Cyclostationary-based Multiband Envelope Spectra Extraction for bearing diagnostics: The Combined Improved Envelope Spectrum

Mauricio, Alexandre; Gryllias, Konstantinos

doi:10.1016/j.ymssp.2020.107150

Cited by 58 publications

(29 citation statements)

References 16 publications

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“…Moreover, the IESFOgram method was extended to accomplish the extraction of the combined improved envelope spectrum, adding the information of other bands. In comparison with the band pass filtering selection based on the Fast Kurtogram-based squared envelope spectrum (SES) and the Autogram-based combined squared envelope spectrum (CSES), it presents better performance in detecting the characteristic frequencies of bearing and gearbox [73].…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

“…(SES) and the Autogram-based combined squared envelope spectrum (CSES), it presents better performance in detecting the characteristic frequencies of bearing and gearbox [73].…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

“…Inspired by the application of CSCoh in signature processing, Chen et al employed it as a tool of feature extraction to the intelligent fault diagnosis of bearings based on convolutional neural network (CNN) [77]. Raw vibration data were processed to accomplish the preliminary feature (SES) and the Autogram-based combined squared envelope spectrum (CSES), it presents better performance in detecting the characteristic frequencies of bearing and gearbox [73].…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

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Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.

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Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

“…(SES) and the Autogram-based combined squared envelope spectrum (CSES), it presents better performance in detecting the characteristic frequencies of bearing and gearbox [73].…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

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Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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“…Kong et al [3] established an adaptive empirical wavelet transform framework for fault-related mode extraction for planet gear. Mauricio et al [4] implemented an improved envelope spectrum via feature optimization-gram for diagnosis for the planet bearings. Feng et al [5] used ensemble empirical mode decomposition (EEMD) and energy separation to diagnose the sun gear of a planetary gearbox.…”

Section: Introductionmentioning

confidence: 99%

Vibration Separation Methodology Compensated by Time-Varying Transfer Function for Fault Diagnosis of Non-Hunting Tooth Planetary Gearbox

Tong

Cong

et al. 2022

Sensors

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Due to planetary movement of planet gears, the vibration signal perceived by a stationary sensor is modulated and difficult to diagnose. This paper proposed a vibration separation methodology compensated by a time-varying transfer function (TVTF-VS), which is a further development of the vibration separation (VS) method in the diagnosis of non-hunting tooth planetary gearboxes. On the basis of VS, multi-teeth VS is proposed to extract and synthesize the meshing signal of a planet gear using a single transducer. Considering the movement regularity of a planetary gearbox, the time-varying transfer function (TVTF) is represented by a generalized expression. The TVTF is constructed using a segment of healthy signal and an evaluation indicator is established to optimize the parameters of the TVTF. The constructed TVTF is applied to overcome the amplitude modulation effect and highlight fault characteristics. After that, experiments with baseline, pitting, and compound localized faults planet gears were conducted on a non-hunting tooth planetary gearbox test rig, respectively. The results demonstrate that incipient failure on a planet gear can be detected effectively, and relative location of the local faults can be determined accurately.

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“…The resonance demodulation is the main way to extract the fault characteristic frequency [10][11]. Resonance demodulation methods include envelope demodulation [12][13][14], cyclostationary demodulation [15,16] and energy demodulation [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction of rolling element bearings based on short-time processing

Chen¹

2022

J. vibroeng.

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Fault diagnosis of bearings is a crucial part of the maintenance process of the rotary machinery. Extracting the cyclic characteristics of the impact force is of significant importance for the bearing diagnosis. To highlight the fault features from signals combined with heavy background noise, a novel approach for bearing fault diagnosis based on the short-time processing is proposed. Fault signals are regarded as periodic impulse response signals. Firstly, a vibration signal is band-pass filtered with a subsequent spectral analysis. Then we integrate the energy of the filtered signal with a constant length, and the natural logarithm is considered to obtain the energy curve. The energy curve is a straight decaying curve, and its spectral energy is more concentrated on the fault characteristic frequency compared with envelope. Finally, the fault characteristic frequency of the bearing is found by the spectral analysis of the energy curve. The effectiveness of the proposed method is verified by simulation and experiments. The harmonics and sidebands in logarithmic energy spectrum are suppressed well, and the fault characteristic frequency is highlighted. Comparison of the proposed method with Hilbert envelope method shows that the proposed method can highlight the fault characteristic frequency.

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Cyclostationary-based Multiband Envelope Spectra Extraction for bearing diagnostics: The Combined Improved Envelope Spectrum

Cited by 58 publications

References 16 publications

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Vibration Separation Methodology Compensated by Time-Varying Transfer Function for Fault Diagnosis of Non-Hunting Tooth Planetary Gearbox

Fault feature extraction of rolling element bearings based on short-time processing

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