@article{
 title = {Multivariable statistical process monitoring method based on multiscale analysis},
 type = {article},
 year = {2009},
 keywords = {Discrete wavelet transform,Multiscale analysis,Principal component analysis,Statistical process monitoring},
 pages = {952-958},
 volume = {60},
 id = {b236d377-f592-3251-855a-efa37d17da88},
 created = {2020-07-31T17:58:46.180Z},
 file_attached = {false},
 profile_id = {c23b2020-6c99-31d5-8474-4a35eb1af667},
 last_modified = {2021-04-09T16:14:43.371Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The existing multiscale principal component analysis method provides an effective way to monitor industrial processes with multiscale features due to the influence of events at different time-frequency values, but there are two main shortcomings with respect to this method: a reconstruction step is needed which results in a great number of monitoring models to be constructed; and Haar wavelet is used to do a wavelet transform for multiscale analysis, but it is not continuous and therefore not good at approximating practical signal. Hence, a modified multiscale monitoring method was proposed, which not only eliminated the reconstruction step based on the analysis of scale feature of the fault but also replaced Haar wavelet with sym wavelet, which was continuous and had a higher order vanishing moment and thus was more suitable to extract multiscale features. In particular, the proposed method gave an alternative multiscale way to determine the location and duration of two typical faults: step fault and oscillating fault, and solve the problem of boundary effect and signal alignment accompanying the introduction of sym wavelet. Finally, the effectiveness of the proposed methods was verified by simulated experiments on a standard CSTR problem. © All Rights Reserved.},
 bibtype = {article},
 author = {Liu, Yuming and Liang, Jun and Hu, Bin and Ye, Lubin and Shi, Xiangrong},
 journal = {Huagong Xuebao/CIESC Journal},
 number = {4}
}

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Hence, a modified multiscale monitoring method was proposed, which not only eliminated the reconstruction step based on the analysis of scale feature of the fault but also replaced Haar wavelet with sym wavelet, which was continuous and had a higher order vanishing moment and thus was more suitable to extract multiscale features. In particular, the proposed method gave an alternative multiscale way to determine the location and duration of two typical faults: step fault and oscillating fault, and solve the problem of boundary effect and signal alignment accompanying the introduction of sym wavelet. Finally, the effectiveness of the proposed methods was verified by simulated experiments on a standard CSTR problem. © All Rights Reserved.","bibtype":"article","author":"Liu, Yuming and Liang, Jun and Hu, Bin and Ye, Lubin and Shi, Xiangrong","journal":"Huagong Xuebao/CIESC Journal","number":"4","bibtex":"@article{\n title = {Multivariable statistical process monitoring method based on multiscale analysis},\n type = {article},\n year = {2009},\n keywords = {Discrete wavelet transform,Multiscale analysis,Principal component analysis,Statistical process monitoring},\n pages = {952-958},\n volume = {60},\n id = {b236d377-f592-3251-855a-efa37d17da88},\n created = {2020-07-31T17:58:46.180Z},\n file_attached = {false},\n profile_id = {c23b2020-6c99-31d5-8474-4a35eb1af667},\n last_modified = {2021-04-09T16:14:43.371Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The existing multiscale principal component analysis method provides an effective way to monitor industrial processes with multiscale features due to the influence of events at different time-frequency values, but there are two main shortcomings with respect to this method: a reconstruction step is needed which results in a great number of monitoring models to be constructed; and Haar wavelet is used to do a wavelet transform for multiscale analysis, but it is not continuous and therefore not good at approximating practical signal. Hence, a modified multiscale monitoring method was proposed, which not only eliminated the reconstruction step based on the analysis of scale feature of the fault but also replaced Haar wavelet with sym wavelet, which was continuous and had a higher order vanishing moment and thus was more suitable to extract multiscale features. In particular, the proposed method gave an alternative multiscale way to determine the location and duration of two typical faults: step fault and oscillating fault, and solve the problem of boundary effect and signal alignment accompanying the introduction of sym wavelet. 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