Fault detection based on squirrel search algorithm and support vector data description for industrial processes

Navarro-Acosta, Jesús Alejandro; García-Calvillo, Irma D.; Reséndiz‐Flores, Edgar O.

doi:10.1007/s00500-022-07337-9

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…As a classic algorithm in the AD field, Support Vector Data Description (SVDD) was proposed by Tax et al [23] in 1999 and has been widely used in biochemistry, cloud computing, fault diagnosis, and other fields [24][25][26]. Since the performance of SVDD is deeply affected by the selection of hyperparameters, various meta-heuristic methods have been used to optimize SVDD hyperparameters [27,28]. However, because SVDD only establishes decision boundaries for normal data, it easily leads to the overfitting of the model and a high false positive rate.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Yang,

Jiang,

Jiang

et al. 2024

Machines

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Because of the difficulty in fault detection for and diagnosing the adjustment hydraulic servomotor, this paper uses feature extraction technology to extract the time domain and frequency domain features of the pressure signal of the adjustment hydraulic servomotor and splice the features of multiple pressure signals through the Multi-source Information Fusion (MSIF) method. The comprehensive expression of device status information is obtained. After that, this paper proposes a fault detection Algorithm GA-SVDD-neg, which uses Genetic Algorithm (GA) to optimize Support Vector Data Description with negative examples (SVDD-neg). Through joint optimization with the Mutual Information (MI) feature selection algorithm, the features that are most sensitive to the state deterioration of the adjustment hydraulic servomotor are selected. Experiments show that the MI algorithm has a better performance than other feature dimensionality reduction algorithms in the field of the abnormal detection of adjustment hydraulic servomotors, and the GA-SVDD-neg algorithm has a stronger robustness and generality than other anomaly detection algorithms. In addition, to make full use of the advantages of deep learning in automatic feature extraction and classification, this paper realizes the fault diagnosis of the adjustment hydraulic servomotor based on 1D Convolutional Neural Network (1DCNN). The experimental results show that this algorithm has the same superior performance as the traditional algorithm in feature extraction and can accurately diagnose the known faults of the adjustment hydraulic servomotor. This research is of great significance for the intelligent transformation of adjustment hydraulic servomotors and can also provide a reference for the fault warning and diagnosis of the Electro-Hydraulic (EH) system of the same type of steam turbine.

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Section: Introductionmentioning

confidence: 99%

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Yang,

Jiang,

Jiang

et al. 2024

Machines

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“…The laws of evolution inspire these algorithms. Metaheuristic algorithms are widely used in signal processing [14], image processing [15,16], fault detection [17], production scheduling [18], feature selection [19], path planning [20], numerical optimization [21], engineering design [22][23][24], etc.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Golden Jackal Optimization and Golden Sine Algorithm with Dynamic Lens-Imaging Learning for Global Optimization Problems

et al. 2022

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Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems.

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Hybrid genetic algorithm and support vector regression for predicting the shear capacity of recycled aggregate concrete beam

Jiang,

Chen,

Jiang

et al. 2024

Soft Comput

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Fault detection based on squirrel search algorithm and support vector data description for industrial processes

Cited by 4 publications

References 32 publications

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

Abnormal Detection and Fault Diagnosis of Adjustment Hydraulic Servomotor Based on Genetic Algorithm to Optimize Support Vector Data Description with Negative Samples and One-Dimensional Convolutional Neural Network

A Hybrid Golden Jackal Optimization and Golden Sine Algorithm with Dynamic Lens-Imaging Learning for Global Optimization Problems

Hybrid genetic algorithm and support vector regression for predicting the shear capacity of recycled aggregate concrete beam

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