A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Miao, Feng; Zhao, Rongzhen

doi:10.1155/2020/6576915

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…Spectrum analysis is essential for a reliable maintenance program and should never be absent from a Condition Monitoring Program. Preventing more costly damage to the airplane engine can be done by using trend monitoring (20).…”

Section: Trend Analysis In Condition Monitoring Of Process Equipment'smentioning

confidence: 99%

A Review on Development and Trend of Condition Monitoring and Fault Diagnosis

Chavan¹,

Kale²,

Deshmukh³

et al. 2022

ECS Trans.

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Machines are employed in a wide range of industrial applications. All machines are prone to fatigue, wear, deformation, and other effects while in operation. As a result of these effects, the clearance between mating parts increases, the rotor becomes unbalanced, and the shafts become misaligned, all of which contribute to an increase in vibration. Vibration levels rise, eventually leading to the machine's malfunction or breakdown. The machine's performance and quality have a direct impact on its life and dependability. Equipment condition monitoring and fault diagnostics are standard procedures used by processing and manufacturing facilities to monitor machine availability, performance, and dependability. Monitoring machine vibration for fault identification has been demonstrated to be the most effective way for machinery condition monitoring. Fault diagnosis sets the stage for fault tolerance, dependability, and security, all of which are critical design elements in complex engineering systems. This paper presents an overview of recent advancements and progress in the philosophy and concepts of condition monitoring, diagnostics, and failure detection. It's critical to have a good bearing condition monitoring and fault detection system in place so that developing bearing defects may be properly identified and analyzed before they cause further damage to the machine. Because higher signal levels are observed in fault cases, vibration analysis provides a suitable foundation for condition monitoring.

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Section: Trend Analysis In Condition Monitoring Of Process Equipment'smentioning

confidence: 99%

A Review on Development and Trend of Condition Monitoring and Fault Diagnosis

Chavan¹,

Kale²,

Deshmukh³

et al. 2022

ECS Trans.

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“…The denoising technique needs to be used to differentiate gearbox vibration signatures from polluted vibration signatures [9]. To de-noise the polluted vibration signatures, a variety of de-noising algorithms were used, including residual Convolution Neural Networks (ResNet) [10], wavelet threshold (WT) denoising [11], correlation analysis (CA), variational mode decomposition (VMD) [12] and the wavelet transform as a basis, denoising [13]. When the characteristic rotational frequency, such as gear mesh frequency or shaft rotational frequency, is less than 100 Hz, the wavelet denoising technique by Mishra et al [14] is appropriate.…”

Section: Introductionmentioning

confidence: 99%

Defect Categorization of Ribbon Blender Worm Gearbox Worm Wheel and Bearing Based on Artificial Neural Network

Barshikar,

Ghongade,

Bhadre

et al. 2024

Eksploatacja i Niezawodność – Maintenance and Reliability

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There is a demand for worm gearboxes in diversified industrial fields that include machinery such as escalators, ribbon blenders, pulverisers, bowl mills, etc. because of their peculiar characteristics like torque and quick retardation. The most commonly occurring defects in a worm gear box are scratches that develop in the worm gear and in bearings. Early defect categorization is required to prevent a sudden breakdown that would decrease production. The defect is depicted in different cases, which include defects in the gear tooth and the outer and inner races of the bearing. In another case, the defect is considered in the gear tooth as well as the bearing. The severity is designated using the ANN. The experiments were performed under these conditions with a good worm gearbox to capture vibration response signatures. Using these values as an input to the ANN, the model is trained. Experimental results show that vibration amplitude increases with fault progression in the worm gearbox, and the trained ANN model effectively categorizes worm gearbox faults with an accuracy of 97.12%.

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“…BSS plays an increasingly important role in the field of digital signal processing and has been widely used in communication [27], speech processing [28], fault diagnosis [29,30], seismic exploration [31], biomedicine [32,33], image processing [34], radar [35], and economic data analysis [36]. In blind signal separation, the typical algorithms commonly used include the fast fixed-point algorithm [37], natural gradient algorithm [38], Equivariant Adaptive Separation via Independence (EASI) algorithm [39,40], and Joint Approximation Diagonalization of Eigen-matrices (JADE) algorithm [41,42], etc.…”

Section: Introductionmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

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A New Fault Diagnosis Method for Rotating Machinery Based on SCA-FastICA

Cited by 15 publications

References 27 publications

A Review on Development and Trend of Condition Monitoring and Fault Diagnosis

A Review on Development and Trend of Condition Monitoring and Fault Diagnosis

Defect Categorization of Ribbon Blender Worm Gearbox Worm Wheel and Bearing Based on Artificial Neural Network

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

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