Abstract
Bearing is an important mechanical component that easily fails in a bad working environment. Support vector machines can be used to diagnose bearing faults; however, the recognition ability of the model is greatly affected by the kernel function and its parameters. Unfortunately, optimal parameters are difficult to select. To address these limitations, an escape mechanism and adaptive convergence conditions were introduced to the ALO algorithm. As a result, the EALO method was proposed and has been applied to the more accurate selection of SVM model parameters. To assess the model, the vibration acceleration signals of normal, inner ring fault, outer ring fault, and ball fault bearings were collected at different rotation speeds (1500 r/min, 1800 r/min, 2100 r/min, and 2400 r/min). The vibration signals were decomposed using the variational mode decomposition (VMD) method. The features were extracted through the kernel function to fuse the energy value of each VMD component. In these experiments, the two most important parameters for the support vector machine—the Gaussian kernel parameter σ and the penalty factor C—were optimized using the EALO algorithm, ALO algorithm, genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. The performance of these four methods to optimize the two parameters was then compared and analyzed, with the EALO method having the best performance. The recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used.
Highlights
Bearing is an important mechanical component that fails in a bad working environment
Is is because the radial basis kernel function has better nonlinear mapping ability in high-dimensional space than other kernel functions, making this model better suited for the fault classification of bearings. erefore, both the binary tree support vector machine model and the radial basis kernel function model were used in these experiments. e EALO, traditional Ant Lion Optimizer” (ALO), genetic algorithm (GA), and particle swarm optimization (PSO) methods with different parameters were used to optimize the SVM model parameters
Based on the classical ALO algorithm, the EALO algorithm was proposed by introducing an escape mechanism and adaptive iterative convergence conditions. is algorithm was applied to the diagnosis of bearing faults
Summary
Bearing is an important mechanical component that fails in a bad working environment. E recognition rates for bearing faults under different tested rotation speeds were improved when the SVM model parameters optimized by the EALO were used. A weak fault results in abnormal vibrations, which hinder the performance of the mechanical equipment and reduce work efficiency. Ese have been adopted to allow for online monitoring of working conditions, which allow for timely fault detection as well as the development of accurate reference points for future maintenance decisions. Given these advantages, it is important to study the application of intelligent fault diagnostic technology to rolling bearing performance. Traditional fault diagnostic methods include analyzing vibration signals from the time, frequency, and timefrequency domains. Traditional approaches have difficulty detecting these signals because they are based on the neural network [7,8,9] and the Bayesian decision [10,11,12] methods, which require a large number of valid samples to function properly. is means that when the sample size is too small, model accuracy decreases; a large number of fault samples are difficult to discern. erefore, the application of traditional pattern recognition methods such as the neural network and the Bayesian decision is restricted
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