Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multivariate statistical process control (MSPC) has been applied to performance monitoring for chemical process. Hover, conventional methods of MSPC are based on the premise that the extracted latent variables must be subjected to normal distribution, which often can't be satisfied. , a new method based on independent component analysis (ICA) and principal component analysis (PCA) is presented for process performance monitoring, in which a two-step procedure is employed. At first step, process operating information with non-normal distribution is extracted by means of ICA, and the density function of this part of information is estimated by means of parzen density estimator for calculating the confidence limits. At the second step, the information with normal distribution is extracted from the underlying residual data sets by PCA, and the confidence limits are determined on Q and hotelling T/sup 2/ statistics. With the primary advantage of which no assumption of normal distribution on process data sets is needed, the proposed method is applied to a double-effect evaporator, and the simulation results verify it effective.
['Guo-jin Chen', 'Jun Liang', 'Jixin Qian']
I am writing a research paper on a idea Process monitoring and fault detection based on multivariate statistical projection analysis
[{"from": "human", "value": "I am writing a research paper on a idea Process monitoring and fault detection based on multivariate statistical projection analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multivariate statistical process control (MSPC) has been applied to performance monitoring for chemical process. Hover, conventional methods of MSPC are based on the premise that the extracted latent variables must be subjected to normal distribution, which often can't be satisfied. , a new method based on independent component analysis (ICA) and principal component analysis (PCA) is presented for process performance monitoring, in which a two-step procedure is employed. At first step, process operating information with non-normal distribution is extracted by means of ICA, and the density function of this part of information is estimated by means of parzen density estimator for calculating the confidence limits. At the second step, the information with normal distribution is extracted from the underlying residual data sets by PCA, and the confidence limits are determined on Q and hotelling T/sup 2/ statistics. With the primary advantage of which no assumption of normal distribution on process data sets is needed, the proposed method is applied to a double-effect evaporator, and the simulation results verify it effective."}]