A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

Jiang, Minlan; Lan, Jiang; Jiang, Dingde; Li, Fēi; Song, Houbing

doi:10.3390/s18010233

Cited by 10 publications

(8 citation statements)

References 28 publications

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“…Moreover, the SVM can theoretically obtain the global optimal solution and can achieve higher accuracy with few samples for the training. In order to improve the prediction accuracy, in it intended to apply the particle swarm optimization (PSO) algorithm in the SVM model to optimize the parameters [17], [18]. In order to resolve the problem of low generalization capacity in predicting the vibration environment of the aircraft platform, Zhang et al [19] combined the PSO and SVM algorithm to propose a novel predicting model.…”

Section: Introductionmentioning

confidence: 99%

Skid-Proof Operation of Wheel Loader Based on Model Prediction and Electro-Hydraulic Proportional Control Technology

et al. 2020

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Wheel loader shovel loading operation tests showed that when the tire slips, it not only causes waste of the engine power, but also increases the tire wear. In this paper, the regression prediction model is combined with the electro-hydraulic proportional control technology. For the first time, the method of adjusting the posture of the working device is proposed to realize the skid-proof shovel control strategy of the wheel loader. In this control strategy: (1) The electro-hydraulic proportional control technology applied to this wheel loader is introduced. (2) The load spectrum of the wheel loader is analyzed during the shovel loading operation. Moreover, combined with the drive shaft torque, pilot pressure and boom cylinder displacement, the particle swarm optimization-support vector machine (PSO-SVM) algorithm is used to complete the construction of the regression prediction model of the boom cylinder displacement.(3) A controller is designed based on the fractional-order PID control algorithm. The skid-proof control strategy is simulated and verified by constructing the joint simulation model. The corresponding program is prepared in the hydraulic system controller and the effectiveness of the control strategy and algorithm are verified by the wheel loader shovel loading operation.INDEX TERMS Electro-hydraulic proportional control technology, PSO-SVM, regression prediction model, skid-proof shovel control strategy.

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Section: Introductionmentioning

confidence: 99%

Skid-Proof Operation of Wheel Loader Based on Model Prediction and Electro-Hydraulic Proportional Control Technology

et al. 2020

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“…However, to overcome the shortcomings of the traditional PSO algorithm, such as slow convergence and the likelihood of falling into a local minimum, its performance was improved by reducing the speed and search range. These modifications were similar to those described for the restricted range PSO algorithm in [22], [23], [28], [31], [32]. In the following, the improved PSO algorithm is referred to as the IPSO algorithm and is used for the optimal selection of SVM learning parameters.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Error Compensation of Capacitive Angular Encoders Based on Improved Particle Swarm Optimization Support Vector Machines

Hou

Zhou

et al. 2020

IEEE Access

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Rotary encoders are widely applied in a variety of industrial fields. However, as the exist of the installation, processing and demodulation circuits errors, the test result of the encoder is superimposed with periodic nonlinear errors and the encoder needs compensation to achieve high measurement accuracy. Traditional methods including the least square method (LSM) and back propagation artificial neural network (BP-ANN), are not capable of addressing nonlinear errors. Thus, a novel method based on improved particle swarm optimization (IPSO) and support vector machines (SVM) is proposed to provide better compensation. The proposed method incorporates the SVM method into the design of the compensation model, and the IPSO algorithm is applied to tune the SVM parameters. To validate the algorithm, four sets of data were obtained from encoders with different numbers of segments. The experimental results show that the IPSO-SVM algorithm has a better prediction precision and the nonlinear standard deviation of 180 petal-shaped numbers has dropped from 0.08 • to 0.0005 • after compensation over 0 • to 360 • measurement range. Based on the results, the proposed IPSO-SVM model provided more accurate compensation on the nonlinear errors to the capacitive angular encoders than other method.

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“…Although SVM has superior ability in pattern recognition, its performance is affected by parameters. In view of this, various optimization algorithms are developed and applied to search the best parameters, such as genetic algorithm (GA) [22], particle swarm optimization (PSO) [23], sine cosine algorithm (SCA) [24], and grey wolf optimizer (GWO) [25]. As novel optimization algorithms, SCA and GWO proposed by Mirjalili et al have proved effective in many previous works [26][27][28].…”

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 Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

Cited by 10 publications

References 28 publications

Skid-Proof Operation of Wheel Loader Based on Model Prediction and Electro-Hydraulic Proportional Control Technology

Skid-Proof Operation of Wheel Loader Based on Model Prediction and Electro-Hydraulic Proportional Control Technology

Nonlinear Error Compensation of Capacitive Angular Encoders Based on Improved Particle Swarm Optimization Support Vector Machines

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

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