A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM

Shi, Pengfei; Liang, Kai; Han, Dongying; Zhang, Ying

doi:10.21595/jve.2017.18380

Cited by 16 publications

(11 citation statements)

References 26 publications

(24 reference statements)

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“…Numerous variations of PSO algorithm have been developed and reported in the literature, such as quantum behaved PSO (QPSO) and deep learning-driven PSO [38]. Variations in different performance include avoiding local maxima and convergence times.…”

Section: The Quantum Particle Swarm Optimization Algorithm (Qpso)mentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.

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Section: The Quantum Particle Swarm Optimization Algorithm (Qpso)mentioning

confidence: 99%

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Alajmi

Almeshal

2020

Materials

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“…Machine learning and physics share some methods as well as goals, as both of them involve the process of gathering and analyzing data to design models that can predict the behavior of complex systems [21]. A support vector machine, developed by Vapnik [22], is a machine learning method based on the statistical learning theory, which solves the problem of over-fitting and low convergence rate, and it has been widely applied in the field of power system fault diagnosis [23]. Selection of kernel functions and kernel parameters is the key to SVM, and it directly affects the generalization ability of SVM.…”

Section: Introductionmentioning

confidence: 99%

Insulation Fault Diagnosis of Disconnecting Switches Based on Wavelet Packet Transform and PCA-IPSO-SVM of Electric Fields

Zhang

Kong

2020

IEEE Access

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Previous studies have shown that switching operations of gas insulated substations (GIS) can generate transient radiation fields outside the enclosure, namely switching transient electric fields (STEF). The waveform features of STEF can reflect the functioning performance of the switch. To monitor online the working states of disconnecting switches (DS), in this paper, we built an experimental platform to simulate their typical faulty types. Then, under different faulty status, a non-invasive three-dimensional (3D) electric field measurement system was applied to obtain STEF produced by DS. It is difficult for conventional methods to establish an accurate fault-diagnosis model, so we presented a novel method to identify the condition of DS. This innovative approach is based on feature extraction and machine learning and combined signal analysis to classify different defect types of DS. Measured STEF signals were analyzed by the wavelet packet transform(WPT) method in the time-frequency domain, which was transformed to the multi-dimensional feature matrix. The principal component analysis (PCA) algorithm was employed to reduce the dimensionality of the obtained feature matrix, which was also compared to other feature extraction algorithms. In addition, a support vector machine (SVM) with an improved particle swarm optimization (IPSO) algorithm was designed to achieve a PCA-IPSO-SVM model which can be used for signal recognition. The proposed IPSO technique can improve the convergence performance of the PSO through the dynamic adjustment of inertia weight and learning factors. Results show that the proposed fault diagnosis method based on WPT and PCA-IPSO-SVM can effectively identify the insulation faulty signals in STEF. INDEX TERMS Gas insulated substation (GIS); disconnecting switch (DS); switching transient electric field (STEF); wavelet packet transform(WPT); principal component analysis (PCA); support vector machine (SV M); improved particle swarm optimization (IPSO)

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“…SVM finds an optimal hyperplane that meets the classification requirements, where the hyperplane maximizes the interval between two classes, thus promoting classification performance. Currently, SVM combined with feature extraction method has been successfully employed in the field of pattern recognition [20,21].…”

Section: Introductionmentioning

confidence: 99%

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

Tan

Zhang

et al. 2019

Complexity

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As a crucial and widely used component in industrial fields with great complexity, the health condition of rotating machinery is directly related to production efficiency and safety. Consequently, recognizing and diagnosing rotating machine faults remain to be one of the main concerns in preventing failures of mechanical systems, which can enhance the reliability and efficiency of mechanical systems. In this paper, a novel approach based on blind parameter identification of MAR model and mutation hybrid GWO-SCA optimization is proposed to diagnose faults for rotating machinery. Signals collected from different types of faults were firstly split into sets of intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the decomposing mode number K of which was preset with central frequency observation method. Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). The optimal SVM model was later employed to classify different fault samples. The engineering application and contrastive analysis indicate the availability and superiority of the proposed method.

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A novel intelligent fault diagnosis method of rotating machinery based on deep learning and PSO-SVM

Cited by 16 publications

References 26 publications

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

Insulation Fault Diagnosis of Disconnecting Switches Based on Wavelet Packet Transform and PCA-IPSO-SVM of Electric Fields

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

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