An evolutionary ensemble convolutional neural network for fault diagnosis problem

Tayarani-Najaran, Mohammad-Hassan

doi:10.1016/j.eswa.2023.120678

Cited by 6 publications

(1 citation statement)

References 71 publications

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“…First, the statistical features, such as the mean, variance, root mean square, etc., obtained from the VS in the TD are not susceptible to SFs. Furthermore, to select SF-sensitive features, extensive feature preprocessing is needed [33,34]. Second, the FD considers signal statistical properties invariant over time; however, the VS of the CP in defective operating conditions is non-stationary, which makes FD analysis less attractive [35].…”

Section: Related Research Studiesmentioning

confidence: 99%

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann–Whitney Test

Ahmad,

Kim,

Kim

2023

Sensors

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This work presents a technique for fault detection and identification in centrifugal pumps (CPs) using a novel fault-specific Mann–Whitney test (FSU Test) and K-nearest neighbor (KNN) classification algorithm. Traditional fault indicators, such as the mean, peak, root mean square, and impulse factor, lack sensitivity in detecting incipient faults. Furthermore, for defect identification, supervised models rely on pre-existing knowledge about pump defects for training purposes. To address these concerns, a new centrifugal pump fault indicator (CPFI) that does not rely on previous knowledge is developed based on a novel fault-specific Mann–Whitney test. The new fault indicator is obtained by decomposing the vibration signature (VS) of the centrifugal pump hierarchically into its respective time-frequency representation using the wavelet packet transform (WPT) in the first step. The node containing the fault-specific frequency band is selected, and the Mann–Whitney test statistic is calculated from it. The combination of hierarchical decomposition of the vibration signal for fault-specific frequency band selection and the Mann–Whitney test form the new fault-specific Mann–Whitney test. The test output statistic yields the centrifugal pump fault indicator, which shows sensitivity toward the health condition of the centrifugal pump. This indicator changes according to the working conditions of the centrifugal pump. To further enhance fault detection, a new effect ratio (ER) is introduced. The KNN algorithm is employed to classify the fault type, resulting in promising improvements in fault classification accuracy, particularly under variable operating conditions.

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Section: Related Research Studiesmentioning

confidence: 99%

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann–Whitney Test

Ahmad,

Kim,

Kim

2023

Sensors

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Distributed statistical process monitoring based on block‐wise residual generator

Tong,

Zhou,

Qian

et al. 2023

Journal of Chemometrics

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The increasing scale of modern chemical plants keeps popularizing investigation as well as application of distributed process monitoring approaches. With a goal of directly quantifying the normal relations between different blocks divided from the whole process, a novel multi‐block modeling strategy called block‐wise residual generator is proposed, which trains a residual generator for each block through using the partial least squares algorithm with single one output, so that the relation between the corresponding block and the others is quantified as a regression model in a block‐wise manner. The deviations caused by the abnormal samples to the normal relations quantified for different blocks could thus be efficiently captured by the residuals generated from the block regression models, which then provide sensitive information for fault detection and contribution‐based fault diagnosis. Moreover, the proposed method is applicable for both disjoint and overlapped block divisions, and the direct consideration of individually quantifying relations between different blocks can always guarantee its salient monitoring performance, as validated through comparisons with classical distributed process monitoring methods.

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Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

Hu,

Chang

2023

Journal of Chemometrics

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With a goal of timely and adaptively exploiting the inconsistency inherited in the monitored samples of current interest, a novel dynamic process monitoring method based on just‐in‐time latent autoregressive residual generation (JITLAR2G) model is proposed. Different from the mainstream dynamic modeling and monitoring methods which usually train a signature generating mechanism and then repeatedly apply it for online monitored samples, the proposed JITLAR2G‐based approach provides a JITLAR2G model for the online monitored samples after data augmentation, so that the corresponding inconsistency within the given consecutive samples could be timely and adaptively uncovered. Instead of expressing the time‐serial relationship that generally accepted by the normal samples in the given dataset, solving the objective function designed for JITLAR2G in a just‐in‐time manner can adaptively and correspondingly seek but only one projecting vector as well as coefficient vector to generate residual, which points to the potential inconsistency inherited in the monitored samples, for the sole purpose of fault detection. As demonstrated through comparisons, the proposed JITLAR2G model can consistently guarantee its effectiveness, in terms of reducing both false alarm rate and missed alarm rate, for dynamic process monitoring, the salient performance achieved by the proposed JITLAR2G‐based method in contrast to the counterparts can be always confirmed.

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An evolutionary ensemble convolutional neural network for fault diagnosis problem

Cited by 6 publications

References 71 publications

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann–Whitney Test

A Technique for Centrifugal Pump Fault Detection and Identification Based on a Novel Fault-Specific Mann–Whitney Test

Distributed statistical process monitoring based on block‐wise residual generator

Just‐in‐time latent autoregressive residual generation for dynamic process monitoring

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