A novel intelligent fault diagnosis method based on fast intrinsic component filtering and pseudo-normalization

Zhang, Zongzhen; Li, Shunming; Lu, Jiming; Wang, Jinrui; Jiang, Xingxing

doi:10.1016/j.ymssp.2020.106923

Cited by 42 publications

(29 citation statements)

References 23 publications

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“…It is evident from Fig. 4 that for the same noise signal, the MGRCMSE entropy curve decreases with the increase in r. This can be explained by the fact that, when r value is low, the number of matching templates increases, 8 increasing the entropy values. Moreover, when r is a small value (e. g., r = 0.1sd), the MGRCMSE entropy curves of both noise signals have slight fluctuation.…”

Section: Fig 2 Influence Of N On Mgrcmsementioning

confidence: 87%

An effective multi-channel fault diagnosis approach for rotating machinery based on multi-variate generalized refined composite multi-scale sample entropy

Wang

Chen

Yao

et al. 2021

Preprint

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Fault diagnosis of critical rotating machinery components is necessary to ensure safe operation. However, the commonly used fault diagnosis methods for rotating machinery are generally based on the single-channel signal processing method, which is not suitable for processing multi-channel signals. Thus, to extract features and carry out the intelligent diagnosis of multi-channel signals, a novel method for rotating machinery fault diagnosis is proposed. Firstly, a novel non-linear dynamics technique, named the multi-variate generalized refined composite multi-scale sample entropy, is presented and applied to extract fusion entropy features of multi-channel signals. Secondly, a practical manifold learning known as supervised isometric mapping is introduced to map the high-dimensional fusion entropy features in a low-dimensional space. In a third step, the Harris hawks optimization-based support vector machine is utilized to achieve intelligent fault recognition. Finally, aiming to verify the effectiveness of the proposed method and present its advantages, it was applied to analyze the rotating machinery system bearing and gear data. The experimental results have shown that the method at hand can accurately identify various bearing and gear faults. Furthermore, in addition to being suitable for multi-channel signal fault diagnosis, it displayed higher recognition accuracy when compared with other multi-channel or single-channel methods.

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Section: Fig 2 Influence Of N On Mgrcmsementioning

confidence: 87%

An effective multi-channel fault diagnosis approach for rotating machinery based on multi-variate generalized refined composite multi-scale sample entropy

Wang

Chen

Yao

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Hence, there might be a need to further investigate the applicability for other faults. [47] Vibration data FICF No Gears and bearing faults FICF is suitable for multi-sample training but the convolution activation limits its performance during single sample operations.…”

Section: Comparison With Closely Related Workmentioning

confidence: 99%

“…Azamfar et al [ 46 ] developed a novel multi-sensor data fusion methodology based on 2-D CNN for gearboxes fault diagnosis using motor current signature analysis. Zhang et al [ 47 ] proposed a novel unsupervised learning algorithm named fast intrinsic component filtering (FICF) for the fault diagnosis of rotating machinery. These studies have no doubt enhanced the knowledge around the fault diagnosis of rotating machines.…”

Section: Comparison With Closely Related Workmentioning

confidence: 99%

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

Cao

Yunusa‐Kaltungo

2021

Sensors

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The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.

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“…13 But the technical maneuverability has hindered its development, and the signal analysis accuracy is still far from enough for the variable rotational speed. Meanwhile, many scholars try to use some related non-stationary signal processing methods, such as Cohen time-frequency analysis, 14 Wigner-Ville distribution (WVD), 15 wavelet transform (WT), 16 sparse representation, 4 etc. These methods have been widely used in equipment monitoring and diagnosis, but they are only suitable for the analysis of some specific signal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis for rotating machinery using L_1/2-SF under variable rotational speed

Wang

Han

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Sparse filtering (SF), as an effective feature extraction technique, has attracted considerable attention in the field of mechanical fault diagnosis. But the generalization ability of SF to handle non-stationary signal under variable rotational speed is still poor. When the rotating parts of mechanical transmission work at a constant speed, the collected vibration signal is strongly correlated with the fault type. However, the mappings will no longer be so simple under the condition of variable rotational speed, which brings a rigorous challenge to intelligent fault diagnosis. To overcome the aforementioned deficiency, a novel L1/2 regularized SF method ( L1/2-SF) is studied in this paper. Specifically, L1/2 regularization strategy is added to the cost function of SF, then the L1/2-SF is directly employed to extract sparse features from the raw vibration data under variable rotational speed condition. In order to understand the sparse feature extraction ability of the L1/2 regularization, a physical explanation of the sparse solution generated by the L1/2 regularization strategy is explored. Next, softmax regression is employed for fault classification connected with the output layer of L1/2-SF. The effectiveness of L1/2-SF method is verified using a planetary gearbox dataset and a bearing dataset, respectively. Experiment results show that L1/2-SF can deal well with the variable rotational speed problem and is superior to other methods.

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A novel intelligent fault diagnosis method based on fast intrinsic component filtering and pseudo-normalization

Cited by 42 publications

References 23 publications

An effective multi-channel fault diagnosis approach for rotating machinery based on multi-variate generalized refined composite multi-scale sample entropy

An effective multi-channel fault diagnosis approach for rotating machinery based on multi-variate generalized refined composite multi-scale sample entropy

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

Intelligent fault diagnosis for rotating machinery using L_1/2-SF under variable rotational speed

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A novel intelligent fault diagnosis method based on fast intrinsic component filtering and pseudo-normalization

Cited by 42 publications

References 23 publications

An effective multi-channel fault diagnosis approach for rotating machinery based on multi-variate generalized refined composite multi-scale sample entropy

An effective multi-channel fault diagnosis approach for rotating machinery based on multi-variate generalized refined composite multi-scale sample entropy

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

Intelligent fault diagnosis for rotating machinery using L1/2-SF under variable rotational speed

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Intelligent fault diagnosis for rotating machinery using L_1/2-SF under variable rotational speed