Vibration-Based Detection and Classification of Mechanical Defects in Induction Motor-Driven Systems During the Starting Transient

Vibration analysis is considered the most common and effective means of detecting mechanical faults such as imbalance, misalignment, and looseness in induction motor driven systems. Most mechanical problems result in an increase in vibration at multiples of the rotor speed frequency (1x) making it difficult to discern the source of vibration. In case of a fault alarm, the maintenance engineer usually performs a walk-around test to identify the source of vibration for planning maintenance, and therefore, is exposed to safety risks. In this paper, a new remote and automated test method for identifying the source of mechanical vibration during the starting transient of induction motors is proposed. The level and speed-dependency of vibration during rotor acceleration are used for identifying imbalance from other mechanical defects that produce 1x vibration. Test results on a 380 V, 5.5 kW induction motor under mechanical defects are given for verification. It is shown that the proposed method can provide automated identification of the source of vibration enabling maintenance to be performed in a safe, low cost, and efficient manner. The data acquired and analyzed for the testing are described and shared through this paper.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Industry Applications
Published
2025-03-26
DOI
https://doi.org/10.1109/tia.2025.3554469
Citations
3
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
3.64
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Vibration-Based Detection and Classification of Mechanical Defects in Induction Motor-Driven Systems During the Starting Transient

Muhammad Faizan Shaikh, Müslüm Arkan, Sang Bin Lee, Taner Göktaş et al.
3 citations
IEEE Transactions on Industry Applications
Machine Fault Diagnosis Techniques
3.64
article

Vibration-Based Detection and Classification of Mechanical Defects in Induction Motor-Driven Systems During the Starting Transient

Muhammad Faizan Shaikh, Müslüm Arkan, Sang Bin Lee, Taner Göktaş, Byambasuren Battulga
article en
3 citations

Abstract

Vibration analysis is considered the most common and effective means of detecting mechanical faults such as imbalance, misalignment, and looseness in induction motor driven systems. Most mechanical problems result in an increase in vibration at multiples of the rotor speed frequency (1x) making it difficult to discern the source of vibration. In case of a fault alarm, the maintenance engineer usually performs a walk-around test to identify the source of vibration for planning maintenance, and therefore, is exposed to safety risks. In this paper, a new remote and automated test method for identifying the source of mechanical vibration during the starting transient of induction motors is proposed. The level and speed-dependency of vibration during rotor acceleration are used for identifying imbalance from other mechanical defects that produce 1x vibration. Test results on a 380 V, 5.5 kW induction motor under mechanical defects are given for verification. It is shown that the proposed method can provide automated identification of the source of vibration enabling maintenance to be performed in a safe, low cost, and efficient manner. The data acquired and analyzed for the testing are described and shared through this paper.

IEEE Transactions on Industry ApplicationsVol. 61(5)
University of Seoul (KR), Dokuz Eylül University (TR), Korea University (KR), Inonu University (TR)
Affordable and clean energy
Openalex Percentile: Top 8%
Machine Fault Diagnosis Techniques
3.64
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.