Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis

Liu, Zhiliang; Jin, Yaqiang; Zuo, Ming J.; Feng, Zhipeng

doi:10.1016/j.ymssp.2017.03.035

Cited by 88 publications

(40 citation statements)

References 16 publications

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“…Over the years, a considerable amount of studies have been conducted to improve the decomposition efficiency of LMD. For example, a soft sifting stopping criterion has been proposed to automatically determine the optimal number of sifting iterations [30]; a signal extending approach based on self-adaptive waveform matching technique was applied to overcome the boundary distortion [31]; and a novel scheme used to automatically determine the fix subset size of the moving average algorithm by Liu et al [32]. However, a desired solution is still difficult to achieve.…”

Section: Discussionmentioning

confidence: 99%

Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background

et al. 2020

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Given the prejudicial environmental effects of fossil-fuel based energy production, renewable energy sources can contribute significantly to the sustainability of human society. As a clean, cost effective and inexhaustible renewable energy source, wind energy harvesting has found a wide application to replace conventional energy productions. However, concerns have been raised over the noise generated by turbine operating, which is helpful in fault diagnose but primarily identified for its adverse effects on the local ecosystems. Therefore, noise monitoring and separation is essential in wind turbine deployment. Recent developments in condition monitoring provide a solution for turbine noise and vibration analysis. However, the major component, aerodynamic noise is often distorted in modulation, which consequently affects the condition monitoring. This study is conducted to explore a novel approach to extract low-frequency elements from the aerodynamic noise background, and to improve the efficiency of online monitoring. A framework built on the spline envelope method and improved local mean decomposition has been developed for low-frequency noise extraction, and a case study with real near-field noises generated by a mountain-located wind turbine was employed to validate the proposed approach. Results indicate successful extractions with high resolution and efficiency. Findings of this research are also expected to further support the fault diagnosis and the improvement in condition monitoring of turbine systems.

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Section: Discussionmentioning

confidence: 99%

Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background

et al. 2020

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“…Local mean decomposition (LMD), one of the popular selfadaptive time-frequency processes, was presented by Smith [1]. LMD can decompose a complex signal into a series of product functions (PFs), each of which is result of an envelope signal multiplied by a pure frequency signal, making each PF contain amplitude-modulated (AM) and frequencymodulated (FM) components [2], also making each PF represent practical physical significance [3]. Based on these features, considerable studies have used LMD in the area of multicomponents signal processing and fault diagnosis, such as voice signal processing [4], EEG signal processing [1], fault diagnosis [5][6][7], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Although LMD has been successfully applied, one of the major problems is end effects [2,8,9], which distort the decomposed waveform at each end of the analyzed signal and influence feature frequency. e main reason is that LMD needs to find out all extrema of analyzed signal itself.…”

Section: Introductionmentioning

confidence: 99%

Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

Liang

Wang

et al. 2020

Mathematical Problems in Engineering

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Local mean decomposition (LMD) is widely used in the area of multicomponents signal processing and fault diagnosis. One of the major problems is end effects, which distort the decomposed waveform at each end of the analyzed signal and influence feature frequency. In order to solve this problem, this paper proposes a novel self-adaptive waveform point extended method based on long short-term memory (LSTM) network. First, based on existing signal points, the LSTM network parameters of right and left ends are trained; then, these parameters are used to extend the waveform point at each end-side of signal; furthermore, the corresponding parameters are adaptively updated. The proposed method is compared with the characteristic segment extension and the traditional neural network extension methods through a simulated signal to verify the effectiveness. By combing the proposed method with LMD, an improved LMD algorithm is obtained. Finally, application of rolling bearing fault signal is carried out by the improved LMD algorithm, and the results show that the feature frequencies of the rolling bearing’s ball and inner and outer rings are successfully extracted.

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“…Most importantly, compared with EMD, the results of LMD are physically plausible, making the conclusions drawn from LMD relevant for various applications. Because of its simple implementation and adequate ability to reveal a signal's nonstationary and nonlinearity information, LMD is widely used as a time-frequency analysis tool for fault diagnosis in rotating machinery [11][12][13][14][15]. However, the modemixing phenomenon has a significant influence on the results.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Time-Frequency Analysis Method for Railway Rolling-Element Bearing Fault Diagnosis

et al. 2019

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The health condition of rolling-element bearings is important for machine performance and operating safety. Due to external interferences, the impulse-related fault information is always buried in the raw vibration signal. To solve this problem, a hybrid time-frequency analysis method combining ensemble local mean decomposition (ELMD) and the Teager-Kaiser energy operator (TKEO) is proposed for the fault diagnosis of high-speed train bearings. The ELMD method is a significant improvement over local mean decomposition (LMD) for addressing the mode-mixing problem. The TKEO method is effective for separating amplitude-modulated (AM) and frequency-modulated (FM) signals from a raw signal. But it is only valid for monocomponent AM-FM signals. The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings. First, a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD. A novel sensitive parameter (SP) is introduced to select the sensitive PF that contains the most fault-related information. Subsequently, the TKEO is applied to extract both the amplitude and frequency modulations from the selected PF. The experimental results of rolling element and outer race fault signals confirmed that the proposed method could effectively recover fault information from raw signals contaminated by strong noise and other interferences.

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Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis

Cited by 88 publications

References 16 publications

Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background

Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background

Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

A Hybrid Time-Frequency Analysis Method for Railway Rolling-Element Bearing Fault Diagnosis

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