Fault diagnosis of planetary gearbox using a novel semi-supervised method of multiple association layers networks

Zhang, Kai; Tang, Baoping; Qin, Yi; Deng, Lei

doi:10.1016/j.ymssp.2019.05.049

Cited by 77 publications

(24 citation statements)

References 23 publications

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“…LGC, and GFHF, are involved. LGC [22] and GFHF [23] are univariate graph regularization-based algorithms, while GGMC is a bivariate label propagation algorithm where both the label information and the classification function are considered as arguments for minimizing the defined objective function [14], [18]- [20].…”

Section: The Fundamental Theory Of the Gsslmentioning

confidence: 99%

An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning

Zaman

Liang

2021

IEEE Access

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Section: The Fundamental Theory Of the Gsslmentioning

confidence: 99%

An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning

Zaman

Liang

2021

IEEE Access

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“…Due to increased attention, numerous AI technologies have been used or developed for FDRM. Generally, AI technologies in FDRM can be divided into three categories: supervised methods [68], semi-supervised methods [69] and unsupervised methods [70]. Among them, the most widely used classifiers include the k-nearest neighbour methods [71], Bayesian methods [72,73], support vector machine methods [74,75], random forest methods [76,77], and artificial neural network methods [78,79].…”

Section: Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery: A Review and Bibliometric Analysis

Chen

Lin

Peng³

et al. 2020

IEEE Access

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Fault diagnosis of rotating machinery (FDRM) has attracted continuous attention because of its great importance to industrial engineering, promoting the healthy and prosperous development of the field. A large number of literature reviews on FDRM have been reported, including signal processing methods in FDRM; artificial intelligence techniques in FDRM; fault diagnosis of bearings, gearboxes and induction motors; and others with broader areas of focus. Using bibliometric techniques, this paper provides objective insight and presents a comprehensive review of FDRM. The review begins with rigorous bibliometric analyses of 2532 published studies. These analyses enable mapping the scope and structure of the discipline, discovering the established collaboration patterns among countries and institutions, and identifying authoritative papers and authors. In addition, a deep analysis of the co-citation network allows graphically classifying the key research topics, illustrating their evolution over time, and identifying the current research interests and potential future research directions. The findings in this paper provide an overall picture of the development of FDRM from 1998 to 2019 and a robust roadmap for future investigations in this field.

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“…For LGC and GFHF algorithms, a fitness function Q is defined by involving two penalty terms: the global smoothness Qsmooth and the local fitting accuracy Qfit. The final classification function F is obtained by minimizing the fitness function Q by [28] F * = arg min ∈ୖ ×ౙ QሺFሻ = arg min ∈ୖ ×ౙ ൫Q ୱ୫୭୭୲୦ ሺFሻ + Q ୧୲ ሺFሻ൯ (4) For LGC, the objective function is formulated by [19]…”

Section: B Three Gssl Algorithms: Ggmc Lgc and Gfhfmentioning

confidence: 99%

“…SSL has also gained more popularity in the field of induction motors fault diagnosis [10], [12], [16], [18], [19]. The published methods include semi-supervised smooth alpha layering for bearing fault diagnosis [10], semi-supervised label consistent dictionary learning framework for machine fault classification [16], semi-supervised deep learning for induction motor gear fault diagnosis [12], manifold regularization-based semisupervised learning for bearing fault diagnosis [18], and a deep SSL method for motor planetary gearbox fault diagnosis [19]. All SSL-based induction motors fault diagnosis methods reported in the literature use vibration signals, and mostly deal with an individual fault detection [10] [16] [18].…”

Section: Introductionmentioning

confidence: 99%

Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

2020

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In this paper, a graph-based semi-supervised learning (GSSL) algorithm, greedy-gradient max cut (GGMC), based fault diagnosis method for direct online induction motors is proposed. Two identical 0.25 HP three-phase squirrel-cage induction motors under healthy, single-and multi-fault conditions were tested in the lab. Three-phase stator currents and three-dimensional vibration signals of the two motors were recorded simultaneously in each test, and used as datasets in this study. Features for machine learning are extracted from experimental stator currents and vibration data by the discrete wavelet transform (DWT). To validate the effectiveness of the proposed GGMC-based fault diagnosis method, its classification accuracy using binary classification and multiclass classification for faults of the two motors are compared with other two GSSL algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). In this study, the performance of stator currents and vibration as a monitoring signal is evaluated, it is found that stator currents perform much better than vibration signals for multiclass classification, while they both perform well for binary classification.

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Fault diagnosis of planetary gearbox using a novel semi-supervised method of multiple association layers networks

Cited by 77 publications

References 23 publications

An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning

An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning

Fault Diagnosis of Rotating Machinery: A Review and Bibliometric Analysis

Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

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