A new autocorrelation-based strategy for multiple fault feature extraction from gearbox vibration signals

He, Xiuzhi; Liu, Qiang; Yu, Wennian; Mechefske, Chris K.; Zhou, Xiaoqin

doi:10.1016/j.measurement.2020.108738

Cited by 27 publications

(12 citation statements)

References 37 publications

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“…Among them, VA needs to install a sensor in the gearbox, while MCSA is more stable and saves costs. [129] overcomes this problem. Experiments show that under the condition of Gaussian and non-Gaussian interference and the resonance frequencies of multiple faults are easily coupled with each other, the LEASgram method has significant fault identification and separation capabilities.…”

Section: B Ai Approaches For Detecting Faults Of Transmissionmentioning

confidence: 99%

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Zhang

Deng

et al. 2022

IEEE Access

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Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

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Section: B Ai Approaches For Detecting Faults Of Transmissionmentioning

confidence: 99%

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Zhang

Deng

et al. 2022

IEEE Access

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“…These methods mainly use the SES and the fault characteristic frequency of interest to construct the evaluation indicator of cyclostationarity as the frequency band identification criterion. Inspired by the works in [37,38] where the log-envelope spectrum was discovered to be a robust tool for characterizing cyclostationarity in the presence of high impulse noise, the IFBI methods based on the log-envelope spectrum were established, such as log-cycligram (LC) [12], cyclic harmonic ratio [39] and LEASgram [40]. In addition, by exploiting the insensitivity of correntropy to impulse noise, FECgram [41] was proposed to identify IFB for bearing diagnostics.…”

Section: Introductionmentioning

confidence: 99%

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

Chen

Dong-li

Cheng

et al. 2022

JDMD

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The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure. Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings. In this paper, based on the ratio of quasi-arithmetic means and Gini index, improved Gini indices (IGIs) are proposed to quantify the transient impulse features of a signal, and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures. Furthermore, an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings. In the new method, an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously, and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features. The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram. The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.

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“…The research on the abovementioned contents has developed a reliable result, but some indicators of signals represent some limitations, and to some extent, the proposed method also affects the overall performance of that method. For example, correlation kurtosis, RCC, and WCHNR are only suitable for the identification of specific frequency components with known prior knowledge and their flexibility and practicability are limited [20]. The entropy index is susceptible to harmonic interference [21] and also insensitive to the periodicity of fault signal, L2/L1 norm, and Gini index which are sensitive to outliers [22], which can easily lead to misleading results in the case of strong interference.…”

Section: Introductionmentioning

confidence: 99%

A Novel Based on the Log-Squared Envelope Harmonic Noise Ratio Fault Transient Feature Extraction Method

Zhu

2022

Journal of Sensors

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To counter the sensitivity of kurtosis for the random pulse, a novel periodic detection index based on the log-squared envelope harmonic noise ratio (LSEHNR) is constructed, and then, a novel LSEHNRgram feature extraction method is proposed by introducing the maximal overlap discrete wavelet packet transform (MODWPT). Firstly, MODWPT is used to decompose the frequency band of the signal and a series of subsignals are obtained from each stage of decomposition, which are called nodes. Secondly, autocorrelation of the log-squared envelope for each node at each level is computed; calculate the LSEHNR, simultaneously. Then, the LSEHNR values of all nodes are calculated and the node with the maximal of the LSEHNR value is selected for spectrum analysis. Finally, the square envelope spectrum (SES) analysis is performed on the in-band filtered signal to extract the transient characteristics of the rolling bearing fault. The experimental results indicate that the LSEHNRgram method can accurately detect the demodulation frequency band and enhance the fault features under the condition of the low noise-to-signal ratio (SNR). The effectiveness of the method is verified on numerical and experimental datasets by comparison with autogram, infogram, and fast kurtgram methods.

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A new autocorrelation-based strategy for multiple fault feature extraction from gearbox vibration signals

Cited by 27 publications

References 37 publications

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

IGIgram: An Improved Gini Index-Based Envelope Analysis for Rolling Bearing Fault Diagnosis

A Novel Based on the Log-Squared Envelope Harmonic Noise Ratio Fault Transient Feature Extraction Method

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