An efficient acoustic-based diagnosis of inter-turn fault in interior mount LSPMSM

Maraaba, Luqman S.; Memon, Azhar M.; Abido, M. A.; Alhems, Luai M.

doi:10.1016/j.apacoust.2020.107661

Cited by 7 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fourier transform is the basis of energy spectrum and power spectrum. The energy spectrum of original signal is defined as shown in equation (7):…”

Section: Principle Theorymentioning

confidence: 99%

“…And the displacement entropy method improved the denoising ability. Maraaba et al [7] combined singular spectrum analysis and fast Fourier transform to extract fault information from acoustic signals and diagnosed fault severity. Because of the lack of feature extraction ability of quaternion singular spectrum analysis, Ma et al [131] proposed a symplectic quaternion singular mode decomposition, which enhanced the feature of gearbox signal.…”

Section: Improvement and Developmentmentioning

confidence: 99%

“…Mechanical equipment fault diagnosis includes the diagnosis of a variety of machinery or parts. The more common ones include rolling bearings [1], gear boxes [2], diesel engines [3], induction motors [4], circuit breakers [5], pumps [6], stators [7], rotors [8], etc. During the operation of mechanical equipment, the failure process may be accelerated due to poor lubrication, rust, corrosion, improper installation, and aging of the motor.…”

Section: Introductionmentioning

confidence: 99%

“…As early as 1822, Fourier transform was proposed by French scholar Fourier, which is different from the previous time-domain analysis method, but the analysis process is completed in the frequency domain. Later, many spectrum analysis techniques were proposed by scholars, and mechanical fault diagnosis methods based on spectrum [18], cepstrum [19], energy spectrum [20], power spectrum [21], higher-order spectrum [22], Hilbert spectrum [23], marginal spectrum [24], envelope spectrum [25], singular spectrum [7], cyclic spectrum [26], full spectrum [27] and so on were also widely used. Although each spectrum analysis technology has some shortcomings, for different mechanical equipment or parts, a spectrum analysis technology is always suitable for achieving the goal of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Review of spectrum analysis in fault diagnosis for mechanical equipment

Wang,

Sun

2023

Eng. Res. Express

View full text Add to dashboard Cite

Various mechanical equipment play a crucial role, and their health or status may affect efficiency and safety seriously. Spectrum analysis of the corresponding signal has been widely used to diagnose the fault in the past decades. The diagnosis method based on spectrum analysis technology covers almost all aspects of mechanical fault diagnosis. However, there is a lack of review of diagnostic methods of spectrum analysis technologies in the field of mechanical equipment fault diagnosis. In order to fill this gap, this paper reviews the spectrum analysis technology in mechanical equipment diagnosis in detail. First of all, in order to let the researchers who are in contact with spectrum analysis technology for the first time quickly understand this field, the principles of spectrum are systematically sorted out, including spectrum, cepstrum, energy spectrum, power spectrum, higher-order spectrum, Hilbert spectrum, marginal spectrum, envelope spectrum, singular spectrum and so on. Furthermore, the characteristics of corresponding spectrum analysis technologies are summarized, and their advantages and disadvantages are analyzed and compared. High-quality references in recent ten years are cited for illustration to enhance persuasiveness. Finally, the prospect of spectrum analysis technology is summarized, and the future development trend of spectrum analysis technology is pointed out. It is believed that the joint diagnosis of fault severity, variable speed fault diagnosis, combined with deep learning and multiple spectrum analysis technologies should be given more attention in the future. This paper is expected to provide a comprehensive overview of mechanical fault diagnosis based on spectrum analysis theory, and help to develop corresponding spectrum analysis technologies in practical engineering.

show abstract

“…Fourier transform is the basis of energy spectrum and power spectrum. The energy spectrum of original signal is defined as shown in equation (7):…”

Section: Principle Theorymentioning

confidence: 99%

Section: Improvement and Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Review of spectrum analysis in fault diagnosis for mechanical equipment

Wang,

Sun

2023

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…A K-Nearest Neighbors classifier was used to classify obtained vectors for single-phase IMs (Glowacz and Glowacz, 2017). The traditional fast Fourier transform method has also been applied to diagnose inter-turn faults (Maraaba et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Comparison of acoustic signal–based fault detection of mechanical faults in induction motors using image classification models

Boztas

2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This study presents a novel deep neural network–based method for fault detection in induction motors. The focus was on identifying five types of mechanical cases: normal operation, shaft/load breakage, misalignment, mounting bolt looseness, and cooling fan problems. To increase the realism of the results, a laboratory-collected dataset of stereo microphone recordings was augmented with real factory noise. The audio data was transformed into image data using Mel-frequency cepstral coefficients as the feature extraction method and then processed with image-based classifiers. A comparison was made among 12 different networks in terms of accuracy and number of parameters, revealing that Mobilenet_v2, EfficientNetV2B0, and NASNetMobile had the best performance in terms of both network size and accuracy.

show abstract

Thermographic fault diagnosis of electrical faults of commutator and induction motors

Głowacz

2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

An efficient acoustic-based diagnosis of inter-turn fault in interior mount LSPMSM

Cited by 7 publications

References 14 publications

Review of spectrum analysis in fault diagnosis for mechanical equipment

Review of spectrum analysis in fault diagnosis for mechanical equipment

Comparison of acoustic signal–based fault detection of mechanical faults in induction motors using image classification models

Thermographic fault diagnosis of electrical faults of commutator and induction motors

Contact Info

Product

Resources

About