Server with maintenance prediction function, and system

Abstract

The invention relates to a server with a maintenance prediction function, and a system. The server with the maintenance prediction function comprises: a collection module which collects parameter data for predictive maintenance of a wave-activated generator; a fault characteristic frequency acquisition module which is used for acquiring the fault characteristic frequency of the wave-activated generator bearing according to the parameter data; a power spectral density acquisition module which is used for acquiring a cyclic autocorrelation function and power spectral density of a vibration signal in the parameter data according to the fault characteristic frequency; an optimal projection acquisition module which is used for acquiring the optimal projection direction of the one-dimensional space according to the sample data of the power spectral density; an operation health degree obtaining module which is used for predicting the operation health degree of the wave-activated generator according to the optimal projection direction of the one-dimensional space; and an early warning module which carries out early warning on the operation of the wave-activated generator according to the operation health degree of the wave-activated generator, so that prediction and early warning on faults of the wave-activated generator are realized, and the defects of high subjectivity and relatively high labor cost of manual maintenance are overcome.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis and monitoring of a wave energy power generation system, and in particular relates to a server and a system with a maintenance prediction function. Background technique [0002] The wave generator is one of the key components of the wave power generation system, and has a high probability of failure and downtime. It is extremely important for its early fault diagnosis. High maintenance costs due to the distance from the land. In addition, general motor faults have the characteristics of small amplitude, instability, and easy to be affected by working conditions. The fault diagnosis of wave generators is relatively difficult. It is of great significance to seek an effective online diagnosis method for early faults of wave generators, so as to avoid downtime and maintenance of wave generators due to faults and increase operating costs. [0003] The bearing is a key part of the wave generato...

AI Technical Summary

Problems solved by technology

[0004] The regular maintenance method is carried out under the static condition of the equipment not running, which has the following disadvantages: the difference between the state of the equipment under the non-working condition and the operation is obvious, which affects the accuracy of judgment

Due to the regular inspection and maintenance, even if the equipment is in good condition, it still needs to be tested and repaired according to the plan, resulting in a waste of manpower and material resources

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