Modeling system for collected data of sensors on numerical control machine tool and method therefor

A method for a modeling system for collected data of sensors on a numerical control machine tool, wherein the method runs on a controller, and comprises the following steps:

• Step 1:
• a plurality of sensors transmitting collected p-channel data to the controller through a multi-channel sensor interface circuit, and the controller converting a received p-channel data into tensor-data-one of the p-channel, the tensor-data-one of the p-channel comprising n sample data collected by the plurality of sensors, each sample data comprising data collected by the plurality of sensors at a certain sampling time, and wherein data format of the i-th sample data Yi(t) being:
• Yi(t)=[Yil(t), . . . , Yip(t)], t is the sampling time of the i-th sample data;
• Yi(t) is expressed by formula (1):
• Yij(t)=Xij(t)+εij(t) (1)
• wherein Xij(t) is a signal function of the collected data of the jth channel, εij(t) is a noise function of the collected data of the jth channel, i is a positive integer less than or equal to N, j is a positive integer less than or equal to p, Yij(t) represents the collected data of the j-th channel at a sampling time t;
• Step 2:
• setting the data format of a tensor-data-two of the p channel Xi(t) as:
• Xi(t)=[Xil(t), . . . ,Xip(t)], wherein the tensor-data-two of the p channel Xi(t) is divided into L sample subspaces Sl, l=1, 2, . . . , L, L is a positive integer, in the same sample subspace, the signal functions representing each signal have cross-correlation, the signal functions of each signal in different sample subspaces have no correlation;
• Step 3:
• setting each sample subspace Sl as a linear superposition of Φl of dl basis functions, as shown in formula (2), wherein Φl=[Φl1(t), . . . , Φldl(t)]:
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• wherein for all signal functions Xij(t), i=1, .. . ,N, the sample subspace to which Xij(t) belongs remains unchanged, while the basis coefficients corresponding to Xij(t) are different, and αijk is the k-th component of the basis coefficient αij, where k is a positive integer less than or equal to dl;
• wherein Xl represent the signals of the respective sample subspaces, wherein Xl={Xj(t)|Xj(t)∈Sl, j=1, . . . ,p}, q and dl are both positive integers, and αq is a set real number, R is the set of real numbers;
• Step 4:
• obtaining formula (3) for each signal Xij(t), Xij(t)∈Xl, according to the self-expression property of data that each data point in the sample subspace can be jointly represented by other data points in the data set:
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• that is, Xij(t) is a signal function in Xl, which can be expressed as a linear combination of other pl-1 signals in this sample subspace, r is a positive integer, and bjr is the rth component of a sparse coefficient;
• Step 5:
• using the Lq norm to minimize an objective function to limit solution set for solution of the sparse coefficient bj.