Fault information fusion diagnosis method based on double-edge linear motor

Abstract

The invention discloses a fault information fusion diagnosis method based on a double-edge linear motor. The mode of data layer fusion, characteristic layer fusion, decision layer fusion and two-stage diagnosis is adopted to mainly avoid the air gap eccentric fault, the short-circuit fault between winding turns, the winding temperature abnormity fault and the vibration abnormity fault of the double-edge linear motor. A park vector fusion method and an improved base 8FFT algorithm are adopted for data layers; a fuzzy artificial neural network method and a fuzzy decision method are adopted for characteristic layers. According to the method, the park vector fusion method, the improved base 8FFT algorithm, the fuzzy artificial neural network method and the fuzzy decision method are combined together, and thus the capacity for diagnosing various faults of the double-edge linear motor is improved.

Description

technical field [0001] The invention relates to a fault information fusion diagnosis method based on a bilateral linear motor. Based on an improved base 8FFT algorithm, a fuzzy neural network algorithm, and a fuzzy determination method, the air gap eccentric fault, winding inter-turn short circuit fault, and The invention relates to diagnosing abnormal winding temperature faults and abnormal vibration faults, and belongs to the field of electrotechnical technology. Background technique [0002] Since the invention of generators and motors in the early 19th century, due to the convenience of using electric energy and the continuous improvement of the performance of rotating machinery, motor technology has developed rapidly. Now motors are widely used and in large quantities. Due to different application environments and methods, some bilateral type Linear motor failures occur frequently. The failure of bilateral linear motors not only affects the production of the enterprise...

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Problems solved by technology

[0003] The current motor fault diagnosis algorithms all use the traditional FFT algorithm to diagnose air gap eccentric faults. It has a series of advantages such as parallel computing, distributed information storage, strong fault tolerance, and self-adaptive learning function, but it is powerless to express rule-based knowledge and deal with structured knowledge; directly use the instantaneous values ​​of temperature curves and vibration curves to Judging abnormal winding temperature faults and bearing vibration faults is not very effective for random and uncertain sudden faults

[0004] The motor fault diagnosis method currently used in engineering can achieve good results in occasions with low requirements, but the traditional online detection and fault diagnosis system cannot meet the requirements for occasions with high requirements for stability, reliability and accuracy. up

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