Abstract
With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.
Highlights
Equipment condition monitoring and fault diagnosis technologies have become an urgent need for equipment management and maintenance
To further quantitatively compare the separation effect of the three methods on the composite fault signal, we introduce the average Pearson correlation coefficient [24] for judging, which is defined by the following equation: ρ=
The fault monitoring and diagnosis of gears and rolling bearings play an important role in the health management of mechanical transmission equipment
Summary
Equipment condition monitoring and fault diagnosis technologies have become an urgent need for equipment management and maintenance. These technologies can foresee accidents, ensure personal equipment safety, and greatly improve production efficiency. One of the common fault diagnosis methods is the analysis of the vibration signals of individual drive components [1]. Compound faults of gears and bearings occur extremely frequently in common transmission component problems [2]. This paper focuses on the effective decomposition and reconstruction of the fault signal energy of transmission devices in mechanical engineering. Signal decomposition plays an important role in fault diagnosis [8,9].
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