Industrial processes are often operated under different modes. However

most of the multivariate statistical process monitoring(MSPM) methods

such as principal component analysis(PCA) which are effective in single mode process

do not perform well in multimode process. A novel multimode fault detection approach named distance space statistics analysis (DSSA) was proposed. First

every sample was represented by the deviations of its k-nearest distances between itself and its neighbor in the training data. All the samples were mapped from the original space into the distance space by this way. Then

different order statistics of the distance samples in a moving window were calculated in the distance space. Finally

principal component analysis(PCA) was used to analyze the new statistics samples. The proposed method

PCA method and a multimode fault detection method using k-nearest neighbor rule (FD-kNN) were applied to the Tennessee Eastman (TE) benchmark process. The comparison of monitoring results showed that the proposed method was superior to the PCA and FD-kNN for fault detection of the multimode process.Industrial processes are often operated under different modes. However

most of the multivariate statistical process monitoring(MSPM) methods

such as principal component analysis(PCA) which are effective in single mode process

do not perform well in multimode process. A novel multimode fault detection approach named distance space statistics analysis (DSSA) was proposed. First

every sample was represented by the deviations of its k-nearest distances between itself and its neighbor in the training data. All the samples were mapped from the original space into the distance space by this way. Then

different order statistics of the distance samples in a moving window were calculated in the distance space. Finally

principal component analysis(PCA) was used to analyze the new statistics samples. The proposed method

PCA method and a multimode fault detection method using k-nearest neighbor rule (FD-kNN) were applied to the Tennessee Eastman (TE) benchmark process. The comparison of monitoring results showed that the proposed method was superior to the PCA and FD-kNN for fault detection of the multimode process.