Minimum Distance-Based Detection of Incipient Induction Motor Faults Using Rayleigh Quotient Spectrum of Conditioned Vibration Signal

Samanta, Arunkumar; Routray, Aurobinda; Khare, Swanand; Naha, Arunava

doi:10.1109/tim.2020.3047433

Cited by 22 publications

(8 citation statements)

References 49 publications

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“…Piston pumps operate under adverse environments and can operate without any discernible change in performance under incipient fault conditions. However, with continual operation, the fault condition deteriorates and can cause major disruption and downtime [1]. Any downtime on these machines incurs huge losses to the customers.…”

Section: Objectives and Scopementioning

confidence: 99%

Predictive Maintenance for Axial Piston Pumps: A Novel Method for Real-Time Health Monitoring and Remaining Useful Life Estimation

Samanta,

Kulkarni,

Hande

2024

14th International Fluid Power Conference

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In this paper, a novel method to estimate and predict the condition of an open circuit piston pump is presented. We introduce the concept of the 'Pump health index' which can assess the health of the pump in real time and use it to estimate the remaining useful life of the pump. The solution is agnostic to pump size, make, and application duty cycle. The solution has been tested with different levels of degradation that were simulated on the physical pump. The algorithms were implemented on multiple embedded platforms to illustrate the agnostic nature of the developed technology.

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Section: Objectives and Scopementioning

confidence: 99%

Predictive Maintenance for Axial Piston Pumps: A Novel Method for Real-Time Health Monitoring and Remaining Useful Life Estimation

Samanta,

Kulkarni,

Hande

2024

14th International Fluid Power Conference

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“…The first effect is bearing fault introduces change in airgap length in the order of few micrometers [20] which depends on clearance between the balls and raceway. The airgap length of the motor with bearing fault is given as in (6) where g0 is the airgap length of the motor without the fault, α is the degree of eccentricity introduced by the fault, θ is angular position of rotor, ϕ(t) is the angle which the defect moved, δ is the dirac delta function and fv is the characteristic frequency of the fault. This periodical change in airgap length causes the permeance of the airgap to vary which leads to change in airgap flux density.…”

Section: Proposed Stray Magnetic Flux Based Bearing Fault Signaturesmentioning

confidence: 99%

“…Bearing fault detection through vibration [2]- [6] and motor current signature analysis (MCSA) [6]- [11], [19] are the most extensively studied methods. Though accelerometer is one of the best candidates for mechanical faults, it is expensive and affected by environment.…”

Section: Introductionmentioning

confidence: 99%

Effect of Magnets Asymmetry on Stray Magnetic Flux Based Bearing Damage Detection in PMSM

Gurusamy¹,

Baruti²,

Zafarani³

et al. 2021

IEEE Access

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In this paper, it is shown that stray magnetic flux is a highly reliable condition monitoring variable which is rich in motor health related information and it is superior to motor current. It is wellknown that the magnetic field distribution in permanent magnet (PM) rotors exhibit asymmetry due to magnet manufacturing process. It is shown that, in addition to the clear characteristic bearing signatures, the magnet imperfections induce multiple new bearing fault signatures in the stray magnetic flux. Different than current, the signatures in stray flux are more immune to noise, consistent and independent of load and speed. It is also shown that the stray flux-based detection method can locate the faulty bearing. To justify the proposed approach, comparative simulations and experiments are carried out on a surface mounted PM synchronous motor (PMSM) and an interior permanent magnet motor (IPM) with outer raceway bearing fault and different degree of magnet field asymmetry.

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“…These machines play a pivotal role in various applications, such as industrial plants, electric vehicles, renewable energy, agriculture, building infrastructures, etc., contributing significantly to advancing technology and improving overall well-being. As essential components in numerous systems, monitoring vibration signals in electric motors is necessary for understanding machine characteristics and conditions, with a particular emphasis on early-stage fault detection [1][2][3][4]. The vibration of motor application in the site plant can be used to identify potential problems with continuous condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks

Jati,

Luthfi,

Yao

et al. 2024

Applied Sciences

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This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques.

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Minimum Distance-Based Detection of Incipient Induction Motor Faults Using Rayleigh Quotient Spectrum of Conditioned Vibration Signal

Cited by 22 publications

References 49 publications

Predictive Maintenance for Axial Piston Pumps: A Novel Method for Real-Time Health Monitoring and Remaining Useful Life Estimation

Predictive Maintenance for Axial Piston Pumps: A Novel Method for Real-Time Health Monitoring and Remaining Useful Life Estimation

Effect of Magnets Asymmetry on Stray Magnetic Flux Based Bearing Damage Detection in PMSM

An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks

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