Robot failure diagnosis method achieved by multi-mode fusion inference

Abstract

The invention provides a robot failure diagnosis method achieved by multi-mode fusion inference. According to the method, results obtained from inference of various inference engines (such as inference engines based on rules, neural networks, the Bayesian network and evidence theories) are fused with the evidence theory method to obtain an inference result with higher credibility. The method mainly comprises the steps of determining an identification frame, converting the inference results of the various inference engines into elementary probability assignments, assigning a weight to each inference method, fusing the elementary probability assignments with the Dempster combination rule, and making decisions by means of gamble probability conversion. According to existing fusing inference methods, either a result obtained from last inference is used by next inference or different kinds of inference are adopted in different stages of a system, and therefore inference of the same information is only conducted with one inference method and the certainty can not be guaranteed. According to the robot failure diagnosis method achieved by multi-mode fusion inference, parallel inference is adopted, and the inference results obtained from the four inference methods are fused to improve the certainty of the inference result. The robot failure diagnosis method achieved by multi-mode fusion inference can be applied to failure diagnosis of multiple fields such as robots and can determine failures of a failure equipment system with a large number of uncertain factors.

Description

technical field [0001] The invention relates to the field of uncertainty reasoning and information fusion in the direction of artificial intelligence, is a multi-mode reasoning and fusion method based on evidence theory, and can be applied to fault diagnosis in multiple fields such as robot fault diagnosis. Background technique [0002] Because things and their relationships in the real world are more complex, random, fuzzy, incomplete and imprecise, people have a certain degree of uncertainty in their cognition. The reasoning done in this case is reasoning under uncertainty. Uncertainty reasoning starts from uncertain initial evidence or facts, uses uncertain knowledge, uses a certain method to propagate uncertainty, and finally draws a certain degree of uncertainty that is reasonable or basic. The process of reasoning to a reasonable conclusion. At present, many theories have been applied to uncertainty reasoning. The main reasoning methods are rule-based reasoning, Baye...

AI Technical Summary

Problems solved by technology

Mainly solve the technical problems of the limitations of existing independent reasoning methods

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