Principal Research Results

Development of Methods to Judge the Reusability of Hardware Using
Rust Pictures
BackgroundIn recent years, each electric power company has enhanced its effective use of the existing equipment and rationalization ofmaintenance checks. The evaluation of whether hardware that has been corroded with rust can be reused, has been carried out by thehuman eye based on the judgment of a sample color pallet photograph. However, the pallet can represent only limited states of rust,
therefore the judgment depends on human expertise, and human evaluation tend to vary. In order to solve these problems, a databaseof rust pictures, which attaches judgment labels, is constructed and maintained and a system which can carry out an appraisal ofreusability is developed by using this enriched database.

Objectives
To develop an efficient construction technique of a rust picture database which attaches judgment labels; and to verify theaccuracy of the reuse judgment system which is based on the rust picture database, for the crossarms of power distribution poles.

Principal Results
1. Construction technique of a picture database with a judgment labels
A construction technique for an efficient rust picture database with the following features was developed using Support
Vector Machine (SVM) - a pattern classification method.
(1) In the case of attaching judgment labels to non-judged image data, the labels are automatically estimated by SVM, which ascertains the optimal discriminant hyper-plane. However, pictures with a low reliability of automatic labeling are judged by humaneyes. By this iterative process, the discriminant hyper-plane becomes even more optimal and will be able to perform more accurateclassifications without a lot of pictures that have had judgment labels attached by a human expert (see figure 1).
(2) The developed method was applied to U.S. postal code numbers for handwritten number recognition, and the effect was evaluated.
When our developed method was applied to 3,747 pictures of the numbers 0, 1, and 2 for “judgment label attachment”, the “rightjudgment label attachment” was able to be performed to all 3,747 pictures with only 166 human-judged pictures (see table 1). Thisis about only 5% of the cost if a human had had to make “judgment label attachments” to all 3,747 pictures.
2. Verification of reuse judgment accuracy
The reuse judgment accuracy of the developed method was verified using the crossarm pictures obtained in joint researchwith the Chubu Electric Power Co., Inc.
(1) When pictures (400 sheets), which consist of “reuse” and “retirement” pictures, were classified by our developed method, the average classification accuracy was 89%.
(2) When pictures (600 sheets), which consist of “reuse” and “retirement” and “reuse after plating” pictures, were classified by ourmethod, the average classification accuracy was 71%.

Future Developments
The testing system for the reuse judgment for crossarms will be further developed, and a field test will be performed.
Moreover, with regard to the construction technique of an efficient picture database with judgment labeling, the required automation
technique of parameter tuning will be developed.
Main Researcher: Takashi Onoda, Ph. D.,
Senior Research Scientist, Information Systems Sector, System Engineering Research Laboratory
Reference
M. Yamana, et.al., 2003, “Effective database construction method based on human and computer interaction for equipment maintenance”, Technical Report R00032 (in Japanese)2

A. Cost reduction and ensuring reliability

Only pictures on or near a discriminant
hyper-plane are displayed△ is estimated as retirement and ☆ is estimated as reuse
Discriminant
hyper-plane

Discriminant hyper-plane

Retirement

Retirement
Reuse

Reuse

Most unstablepictureMost unstablepictureGray area: unstable classification area-the pictures on thediscriminant hyper-plane is the most unstable for classification

Generated new discriminanthyper-plane

Fig.1 Displayed pictures to human experts to attach correct labels

Table 1 Comparison of cost for attachment judgment labels

Number of required pictures
Relative cost ratio
Required time to classify 100,000 pictures(10sec./sheet)
Required working days to classify 100,000pictures

Reuse

Proposed method
166±5 pictures
0.044

H.W.S.
3,747 pictures(all)112.3 hours

278 hours

1.8 days

40 days

Reuse after plating

Retirement

Fig.2 Examples of rust pictures on crossarm

Table 2 Classification accuracy for two-class test data

Classification accuracy
Total(averaged)
Reuse
89%
86%

Retirement
91%

Table 3 Classification accuracy for three-class test data

Total(averaged)
71%

Classification accuracy
Reuse
Retirement
78%
78%

3

Reuse after plating
58%