Empirical Fourier decomposition: An accurate signal decomposition method for nonlinear and non-stationary time series analysis

Zhou, Wei; Zhang, Feng; Xu, Yongfeng; Wang, Xiongjiang; Lv, Hao

doi:10.1016/j.ymssp.2021.108155

Cited by 68 publications

(34 citation statements)

References 31 publications

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“…16a-d and f. These results indicate that five frequency bands (4,800,3,200), (5,600, 1,600), (4,800, 1,067), (5,067,533) and (5,333,2,133) contain repetitive impulse features caused by bearing outer race fault. Because the frequency bands (5,600, 1,600), (4,800, 1,067) and (5,067, 533) have relatively narrow bandwidths, the SESs of their corresponding filtered signals exhibit better fault diagnosis effects.…”

Section: A Bearing Outer Race Faultmentioning

confidence: 76%

“…Thus, fault diagnosis of rolling bearings in key transportation, energy and chemical equipment is of great significance to ensure operational safety and improve economic benefits. Up to now, many signal processing-based methods have been developed for fault diagnosis of rolling bearings, such as blind deconvolution [2], signal decomposition [3], envelope analysis [4], stochastic resonance [5,6], morphological filtering [7], cyclic spectral analysis [8,9] and modulation signal bispectrum [10]. Among these methods, envelope analysis is one of the most commonly used methods for fault diagnosis of rolling bearings and other rotating mechanical components.…”

Section: Introductionmentioning

confidence: 99%

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IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Chen

Dong-li

Cheng

et al. 2022

JDMD

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The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure. Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings. In this paper, based on the ratio of quasi-arithmetic means and Gini index, improved Gini indices (IGIs) are proposed to quantify the transient impulse features of a signal, and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures. Furthermore, an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings. In the new method, an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously, and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features. The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram. The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.

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Section: A Bearing Outer Race Faultmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Chen

Dong-li

Cheng

et al. 2022

JDMD

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“…A recently proposed accurate adaptive signal decomposition method, termed Empirical Fourier decomposition (EFD), can overcome several shortcomings of its preceding algorithms [ 275 ]. However, future work needs to be dedicated to exploring the application of this method in damage detection of different structures, such as composite structures.…”

Section: Advanced Hybrid Vibration Methodsmentioning

confidence: 99%

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Hassani

Mousavi

Gandomi

2021

Sensors

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This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

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“…Signal-processing algorithms applicable to real-world engineering solutions are a hotspot of data science research [ 9 , 10 ]. Although the related research is in the development stage, due to the aforementioned reasons, there is much scientific literature on this common research direction in the sub-sections of transportation [ 11 , 12 ] and computer science [ 13 , 14 , 15 ].…”

Section: Review Of Recent Advancementsmentioning

confidence: 99%

“…Their applicability and effectiveness in solving specific monitoring and local decision support tasks are astonishing [ 55 ] compared to conventional methods. Most of these systems rely on simple sensory signal filtering methods [ 10 ], while others use AI-based solutions [ 21 , 56 ], deep learning [ 57 ], and other machine learning methods [ 58 ] to filter noises in the raw data [ 59 ], classifying events [ 60 ], detecting patterns [ 61 ], eliminating cyber threats in communication networks [ 60 , 62 , 63 ], compressing the data [ 64 ], etc.…”

Section: Review Of Recent Advancementsmentioning

confidence: 99%

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

Jakovlev

Eglynas

Vozňák

et al. 2022

Sensors

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Due to the mechanical nature of container handling operations, as well as natural factors, container and handling infrastructure suffers various types of damage during use, especially within the tight and enclosed environments of a ship’s hull. In this operational environment, it is critical to detect any sort of physical impacts between the vertical cell guides of the ship’s hull and the container. Currently, an inspection of impacts and evaluation of any consequences is performed manually, via visual inspection processes. This process is time-consuming and relies on the technical expertise of the personnel involved. In this paper, we propose a five-step impact-detection methodology (IDM), intended to detect only the most significant impact events based on acceleration data. We conducted real measurements in a container terminal using a sensory device placed on the spreader of the quay crane. The proposed solution identified an average of 12.8 container impacts with the vertical cell guides during common handling operations. In addition, the results indicate that the presented IDM can be used to recognize repeated impacts in the same space of each bay of the ship, and can be used as a decision support tool for predictive maintenance systems.

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Empirical Fourier decomposition: An accurate signal decomposition method for nonlinear and non-stationary time series analysis

Cited by 68 publications

References 31 publications

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Detecting Shipping Container Impacts with Vertical Cell Guides inside Container Ships during Handling Operations

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