Multiple time-frequency curve extraction Matlab code and its application to automatic bearing fault diagnosis under time-varying speed conditions

Huang, Huan; Baddour, Natalie; Liang, Ming

doi:10.1016/j.mex.2019.05.020

Cited by 26 publications

(14 citation statements)

References 19 publications

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“…Vibration signal samples in the following four health states are collected: health (CH), inner-race fault (CI), outer-race fault (CO), and ball fault (CB), which are collected under four variable speed conditions: speed-up, speed-down, speed-up then speed-down, and speed-down then speed-up. e sampling frequency of the vibration signals is 200 kHz, and the signal is down-sampled by a factor of 10 to reduce the calculation [40]. By performing nonoverlapping division of samples with a window of 0.1 s, 100 samples are obtained in each health condition under each speed condition, so each health condition is arranged with 400 sets of vibration data with different variable speeds.…”

Section: Methodsmentioning

confidence: 99%

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Huang

Xu²,

Cao³

et al. 2022

Shock and Vibration

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Fault diagnosis of rolling bearings under variable speed is a common issue in engineering practice, but it lacks an effective diagnosis algorithm, while approaches developed for steady speed cannot be directly applied. Therefore, for effectively identifying bearing faults under variable speed, this paper proposed a multiscale fractional dimensionless indicator (MSFDI) and put forward a fault diagnosis method with random forest (RF). It can overcome the feature space aliasing problem of traditional dimensionless indicators, which will lead to increased diagnosis uncertainty. The multiorder fractional Fourier transform is carried out on bearing signals to get a series of fractional Fourier domain components, which will be used to construct the original MSFDI feature set. Moreover, reliefF selects the sensitive MSFDIs as the input of the RF algorithm to determine the health condition. The effectiveness of the proposed method is verified by experiments and case studies.

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Section: Methodsmentioning

confidence: 99%

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Huang

Xu²,

Cao³

et al. 2022

Shock and Vibration

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show abstract

“…The time-varying speed datasets of the rolling bearing are got from the department of mechanical engineering of Ottawa University, Canada [46]. The experimental platform is a SpectraQuest machinery fault simulator (PK5M), as shown in figure 6.…”

Section: Data Description and Experimental Setupmentioning

confidence: 99%

Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions

Lu¹,

Tong²,

Feng³

et al. 2022

Meas. Sci. Technol.

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Intelligent fault diagnosis of rolling bearings under non-stationary and time-varying speed conditions is still a challenging task. At the same time, a reasonable explanation for an intelligent diagnosis model based on features is currently lacking. Therefore, we exploit an explainable one-dimensional convolutional neural network (1DCNN) model by combining with the demodulated frequency features of vibration signals and apply it to the fault classification of rolling bearings under time-varying speed conditions. First, the speed signals obtained by the speed encoder were transformed into generalized demodulation operator (GDO). Second, combined with the sensitive frequency band and GDO, the generalized demodulation algorithm was used to extract the frequency features from the amplitude envelope of the vibration signal. Subsequently, the proposed lightweight 1DCNN was trained to classify the frequency features and identify the health states of the rolling bearing. Finally, the local interpretable model-agnostic explanations model was utilized to explain the proposed model based on the features which own weight. It is found that the internal classification mechanism of the lightweight 1DCNN is realized according to the distribution of fault features, which is consistent with the process of human brain analysis. Two kinds of time-varying speed datasets which come from the University of Ottawa and XJTU are tested and verified. The results show that compared with other intelligent fault diagnosis methods, the identification error of the proposed method is lower and the diagnosis stability is better. The average diagnostic accuracy was 96.26% and 99.82%, respectively.

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“…To verify the feasibility of the approach, this study conducts vibration signal analysis under time-varying rotational speed [34,35]. This is aimed at achieving high-resolution representation of fault characteristic components in the TF plane under variable conditions.…”

Section: Case 2: Application To Equipment Failure Signalmentioning

confidence: 99%

Time-varying fault feature extraction of rolling bearing via time–frequency sparsity

Qin

Huang

et al. 2020

Meas. Sci. Technol.

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The joint time–frequency (TF) distribution is a critical method of describing the instantaneous frequency that changes with time. To eliminate the errors caused by strong modulation and noise interference in the process of time-varying fault feature extraction, this paper proposes a novel approach called second-order time–frequency sparse representation (SOTFSR), which is based on convex optimization in the domain of second-order short-time Fourier transform (SOSTFT) where the TF feature manifests itself as a relative sparsity. According to the second-order local estimation of the phase function, SOSTFT can provide a sparse TF coefficient in the short-time Fourier transform (STFT) domain. To obtain the optimal TF coefficient matrix from noisy observations, it is innovatively formulated as a typical convex optimization problem. Subsequently, a multivariate generalized minimax concave penalty is employed to maintain the convexity of the least-squares cost function to be minimized. The aim of the proposed SOTFSR is to obtain the optimal STFT coefficient in the TF domain for extraction of time-varying features and for perfect signal reconstruction. To verify the superiority of the proposed method, we collect the multi-component simulation signals and the signals under variable speed from a rolling bearing with an inner ring fault. The experimental results show that the proposed method can effectively extract the time-varying fault characteristics.

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Multiple time-frequency curve extraction Matlab code and its application to automatic bearing fault diagnosis under time-varying speed conditions

Abstract: Graphical abstract

Cited by 26 publications

References 19 publications

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Fractional Dimensionless Indicator with Random Forest for Bearing Fault Diagnosis under Variable Speed Conditions

Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions

Time-varying fault feature extraction of rolling bearing via time–frequency sparsity

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