Wavelet and Scalar Indicator based Fault Assessment Approach for Rolling Element Bearings

Paliwal, D.N.; Choudhury, Achintya; Tingarikar, Govardhan

doi:10.1016/j.mspro.2014.07.478

Cited by 19 publications

(6 citation statements)

References 9 publications

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“…S i ) is the ratio of the ith power spectrum to the whole spectrum. Similarly, normalized by the white noise signal [19][20][21]. The PSE of the white noise is H f,max = logN.…”

Section: Frequency Domain Information Entropy Featurementioning

confidence: 99%

“…The segmentation criterion consists of a segmentation variable and segmentation predication and is measured by impurity function. The Gini coefficient [20] reflects the inconformity probability of the category labels, in which the two samples randomly selected in the data set. The Gini coefficient is proportional to the impurity level.…”

Section: Random Forest Algorithm Buildingmentioning

confidence: 99%

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Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Jun

Liu

Zhang

et al. 2019

Entropy

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Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

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“…S i ) is the ratio of the ith power spectrum to the whole spectrum. Similarly, normalized by the white noise signal [19][20][21]. The PSE of the white noise is H f,max = logN.…”

Section: Frequency Domain Information Entropy Featurementioning

confidence: 99%

Section: Random Forest Algorithm Buildingmentioning

confidence: 99%

Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Jun

Liu

Zhang

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…Wavelet analysis is a time-frequency analysis method that is developed based on overcoming the shortcomings of the Fourier transform [24,25]. For an AE signal f(t) of Shock and Vibration a measured point, the energy conservation of the limited energy contained in the function before and after the wavelet transform is…”

Section: Time-frequency Domain Information Exergy Of Ae Signalmentioning

confidence: 99%

Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method

Jun

Fei

et al. 2018

Shock and Vibration

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For the fault diagnosis of intershaft bearings, the fusion information exergy distance method (FIEDM) is proposed by fusing four information exergies, such as singular spectrum exergy, power spectrum exergy, wavelet energy spectrum exergy, and wavelet space spectrum exergy, which are extracted from acoustic emission (AE) signals under multiple rotational speeds and multimeasuring points. The theory of FIEDM is investigated based on four information exergy distances under multirotational speeds. As for rolling bearings, four faults and one normal condition are simulated on a birotor test rig to collect the AE signals, in which the four faults are inner ring fault, outer ring fault, rolling element fault, and inner race-rolling element coupling fault. The faults of the intershaft bearings are analyzed and diagnosed by using the FIEDM. From the investigation, it is demonstrated that the faults of the intershaft bearings are accurately diagnosed and identified, and the FIEDM is effective for the analysis and diagnosis of intershaft bearing faults. Furthermore, the fault diagnosis precision of intershaft bearings becomes higher with increasing rotational speed.

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“…To monitor the health condition and detect localized defects in a rolling-element bearing, many signal-processing techniques have been proposed and developed in recent years. As one of the most widely-used methods for vibration signal analysis, timefrequency methods, including wavelet transform [1,2], timefrequency distribution [3,4], time series model [5,6], matching pursuit [7,8], and empirical mode decomposition (EMD) [9], have been explored as powerful tools for fault detection and diagnosis of rolling-element bearings.…”

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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Wavelet and Scalar Indicator based Fault Assessment Approach for Rolling Element Bearings

Cited by 19 publications

References 9 publications

Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method

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

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