Method for diagnosing vibration faults of rotary machine

Abstract

The invention discloses a method for diagnosing vibration faults of a rotary machine. According to the method for diagnosing the vibration faults of the rotary machine, vibration data in various states are collected, process amount data in various states are correspondingly acquired at the same time, and therefore the vibration data and the process amount data in various typical vibration fault states of the rotary machine can be obtained. Characteristic values are extracted from the vibration data and the process amount data respectively to form characteristic value matrices, the characteristic value matrices completely describe the process varying rules of various typical faults, namely a sample matrix database of the various typical faults. On the basis, according to the method for diagnosing the vibration faults of the rotary machine, the image recognition technology is applied to the field of fault diagnosis, the defects of manual fault recognition and single characteristic quantity fault recognition in the present field of fault diagnosis are overcome through matching calculation of the characteristic value matrices, and high-accuracy automatic fault diagnosis is achieved.

Description

technical field [0001] The invention relates to the field of vibration fault diagnosis of rotating machinery, in particular to a method for monitoring and high-precision automatic diagnosis of vibration fault types of rotating machinery by using heterogeneous sensor process information fusion technology and image recognition technology. Background technique [0002] Rotating machinery is key equipment widely used in electric power, metallurgy, petrochemical, transportation and other industries. On-line monitoring and fault diagnosis of rotating machinery is an important means to ensure the safe and stable operation of rotating machinery. The modern fault diagnosis technology of rotating machinery based on vibration data has been developed for more than 30 years, but most of the current fault diagnosis methods rely on the original vibration data collected for diagnosis. These raw vibration data are often a section of waveform collected for a specific measuring point in a shor...

AI Technical Summary

Problems solved by technology

Therefore, if the vibration data is simply analyzed without considering the process data, the relevant fault types cannot be well distinguished