Industrial processes often operate in multiple production modes, for the characteristics of larger variables, the center drift and the larger modal variance of the multi-modal data, a weighed k-nearest-neighbor fault detection method(FD-wkNN) is proposed. First, find the k nearest neighbor in the training data set and calculate the distance between the training sample and the k nearest neighbor, and calculate the average distance of between the k nearest neighbor and the first K local nearest neighbors, take the reciprocal of the average distance as the distance weight, take the weighted distance as statistic D, D is able to eliminate the influence of center drift and difference of modal variances. Second determine the control line using D distribution. Finally compare the calculation D statistics of online sample and control line. On-line fault detection is realized. Using multi-mode example, as well as examples of penicillin data simulation experiments, compared with the PCA, kPCA, FD-kNN method to verify the effectiveness of this method.

To the speciality of mixed noise constituted by pulse noise and Gauss noise，we present a comprehensive algorithm in this
text，which is based on the simplified PCNN model，utilizing several technique specialities of the model，selecting parameters properly，
and combining with mathematical morphology method，median filtering and wiener filtering． This method performs better than average
filters and median filters on hybrid noise reduction while retaining edges and detail information of the image． Experiments show that the
effect of eliminating grey image mixed noise which applying the simplified PCNN eliminating algorithm proposed in this paper is good．
This algorithm can show a big advantage when in the comparison with other algorithms． This algorithm not only can effectively filter hybrid
noise but also can excel in real-time tasks because of its reduced computation complexity． With the increase of the image populated
by blends noise，the advantage is obvious．