A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

Isham, M. Firdaus; Leong, M. Salman; Lim, Meng Hee; Zakaria, Muhammad

doi:10.1051/matecconf/201925502017

Cited by 16 publications

(10 citation statements)

References 42 publications

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“…In particular, certain studies have applied decomposing methods, such as Wavelet (Jammazi and Aloui, 2012;Hamid and Shabri, 2017;Uddin et al, 2019), EMD (Hu et al, 2018;Ding, 2018), and VMD (Jianwei et al, 2017;Lahmiri, 2015;He et al, 2018) to decompose the input data series into subsets of data and then they use optimization methods, namely GA, Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) to obtain the optimal parameters of the forecasting methods of ANN and SVM. The recent techniques, namely, EMD is facing mode mixing problems, while the VMD approach has its own problems in setting parameters, leading either to over decomposing the series or to under decomposing the series; this, in turn, implies lower accuracy in the forecasting process (Isham et al, 2019). Though the machine learning methods are efficient in handling nonlinear and nonstationary data, MARSplines methods are capable of finding important input variables for models, such as BPNN, SVM and RF.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

A novel hybrid approach to forecast crude oil futures using intraday data

Manickavasagam

Visalakshmi

Apergis

2020

Technological Forecasting and Social Change

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Prediction of oil prices is an implausible task due to the multifaceted nature of oil markets. This study presents two novel hybrid models to forecast WTI and Brent crude oil prices using combinations of machine learning and nature inspired algorithms. The first approach, MARSplines-IPSO-BPNN, Multivariate Adaptive Regression Splines (MARSPlines) find the important variables that affect crude oil prices. Then, the selected variables are fed into an Improved Particle Swarm Optimization (IPSO) method to obtain the best estimates of the parameters of the Backpropagation Neural Network (BPNN). Once these parameters are obtained, the variables are fed into the BPNN model to generate the required forecasts. The second approach, MARSplines-FPA-BPNN, generates the parameters of BPNN through the Flower Pollination Algorithm (FPA). The forecasting ability of these new models is compared to certain benchmark models. The findings document that the MARSplines-FPA-BPNN model performs better than the other competitive models.

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Section: Machine Learning Modelsmentioning

confidence: 99%

A novel hybrid approach to forecast crude oil futures using intraday data

Manickavasagam

Visalakshmi

Apergis

2020

Technological Forecasting and Social Change

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“…These are the EMD, the HVD, and the VMD. All of them have been successfully applied for fault and damage detection (see e.g., References [ 14 , 15 ]). These three methods are here quantitatively compared.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark

Civera

Surace

2021

Sensors

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Signal Processing is, arguably, the fundamental enabling technology for vibration-based Structural Health Monitoring (SHM), which includes damage detection and more advanced tasks. However, the investigation of real-life vibration measurements is quite compelling. For a better understanding of its dynamic behaviour, a multi-degree-of-freedom system should be efficiently decomposed into its independent components. However, the target structure may be affected by (damage-related or not) nonlinearities, which appear as noise-like distortions in its vibrational response. This response can be nonstationary as well and thus requires a time-frequency analysis. Adaptive mode decomposition methods are the most apt strategy under these circumstances. Here, a shortlist of three well-established algorithms has been selected for an in-depth analysis. These signal decomposition approaches—namely, the Empirical Mode Decomposition (EMD), the Hilbert Vibration Decomposition (HVD), and the Variational Mode Decomposition (VMD)—are deemed to be the most representative ones because of their extensive use and favourable reception from the research community. The main aspects and properties of these data-adaptive methods, as well as their advantages, limitations, and drawbacks, are discussed and compared. Then, the potentialities of the three algorithms are assessed firstly on a numerical case study and then on a well-known experimental benchmark, including nonlinear cases and nonstationary signals.

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“…Further, it is not only different from the existing recursive filtering patterns of EMD and LMD but also solves variational problems based on classical Wiener filtering, frequency mixing, and Hilbert transforms. Because of these advantages, it has been used in for machinery diagnosis, image processing, speech recognition, pipeline monitoring, oil price forecasting, etc 19 . Feng et al 20 proposed a method to provide stationary component signals from a vibration signal at constant speed via demodulation analysis based on VMD, improving the accuracy of planetary gearbox fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

Sharma

2020

Wind Energy

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Structural compactness with multiple meshing points excites multiple vibrations in a planetary gearbox of a wind turbine operating at varying speeds. The nonstationary, multicomponent vibration signals result in complex modulations challenging the effectiveness of a signal processing technique‐based fault diagnosis method. This paper aims to detect gear tooth faults under varying speed conditions based on the instantaneous frequency (IF) estimate of the decomposed component. Vibration signals were decomposed using variational mode decomposition (VMD), which is capable of frequency demodulation by exhibiting mono‐oscillatory components, followed by the IF estimation to select the most sensitive decomposed mode. The selected sensitive mode was further analyzed to deduce envelope spectrums for the extraction of frequency components highlighting fault symptoms. Furthermore, permutation entropy (PE) was evaluated to characterize the presence of randomness due to fault severity and to validate the proposed signal processing approach. In this paper, the vibration signals simulating planet gear faults were analyzed using the proposed signal processing approach. Symptoms of planet tooth faults were analyzed using the experimental investigation under real‐time varying speed to validate and exhibit the effectiveness of the proposed fault diagnosis approach for a planetary gearbox.

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A Review on Variational Mode Decomposition for Rotating Machinery Diagnosis

Cited by 16 publications

References 42 publications

A novel hybrid approach to forecast crude oil futures using intraday data

A novel hybrid approach to forecast crude oil futures using intraday data

A Comparative Analysis of Signal Decomposition Techniques for Structural Health Monitoring on an Experimental Benchmark

Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

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