2021
DOI: 10.1016/j.oceaneng.2021.109723
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Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study

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Cited by 48 publications
(17 citation statements)
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“…Authors in [ 7 , 8 ] also pointed out the problem that the data distribution will hardly influence the performance of deep neural network. When deep models are trained in such imbalanced scenarios, standard approaches usually fail to achieve satisfactory results, leading to a significant drop in performance.…”
Section: Introductionmentioning
confidence: 99%
“…Authors in [ 7 , 8 ] also pointed out the problem that the data distribution will hardly influence the performance of deep neural network. When deep models are trained in such imbalanced scenarios, standard approaches usually fail to achieve satisfactory results, leading to a significant drop in performance.…”
Section: Introductionmentioning
confidence: 99%
“…is method only uses a set of vibration sensors to classify the faults in rotating machinery and has a high accuracy rate. Tan et al [14] carried out a comparative study of several state-of-the-art multilabel classification algorithms for simultaneous fault diagnosis of marine machinery based on single fault data and proved the effectiveness of the proposed method.…”
Section: Introductionmentioning
confidence: 95%
“…Among them, the datadriven approaches usually do not rely on accurate mathematical models, but directly extract the latent information from historical data sets [5]. Therefore, the data-driven approaches were mostly used in sensors' FDI, such as the PCA methods [6][7][8], time-frequency analysis methods [9,10], machine learning-based methods [11][12][13][14][15][16][17], and the combination of the above methods [18][19][20][21][22]. Huang et al defined the isolation index by setting each sensor in turn as a missing variable and then recalculating the corresponding fault detection statistic.…”
Section: Introductionmentioning
confidence: 99%
“…Based on a multilabel classification algorithm, Tan et al used a single fault data for simultaneous fault diagnosis, which significantly reduced the requirements for training data in machine learning. However, he also pointed out that the algorithm is difficult to deal with the problem of data imbalance [17]. And Lei et al also pointed out that intelligent fault diagnosis based on machine learning still relies on expert knowledge; although deep learning can automatically learn features, it requires enough labelled samples, which are impractical in engineering scenarios [23].…”
Section: Introductionmentioning
confidence: 99%