1. A fault detection method in a continuous annealing process based on a recursive kernel principal component analysis (RKPCA), comprising the following steps: Step 1: collecting data and standardizing samples using a processor by detecting roll speed, current and tension of an entry loop (ELP); Step 2: extracting principal factors P of the fault in the continuous annealing process using the processor by building an initial monitoring model of the continuous annealing process with N standardized samples in Step 1; monitoring a new sample x new of the continuous annealing process, and if it is abnormal, generating an alarm, otherwise going to Step 3; wherein the extracted principal factor P in the continuous annealing process is as follows: P = Φ ⁡ ( X ) ⁡ [ 1 h Φ ⁢ N - 1 N ⁡ ( N - 2 ) 0 T - 1 h Φ ⁢ N - 1 N ⁡ ( N - 2 ) ⁢ B A ~ ] ⁢ U Φ ′ where Φ(X) is a mapping matrix of N samples X=[x 1 , x 2 ,. .. , x N ], N is a sample number, the regulating factor of the initial monitoring model in the continuous annealing process is h Φ = N - 1 N ⁡ ( N - 2 ) ⁢ 1 - 2 ⁢ B T ⁢ k ⁡ ( X , x 1 ) + B T ⁢ K ⁡ ( X ) ⁢ B , the correcting matrix of the initial monitoring model in the continuous annealing process is B = 1 N - 1 ⁢ 1 N - 1 + A ~ ⁢ Λ ~ ⁢ A ~ T ⁡ ( k ⁡ ( X ~ , x 1 ) - 1 N - 1 ⁢ K ⁡ ( X ~ ) ⁢ 1 N - 1 ) , k(X,x 1 ) indicates the inner product of x and x 1 , K(X) indicates the inner product of the sample matrix, k({tilde over (X)},x 1 ) is the inner product of {tilde over (X)} and x 1 , {tilde over (X)} is the middle matrix, K({tilde over (X)}) indicates the inner product of the middle matrix, {tilde over (Λ)} is the eigenvalues matrix of the middle matrix covariance, U′ Φ is the eigenvectors matrix of the process variables, 1 N-1 is the unit vector in N−1 column; Extracting the transmission factor of the continuous annealing process, which is expressed as [ 1 h Φ ⁢ N - 1 N ⁡ ( N - 2 ) 0 T - 1 h Φ ⁢ N - 1 N ⁡ ( N - 2 ) ⁢ B A ~ ] = A ⁡ ( U Φ ′ ) - 1 the Step 3: when the continuous annealing process sample x new is normal data, updating, using the processor, the initial monitoring model of the continuous annealing process built in the Step 2 and calculating the principal factor {circumflex over (P)} of the fault in the updated continuous annealing process model by using the RKPCA, in which {circumflex over (P)} is expressed as follows: P ^ = Φ ⁡ ( [ X ~ x new ] ) ⁡ [ A ~ - 1 h Φ ′ ⁢ N - 1 N ⁡ ( N - 2 ) ⁢ B ′ 0 T 1 h Φ ′ ⁢ N - 1 N ⁡ ( N - 2 ) ] ⁢ U Φ ″ = Φ ⁡ ( X new ) ⁢ A ^ where Φ(X new )=Φ([{tilde over (X)} x new ]) is the updated mapping matrix, the regulating factor of the updated monitoring model in the continuous annealing process is h Φ ′ = N - 1 N ⁡ ( N - 2 ) ⁢ 1 - 2 ⁢ B ′ ⁢ ⁢ T ⁢ k ⁡ ( X ~ , x new ) + B ′ ⁢ ⁢ T ⁢ K ⁡ ( X ~ ) ⁢ B ′ , the regulating matrix of the updating monitoring model in the continuous annealing process is B ′ = 1 N - 1 ⁢ 1 N - 1 + A ~ ⁢ Λ ~ ⁢ A ~ T ⁡ ( k ⁡ ( X ~ , x new ) - 1 N - 1 ⁢ K ⁡ ( X ~ ) ⁢ 1 N - 1 ) , k({tilde over (X)}, x new ) indicates the inner product of {tilde over (X)} and x new ; Step 4: detecting, using the processor, the fault for the continuous annealing process; wherein the fault of the continuous annealing process can be judged by using Hotelling's T 2 statistic and squared prediction error (SPE) statistic, when the T 2 statistic and SPE statistic exceed their confidence limit, a failure is identified; on the contrary, the whole process is normal, go to the step 3 to continue to update the initial monitoring model of the continuous annealing process, and outputting the identified failure.