A self-adaptive time-frequency analysis method based on local mean decomposition and its application in defect diagnosis

The local mean decomposition (LMD), a new adaptive time-frequency analysis method, is the research focus in the fault diagnosis field in recent years. In this paper, the LMD’s characteristics are obtained by processing multi-component frequency and amplitude modulation signal, which are usually used to describe the gear pitting corrosion fault signals. Base on the simulation analysis, LMD is presented to deal with the vibration signals of gear pitting corrosion fault, comparing with traditional method. The results show that the gear pitting corrosion defect can be diagnosed by LMD effectively, and LMD can eliminate the false composition effect, thus improving the accuracy of gear fault diagnosis.

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Signal Feature Extraction about the Oil Whirl and Oil Oscillation Based on EMD

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.141.483 ◽

2011 ◽

Vol 141 ◽

pp. 483-487

Author(s):

Bao Jie Xu ◽

Ran Liu

Keyword(s):

Feature Extraction ◽

Frequency Analysis ◽

Analysis Method ◽

Time Frequency Analysis ◽

Time Frequency ◽

Oil Whirl ◽

Adaptive Time

The article discusses the EMD and adaptive time-frequency analysis method based on EMD, and explains the characteristics about oil whirl, oil oscillation. Apply EMD and Hilbert-Huang t transformation to extract characteristics, which verifies applying EMD into feature extraction is effective.

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

Journal of Sensors ◽

10.1155/2019/8498496 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12

Author(s):

Yao Cheng ◽

Dong Zou ◽

Weihua Zhang ◽

Zhiwei Wang

Keyword(s):

Fault Diagnosis ◽

Frequency Analysis ◽

Rolling Element Bearings ◽

Analysis Method ◽

Local Mean Decomposition ◽

Time Frequency ◽

Fm Signals ◽

Rolling Element ◽

Local Mean ◽

Fault Information

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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