IJERT-Vibration and Acoustic Emission Signal Monitoring for Detection of Induction Motor Bearing Fault (original) (raw)

Bearing Fault Detection in Single Phase Induction Motor using Sound Signal Analysis

International journal of engineering research and technology, 2018

Induction motors are one of the most commonly used electrical machines in industry because of various technical and economic reasons. The bearings are common elements of induction machine. The performance of bearing is influential on the performance of induction machine. The presence of bearing defects causes, a reduction in the efficiency of the machine or severe damage in induction machine. Therefore, this diagnosis is an intensively investigated field of research. Recently, many research activities were focused on the diagnosis of bearing faults using current signals and vibration signals. In this paper, the sound of electric motors is analyzed in order to obtain information for the detection of faults. Significant sound spectrum differences between healthy motor and motors with bearing faults are observed. The signal processing techniques that are being currently used for the analysis of sound signals of different induction motors are also investigated in this paper.

Methodology for fault detection in induction motors via sound and vibration signals

Mechanical Systems and Signal Processing, 2017

Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. There are currently a variety of methodologies for fault diagnosis. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. The faults diagnosed in this work are two broken rotor bars, mechanical unbalance and bearing defects.

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Advances in Mechanical Engineering, 2021

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and...

Bearing Fault Detection in Induction Motor Using Time Domain Analysis

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2014

Approximately half of all operating costs in most processing and manufacturing operations can be attributed to maintenance. This is ample motivation for studying induction motor faults diagnosis and monitoring techniques that can reduce the maintenance costs. In the present scenario every industry need condition monitoring system to avoid unwanted faults in the process components. Vibration condition monitoring technique is widely used for fault detection. In this paper we analysed acceleration documented data of a 2 HP electric motor for bearing fault detection, for this we have use time domain analysis technique.

Vibration Analysis for Bearing Faults of Three Phase Induction Motor Using Time-Frequency Domain

2020

Induction motors plays a major role in almost all the industrial drive systems because of their simple, efficient and robust nature offering high degree of reliability. Detection and diagnosis of faults while the system is running can help to reduce all kind of losses because it causes decrease in production, loss in valuable time and above all repairing cost. Rolling element bearings are interpretative components in induction motors and monitoring their condition is important to avoid failures. In this paper fault detection in induction motor is done using vibration analysis.Any fault present in the rolling bearing element will generate a mechanical impulse of higher amplitude as compared with the healthy bearing element. If the amplitude of the vibrations reaches to a certain level the fault can be detected and identified. In this paper, the signals are analysed using Fourier transformations that transform time domain signals into frequency domain. This work proposes the use of ti...

A Novel Approach for Bearing Fault Detection and Classification using Acoustic Emission Technique

Ball bearings are one of the most important components in the machine involving the rotary motion. Ball bearing failure can cause a significant amount of maintenance cost and serious safety problems. Hence detection of the fault at its early stage is important. In this paper, a novel approach for fault detection of ball bearing by combining time, frequency and time-frequency domains are used.Two layer multiclass svm is used for the classification of fault into an outer race fault, inner race fault, ball fault and healthy bearing. A comparison between Envelope Wavelet Packet Transform, Wavelet Packet Transform and proposed method is carried out. The experimental observation shows that the proposed method is able to detect the faulty condition with high accuracy.

Bearing Fault Detection in Induction Motor Using Fast Fourier Transform

2013

ABSTACT: In the present scenario every industry need Condition Based Monitoring System to avoid unwanted faults in the process components. Vibration condition monitoring technique is widely used for fault detection. Vibration monitoring is the most reliable method of assessing the overall health of a motor system. In this paper we work on 2 Hp inductions motor. Ball bearing fault is widely occurred fault in induction motor. We collect vibration signal of induction motor with and without ball bearing fault. Characteristic frequencies of ball bearing are observed and calculated for acquired vibration signal.

Induction machine bearing fault diagnosis based on the axial vibration analytic signal

International Journal of Hydrogen Energy, 2016

This paper deals with a new induction motor defects diagnosis using the Axial Vibration Analytical Signal (AVAS). The signal is generated by a bearing-defected induction machine. The calculation method may be divided into two main parts; the former is the Hilbert transform that consists in the first part normalization of the axial vibration and its comparison with the AVAS module. The second part consists in the extraction of feature vectors using the Signal Class Dependent Time Frequency Representation ðTFR SCD Þ based on the Fisher contrast design of the non parametrically kernel. The Particle Swarm Optimization (PSO) is used to optimize the feature vectors size. The vibration severity caused by the bearing fault is investigated for different loads. This last decreases with the increasing level of the load. The obtained results are experimentally validated on a 5500 W induction motor test bench.