Fault Identification Of RV Reducer Based On Acoustic Emission Technique

RV Reducer was applied in the industrial robot as one of the most common reducer; it has the highlights of small volume, light weight, large range of transmission, long service time, highly stable accuracy, high efficient and smooth transmission. It comes into the trend of replacing harmonic reducer. It requires high standard for the components of mechanical reducer in manufacturing process. However, the fault diagnosis and recognizes is not systemized and inaccuracy in domestic, especially it exists the problem of inaccuracy on the dimensioning of fault parameters. It cannot verdict the operating status of machinery in the manufacture process, which makes inconvenience on the producing. Thus, it is significant of researching the timing, effective and convenient diagnosing methods to improve efficient of status monitoring as well as fault diagnosis. This article will carry out the experiment research on the fault diagnosis and recognition methods of RV reducer, the main contents of the research addressed beneath.1. Researching the development trend of fault diagnosis technology, introducing the development situation of RV reducer, expounding the advantages of RV reducer, analyzing the current difficulties on RV reducer fault diagnosis and clarifying the research purpose of this article.2. Researching the basic theory of fault diagnosis technology, analyzing the pick of characteristic parameter, introducing the method of signal processing briefly, analyzing the fault parameters of RV reducer and presenting the clustering method of K-means.3. Processing and analyzing the acoustic emission signal of RV reducer, analyzing the common features and differences of the signal.4. Carrying out the clustering research on existing data through K-means clustering method, exploring the clustering effect on different characteristic parameters, figuring the solution of automatic fault diagnosis.5. Establishing online robot acoustic emission system, it can extract the signal from RV reducer on site and judge the type of the failure through existing data base.