An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

Mishra, Rismaya Kumar; Choudhary, Anurag; Mohanty, Amiya Ranjan; Fatima, Shahab

doi:10.1177/09544062221101737

Cited by 34 publications

(9 citation statements)

References 50 publications

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“…Accordingly, best parameters for each algorithm are used for solving the IKM of a three-section CR during tracking a spiral-like trajectory ( X = 8 t sin ( t / 2 ) , Y = 8 t cos ( t / 2 ) , 650 − 10 t ) ( t = 0 : 0 . 25 : 30 ) . The used codes to test the previously mentioned algorithm is inspired from HGSO, 38–41 GA, 42 ABC, 43 and TLBO. 44,45…”

Section: Comparison Of Pso With Four Meta-heuristic Approaches In Ter...mentioning

confidence: 99%

“…Accordingly, best parameters for each algorithm are used for solving the IKM of a threesection CR during tracking a spiral-like trajectory (X =8t sin (t=2), Y = 8t cos (t=2), 650 À 10t) (t = 0 : 0:25 : 30). The used codes to test the previously mentioned algorithm is inspired from HGSO, [38][39][40][41] GA, 42 ABC, 43 and TLBO. 44,45 Comparatively speaking, the adopted meta-heuristic approaches for solving the IKM of a three-section CR are reliably accurate since the maximum error is less than 1 3 10 À5 mm (see Table 5 and Figure 14).…”

Section: Comparison Of Pso With Four Metaheuristic Approaches In Term...mentioning

confidence: 99%

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Inverse kinematic model of multi-section continuum robots using particle swarm optimization and comparison to four meta-heuristic approaches

Djeffal

Mahfoudi

2023

SIMULATION

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Multi-section continuum robots’ (CRs) behavior is still an outstanding problem because of the highly non-linearity of its equation of motions. To this end, in this paper, particle swarm optimization (PSO) is adopted to solve the inverse kinematic model (IKM) of CRs. First, the CR’s structure is properly described. Then, the aforementioned algorithm is elaborately discussed and implemented in figuring out the IKM of CR and verified through forward kinematic model by choosing the PSO parameters, namely, cognitive factors [Formula: see text] and inertia weight [Formula: see text] for 200 positions on an arc-like trajectory. The optimal angle values ([Formula: see text] and [Formula: see text]) which ensure the lowest distance between the attainably desired position and the robot’s end effector are [Formula: see text] which is perfectly accurate. After that, simulation through MATLAB is carried out, namely, in the first simulation, a three-section CR follows a linear trajectory with a precision approximately equal to [Formula: see text]. Furthermore, PSO takes 7 ms as a mean consumption time to make the robot’s end effector attain to each position. Then, a circular trajectory is followed using PSO. Comparatively speaking, PSO is compared with four meta-heuristic approaches; it is remarked that PSO is a good compromise between accuracy and time consumption. Based on the obtained results, PSO can be considered as a trade-off between accuracy and time consumption for solving the IKM of CRs with complex structure.

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Section: Comparison Of Pso With Four Meta-heuristic Approaches In Ter...mentioning

confidence: 99%

Section: Comparison Of Pso With Four Metaheuristic Approaches In Term...mentioning

confidence: 99%

Inverse kinematic model of multi-section continuum robots using particle swarm optimization and comparison to four meta-heuristic approaches

Djeffal

Mahfoudi

2023

SIMULATION

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“…Finally, the features are classified and learned. Commonly used traditional machine learning classification methods include K-nearest neighbors [16], support vector machines (SVMs) [17], and artificial neural networks [18], among others. The aforementioned methods excessively depend on expert knowledge and possess a shallow network structure, thereby impeding the extraction of profound feature information and failing to fulfill the present-day demands for extensive integration and maintenance.…”

Section: Introductionmentioning

confidence: 99%

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

Sun,

Zhu,

Xiao

et al. 2024

Meas. Sci. Technol.

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The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network(GGRU), one-dimensional convolutional neural network(1DCNN), and adaptive batch normalization(AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer (1D-GAP) to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network's feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model's domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University (CWRU), aero-engine bearings, Xi'an Jiaotong University, and the Changxing Sumyoung Technology (XJTU-SY). The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

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“…The schemes of time-frequency combined with neural networks have been maturely applied in the field of mechanical fault diagnosis [18,19]. However, it can be observed that the following challenges still exist in the above-mentioned methods.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of industrial motors under few-shot feature conditions based on causality

Cen¹,

Li²,

Cen³

et al. 2023

Meas. Sci. Technol.

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It is observed that previous research studies focusing on few-shot feature data for fault diagnosis or anomaly detection have a limitation, that is, feature extraction methods to solve few-shot feature data problems will also have scenarios where they may not always be applicable. In this paper, a motor anomaly detection model with generalization performance is proposed to meet the anomaly detection needs in the above scenarios. The model consists of a reinforcement unit and a diagnosis unit. Firstly, the reinforcement unit extracts the adjacent features with different timestamps through ensemble learning. Secondly, the temporal convolutional network (TCN) model is nested to increase the receptive field of the reinforcement unit. Additionally, a residual network is introduced to improve the generalization performance. Finally, features obtained from the reinforcement unit are used for final anomaly detection through neural networks in the diagnosis unit. Experimental results indicate that the proposed model achieve an anomaly detection accuracy of 97.96% in factory motor dataset, while the model has the superior generalization ability.

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An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

Cited by 34 publications

References 50 publications

Inverse kinematic model of multi-section continuum robots using particle swarm optimization and comparison to four meta-heuristic approaches

Inverse kinematic model of multi-section continuum robots using particle swarm optimization and comparison to four meta-heuristic approaches

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

Anomaly detection of industrial motors under few-shot feature conditions based on causality

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