Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

Duan, Lixiang; Yao, Mingchao; Wang, Jinjiang; Bai, Tangbo; Yue, Jingjing

doi:10.1177/1687814016665747

Cited by 9 publications

(8 citation statements)

References 39 publications

(41 reference statements)

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“…For example, Lin and Chang proposed a rolling bearing fault diagnosis method based on an enhanced kurtosis spectrum and intrinsic time-scale decomposition [13]. Duan and Yao et al proposed to apply intrinsic time-scale decomposition to the fault diagnosis of gearboxes under variable operating conditions [14]. Xing and Qu et al proposed a gear fault diagnosis method with variable working conditions based on intrinsic time-scale and singular value decomposition [15].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Shi

et al. 2021

Symmetry

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When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Shi

et al. 2021

Symmetry

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“…Time-frequency analysis based on the intrinsic time-scale decomposition can quantitatively describe the relationship between frequency and time, accurately analyzing time-varying signals [10]. On the basis of these advantages, scholars introduced this method from the medical field to the fault diagnosis of mechanical signals [11][12][13][14][15][16][17][18][19][20][21][22]. For example, Lin and Chang published a rolling-bearing fault diagnosis method based on an enhanced kurtosis spectrum and intrinsic time-scale decomposition [11].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Lin and Chang published a rolling-bearing fault diagnosis method based on an enhanced kurtosis spectrum and intrinsic time-scale decomposition [11]. Duan and Yao et al proposed a comprehensive eigentime decomposition method for the fault diagnosis of a gearbox under variable operating conditions [12]. Xiang and Qu et al proposed intrinsic time-scale decomposition and singular-value decomposition for variable-condition gear fault diagnosis [13].…”

Section: Introductionmentioning

confidence: 99%

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

Han

et al. 2021

Entropy

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The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

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“…For the second difficulty, Duan L, et al present an integrative approach of intrinsic time-scale decomposition and hierarchical temporal memory for gearbox diagnosis under variable operating conditions. [15] A hybrid method based on VMD, SKE, and WSaE-ELM is implemented for fault diagnosis of real bogies under variable conditions [16]. A technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions [17].…”

Section: Introductionmentioning

confidence: 99%

Multi-Model Switching Based Fault Detection for the Suspension System of Maglev Train

Wang

Dang³

2019

IEEE Access

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In order to satisfy the requirements of on-line monitoring of the suspension system under complex conditions, a fault detection method for maglev train suspension system based on multi-model switching is proposed. In the proposed method, the healthy samples are extracted through the moving time window, and then the features of the healthy samples are extracted by the Fast Walsh-Hadamard transform and filtered by the median filter. Then, the normalization is used to eliminate the difference of feature vectors, and then the principal component analysis method is used to reduce the dimension and de-correlation of the feature matrix contributing to a hyper-sphere space. Finally, the health threshold and the fault threshold are determined by Euclidean distance, then fault detection models are established for various operating conditions. Taking the positive line operation condition as an example, the proposed method is compared with the method based on the original feature and support vector data description based on the original feature. The results demonstrate that the proposed method is superior to the other two methods in terms of health detection rate and false positive rate. In addition, the proposed method is of the characteristics of low computational complexity, no-parameter optimization, and good robustness. It can be applied in practical engineering providing a certain research basis for data deep mining such as fault diagnosis and fault prediction.INDEX TERMS Maglev train suspension system, complex conditions, multi-model switching, fault detection models.

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Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

Cited by 9 publications

References 39 publications

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

Multi-Model Switching Based Fault Detection for the Suspension System of Maglev Train

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