Simulating calculation based intelligent rotary mechanical fault diagnosis system and method

Abstract

The invention relates to a simulating calculation based intelligent rotary mechanical fault diagnosis system and method. A data acquisition module acquires vibration data of an original analog quantity in the operation process of a unit, processes frequency-domain data to obtain characteristic parameters of vibration, and obtains thermodynamic data, including temperature, pressure and flow, of theunit; a simulating calculation module carries out simulating calculation, of a dynamic system, on the acquired vibration data and thermodynamic data; and a health monitoring and fault diagnosis module compares a real-time simulation calculation result with actual operation data of the unit to evaluate the operation state of the set, and feeds back the evaluated operation state to a human-computerinterface, so that operation staff can use the operation state as reference. Thus, the operation staff can pre-discriminate the mechanical vibration fault timely, and loss generated by vibration fault can be prevented.

Description

technical field [0001] The invention relates to an intelligent diagnosis system and method for vibration faults of rotating machinery based on simulation calculation, in particular to an intelligent diagnosis system and method for mechanical vibration faults of steam turbines. Background technique [0002] In the past ten years, various units have developed many intelligent fault diagnosis systems. In the intelligent vibration fault diagnosis system, AI algorithms such as neural network, fault tree, and genetic algorithm are used to analyze the vibration fault data, and judge the internal connection mechanism of all parameters when the fault occurs according to the fuzzy rules generated by the big data. The disadvantage of the system is that only when a fault occurs, there will be big data of the fault, and the numerical training of the model will be carried out. It is the reverse empirical rule to judge the fault instead of the positive rule judgment. This diagnosis method ...

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Problems solved by technology

The disadvantage of the system is that only when a fault occurs, there will be big data of the fault, and the numerical training of the model is carried out. It is a reverse empirical rule to judge the fault instead of a positive rule judgment. This diagnosis method cannot prevent the fault in time

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