Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ronny Francis Ribeiro Junior ◽  
Isac Antônio dos Santos Areias ◽  
Guilherme Ferreira Gomes
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Electric Motors ◽  
Content Type ◽  
Operation Conditions ◽  
Vibration Pattern ◽  
Vibration Signal Analysis ◽  
Detection And Diagnosis

Purpose Electric motors are present in most industries today, being the main source of power. Thus, detection of faults is very important to rise reliability, reduce the production cost, improving uptime and safety. Vibration analysis for condition-based maintenance is a mature technique in view of these objectives. Design/methodology/approach This paper shows a methodology to analyze the vibration signal of electric rotating motors and diagnosis the health of the motor using time and frequency domain responses. The analysis lies in the fact that all rotating motor has a stable vibration pattern on health conditions. If the motor becomes faulty, the vibration pattern gets changed. Findings Results showed that through the vibration analysis using the frequency domain response it is possible to detect and classify the motors in several induced operation conditions: healthy, unbalanced, mechanical looseness, misalignment, bent shaft, broken bar and bearing fault condition. Originality/value The proposed methodology is verified through a real experimental setup.

Study on the Defects Size of Ball Bearings Elements Using Vibration Analysis

2014 ◽  
Vol 658 ◽  
pp. 289-294 ◽  
Author(s):  
Carmen Bujoreanu ◽  
Razvan Monoranu ◽  
N. Dumitru Olaru
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Ball Bearing ◽  
Fault Detection And Diagnosis ◽  
Response Measurement ◽  
Time Frequency ◽  
Bearing Life ◽  
Rolling Element ◽  
Detection And Diagnosis ◽  
The Time Domain

The vibration analysis aims to extract features from the measurements in order to be used for fault detection and diagnosis. Vibration response measurement is an important and effective technique for the detection of the defects in rolling element bearings. The corresponding analysis methods operate in the time domain, in the frequency domain and recently in the time-frequency domain. A quantitative determination of the defect severity and its development are useful to be determined in order to estimate the remaining useful ball bearing life. Experimental data from a bearing with a defect are collected by an accelerometer then processed to identify the passing time of a ball over a defect. The paper presents a computation model corroborated to an experimental investigation to establish the defect length of a ball bearing inner race.

Health Monitoring of a Gear Box Using Vibration Signal Analysis

2015 ◽  
Vol 813-814 ◽  
pp. 1012-1017 ◽  
Author(s):  
M.R. Praveen ◽  
M. Saimurugan
Keyword(s):  
Frequency Domain ◽  
Health Monitoring ◽  
Crucial Role ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Nearest Neighbours ◽  
Gear Box ◽  
Vibration Signal Analysis

A gear plays a crucial role in the performance of a gear box. The faults in a gear reduces the gear life and if problem arises in shaft it affects bearing. Gear box is finally affected due to these faults. Vibration signals carries information about condition of a gear box which are captured using piezoelectric accelerometer. In this paper, features are extracted and classified using K nearest neighbours (KNN) algorithms for both time and frequency domain. The effectiveness of KNN in classification of gear faults for both time and frequency domain is discussed and compared.

EMD and Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 233-237 ◽  
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Oscillation Modes ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
Envelope Spectrum

A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the bearing. The experimental results show that the proposed method based on EMD and envelope spectrum analysis technique can effectively diagnose the faults of bearing.

Gearbox Fault Detection Using Hilbert and TT-Transform

2005 ◽  
Vol 293-294 ◽  
pp. 79-86 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Hilbert Transform ◽  
Detection Method ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Time Representation ◽  
Machine Vibration ◽  
S Transform ◽  
Gearbox Vibration ◽  
Detection And Diagnosis

Machine vibration signal has been used in fault detection and diagnosis. Modulation and non-stationarity existing in the signal generated by a faulty gearbox present challenges to effective fault detection. Hilbert transform has the ability to address the modulation issue. This paper outlines a novel fault detection method called Hilbert & TT-transform (HTT-transform) which combines Hilbert transform and TT-transform obtained from the inverse Fourier transform of the S-transform. The principle of the proposed method is to analyze the modulating signal created by a faulty gear using a time-time representation. The method has the advantage of providing a new way of localizing the time features of the modulating signal around a particular point on the time axis through scaled windows. It is verified with simulated signals and real gearbox vibration signals. The results obtained by CWT, S-transform, TT- transform, and HTT-transform are compared. They show that utilizing the proposed method can improve the effectiveness of gearbox fault detection.

Magnetic Bearing Rotor Vibration Analysis Based on the Harmonic Wavelet Package Algorithm

2012 ◽  
Vol 588-589 ◽  
pp. 152-155
Author(s):  
De Guang Li ◽  
Shu Qin Liu
Keyword(s):  
Frequency Domain ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Magnetic Bearing ◽  
Rotor Vibration ◽  
Time Frequency ◽  
Optimizing Design ◽  
Harmonic Wavelet ◽  
Frequency Map

Analysis of the magnetic bearing rotor vibration is the base of the optimizing design, supervise and diagnosis of the magnetic bearing. Harmonic wavelet package was used for the analysis of the vibration signal, and the 3 dimension time-frequency domain energy map was constructed, then the analysis of the rotor vibration became convenient. Via analysis of the time-frequency map, the vibration in each time and each frequency was obtained, and the supervise and the diagnosis of the rotor can be realized.

HMM-Based Fault Detection and Diagnosis Scheme for Rolling Element Bearings

2004 ◽  
Vol 127 (4) ◽  
pp. 299-306 ◽  
Author(s):  
Hasan Ocak ◽  
Kenneth A. Loparo
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Markov Modeling ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Hidden Markov Modeling ◽  
Rolling Element ◽  
Detection And Diagnosis

In this paper, we introduce a new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals. Features extracted from amplitude demodulated vibration signals from both normal and faulty bearings were used to train HMMs to represent various bearing conditions. The features were based on the reflection coefficients of the polynomial transfer function of an autoregressive model of the vibration signals. Faults can be detected online by monitoring the probabilities of the pretrained HMM for the normal case given the features extracted from the vibration signals. The new technique also allows for diagnosis of the type of bearing fault by selecting the HMM with the highest probability. The new scheme was also adapted to diagnose multiple bearing faults. In this adapted scheme, features were based on the selected node energies of a wavelet packet decomposition of the vibration signal. For each fault, a different set of nodes, which correlates with the fault, is chosen. Both schemes were tested with experimental data collected from an accelerometer measuring the vibration from the drive-end ball bearing of an induction motor (Reliance Electric 2 HP IQPreAlert) driven mechanical system and have proven to be very accurate.

scholarly journals Early Fault Detection in Bearing with Fault Seeded on Outer Raceway at Three Different Position: Orthogonal, Centered and Opposite

2019 ◽  
Vol 8 (4) ◽  
pp. 6448-6453
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Low Voltage ◽  
Vibration Signal ◽  
Industrial Applications ◽  
Fault Detection And Diagnosis ◽  
Economic Losses ◽  
Detection And Diagnosis ◽  
Early Fault Detection ◽  
The Time Domain

Rotating machine such as a small low voltage motor or a power plant generator is an essential asset to the industrial applications. The execution and efficiency of these rotating machines are being reduced due to faulty rotating machinery parts. The faulty parts also generate various forces, thus increases the amplitude of vibration as well as energy consumption. Early fault detection and diagnosis have been widely used with various methods as they were able to reduce accidents and machine breakdowns along with economic losses. This study aims to present the faulty bearings which were seeded in the bearings. The fault size are ranging from 0.007 inches to 0.021 inches in diameter. Among the methods, vibration signal data is one of the champions. In this study, early fault detection was focused on bearing using the time domain technique and the data were analyzed. Particularly, the fault was introduced on the outer raceway at three different positions; orthogonal (3 o’clock), centered (6 o’clock) and opposite (12 o’clock). The MATLAB software was used to determine the time domain parameters, comprising of the standard deviation, Root Mean Square (RMS), skewness and shape factor as the representation of the best reflection of the failure. The time domain parameters for healthy and faulty bearing were plotted and compared in graphical presentation. The result shows all the four parameters have greater value in contrast with the healthy bearing value except for skewness data in the opposite (12 o’clock) position.

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