Condition Monitoring of BLDC Motor Using Short Time Fourier Transform

Vippala, Shanmukha Reddy; Bhat, Sunil; Reddy, Agarala Ajaysekhar

doi:10.1109/cmi50323.2021.9362938

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Unlike the FFT, the frequency-time characteristic is obtained. Unfortunately, the STFT has a significant disadvantage, which is related to the Heisenberg's principle [56,57]. This principle is based on two characteristics (momentum and position) of a point in an area that cannot be found with the same accuracy.…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Autsou¹,

Kudelina²,

Rassõlkin³

et al. 2022

PEAS

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The paper discusses problems that occur in robotic arm control. The specific problems arise due to the wear of various types of gears (in the presented case, belt gear and worm gear). It is important to note that such errors need to be diagnosed in time, and the way of their elimination has to be determined, which should be resource-intensive and cost-effective. This article describes the basic types of robotic manipulators (robotic arm, telpher and Cartesian), presenting a review and study of the possibilities of errors in the movement of a robot, adjustment of a mechanical system, and determination of a strategy for solving the emerging problems. A comparison between various types of gear faults is also provided. Different ways of diagnosing faults are discussed, based on the advantages and disadvantages of the methods. The main objective of this study is to provide a complete overview of the mechanical areas where disturbances occur, their diagnosing, and methods of their elimination.

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Section: Short-time Fourier Transformmentioning

confidence: 99%

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Autsou¹,

Kudelina²,

Rassõlkin³

et al. 2022

PEAS

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“…In [10], an approach named as complete ensemble empirical mode decomposition with adaptive noise is proposed to extract distinct intrinsic mode functions of current signal; the most discriminant among them are used to detect bearing faults. In [11], STFT is adopted to obtain spectrogram of stator current and identify BLDC motor defects. Motor current analysis is also proposed to detect mechanical and electrical faults in induction motors [12].…”

Section: Literature Reviewmentioning

confidence: 99%

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

Kafeel¹,

Aziz

Awais

et al. 2021

Sensors

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Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine’s health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.

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“…In the field of WT, several approaches exist for different assets. CM of brushless DC motors is investigated based on energies for characteristic frequencies based on both STFT and WT in [17]. A decomposition rate is used in [18] for CM of electric drives based on WT.…”

Section: Time-frequency-based Health Indicatorsmentioning

confidence: 99%

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Nentwich

Reinhart

2021

Robotics

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Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.

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Condition Monitoring of BLDC Motor Using Short Time Fourier Transform

Cited by 5 publications

References 7 publications

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

An Expert System for Rotating Machine Fault Detection Using Vibration Signal Analysis

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

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