Identifying and assessing system for deformation and damage of 16Mn steel welded structure on basis of SNF (Strongest Neighbor Filter) and DSD (Deformation Sensitive Decimation) policies

Abstract

The invention discloses an identifying and assessing system for deformation and damage of a 16Mn steel welded structure on the basis of SNF (Strongest Neighbor Filter) and DSD (Deformation Sensitive Decimation) policies, which comprises a plurality of acoustic emission transducers, a multipath preamplifier, an acoustic emission device and a nondestructive testing unit for the deformation and damage of the 16Mn steel welded structure, wherein the nondestructive testing unit for the deformation and damage of the 16Mn steel welded structure is composed of a SNF filter module, a sample extractingmodule, a neural network forecasting module and a DSD deformation and damage identifying module. In a state identification process, firstly, a SNF policy is used for multi-dimensionally filtering theinformation acquired by a plurality of acoustic emission transducers, and then an artificial neural network method is used for training and forecasting the filtered signals, thereby acquiring a deformation and damage parameter of the 16Mn steel welded structure; and secondly, a DSD policy is used for judging a result outputted by the neural network, thereby confirming the deformation and damage state of a welded part of a detected workpiece. In a working state, the identifying and assessing system disclosed by the invention can be used for extracting the deformation and damage parameter of the 16Mn steel welded structure in service, identifying different damage states and early warning according to an identified result.

Description

technical field [0001] The invention relates to a method for identifying deformation damage of an in-service 16Mn steel welded structure during service. More specifically, it refers to a system based on SNF filtering strategy and DSD judgment strategy, using acoustic emission technology and neural network method, to identify and quantitatively evaluate the deformation damage of 16Mn steel welded structures in large-scale mechanical equipment in ports. Background technique [0002] 16 manganese steel is often used as the key load-bearing parts for shoreside equipment in large-scale mechanical equipment in ports: such as ship loaders, ship unloaders, and grab buckets. After the shore equipment has been used for a period of time, the damage state of the 16 manganese steel as the main bearing part will have an important impact on the service life of the entire shore equipment. [0003] 16Mn steel (16 manganese steel) is a low-alloy steel developed in combination with the resour...

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Problems solved by technology

[0008] (2) The task is heavy. With the expansion of production scale and the lag of crane update, the work of many cranes is becoming more and more heavy, and overloading occurs from time to time;

[0009] (3) The current damage detection method is immature. The partial sampling inspection of cranes by ultrasonic testing and magnetic particle testing is blind, prone to missing inspections, and the detection cycle is long, heavy workload, and expensive;

[0010] (4) The early warning evaluation system is not perfect, and the analysis and discrimination technology currently applied cannot make accurate, timely and comprehensive early warning and safety assessment for the damage of the welding structure deformation of the crane load-bearing parts, especially in the case of large-scale mechanical equipment safety accidents in my country's ports Among them, the deformation damage of the welded structure is one of the main damage modes of the load-bearing parts of the large shore crane equipment in the port

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