ◽  
Fixational Eye Movements ◽  
Definition Of

During visual fixation on a target, humans perform miniature (or fixational) eye movements consisting of three components, i.e., tremor, drift, and microsaccades. Microsaccades are high velocity components with small amplitudes within fixational eye movements. However, microsaccade shapes and statistical properties vary between individual observers. Here we show that microsaccades can be formally represented with two significant shapes which we identfied using the mathematical definition of singularities for the detection of the former in real data with the continuous wavelet transform. For character-ization and model selection, we carried out a principal component analysis, which identified a step shape with an overshoot as first and a bump which regulates the overshoot as second component. We conclude that microsaccades are singular events with an overshoot component which can be detected by the continuous wavelet transform.

scholarly journals A Study On Ventricular Fibrillation And Ventricular Tachyarrythmia Classification Methods Using Continuous Wavelet Transform

2021 ◽  
Author(s):  
Elnaz Afatmirni
Keyword(s):  
Wavelet Transform ◽  
Ventricular Fibrillation ◽  
Continuous Wavelet Transform ◽  
Principal Component ◽  
Continuous Wavelet ◽  
Classification Methods ◽  
Eigen Values ◽  
Leave One Out ◽  
Case Feature

Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are fatal cardiac diseases associated with cardiac arrest. It is difficult to manually classify VT and VF signals. However, precise classification of VT and VF signals can assist cardiologists to identify and ultimately prevent onset of VF or VT. In this thesis, some of the underlying features which characterize VF and VT are extracted and are used to efficiently classifying these signals. The features are acquired from energy coefficients matrices using Continuous Wavelet Transform (CWT) through application of Principal Component Analysis (PCA). The features are the vector containing newly generated energy projection coefficients and the vector containing the number of the top 99% principal components (Eigen-Values) for each case. Feature vectors are then passed through Fast Forward Neural Network (FFNN) and Leave One Out Method (LOOM) classifiers for discrimination. The results are then compared for the highest classification results for VF and VT signals.

scholarly journals Development and Evaluation of Traffic Count Sensor with Low-Cost Light-Detection and Ranging and Continuous Wavelet Transform: Initial Results

2019 ◽  
Vol 2673 (11) ◽  
pp. 209-219
Author(s):  
Ravi Jagirdar ◽  
Joyoung Lee ◽  
Kitae Kim ◽  
Min-Wook Kang
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Supervised Machine Learning ◽  
Light Detection And Ranging ◽  
Support Vector ◽  
Continuous Wavelet ◽  
Light Detection ◽  
Distance Information ◽  
Traffic Count ◽  
Distance Data

This paper presents a cost-effective, non-intrusive, and easy-to-deploy traffic count data collection method using two-dimensional light-detection and ranging (LiDAR) technology. The proposed method integrates a LiDAR sensor, continuous wavelet transform (CWT), and support vector machine (SVM) into a single framework for traffic count. LiDAR is adopted since the technology is economical and easily accessible. Moreover, its 360° visibility and accurate distance information make it more reliable compared with radar, which uses electromagnetic waves instead of light rays. The obtained distance data are converted into the signals. CWT is employed to detect any deviation in distance profile, because of its efficiency in detecting modest changes over a period of time. SVM is one of the supervised machine learning tools for data classification and regression. In the methodology, the SVM is applied to classify the distance data points obtained from the sensor into detection and non-detection cases, which are highly complex. Proof-of-concept (POC) test is conducted in three different places in Newark, New Jersey, to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances in vehicle count collection, resulting in 83–94% accuracy. It is discovered that the accuracy of the proposed method is affected by the color of the exterior surface of a vehicle.

scholarly journals A Study On Ventricular Fibrillation And Ventricular Tachyarrythmia Classification Methods Using Continuous Wavelet Transform

2021 ◽  
Author(s):  
Elnaz Afatmirni
Keyword(s):  
Wavelet Transform ◽  
Ventricular Fibrillation ◽  
Continuous Wavelet Transform ◽  
Principal Component ◽  
Continuous Wavelet ◽  
Classification Methods ◽  
Eigen Values ◽  
Leave One Out ◽  
Case Feature

Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) are fatal cardiac diseases associated with cardiac arrest. It is difficult to manually classify VT and VF signals. However, precise classification of VT and VF signals can assist cardiologists to identify and ultimately prevent onset of VF or VT. In this thesis, some of the underlying features which characterize VF and VT are extracted and are used to efficiently classifying these signals. The features are acquired from energy coefficients matrices using Continuous Wavelet Transform (CWT) through application of Principal Component Analysis (PCA). The features are the vector containing newly generated energy projection coefficients and the vector containing the number of the top 99% principal components (Eigen-Values) for each case. Feature vectors are then passed through Fast Forward Neural Network (FFNN) and Leave One Out Method (LOOM) classifiers for discrimination. The results are then compared for the highest classification results for VF and VT signals.

scholarly journals Classification of Refrigerant Flow Noise of Air Conditioners Based on Continuous Wavelet Transform and Support Vector Machine

2020 ◽  
Vol 10 (11) ◽  
pp. 3959
Author(s):  
Un-Chang Jeong
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Support Vector ◽  
Continuous Wavelet ◽  
Air Conditioner ◽  
Time Frequency ◽  
Flow Noise ◽  
The Time Domain

This study proposes a classification method that uses the continuous wavelet transform and the support vector machine approach to classify refrigerant flow noises generated in an air conditioner. The air conditioning noise was identified as an abnormal signal by the use of the first- and second-order moments. The start and end times of refrigerant flow noises were identified by detecting the singularities of the continuous wavelet transform coefficient in the time domain and by means of listening to the measured sounds. Further, the time-frequency characteristics of refrigerant flow noise were analyzed with the continuous wavelet transform. For the support vector machine-based classification of refrigerant flow noise in an air conditioner, the grid search method was used to determine kernel hyperparameters. Five-fold cross validation was employed for the application of the support vector machine to the classification of air conditioner refrigerant noise. In addition, measured sound sources were modified based on classified refrigerant flow noise to compare the classification accuracy of a jury test with the results of the support vector machine.

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