Nested SVDD in DAG SVM for induction motor condition monitoring

Zgarni, Slaheddine; Keskes, Hassen; Braham, Ahmed

doi:10.1016/j.engappai.2018.02.019

Cited by 37 publications

(13 citation statements)

References 17 publications

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“…Platt et al [172] and Hsu et al [173] compared the performance of OAA with OAO, and provided valuable suggestion in selecting prior strategy to obtain better diagnosis accuracy. After that, some publications further introduced some advanced multi-class strategies in applications of SVM, such as the direct acyclic graph [174][175][176] and the binary tree [164][165][166][177][178][179][180][181][182][183][184][185], which effectively overcame the weaknesses of OAA and OAO. In order to improve the diagnosis accuracy of SVM-based models, researchers mainly focused on two branches, i.e., the modified SVM and the algorithm optimization.…”

Section: ) Applications Of Svm To Machine Fault Diagnosismentioning

confidence: 99%

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei

Yang

Jiang

et al. 2020

Mechanical Systems and Signal Processing

1,650

603

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Section: ) Applications Of Svm To Machine Fault Diagnosismentioning

confidence: 99%

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei

Yang

Jiang

et al. 2020

Mechanical Systems and Signal Processing

1,650

603

View full text Add to dashboard Cite

“…For dealing with the fault detection of rolling element bearings, Liu et al [17] proposed a semi-supervised SVDD method to overcome the limitation of labeling samples. Some other related studies can be seen in literature [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Deng¹,

Zhang²

2020

Sensors

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As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.

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“…That is lead to automate feature extraction and classification process. Slaheddine et al [40] improve the standard support vectors machines (SVM) by using support vector data description (SVDD) based on MCSA and stationary wavelet packet transform (SWPT) for feature extraction to diagnose broken rotor bar fault. Bensaoucha et al [41] proposed a diagnostic technique based on NN for detecting and locating the inter turns short-circuit in one of three stator winding phases of IM, where the three-phase shift between the stator voltages and its currents are considered as inputs of the NN in order to develop an automatic fault detection and classification system.…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Harzelli¹,

Benakcha²,

Ameid³

et al. 2021

J. Model. Optim.

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In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

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Nested SVDD in DAG SVM for induction motor condition monitoring

Cited by 37 publications

References 17 publications

Applications of machine learning to machine fault diagnosis: A review and roadmap

Applications of machine learning to machine fault diagnosis: A review and roadmap

Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

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