Improved local mean decomposition for modulation information mining and its application to machinery fault diagnosis

Liu, Zhiliang; Zuo, Ming J.; Jin, Yaqiang; Deng, Pan; Qin, Yong

doi:10.1016/j.jsv.2017.02.055

Cited by 52 publications

(32 citation statements)

References 17 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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“…where f m and Fs are the fault frequency and the sampling frequency, respectively. When the fault period T, calculated according to Equation (17), is a fractional number, the resampling technique is necessary before the iterative procedure can be implemented [26]. Compared to kurtosis, correlated kurtosis remains unaffected by the random impulses and is more robust and more suitable for detecting the fault-related information contained in PFs.…”

Section: Time-frequency Analysis Methods Based On Elmd and Tkeomentioning

confidence: 99%

“…The ELMD method can adaptively filter local oscillations into appropriate PFs through the uniform filtering characteristics of white noise, reducing the modemixing phenomenon and achieving better decomposition performance compared with LMD. Owing to this significant improvement, ELMD has a diverse range of applications in rotating machinery such as bearing [17,18] and gear fault diagnoses [19]. However, after performing ELMD on a vibration signal, the added white noise in the raw signal pollutes the PFs and residual signal according to the filter bank structure of LMD.…”

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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“…The WPT has good de-noising performance, but the basic functions need to be provided in advance [10,11]. The LMD is an adaptive analysis method for non-stationary signals, but it is affected by mode mixing [12,13]. The VMD is an adaptive and quasi-orthogonal signal decomposition approaches, but its penalty parameter and decomposition number are difficult to determine [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis

Zhen

Guo

et al. 2019

Sensors

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To realize the accurate fault detection of rolling element bearings, a novel fault detection method based on non-stationary vibration signal analysis using weighted average ensemble empirical mode decomposition (WAEEMD) and modulation signal bispectrum (MSB) is proposed in this paper. Bispectrum is a third-order statistic, which can not only effectively suppress Gaussian noise, but also help identify phase coupling. However, it cannot effectively decompose the modulation components which are inherent in vibration signals. To alleviate this issue, MSB based on the modulation characteristics of the signals is developed for demodulation and noise reduction. Still, the direct application of MSB has some interfering frequency components when extracting fault features from non-stationary signals. Ensemble empirical mode decomposition (EEMD) is an advanced nonlinear and non-stationary signal processing approach that can decompose the signal into a list of stationary intrinsic mode functions (IMFs). The proposed method takes advantage of WAEEMD and MSB for bearing fault diagnosis based on vibration signature analysis. Firstly, the vibration signal is decomposed into IMFs with a different frequency band using EEMD. Then, the IMFs are reconstructed into a new signal by the weighted average method, called WAEEMD, based on Teager energy kurtosis (TEK). Finally, MSB is applied to decompose the modulated components in the reconstructed signal and extract the fault characteristic frequencies for fault detection. Furthermore, the efficiency and performance of the proposed WAEEMD-MSB approach is demonstrated on the fault diagnosis for a motor bearing outer race fault and a gearbox bearing inner race fault. The experimental results verify that the WAEEMD-MSB has superior performance over conventional MSB and EEMD-MSB in extracting fault features and has precise and effective advantages for rolling element bearing fault detection.

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Improved local mean decomposition for modulation information mining and its application to machinery fault diagnosis

Cited by 52 publications

References 17 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

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

A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis

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