Computer-implemented method and system for automatically monitoring and determining the status of entire process segments in a process unit

• US 10,261,480 B2
• Filed: 12/05/2014
• Issued: 04/16/2019
• Est. Priority Date: 12/05/2013
• Status: Active Grant

• Current Assignee: Bayer AG
• Original Assignee: Bayer AG
• Inventors: Ross, Thomas
• Primary Examiner(s): Elamin, Abdelmoniem I
• Application Number:
  US15/101,614
• Publication Number:
  US 20170082988A1
• Time in Patent Office: 1,593 Days
• CPC Class Codes: G05B 13/0265 the criterion being a learning criterion
• CPC Class Codes: G05B 19/41885 characterised by modeling, simulation of the manufacturing system
• CPC Class Codes: G05B 2219/24015 Monitoring
• CPC Class Codes: G05B 2219/33037 Learn parameters of network offline, not while controlling system
• CPC Class Codes: G05B 2219/33322 Failure driven learning
• CPC Class Codes: G05B 23/0216 Human interface functionality, e.g. monitoring system providing help to the user in the selection of tests or in its configuration
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A method and a computer-implemented system for automatically monitors and determines the status of entire process sections in a process unit in a computer-implemented manner.

Claims

1. A computer-implemented method for monitoring installation parts which are connected by process engineering and comprise one or more process units, wherein the computer of the computer-implemented process includes at least one non-transitory computer-readable medium, the at least one computer-readable program comprising a program which, when executed by the computer causes the computer to execute the method for monitoring installation parts having the following steps:

in step a), M process parameters of each of the one or more process units to be monitored and their measurement range limits are input at a user interface and are forwarded to a module for defining the process unit,

in step b), learning data phases consisting of one or more learning vectors are imported to a database module, wherein each of the one or more learning vectors comprise one of more of the M inputted process parameters,

in step c), in the database module, at least one of the one or more learning vectors from the learning data phases from b) is automatically selected via the user interface using the measurement range limits from step a),

in step d), the selected learning vectors from step c) are transmitted from the database module to a model module connected to the latter;

wherein in the model module, a model based on a neural network is generated by assigning each selected learning vector to a best matching unit defined by its M process parameters identified by adapting the best matching units to the learning vectors in a self-organizing neural map made of neurons, said neurons being the best matching units assigned to the learning vectors,

in step e), the following features of the model are automatically calculated;

for each of the one or more learning vectors, a deviation error as a distance between each inputted process parameter value of the one or more learning vectors and the corresponding process parameter value of the best matching unit assigned to it,

for each of the one or more learning vectors, a total deviation error as the sum of the deviation errors of its M inputted process parameters from the assigned best matching unit of each of the one or more learning vectors,

in step f), the total deviation error for each learning vector from step e) is transmitted to a module for analysing the deviation errors and a minimum and a maximum value of the calculated total deviation errors are automatically determined there,

in step g), to monitor the process units, the processing M process parameters monitored online are automatically transmitted, as a monitoring vector of the process unit over time, to the database module at a time stamp t followed by the transmission of the monitoring vector to the model module,

in step h), each monitoring vector from step g) is automatically assigned to the best matching units of the self-organizing neural map (SOM) from step e) in the model module by comparing the processing parameter values of the monitoring vectors with the process parameter values of each neuron in the neural map and making a selection of the neuron which is the best matching unit to the monitoring vector on the basis of the neuron having a shortest distance from each of the monitoring vectors,

in step i), the deviation errors are calculated and the total deviation errors of the monitoring vector are calculated as the sum of the deviation errors of its processing M process parameters, and,

in step j), the total deviation error of the processing monitoring vectors of the processing M process parameters is transmitted to the module for analysing the deviation errors and is compared with the minimum and maximum values of the calculated N total deviation errors of the learning vectors from f), and a result of the comparison is displayed via the user interface.

2. The computer-implemented method according to claim 1, wherein a range between the minimum and maximum values of the calculated total deviation errors calculated in step f) is subdivided into 10 to 100 equidistant segments, the learning vectors are sorted into the segments using their total deviation errors, and at least one threshold for the total deviation error is automatically set using the following steps, threshold is equal to first segment supporting point, for which greater than 5 to 15% of the highest total deviation errors are within segments starting from the maximum value, and the threshold is in the range of the uppermost 7/10 of the segments starting from the maximum value.

3. The computer-implemented method according to claim 2, wherein the calculated threshold is multiplied by a tolerance range in the form of an integer positive factor of usually 2-4.

4. A computer system for monitoring installation parts which are connected by process engineering and comprise one or more process units, the computer system comprising the following modules:

a) a user interface for defining i) M process parameters to be monitored and ii) input of the measurement range limits of the M process parameters of one or more process units to be monitored,

b) a module for defining the process unit for storing the inputs i) and ii), connected to the user interface,

c) a database module for importing and storing learning data phases, learning vectors and monitoring vectors via a data interface, wherein the database module is connected to the module for defining the process unit and to the user interface,

d) a model module which is based on a neural network and is intended to train

a model based on a neural network in a fully automatic manner in a self-organizing neural map by automatically calculating;

a deviation error as a distance between each process parameter value of a learning vector and the corresponding process parameter value of the best matching unit assigned to it,

for each of the learning vectors, a total deviation error as a sum of the deviation errors of its M process parameters,

and to assign a monitoring vector to one of the best matching units of the self-organizing neural map and calculate;

a deviation error as the distance between each process parameter value of the monitoring vector and the corresponding process parameter value of the best matching unit assigned to it,

for each of the monitoring vectors, a total deviation error as the sum of the deviation errors of its M process parameters,

e) a module for analysing the deviation errors by automatically determining a range of the calculated total deviation errors, sorting the learning and monitoring vectors using their total deviation errors in the range, wherein the module for analysing the deviation errors is connected to the model module, and

wherein the module for analysing the deviation errors is connected to the user interface for displaying the sorting of the monitoring vectors in the range of the calculated total deviation errors wherein the computer system includes at least one non-transitory computer-readable medium, the computer-readable medium comprising a program which, when executed by a computer in the computer system operates the user interface, the module for defining the process unit, the database module, the model module, and the module for analysing the deviation errors.