scholarly journals A New Process Monitoring Method Based on Waveform Signal by Using Recurrence Plot

Entropy ◽  
2015 ◽  
Vol 17 (12) ◽  
pp. 6379-6396 ◽  
Author(s):  
Cheng Zhou ◽  
Weidong Zhang
Keyword(s):  
Process Monitoring ◽  
Recurrence Plot ◽  
Monitoring Method ◽  
New Process

Time domain NMR as a new process monitoring method for characterization of pharmaceutical hydrates

2017 ◽  
Vol 137 ◽  
pp. 96-103 ◽  
Author(s):  
Stefanie Ulrike Schumacher ◽  
Benno Rothenhäusler ◽  
Alf Willmann ◽  
Jürgen Thun ◽  
Regina Moog ◽  
...  
Keyword(s):  
Process Monitoring ◽  
Time Domain ◽  
Monitoring Method ◽  
New Process

An Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processes

2016 ◽  
Vol 13 (2) ◽  
pp. 1102-1111 ◽  
Author(s):  
Cheng Zhou ◽  
Kaibo Liu ◽  
Xi Zhang ◽  
Weidong Zhang ◽  
Jianjun Shi
Keyword(s):  
Process Monitoring ◽  
Recurrence Plot ◽  
Automatic Process ◽  
Monitoring Method

scholarly journals Development of a Rapid Process Monitoring Method for Dry-Coated Tableting Process by Using Near-Infrared Spectroscopy

2011 ◽  
Vol 59 (7) ◽  
pp. 868-873 ◽  
Author(s):  
Masatomo Ito ◽  
Tatsuya Suzuki ◽  
Naoki Wakiyama ◽  
Hiroshi Teramoto ◽  
Etsuo Yonemochi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Process Monitoring ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Monitoring Method ◽  
Rapid Process

Sparse representation preserving embedding based on extreme learning machine for process monitoring

2020 ◽  
Vol 42 (10) ◽  
pp. 1895-1907
Author(s):  
Hui Yongyong ◽  
Zhao Xiaoqiang
Keyword(s):  
Sparse Representation ◽  
Extreme Learning Machine ◽  
Process Monitoring ◽  
Nonlinear Dynamic ◽  
Monitoring Method ◽  
Adjacency Graph ◽  
Manifold Structure ◽  
Fast Learning ◽  
Learning Machine ◽  
Adaptive Neighborhood

Extreme learning machine (ELM) is a fast learning mechanism used in many domains. Unsupervised ELM has improved to extract nonlinear features. A nonlinear dynamic process monitoring method named sparse representation preserving embedding based on ELM (SRPE-ELM) is proposed in this paper. First, the noise is removed by sparse representation and the sparse coefficient is applied to construct the adjacency graph. The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter. Secondly, a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure. Thirdly, the statistic SPE and T2 based on SRPE-ELM are built to monitor the whole process. Finally, SRPE-ELM is applied in the IRIS data classification example, a numerical case and Tennessee Eastman benchmark process to verify the effectiveness of process monitoring.

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