APPARATUS, SYSTEM, AND METHOD FOR CALCULATING DEFECT RATE

• US 20120226476A1
• Filed: 03/02/2012
• Published: 09/06/2012
• Est. Priority Date: 09/18/2009
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Nishikawa, Takeichiro, Nakatsugawa, Minoru, Shingaki, Ryusei
• Application Number:
  US13/410,645
• Publication Number:
  US 20120226476A1
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G05B 23/0245 based on a qualitative model, e.g. rule based; if-then decisions
• 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

Abstract

According to one embodiment, a first reading unit reads, from a defect database, first identification information corresponding to a first product type. A second reading unit reads, from a monitoring database, defect monitoring information corresponding to the first identification information and non-defect monitoring information that corresponds to the first product'"'"'type and that is other than the defect monitoring information. A generating unit generates a defect, model that models a probability of products becoming defective within a predetermined time period with respect to the monitoring information, based on the defect monitoring information and the non-defect monitoring information. A first calculating unit calculates a defect probability of products of a second product type by inputting the monitoring information corresponding to the second product type into the defect model. A second calculating unit calculates a defect rate of the products of the second product type based on the defect probability.

Claims

1. A defect rate calculating apparatus comprising:

a first reading unit that reads, from a defect database storing therein product types of defective products and pieces of identification information of the defective products that are kept in correspondence with one another, first identification information that is one of the pieces of identification information corresponding to a first product type being specified;

a second reading unit that reads, from a monitoring information database storing therein monitoring information indicating usage statuses of products, the product types, and the pieces of identification information that are kept in correspondence with one another, defect monitoring information and non-defect monitoring information, defect monitoring information being the monitoring information corresponding to the first identification information, non-defect monitoring information being the monitoring information that corresponds to the first product type and that is other than the defect monitoring information;

a generating unit that generates a defect model that models a probability of the products becoming defective within a predetermined time period with respect to the monitoring information, based on the defect monitoring information and the non-defect monitoring information;

a first calculating unit that reads, from the monitoring information database, a pieces of the monitoring information corresponding to a second product type being specified and calculates a defect probability expressing a probability of products of the second product type becoming defective within the predetermined time period, based on an output value obtained by inputting the pieces of monitoring information into the defect model; and

a second calculating unit that calculates a defect rate expressing a probability of the products of the second product type becoming defective within a unit time period, based on an operating quantity of the products of the second product type, a defect quantity expressing a quantity of products that have become defective among the products of the second product type, and the defect probability.

2. The defect rate calculating apparatus according to claim 1, wherein

the monitoring information database stores therein a plurality of pieces of monitoring information in correspondence with the product types and the identification information, and

the generating unit calculates a plurality of first density functions expressing probability density functions of a plurality of pieces of defect monitoring information, respectively, further calculates a plurality of second density functions expressing probability density functions of a plurality of pieces of non-defect monitoring information, respectively, and generates the defect model including a first joint density function that is a mathematical product of the plurality of first density functions and a second joint density function that is a mathematical product of the plurality of second density functions.

3. The defect rate calculating apparatus according to claim 2, wherein the first calculating unit reads, from the monitoring information database, the pieces of the monitoring information corresponding to the second product type being specified, calculates a first output value and a second output value by inputting the pieces of monitoring information into the first joint density function and the second joint density function, respectively, that are included in the defect model, and calculates the defect probability that is a posterior defect probability based on a Bayes'"'"' theorem, from a prior defect probability being specified, while using the first output value and the second output value as likelihood values.

4. The defect rate calculating apparatus according to claim 1, wherein the generating unit generates the defect model obtained by learning from a neural network while using the defect monitoring information and the non-defect monitoring information.

5. The defect rate calculating apparatus according to claim 1, wherein, while using a value obtained by dividing the defect quantity by the operating quantity as an initial value of the defect rate, the second calculating unit selects a sample of the defect rate according to a selection probability calculated from the defect probability by using a Metropolis-Hastings algorithm and further calculates a probability density function of the defect rate based on the sample.

6. A defect rate calculating system including a terminal apparatus and a defect rate calculating apparatus connected to the terminal apparatus via a network, wherein the terminal apparatus comprises:

a first communicating unit that transmits a first product type and a second product type specified from among product types of products, to the defect rate calculating apparatus, and

the defect rate calculating apparatus comprises;

a second communicating unit that receives the first product type and the second product type from the terminal apparatus;

a first reading unit that reads, from a defect database storing therein product types of defective products and pieces of identification information of the defective products that are kept in correspondence with one another, first identification information that is one of the pieces of identification information corresponding to the received first product type;

a second reading unit that reads, from a monitoring information database storing therein monitoring information indicating usage statuses of products, the product types, and the pieces of identification information that are kept in correspondence with one another, defect monitoring information and non-defect monitoring information, defect monitoring information being the monitoring information corresponding to the first identification information, non-defect monitoring information being the monitoring information that corresponds to the first product type and that is other than the defect monitoring information;

a generating unit that generates a defect model that models a probability of the products becoming defective within a predetermined time period with respect to the monitoring information, based on the defect monitoring information and the non-defect monitoring information;

a first calculating unit that reads, from the monitoring information database, a pieces of the monitoring information corresponding to a second product type being specified and calculates a defect probability expressing a probability of products of the second product type becoming defective within the predetermined time period, based on an output value obtained by inputting the pieces of monitoring information into the defect model;

a second calculating unit that calculates a defect rate expressing a probability of the products of the second product type becoming defective within a unit time period, based on an operating quantity of the products of the second product type, a defect quantity expressing a quantity of products that have become defective among the products of the second product type, and the defect probability.

7. A defect rate calculating method comprising:

reading, from a defect database storing therein product types of defective products and pieces of identification information of the defective products that are kept in correspondence with one another, first identification information that is one of the pieces of identification information corresponding to a first product type being specified;

reading, from a monitoring information database storing therein monitoring information indicating usage statuses of products, the product types, and the pieces of identification information that are kept in correspondence with one another, defect monitoring information and non-defect monitoring information, defect monitoring information being the monitoring information corresponding to the first identification information, non-defect monitoring information being the monitoring information that corresponds to the first product type and that is other than the defect monitoring information;

generating a defect model that models a probability of the products becoming defective within a predetermined time period with respect to the monitoring information, based on the defect monitoring information and the non-defect monitoring information;

reading, from the monitoring information database, a pieces of the monitoring information corresponding to a second product type being specified and calculating a defect probability expressing a probability of products of the second product type becoming defective within the predetermined time period, based on an output value obtained by inputting the pieces of monitoring information into the defect model; and

calculating a defect rate expressing a probability of the products of the second product type becoming defective within a unit time period, based on an operating quantity of the products of the second product type, a defect quantity expressing a quantity of products that have become defective among the products of the second product type, and the defect probability.