Wavelet Denoising for the Vibration Signals of Wind Turbines Based on Variational Mode Decomposition and Multiscale Permutation Entropy

Chen, Xuejun; Yang, Yongming; Cui, Zhixin; Shen, Jun

doi:10.1109/access.2020.2975875

Cited by 37 publications

(37 citation statements)

References 31 publications

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“…This has essentially prompted the need for several signal processing techniques for reliable feature extraction. Particularly, choosing a safe threshold for signal de-noising depends on the targeted system's dynamics, engineer/analyst's level of expertise, and/or familiarity in the domain; nevertheless, by separating useful signals from background noise, a more reliable health assessment for accurate prognostics can be achieved [17], [18].…”

Section: A Signal De-noisingmentioning

confidence: 99%

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Akpudo

Hur

2021

IEEE Access

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The remaining useful life (RUL) prediction of industrial cyber-physical system components demands the use of reliable prognostics parameters and frameworks. Against the traditional use of a single measure of degradation, data from multiple sensors provide abundant characteristic information for modeling, assessing, and extracting useful parameters via appropriate signal processing and sensor fusion methods. This study introduces a multi-sensor prognostics approach which merges highly prognosible statistical features from vibrational and pressure sensor measurements after a multi-level wavelet decomposition of the signals. The prognostic algorithm presented in this work for solenoid pump RUL prediction is a multi-objective genetic algorithm-optimized long short-term memory (MOGA-LSTM) which accepts the fused sensor features as input and returns the RUL of the pump as output. The framework was tested on a run-to-failure experiment on a VSC63A5 Solenoid pump following a significant pump malfunction caused by a clogged suction filter after the test. Using standard prognostic performance evaluation metrics, the performance of the prognostics framework was compared with other reliable state-of-the-art methods with a remarkable comparative advantage in addition to better automation potentials for real-time condition monitoring and RUL prediction.

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Section: A Signal De-noisingmentioning

confidence: 99%

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

Akpudo

Hur

2021

IEEE Access

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“…Also, Continuous wavelet transform (CWT) is popular for nonlinear characterization of nonstationary and transient signals [28]. This versatile technique which employs wavelet function(s) in place of sinusoidal functions enhances a scale variable in addition to the time variable in the inner product transformation and is popularly used for signal demodulation and band-pass filtering [29], [30]. For fault diagnosis, CWT coefficients provide a wide array of nonlinear properties for fault isolation/classification and these have shown strong effectiveness for planetary gearbox fault detection [31], water pump impeller damage detection [32], bearing fault detection [33], etc.…”

Section: Motivation and Related Work A Vibration Monitoring Andmentioning

confidence: 99%

“…As a result of the wavelet basis function of the WT, wavelet decomposition of a non-stationary signal into linear forms of time-scale units is possible, thereby reconstructing the signal into several components according to the wavelet function translation [29], [30]; however, as fault features, wavelet coefficients are quite efficient [32], [33].…”

Section: ) Continuous Wavelet Transformmentioning

confidence: 99%

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Akpudo

Hur

2020

IEEE Access

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Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequencydomain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson's correlation-BPSO (ρ-BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filterwrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed ρ-BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods.

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“…VMD is a completely non-recursive adaptive signal processing technology employed by Dragomiretskiy et al [32], which can effectively achieve the adaptive separation of signals in the frequency domain.…”

Section: Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

et al. 2020

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As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

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Wavelet Denoising for the Vibration Signals of Wind Turbines Based on Variational Mode Decomposition and Multiscale Permutation Entropy

Cited by 37 publications

References 31 publications

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

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