With the structural complexity of machine tool and the diversity and time-sequence characteristic of state data,in order to solve the problem that the future state of machine tool is difficult to accurately predict,a novel state prediction method based on multidimensional time series was proposed.Firstly,the ole for process control (OPC) technology was used to collect data of machine tool,and the Min-max normalization and autoregressive moving average model were adopted to data preprocessing.The state and evaluation models of multidimensional time series were established.Meanwhile,the feature vector and trend distance were also used to represent state model,and then the state match of multidimensional time series was completed by difference degree analysis.Secondly,through constructing the time sliding window technique,the historical state sets of machine tool were obtained by the length of time window length and sliding.On this basis,multiple matching technique based on window sliding was developed,and then theβ-coupling similarity metrics was also used to find a set of historical states that were the most similar to the current state matrix.According to the similarity threshold,the optimal sliding time and prediction time were obtained.Further,the density-based spatial clustering algorithm was adopted to perform state series analysis,and the optimal historical sate matrix which can represent the current state of machine tool was obtained,and then the next state was regarded as prediction state.Finally,the state prediction experiments were carried out for four parameters of machine tool spindle.The optimal prediction time and sliding unit were 24 s and 2 s respectively,and then the state-sequence matching was completed by using state-sequence clustering analysis.The prediction results showed that the maximum error,mean error,mean square error and relative error of the matrix and vector state prediction approach based on multidimensional time series were lower than those of traditional AR prediction model,which also verified the effectiveness and accuracy of state prediction method.
