Method and system for monitoring and analyzing a paper manufacturing process

• US 20030139904A1
• Filed: 10/02/2002
• Published: 07/24/2003
• Est. Priority Date: 04/05/2000
• Status: Active Grant

• Current Assignee: Liqum OY
• Original Assignee: Liqum OY
• Inventors: Laitinen-Vellonen, Sakari
• Granted Patent: US 6,792,388 B2
• Time in Patent Office: Days
• US Class Current: 702/182
• CPC Class Codes: D21G 9/0009 Paper-making control systems
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0267 Fault communication, e.g. human machine interface [HMI]
• CPC Class Codes: G05B 23/0272 Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user

Abstract

The invention concerns a method for monitoring and analyzing a paper manufacturing process, in which method a large number of quantities (x_i, t) are measured from the process, the measured quantities (x_i, t) are entered as an input vector ({overscore (X)}, t) into a neural network, which, in response to the input vector, produces an output vector ({overscore (Y)}, t) as a continuous quantity, at least one fingerprint consistent with a good process situation in regard of runnability, i.e. an optimal output vector ({overscore (Y)}_o, t) is determined and stored in memory, the stored fingerprints and fingerprints or output vectors obtained in a normal process situation are compared substantially in real time, based on the comparison, a difference to be presented in a graphic form to the user is determined. According to the invention, a continuous time-dependent scalar quantity k=k(t) to be presented as a result to the user is determined as a geometric distance between the instantaneous measured fingerprint ({overscore (Y)}, t) and the taught fingerprint ({overscore (Y)}_o), and the scalar quantity k=k(t) is displayed via a display device to the user.

Claims

1. Method for monitoring and analyzing a paper manufacturing process, in which method

a large number of quantities (x_i, t) are measured from the process,

the measured quantities (x_i, t) are entered as an input vector ({overscore (X)}, t) into a neural network, which, in response to the input vector, produces an output vector ({overscore (Y)}, t) as a continuous quantity,

at least one fingerprint consistent with a good process situation in regard of runnability, i.e. an optimal output vector ({overscore (Y)}_o, t) is determined and stored in memory,

the stored fingerprints and the fingerprints or output vectors obtained in a normal process situation are compared substantially in real time,

based on the comparison, a difference to be presented in a graphic form to the user is determined,

characterized in that

a continuous time-dependent quantity k=k(t) to be presented as a result to the user is determined as a geometric distance between the instantaneous measured fingerprint ({overscore (Y)}, t) and the taught fingerprint ({overscore (Y)}_o), and

the aforesaid quantity k=k(t) for the period of a selected trend T is displayed via a display device to the user.

2. Method as defined in claim 1, characterized in that a multi-layer perceptron network (MLP) is selected as the neural network to be used.

3. Method as defined in claim 1 or 2, characterized in that a polar conversion k(φ

, t) to be presented in a graphic form is generated from the said quantity k=k(t) as follows;

φ

(t)=t/T*2π

, where T is the selected trend r(t)=k.

4. Method as defined in claim 3, characterized in that a set of history values is determined from the measured quantities (x_i, t_n) over a period of time corresponding to a multiple of the trend, by periodically saving the dimensionless value (y_i, t_n) of each input quantity, which is stored and coded so as to produce brightness/color information, said values being displayed graphically in succession as a bar (13′

) in which each value (y_i, t_n) is visible as a transverse line or similar area of corresponding brightness/color.

5. Method as defined in claim 4, characterized in that both a graph of the polar conversion k(φ

, t) and the history bars (13) are presented to the user at the same time, especially via the same display.

6. Method as defined in any one of the preceding claims 3-5, characterized in that the information presented to the user on the same display additionally comprises the measured quantities and the good fingerprint as sets of values converted into a dimensionless and graphic form, e.g. into sequences of bars (11, 12).

7. Method as defined in any one of the preceding claims 2-6, characterized in that a set of difference quantities ({overscore (Y)}_o-{overscore (Y)}) together with a sequence of times is presented to the user in a graphic form.

8. Method as defined in any one of the preceding claims 1-7, characterized in that the dimension of the output vector of the neural network is in the range of 3-10, preferably equaling the number of categories to be distinguished.

9. System for monitoring and analyzing the paper production process in a paper machine (1), said system comprising

a neural network (2) having inputs for the entering of a large number of quantities (x_i, t) measured from the process and outputs for the delivery of a response, and

computing means (3) for the processing of the response, and

storage means (4) for the storage of a fingerprint consistent with a good process situation, i.e. an optimal output vector ({overscore (Y)}_o, t),

means for comparing substantially in real time the stored fingerprints and the fingerprints or output vectors obtained in a normal process situation and for determining on the basis of the comparison a difference to be presented to the user,

a display device (5) for displaying the difference in a graphic form,

characterized in that said neural network (2) has been adapted to implement a multi-layer perceptron network (MLP), and

the computing means (3) have been adapted to determine mine from the output vector a continuous time-dependent scalar quantity k=k(t) as a geometric distance between the instantaneous measured fingerprint ({overscore (Y)}, t) and the taught fingerprint ({overscore (Y)}_o), and

the display device (5) has been adapted to display the said scalar quantity k=k(t).