Oil state self-learning quantitative characterization method, storage medium and equipment

Abstract

The invention discloses an oil state self-learning quantitative characterization method, a storage medium and equipment. The method comprises the following steps: constructing an index-attribute-state three-layer fuzzy state characterization system model; obtaining the membership probability of each monitoring index corresponding to each state grade as an index membership degree; carrying out weighted fusion calculation to obtain an attribute membership degree; formulating an inference rule base, and obtaining the reliability of the oil comprehensive state belonging to each state grade; performing reliability assignment on each state level by applying the maximum, minimum and average utility intervals, so the oil state characterization result is a quantitative output result; establishing a KBNN neural network model; enabling the connections among the layers of the KBNN neural network model to respectively correspond to the connections among the layers of the index-attribute-state three-layer fuzzy state representation system model; training the connected KBNN neural network model, putting the trained KBNN model into use, and carrying out oil state quantitative characterization. According to the method, the accuracy and precision of oil state diagnosis characterization are greatly improved.

Description

technical field [0001] The invention belongs to the technical field of oil state monitoring, and in particular relates to an oil state self-learning quantitative characterization method, storage medium and equipment. Background technique [0002] Oil, as the blood of the machine, can reflect the decay of the tribological state of the machine from the mechanism, and then comprehensively reflect the health status of large-scale equipment, so it becomes a reliable carrier of equipment status. Although oil is a complex of physical and chemical properties, the uncertainty of its information representation due to factors such as multiple indicators and non-monotonicity seriously restricts the development of oil monitoring technology. Therefore, it is of great significance to study the characterization technology of oil uncertain state information. [0003] Expert knowledge decision-making is an effective method to solve uncertain problems such as oil information conflict and non-...

AI Technical Summary

Problems solved by technology

However, these data-driven models, like black-box neural network models, completely rely on the mapping relationship between data and state. applications in monitoring

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