A fault detection algorithm for uneven-length multimode batch processes is proposed. First

the local weighted algorithm is used to preprocess the uneven-length batch data. In the training sample

the maximum retention length of uneven-length data is determined. Using the k-nearest neighbor information

the missing data points are reconstructed by weighting reconstruction. Secondly

the local neighbor normalized matrix is estimated for the training set of equal length. The K-means algorithm is used to classify the models. The local outlier factor method is used to determine the first control limits and remove outliers. Finally

the MPCA model is established and the second control limits are determined for each model. The unified statistics and control limits are calculated according to the matching coefficients of the control limit of the various models. The multimode fault detection is carried out under the unified control limits. The algorithm is applied to the semiconductor industrial process. Simulation results show that the proposed algorithm improves the fault detection rate relative to the traditional fault detection algorithms. The effectiveness of the proposed method is verified.