METHOD AND APPARATUS FOR ALARM MONITORING

• US 20140249656A1
• Filed: 09/03/2013
• Published: 09/04/2014
• Est. Priority Date: 03/01/2013
• Status: Active Grant

• Current Assignee: Semiconductor Manufacturing International (Shanghai) Corporation (Semiconductor Manufacturing International Corporation)
• Original Assignee: Semiconductor Manufacturing International (Shanghai) Corporation (Semiconductor Manufacturing International Corporation)
• Inventors: KANG, RANDY, Ma, Jenny, Wang, Elaine, Ji, Serena, Ting, Chien Wei
• Granted Patent: US 9,798,320 B2
• Time in Patent Office: Days
• US Class Current: 700/80
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: F25J 3/044 using a single pressure main column system only F25J3/0446, F25J3/04624, F25J3/04636 take precedence
• CPC Class Codes: G05B 13/02 electric
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04

Abstract

A method for alarm monitoring abnormal conditions associated with a process operation includes collecting N sample data representing one or more parameters of a process, determining a distribution type of the sample data, obtaining an alarm monitoring strategy associated with the distribution type, and monitoring the process using the alarm monitoring strategy. The distribution type may be single-constant, multi-level discrete, normally distributed, continuous non-normally distributed, cyclical trend-up/down, and drifting away after a period maintenance. The alarm monitoring strategy includes a unilateral or bilateral control chart. The one-side control chart has either an upper control limit (UCL) of (100−p) or a lower control limit (LCL) of (p), and the two-sided control chart has a UCL of (100−p/2) and an LCL of (p/2), where p is a predetermined false alarm rate which determines the number N. The control limit may be a difference between two adjacent sample values of the sample data.

Claims

1. A computer-implemented method for alarm monitoring of a process operation, the method comprising:

collecting sample data representing one or more parameters of a process;

determining a distribution type of the sample data;

obtaining an alarm monitoring strategy corresponding to the distribution type; and

monitoring the process using the alarm monitoring strategy.

2. The computer-implemented method of claim 2, wherein obtaining the alarm monitoring strategy comprises:

determining a control limit of a control chart associated with the distribution type; and

monitoring the production process using the control limit of the control chart.

3. The computer-implemented method of claim 2, wherein determining the distribution type of sample data comprises:

determining whether a value level of the sample data is greater than a first threshold value;

if the value level is not greater than the first threshold value, determining whether the distribution type is a single constant-type or multi-level discrete type;

otherwise

determining whether the sample data is normally distributed;

if the sample data is normally distributed, setting the upper control limit to a maximum value of the sample data and the lower control limit to a minimum value of the sample data, and

if the sample data is not normally distributed, determining whether the distribution type is a continuous non-normal distribution type, cyclical trend-up/down-type, or a drift after a periodic maintenance (PM) type.

4. The computer-implemented method of claim 3, wherein determining whether the distribution type is a single constant-type or multi-level discrete comprises:

if the value level is equal to 1 (unity), setting the control limit to a predetermined constant;

otherwise;

otherwise,

setting an upper limit of the control limit to the maximum value of the sample data;

setting a lower control limit of the control limit to a minimum value of the sample data.

5. The computer-implemented method of claim 3, wherein determining the distribution type of the continuous non-normal distribution type, cyclical trend-up/down type, or drift after the PM type comprises:

determining whether the distribution of the sample data satisfies PM changing conditions;

if the sample data distribution does not satisfy the PM changing conditions, the distribution type is the continuous non-normal distribution type;

otherwise,

the distribution type is the cyclical trend-up/down type, or drift after the PM type.

6. The computer-implemented method of claim 5, wherein determining the continuous non-normal distribution type comprises:

determining whether the control chart is a two-sided (bilateral) control chart;

if the control chart is the two-sided (bilateral control chart having an upper control limit (UCL) and a lower control limit (LCL), setting the UCL to (100−

p/2) and LCL to (p/2), otherwise

setting the UCL to (100−

p) or LCL to (p), wherein p is a predetermined alarm rate.

7. The computer-implemented method of claim 5, wherein determining the cyclical trend-up/down type or drift after the PM type comprises:

setting the control limit based on a difference between two adjacent sample values of the sample data, wherein the difference and an upper control limit of the control limit are computed using the following equations;

8. The computer-implemented method of claim 3, wherein determining the distribution type further comprises:

collecting at least a number N of the sample data, wherein N=2/p %, and p % is a predetermined alarm rate.

9. The computer-implemented method of claim 5, wherein determining the distribution type comprises:

if the distribution type is determined to be the normal distribution type, and the control chart is a single-sided (unilateral) control chart having an upper control limit (UCL) or the lower control limit LCL, the UCL or the LCL is determined using the following expressions;

if the distribution type is determined to be the normal distribution type, and the control chart is a bilateral sided (bilateral) control chart having the upper control limit UCL and the lower control limit LCL, the UCL and the LCL are determined using the following expressions;

10. An apparatus for alarm monitoring of a process operation, the apparatus comprising:

a sampling unit configured to obtain a number N of sample data representing one or more parameters in a production process;

a determining unit configured to determine a distribution type of the sample data;

an acquisition unit configured to acquire an alarm monitoring strategy for the distribution type; and

a monitoring unit configured to monitor the production process using the alarm monitoring strategy.

11. The apparatus of claim 10, wherein the number N is a function of a predetermined false alarm rate.

12. The apparatus of claim 10, wherein the distribution type comprises a single constant-type, a multi-level discrete type, a normally distributed type, a continuous non-normal distribution type, cyclical trend-up/down type, and drift after the PM type.

13. The apparatus of claim 10, wherein the determining unit removes outliers of the sample data located outside a predetermined range.

14. The apparatus of claim 10, wherein the acquisition unit determines a control limit of a control chart associated with the distribution type, the control limit having an upper control limit (UCL), a lower control limit (LCL), or both dependent upon whether the control chart is single-sided (unilateral) or two-sided (bilateral).

15. A method for alarm monitoring abnormal conditions associated with a process operation, the method comprising:

collecting at least N sample data representing one or more parameters of a process;

determining a distribution type of the sample data;

obtaining a control chart in response to the determined distribution type; and

monitoring the process using the control chart,

wherein N is a function of a predetermined false alarm rate p.

16. The method of claim 15 further comprising:

removing outliers of the sample data located outside a predetermined range.

17. The method of claim 15, wherein the control chart comprises an upper control limit and a lower control limit if the distribution type is a multi-level discrete type or a continuous non-normal distribution type.

18. The method of claim 17, wherein the upper control limit is a maximum value of the sample data and the lower control limit is a minimum value of the sample data.

19. The method of claim 15, wherein the control chart is single-sided having an upper control limit equal to (1−

p) or a lower control limit equal to p.

20. The method of claim 15, wherein the control chart is two-sided having an upper control limit equal to (1−

p/2) and a lower control limit equal to p/2.