Support and hanger degradation alarm method based on fundamental frequency data K-Means clustering

Abstract

The invention discloses a support hanger degradation alarm method based on fundamental frequency data K-Means clustering, and the method comprises the following steps: additionally installing a vibration sensor on a support hanger, carrying out vibration testing to acquire N groups of vibration signals; performing spectral analysis on the N groups of vibration signals of the support hanger by adopting a fast Fourier transform method; extracting the maximum peak value of each group of signal frequency spectrums or frequency spectrum envelope lines in an empirical domain as a fundamental frequency observation point of the support hanger by adopting a peak value method identification method according to a frequency spectrum analysis result, and forming a fundamental frequency observation point sequence by all the fundamental frequency observation points as the support hanger; performing data clustering analysis on the fundamental frequency observation sequence by adopting a K-Means clustering method, taking a cluster closest to the mean value of the sequence as a fundamental frequency true value data cluster, and taking the mass center position of the data cluster as a state index ofthe current performance of the support hanger; and when the calculation index obtained by testing the numerical value in real time in the later period deviates from the support and hanger intact state index value distance D for M times continuously, overhauling the deteriorated support hanger.

Description

technical field [0001] The present invention relates to the field of performance monitoring, detection, early warning and evaluation of existing building structures and their auxiliary facilities, in particular, a kind of K-Means clustering result based on the fundamental frequency data identified by the vibration test to monitor the performance degradation of the support and hanger during the operation period, The method of inspection and warning, that is, the method of warning the deterioration of supports and hangers based on K-Means clustering of fundamental frequency data. Background technique [0002] Supports and hangers are one of the important load-bearing components of building structures and their ancillary facilities such as electromechanical and pipelines. During the long-term operation of the building, the supports and hangers often suffer from loose bolts, severe corrosion and local damage. These diseases will not only cause the hanger itself to withdraw from...

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Problems solved by technology

The commonly used methods are as follows: (1) The deterioration of the hanger is found based on the results of manual inspection: this method is to use the building management and inspection personnel to approach the hanger, manually check the appearance of the hanger, and further determine the quality of the hanger. Whether there are diseases such as bolt looseness, severe corrosion and local damage, this method relies on the experience and subjective judgment of the inspectors. It is an uneconomical and unsafe method for personnel to approach vulnerable locations, which is not only time-consuming and laborious, but may also be dangerous; (2 ) The deterioration of the hanger is found based on the natural frequency change of the vibration test data of the hanger: the acceleration sensor test signal installed on the hanger is used to identify the natural frequency of the hanger, and when the natural frequency drops to a certain level, the The support and hanger has some kind of disease and leads to performance degradation. However, this method does not consider the error information caused by some abnormal conditions (such as noise interference, improper excitation mode, sensor abnormality, etc.) during the test process, so it is difficult to guarantee the effectiveness of the method. , resulting in a large number of false positives

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