Fault Diagnosis of Gearbox Using Particle Swarm Optimization and Second-Order Transient Analysis

2017 ◽  
Vol 139 (2) ◽  
Author(s):  
Sajid Hussain
Keyword(s):  
Experimental Data ◽  
Particle Swarm Optimization ◽  
Transient Analysis ◽  
Bandpass Filter ◽  
Particle Swarm ◽  
Second Order ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Health Related ◽  
Feature Extraction Technique

Detection of faults in a gearbox is a first and foremost step before diagnostic and prognostic operations are performed. This paper proposes a novel gearbox fault detection and feature extraction technique. The proposed method adaptively filters the vibration signals emanating from a gearbox. A bandpass filter is designed and optimized through particle swarm optimization (PSO) to maximize kurtosis as an objective function. Gearbox health-related features are extracted from the filtered signals using second-order transient analysis. The method is validated on experimental data collected from a running gearbox in healthy and faulty conditions. The proposed method has successfully identified the faulty conditions inside the gearbox.

Signal-Noise Separation Method Based on PSO for Vibration Signals

2010 ◽  
Vol 159 ◽  
pp. 686-690
Author(s):  
Hong Xia Pan ◽  
Jin Ying Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Wavelet Analysis ◽  
Blind Source Separation ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Separation Method ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Signal Noise ◽  
Fault Information

The wavelet analysis is combined with particle swarm optimization (PSO) which is applied to the de-noise process of vibration responding signals in this paper. The fault information has been enhanced. Furthermore, the signal-noise separation method based on particle swarm optimization for vibration signal and the diagnosis precision enhancement technology are studied, by means of the blind source separation technique.

scholarly journals Intelligent Bearing Fault Diagnosis using Swarm Decomposition Method and New Hybrid Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Saeed Nezamivand Chegini ◽  
Pouriya Amini ◽  
Bahman Ahmadi ◽  
Ahmad Bagheri ◽  
Illia Amirmostofian
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Support Vector ◽  
Svm Classifier ◽  
Features Selection ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Hybrid Particle Swarm Optimization

Abstract The quality of information extracted from the vibration signals, and the accuracy of the bearing status detection depend on the methods used to process the signal and select the informative features. In this paper, a new hybrid approach is introduced in which the relatively new Swarm Decomposition (SWD) method and the optimized Compensation Distance Evaluation Technique (OCDET) are used to enhance the signal processing stage and to improve the optimal features selection process, respectively. Firstly, the vibration signals are decomposed into their Oscillatory Components (OCs) using the SWD. The feature matrix is constructed by computing the time-domain features for the OCs. The CDET method is consequently utilized to select the most sensitive features corresponding to the bearing status. On the other hand, The CDET approach contains a parameter called threshold which affects the number of the selected features. In this way, the hybrid optimization algorithm, which is a combination of the Particle Swarm Optimization (PSO) algorithm with the Sine-Cosine Algorithm (SCA) and the Levy flight distribution, has been used to select the optimal CDET threshold and improve the Support Vector Machine (SVM) classifier. The proposed technique ability is evaluated by vibration signals corresponding to different bearing defects and various speeds. The results indicate the capability of the proposed fault diagnosis method in identifying the very small-size defects under various bearing conditions. Finally, the presented method shows better performance in comparison with other well-known methods in the most of the case studies.

scholarly journals Evolutionary Optimization of Machining Parameters Based on Surface Roughness in End Milling of Hot Rolled Steel

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5494
Author(s):  
Issam Abu-Mahfouz ◽  
Amit Banerjee ◽  
Esfakur Rahman
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
End Milling ◽  
Particle Swarm ◽  
Depth Of Cut ◽  
Selection Methods ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Better Than

Surface roughness measurements of machined parts are usually performed off-line after the completion of the machining operation. The objective of this work is to develop a surface roughness prediction method based on the processing of vibration signals during steel end milling operation performed on a vertical CNC machining center. The milling cuts were run under varying conditions (such as the spindle speed, feed rate, and depth of cut). This is a first step in the attempt to develop an online milling process monitoring system. The study presented here involves the analysis of vibration signals using statistical time parameters, frequency spectrum, and time-frequency wavelet decomposition. The analysis resulted in the extraction of 245 features that were used in the evolutionary optimization study to determine optimal cutting conditions based on the measured surface roughness of the milled specimen. Three feature selection methods were used to reduce the extracted feature set to smaller subsets, followed by binarization using two binarization methods. Three evolutionary algorithms—a genetic algorithm, particle swarm optimization and two variants, differential evolution and one of its variants, have been used to identify features that relate to the “best” surface finish measurements. These optimal features can then be related to cutting conditions (cutting speed, feed rate, and axial depth of cut). It is shown that the differential evolution and its variant performed better than the particle swarm optimization and its variants, and both differential evolution and particle swarm optimization perform better than the canonical genetic algorithm. Significant differences are found in the feature selection methods too, but no difference in performance was found between the two binarization methods.

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