Experimental investigation on misalignment fault detection in induction motors using current and vibration signature analysis

Kumar, Chandan; Krishnan, Geeta; Sarangi, Somnath

doi:10.1109/ablaze.2015.7154971

Cited by 20 publications

(9 citation statements)

References 11 publications

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“…Induction motors which involve industrial processes typically experience misalignment due to various factors such as improper assembly of machines, thermal distortion of machines, asymmetry in applied loads, unequal settlement of the foundation, etc. [17]. For some reason, ultimately, there is an incorrect positioning along the shaft centreline between bearings when two machines are coupled.…”

Section: Misalignmentmentioning

confidence: 99%

Predictive and Standalone Fault Diagnosis System for Induction Motors

Jayasinghe

Ahangama

Dharmasiri³

et al. 2021

Engineer

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Sudden faults created in induction motors result in catastrophic failures and loss of production. Therefore, the industry is in need of a predictive based system that can identify developing faults in advance. Condition monitoring is used as the general method of identifying faults and taking measures before the dreadful situation. However, there is limited work done on the predictive methodologies based on the trend analysis. The study presented in this paper proposes a novel method that identifies trend variation of critical harmonics of the vibration spectrum with increasing fault severity for frequent mechanical faults; structural looseness, misalignment, bearing eccentricity and bearing inner race fault. Faults were artificially induced on a three-phase induction motor and vibration data obtained was analysed with a MATLAB based algorithm.

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Section: Misalignmentmentioning

confidence: 99%

Predictive and Standalone Fault Diagnosis System for Induction Motors

Jayasinghe

Ahangama

Dharmasiri³

et al. 2021

Engineer

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“…This causes heating on the ball bearings and therefore on the engine surface. As a result of these warm-ups and wears, motor noise problems and coupling problems due to heat can be seen [6,21].…”

Section: Misalignmentmentioning

confidence: 99%

“…Carrier occurs depending on frequency. This carrier frequency can be the gear clutch frequency, the ball bearing frequency multiples, the resonance frequency of the machine or the structure, or the resonance frequency carrier frequency of the acceleration metric [16,21].…”

Section: Side Band Formationmentioning

confidence: 99%

Misalignment fault detection by wavelet analysis of vibration signals

Yılmaz

Aksoy

Kesilmiş

2019

International Advanced Researches and Engineering Journal

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Article history:Asynchronous motors are frequently used in many industrial applications, especially pumps and fans. Placement, bearing and coupling faults are common faults in these types of engines. Misalignment error is a common type of error that is seen very often among these errors. This error may cause efficiency decrease in a short run and vibration may cause short circuit and wear in moving parts in the stator windings in a long run. Early diagnosis of such faults is important in terms of machine health and productivity. In this study, loose connection and angular imbalance of the asynchronous machine were investigated. In the experimental works, a 1 Phase 0.75 KW power asynchronous motor, Y-0036-024A Electromagnetic Brake and SKF Microlog vibration meter were used during the measurements. The Frequency components of motor caused by the settlement errors were investigated under the different loads. A loose assembly error and angular imbalance were investigated from the misalignment errors. The engine was run idle and without any positioning errors and measurements were taken from different points with the accelerometer and the frequency spectrum examined. Measurements are repeated when the misalignment errors are occurred on purpose and the FFT frequency components were compared under the load of 12.50Nm using magnetic brake. The results show that the FFT frequency components are examined and the placement error can be determined with high success and accuracy. It has been found that harmonic components are formed in the frequency spectrum at 25Hz Coefficients. After the settlement error is generated it is seen that, undesired frequency components that are unloaded are lowered under load when the frequency spectra is examined. In this study, theoretical and experimental comparisons of settlement errors are made. Although many errors in this subject are examined in the same publication in general, only the results of the settlement errors are examined specifically as a contribution to the literature. The results and graphs are presented comparatively to the reader's knowledge. © 2019, Advanced Researches and Engineering Journal (IAREJ) and the Author(s).

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“…In addition, ref. [6] compared the MCSA approach with vibration analysis with good results for the analysis of single components within the spectra. This knowledge is sufficient for many applications, but MCSA fails in the case of varying load, as examined in [7].…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Discovery Process Extended to Experimental Data for the Identification of Motor Misalignment Patterns

Bold

Urschel

2023

Machines

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The diagnosis of misalignment plays a crucial role in the area of maintenance and repair since misalignment can lead to expensive downtime. To address this issue, several solutions have been developed, and both offline and online approaches are available. However, online strategies using a small number of sensors show a higher false positive rate than other approaches. The problem is a lack of knowledge regarding the interrelations of a fault, disturbances during the diagnosis process, and capable features and feature vectors. Knowledge discovery in database is a framework that allows extracting the missing knowledge. For technical systems, optimal results were achieved by aligning (partially) automated experiments with a data mining strategy, in this case classification. The results yield a greater understanding of the interrelations regarding parallel misalignment, i.e., feature vectors that show good results also with varying load and realistic fault levels. Moreover, the test data confirm a specificity (range 0 to 1) for classification between 0.87 and 1 with the found feature vectors. For angular misalignment, potential vectors were identified, but these need further validation with a modified experiment in future work. For the study, two induction motors with 1.1 kW and 7.5 kW were considered. Furthermore, the findings were compared with additional motors of the same rated power. The findings of this work can help to improve the implementation of sensorless diagnostics on machines and advance the research in this field.

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Experimental investigation on misalignment fault detection in induction motors using current and vibration signature analysis

Cited by 20 publications

References 11 publications

Predictive and Standalone Fault Diagnosis System for Induction Motors

Predictive and Standalone Fault Diagnosis System for Induction Motors

Misalignment fault detection by wavelet analysis of vibration signals

A Knowledge Discovery Process Extended to Experimental Data for the Identification of Motor Misalignment Patterns

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