Abstract
Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on multi‐genetic algorithm. The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. The experimental results demonstrate that by combining the genetic algorithm and SVM algorithm, fault diagnosis can be effectively realized for bearings of rotating machinery.
Highlights
Rotating machinery has a wide range of applications in the modern industry, within the petrochemical, motor vehicle, power, metallurgy, manufacturing, and other important engineering fields [1,2,3,4,5]
Approximately 30% of rotating machinery faults are caused by bearing faults since the running state of the bearing can directly affect the performance of the machine, and the faults can result in violent vibrations of the mechanical equipment, leading to damage [5]
The working status of machinery affects its operation but can adversely affect follow-up production [6], causing significant losses to the national economy as well as posing a tremendous threat to the lives and safety of personnel working with the equipment. erefore, the discussion and investigation into fault diagnosis technologies for rotating machinery is of critical importance [1, 2, 7, 8]
Summary
Rotating machinery has a wide range of applications in the modern industry, within the petrochemical, motor vehicle, power, metallurgy, manufacturing, and other important engineering fields [1,2,3,4,5]. Bearings are one of the most important components of rotating machinery and are relatively easy to damage, which makes the technology for diagnosing bearing faults an important scientific research. Erefore, the discussion and investigation into fault diagnosis technologies for rotating machinery is of critical importance [1, 2, 7, 8]. Research in the field of fault diagnosis for rolling bearings is booming, and the theory and technology are developing rapidly [9]. Important research is being carried out in the field of rotating machinery fault diagnosis using algorithms. In the field of mechanical fault diagnosis, the neural network has some value in certain applications and has been widely used since it offers major advantages in solving complex nonlinear problems. Slow convergence, local minima, overlearning, and underlearning still exist, and the neural network algorithm requires a large amount of fault data, restricting further application and development of neural
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