Electrical Fault Detection in Three Phase Squirrel Cage Induction Motor by Vibration Analysis using MEMS Accelerometer

Maruthi, G. S.; Vittal, K. Panduranga

doi:10.1109/peds.2005.1619804

Cited by 21 publications

(6 citation statements)

References 3 publications

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“…In [28], voltages at several points of the PWM motor drive system are compared, and in [27] the neural network technique is employed for diagnosis of an actuation system. Vibration analysis algorithms are often used to detect faults in bearings, ball screws and gears [29][30][31]. They are based on accelerometer data, which is not normally available for electrical actuators.…”

Section: Health Monitoring and Fault Detectionmentioning

confidence: 99%

Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

Yaun¹

2021

Preprint

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Power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation conditions. Inspired by this observation, in this thesis work, a simple yet effective power efficiency estimation base health monitoring and fault detection technique is proposed for modular and reconfigurable robot with joint torque sensor. The design of the Ryerson modular and reconfigurable robot system is first introduced, which aims to achieve modularity and compactness of the robot modules. Critical components, such as the joint motor, motor driver, harmonic drive, sensors, and joint brake, have been selected according to the requirement. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on the developed modular and reconfigurable robot with joint torque sensing and a distributed control system. Experimental results have demonstrated the effectiveness of the proposed method.

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Section: Health Monitoring and Fault Detectionmentioning

confidence: 99%

Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

Yaun¹

2021

Preprint

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“…The small size of MEMS devices also motivates its use in some monitoring processes such as the Structural Health Monitoring (SHM) [23][24][25][26], the Machine Condition Monitoring [27][28][29], damage detection [30] and for navigation systems such as strap-down Attitude and Heading Reference Systems (AHRS) and Inertial navigation system (INS) [31]. For some examples, in [32] a MEMS accelerometer is designed for SHM applications, where sensing low frequency and low amplitudes with good resolution is essential; in [33], a computer-aided analysis of three bulk micromachined piezoresistive MEMS accelerometer is made for concrete SHM applications, where three accelerometers have different design characteristics.…”

Section: Introductionmentioning

confidence: 99%

MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications

Varanis

Silva

Mereles

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

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In this paper, the use of MEMS accelerometers for measuring mechanical vibrations is presented. Also a wide review of the literature is performed by presenting the uses of the MEMS accelerometers in a great number of applications. These sensors are known for their low prices, low power consumption and low sizes, which enhance their use in applications such as energy harvesters, monitoring processes and for educational purposes. In order to propose these sensors for measuring vibrations, a complete evaluation of the MEMS accelerometers was performed by measuring amplitudes and frequencies of oscillations and comparing their dynamic characteristics with other accelerometers with higher precision. Moreover, two experiments were conducted: In the first one, the measurements of the amplitude given by a MEMS and a standard accelerometer while being excited with sinusoidal waves with different frequencies using a vibration exciter were taken and compared. For the second experiment, three MEMS sensors and a piezoelectric accelerometer were used to measure the accelerations of a 3-DOF shear-building excited by an unbalanced DC motor. The signals obtained were compared in the time and frequency domains; for the last case, the wavelet transform, the wavelet coherence and the power spectrum density were used.

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“…Some researchers have investigated the potentiality of reading vibration signals to diagnose electrical faults [15][16][17]. However, the techniques are invasive since they depend on the signals measured and acquired by accelerometers, which placed at the equipment structures.…”

Section: Introductionmentioning

confidence: 99%

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

Baccarini¹,

Avelar²,

Silva³

et al. 2013

JSEA

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The short circuit is a severe fault that occurs in the stator windings. Therefore, it is very important to diagnose this type of failure in its beginning before it causes unscheduled stop and the machine loss. In this context, the Support Vector Machine (SVM) is a tool of considerable importance for standard classification. From some training data, it can diagnose whether or not there is a short circuit beginning, and which is important for predictive maintenance. This work proposes a technique for early detection of a short circuit between the turns aiming at its implementation in a real plant. The paper shows simulation and experimental results, and validates the proposed technique.

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Electrical Fault Detection in Three Phase Squirrel Cage Induction Motor by Vibration Analysis using MEMS Accelerometer

Cited by 21 publications

References 3 publications

Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot

MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications

Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

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