Asia Pacific Conference of
the Prognostics and Health Management Society 2023

September 11-14, 2023
Hitotsubashi Hall, National Center of Sciences Building,Tokyo, Japan

Hosted byContents
1. Welcome Message
2. PHMAP23 Organizing Committee
3. Sponsorship
4. General Information
5. Floor Guide
6. Program at a Glance
7. Oral Sessions
8. Poster Sessions
9. Keynote Speakers
10. Panel Sessions
11. Exhibitions
12. Welcome Reception and Banquet
13. Short Course

7

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Welcome Message
As the general chair, I am deeply honored to host the 4th Asia Pacific Conference of the Prognostics and
Health Management Society (PHMAP23) in Tokyo, and to welcome numerous researchers, experts,
engineers and students who are interested in PHM, finally in person after overcoming the prolonged
pandemic.
Looking back on the history of PHMAP, the first meeting was initiated in 2017 in Korea and two successive
meetings have been held in 2019 in China and 2021 again in Korea. After the series of successful
meetings, we finally obtained an opportunity to host this young and energetic event in Japan, thanks to thegenerous support of the PHM Society (PHMS) and the Japan Society for Aeronautical and Space Sciences(JSASS). We have also had many valuable pieces of advice from the PHMAP21 committee, financial
support from SECOM Science and Technology Foundation and KDDI Foundation, sponsorship from many
companies, and a lot of support from academic societies. We would like to express our gratitude to all theseorganizations.
As far as I know, PHMAP23 is the first international conference specialized in prognostics and healthmanagement to be held in Japan. In my view, the main reason why no other PHM conferences have notbeen held in Japan in the past even though a number of people are interested in PHM is that there have notbeen academic communities specialized in the methodologies of PHM because the PHM-related studies
have long been conducted in individual industries such as automobile, electronics, construction and so on
in Japan. In recent years, however, emerging technologies such as AI, IoT, digital twins are about to break
down the barriers between the industries, and we are witnessing a growing interest in PHM and predictive
maintenance across all industries. In fact, PHMAP23 has collected more than 160 submissions from avariety of industries including aerospace, production, civil engineering, electronics, and so on. I can't help
getting excited to imagine the discussions, exchanges of ideas, and interactions among people withdifferent backgrounds, leading to numerous chemical reactions during the four days from September 11th
to 14th in Tokyo.
In PHMAP23, in addition to regular / organized sessions, there will be plenary talks by leading researchers
in various fields, a short course for PHM beginners, panel discussions, data challenge and social events
featuring Japanese hospitality to provide all participants with global networking opportunities. We kindly
ask all participants of PHMAP23 to contribute continuously to the future development of the PHM researchcommunity, and also hope that you will actively leverage this experience to establish connections withprofessionals from other fields and industries.

Takehisa Yairi
General Chair of PHM Asia Pacific 2023

8

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Organizing Committee●General Chair
Takehisa Yairi, Univ. of Tokyo, Japan●Secretary General
Seiji Tsutsumi, JAXA, Japan
Samir Khan, Univ. of Tokyo, Japan●Outsourcing Committee●Short Course Committee
Michio Inoue, MathWorks, JapanKen Ueno, Toshiba, Japan●Data Challenge Committee
Hiroshi Tanaka, ISID, Japan
Masashi Miyasaka, ISID, Japan

Noriyasu Omata, JAXA, Japan

Osamu Yoshimatsu, NSK, Japan

Akiko Otsuka, JAXA, Japan

Kota Dohi, Hitachi, Japan●Finance Committee

Kohji Tominaga, JAXA, Japan

Akiko Otsuka, JAXA, Japan

Jun Naito, ISID, Japan

Akiko Sato, JAXA, Japan

Satoshi Suzuki, ISID, Japan●Technical Program Committee●Sponsorship Committee

Makoto Imamura,Tokai Univ., Japan

Chiaki Ichikawa, JAXA, Japan

Hidetoshi Mishima, MELCO, Japan

Taiichi Nagata, JAXA, Japan

Masaru Nishiwaki, ANA, Japan

Kohji Tominaga, JAXA, Japan

Manabu Kano, Kyoto Univ., Japan

Kota Dohi, Hitachi, JapanKen Ueno, Toshiba, Japan●Social Activity Committee

Tadahiro Shibutani, Yokohama National Univ., Japan

Kohji Tominaga, JAXA, Japan

Deng Mingcong, Tokyo Univ. of Agriculture and Tech., Japan

Shota Iino, JAMSS, Japan

Takato Yasuno, Yachiyo Engineering, Japan

Kengo Asada, TUS, Japan●Plenary talk, Panel, Poster, Best Practice Session Committee
Nozomu Kogiso, Osaka Metropolitan Univ., Japan

Yutaka Watanabe, CRIEPI, Japan●Award Committee

Yasushi Umeda, Univ. of Tokyo, Japan

Kengo Asada, TUS, Japan

Junichiro Kaya, ANA, Japan

Shota Iino, JAMSS, Japan

Samir Khan, Univ. of Tokyo, Japan●Paper Submission Committee
Samir Khan, Univ. of Tokyo, Japan
Yutaka Watanabe, CRIEPI, Japan●WebmasterYutaka Watanabe, CRIEPI, Japan
Masanao Natsumeda, NEC, Japan●Co-chair

Tadahiro Shibutani, Yokohama National Univ., Japan

Eric Bechhoefer, GPMS International, Inc, USA

Deng Mingcong, Tokyo Univ. of Agriculture and Tech., Japan

Jongsoo Lee, Yonsei University, South Korea

Tadao Kawai, Osaka Metropolitan Univ., Japan●Advisors

Arata Masuda, Kyoto Institute of Technology, JapanDavid He, University of Illinois at Chicago, USA

Takato Yasuno, Yachiyo Engineering, JapanIan Jennions, Cranfield University, UK●International Coordination Committee
Samir Khan, Univ. of Tokyo, Japan

Jeff Bird, TECnos, Canada, Canada●Honorary Chairs

Atsuhiro Takasu, NII, Japan

Joo-Ho Choi, Korea Aerospace University, South Korea

Makoto Imamura, Tokai Univ., Japan

Shinichi Nakasuka, Univ. of Tokyo, Japan

Manabu Kano, Kyoto Univ., Japan

Andrew Hess, The Hess PHM Group, USA●Venue Committee
Noriyasu Omata, JAXA, Japan

Jay Lee, University of Maryland College Park, USA
Byeng Dong Youn, ONEPREDICT, INC., USA

Yusuke Motegi, IHI, Japan
Kazuma Matsuda, IHI, Japan
Shiyunichi Isozaki, IHI, Japan

Seoul National University, Korea●International Committee
David Siegel, Predictronics, USA

Yuki Hashimoto, IHI, Japan
Shotaro Hamato, JAXA, Japan

9

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Sponsorship
The PHM Asia Pacific 2023 Organizing Committee greatly Appreciates the Supportof Our Sponsors!

Supported by
SECOM Science and Technology Foundation
KDDI Foundation
The Society of Chemical Engineers, Japan
Japan Society of Civil Engineers
SAE International
Japanese Society of Tribologists
The Society of Instrument and Control Engineers
Japan Society of Maintenology
Gas Turbine Society of JapanThe Japan Society of Mechanical Engineers
The Japanese Society for Artificial Intelligence
Atomic Energy Society of JapanThe Institute of Image Information and Television Engineers
The Operations Research Society of JapanThe Institute of Systems, Control and Information Engineers

10

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Sponsored by

Mars

Moon

Mitsubishi Electric Corporation
Lufthansa Technik

PHM Technology
Mathworks
Toshiba Corporation
ISID

Earth

Japan Manned Space Systems Corporation
ONEPREDICTThe Boeing Company

11

Asia Pacific Conference of the Prognostics and Health Management Society 2023

General Information●Conference Proceedings
The conference proceedings can be accessed via the link below. Attendees can also
access the proceedings via Whova.
https://papers.phmsociety.org/index.php/phmap/issue/view/64●WhovaThe conference program is live on Whova. Attendees can personalize your experience by
creating your own itinerary. Using the Whova platform, you can select the sessions you're
most interested in and plan out your day with ease. Do not miss to download the Whova app
to your smartphone or other devices before arriving at the conference.
●Registration desk
Registration desk will open at the following time and locations. If you have any trouble,
please contact to the staff at the desk.
Sep. 11, 9:30-16:30, 2F Lobby
Sep. 12, 9:30-19:00, 1F Lobby
Sep. 13, 9:30-18:00, 1F Lobby
Sep. 14, 9:30-15:00, 1F Lobby●Wireless Internet
Wireless internet is available for the participants at the conference venue. ID/PW will be
informed on-site.
●Hybrid Meeting
PHMAP23 will be held as a hybrid format of in-person and online.
The online venue will be hosted by- Whova for Poster sessions,- Zoom for Technical sessions and Panel session 1,- YouTube for Keynotes and Panel sessions 2 and 3.
Access information for online participation will be made available to participants via
the web page.
●Coffee service
Free coffee will be served in the exhibition area.

● !! Important Notice !!
■ Eating and drinking in the Hall is prohibited.

■ Please take home any garbage such as food trays or empty bottles
that you brought with you.
Thank you for your cooperation.
12

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Floor Guide●Eating / Drinking
Eating and drinking are prohibitedin the hall.

13

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Program at a Glance
Time/Date
JST(UTC+9)
9:00
9:10
9:20
9
9:30

10

11

9:40
9:50
10:00
10:10
10:20
10:30
10:40
10:50
11:00
11:10
11:20
11:30

Sep. 11
Room
A

Room
B

Room
C

Sep. 12
Room
D

2F
Lobby

Hall

Room
A

Room
B

Room
C

Room
D

Time/Date
Room
E

Exhibition

1F
Lobby

JST
(UTC+9)9Opening Ceremony (Hall)

Registration

Short Course

10

Keynote1 (Hall)
Asteroid Sample Return Mission “Hayabusa2” – A Successful
Example of Large-Scale Scientific Project Management
Prof. Yuichi Tsuda (JAXA)

Sponsor Talk 1 (Hall)

11

11:40
11:50

12

12:00
12:10
12:20
12:30
12:40

Lunch

Registration 12
OS04-1

12:50
13:00

13

13:10
13:20
13:30

15

16

17

18

14

14:20
14:30
14:40

OS01-1OS06-1

9:40
9:50
10:00
10:10
10:20
10:30
10:40

PHM
PHM forPrognostic
Electric
Discriminator Applications
for Urban
Power
under
Systems 1 Uncertainty 1 Infrastructure

Resilienceand
Maintenance 1

Exhibition

13

Short Course

OS04-2OS05-2OS12-1OS01-2

OS6-2

Evolving
PHM forAdvances in Prognostic
PHM
Smart
Commercial
PHM for
Discriminator Applications
Airplanes 2 Manufacturing Tribology 1
underfor Urban
System by
Uncertainty 2 Infrastructure
design andResilience
implementationandof PHM 2
Maintenance 2

15

Coffee Break
OS04-3OS05-3OS12-2OS01-3OS6-3

Evolving
PHM forAdvances in Prognostic
PHM
Smart
Discriminator Applications
Commercial
PHM forunder
Airplanes 3 Manufacturing Tribology 2
for Urban
System by
Uncertainty 3 Infrastructure
design andResilience
implementationandof PHM 3
Maintenance 3

Coffee Break

Welcome Reception

17

OS04-4
PHM forCommercial
Airplanes 4
Panel
Session 1
Full Flight
SensorData sharing
within thestakeholdersof PHM

OS12-3

16

R02-1

OS6-4

Advances in
PHM
Smart
PHM forMonitoring/ Applications
Tribology 3 Maintenance/ for Urban
Operation 1 Infrastructure
Resilienceand
Maintenance 4

18

Sep. 14

Room
D

Room
E

Exhibition

1F
Lobby

16:30
16:40
16:50
17:00
17:10
17:20
17:30
17:40
17:50
18:00
18:10
18:20
18:30
18:40
18:50

Hall

Room
B

Room
C

Room
D

2F
Lobby
P02

Registration

Coffee BreakRegistration

Keynote 3 (Hall)

Panel Session 2

How is Industrial AI Transforming Manufacturing Industry?

Quantum
Computingmeets PHM

Prof. Byeng Dong Youn
(Seoul National University and OnePredict)

1F
Lobby

PosterSession 2

Prof. Katsuo Sasahara (Kochi University)

Exhibition

Exhibition

P01

Prof. Jay Lee (University of Maryland College Park)

Sponsor Talk 2 (Hall)

Room
E

PosterSession 1

Keynote 4 (Hall)PHM forDeviceMonitoring

OS2-3

R07-1

New trends
andchallenges
in aerospace
PHM

RUL
Prediction 1

PHM forRotaryMachines

R03-3
MachineLearning/
Deep
Learning for
PHM 3

Exhibition

Lunch
Lunch
R01-2
PHM forElectric
Power
Systems 2

OS02-1OS07-1

R02-2

R03-1

New trends Autonomous
Smart
Machineanddecision
Monitoring/
Learning/
challenges making under Maintenance/
Deepin aerospacecomplexOperation 2 Learning for
PHM 1
conditions or
PHM 1
situations 1

14:20
14:30
14:40
14:50
15:00
15:10
15:20
15:30
15:40
15:50
16:00
16:10
16:20

Room
A

Prediction of the Movement of a Landslide for
the Early Warning System Against Disaster in Japan

12:00
12:10
12:20
12:30
12:40

13:10
13:20
13:30

2F
Lobby

Keynote 2 (Hall)

10:50
11:00
11:10
11:20
11:30

13:40
13:50
14:00
14:10

14

Room
C

Industrial AI-Augmented Prognostics
for Highly Connected and Complex Industrial Systems

12:50
13:00

Coffee Break

16:30
16:40
16:50
17:00
17:10

18:20
18:30
18:40
18:50

System bydesign andimplementationof PHM 1

Waiting place forwelcome recpetion

14:50
15:00
15:10
15:20
15:30
15:40
15:50
16:00
16:10
16:20

17:20
17:30
17:40
17:50
18:00
18:10

R01-1

Evolving
PHM for
Smart
Commercial
Airplanes 1 Manufacturing

13:40
13:50
14:00
14:10

14

OS05-1

Hall

Room
B

9:00
9:10
9:20
9:30

11:40
11:50

Lunch

Sep. 13
Room
A

OS08

OS02-2OS07-2

Predictive
Maintenance
Challenges andSolutions:
VendorInsights

PHM forTurbines

Digital Twin
for PHM

R07-2

R02-5

RUL
Prediction 2

Smart
Monitoring/
Maintenance/
Operation 5

Coffee BreakCoffee BreakTechnologies New trends
and methods
andfor achieving challenges
Zero DefectinManufacturing aerospacein the era ofPHM 2Industry
4.0 / 5.0

Panel Session 3

R02-3

OS09

Autonomous
Smart
AI baseddecision
Monitoring/
PHM formakingMaintenance/
Power
under
Operation 3 Electronics
complexcomponents
conditions orandsituations 2
systems

Keynote 5 (Hall)
From Physics to Machine Learning and Back:
Applications to Prognostics and Health Management
Prof. Olga Fink (EPFL, Lausanne)

Closing/Award Ceremony (Hall)

Coffee Break
R04

R05

PHM forPower
Electronics/
Battery

PHM forMotors

R03-2

R02-4

MachineSmart
Learning/
Monitoring/
Deep
Maintenance/
Learning for Operation 4
PHM 2

D01
Data
Challenge

BanquetAsia Pacific Conference of the Prognostics and Health Management Society 2023

Oral Sessions
Sep. 12

Time/Date
JST(UTC+9)

10

11

12

12

13

14

15

Hall

10:00
10:10
10:20

Room
B

Room
C

11:10
11:20
11:30
11:40

Sponsor Talk 1 (Hall)

13:00
13:10
13:20
13:30
13:40
13:50
14:00

OS04-1
PHM for Commercial Airplanes1
Chair: Junichiro Kaya
(ANA)

OS05-1
Evolving Smart Manufacturing System
by design and implementation of PHM 1
Chair: Makoto Imamura (Tokai University)

R01-1
PHM for Electric Power Systems 1
Chair: Eric Bechhoefer(GPMS Inc.)

OS04-01

OS05-01

R01-01A monitoring method for detecting and localizing
overheat faults in wind turbine nacelle
Minsoo Lee, Eunchan Do, and Ki-Yong Oh1
1. Hanyang University

Automatic Detection of Concrete Surface Defects Using Deep
Learning and Laser Ultrasonic Visualization Testing
Takahiro Saitoh1, Syumpei Ohyama1, Tsuyoshi Kato1,
and Sohichi Hirose2
1. Gunma University, 2. Tokyo Institute of Technology

OS01-01

OS06-01

OS04-02

OS05-02

R01-02OS01-02OS06-02

OS04-03

OS05-03

R01-03OS01-03

A Method for Executing Digital System Models and Digital
Twins at Scale to Enrich Fleet Health Management
Daniel Newman1
1. Boeing
Operational Prognostic Model Evaluation
Shashvat Prakash1, and Katarina Vuckovic1
1. Raytheon

Applicability of Traditional and AI Models in Predictive
Maintenance in Aviation
Dimitri Reiswich1
1. Lufthansa Technik

Quality Management Issues for Machine Learningbased Systems
Yutaka Oiwa1
1. National Institute of Advanced Industrial Science and Technology
Smart Manufacturing Kaizen Level(SMKL) based maturity
evaluation access to DX and decarbonization
Nicole S. Otoki1, Yasuo Onodera1, and Mitsushiro Fujishima1
1. Mitsubishi Electric
Development and operation of a piping corrosion external surface
inspection system using Human in the Loop Machine Learning
Toshihiro Shimbo1, Yousuke Okada2, and Hitoshi Matsubara3
1. Mitsubishi Gas Chemical Company, Inc, 2. ABEJA, Inc,
3. The University of Tokyo

Research on the method of digital twin operation and maintenanceplatform for intelligent early warning of wind turbine tower
Yu Jia1, and Xiaomo Jiang1
1. Dalian University Of Technology
A Survey of Prognostics and Health Management for Transformers:
From Statistical Analysis to Condition-Based Diagnostics

Jiaxiang Cheng1, Sungin Cho2, Yap Peng Tan1, and Guoqiang Hu1

1. Nanyang Technological University, 2. Singapore Power

17

16:40
16:50
17:00
17:10

17

18

15

18:20
18:30
18:40
18:50

Digital Twin for Diagnosis of Belt Looseness in HVAC Systems using
multi-body dynamics simulation
Daeguen Lim1, Wonho Jung1, Sung Hyun Yun1, Yong Hwa Park1,
and Gil-Yong Lee1
1. Korea Advanced Institute of Science and Technology

OS01-04
Outlier Analysis of Bridge Deffections using Satellite
SAR and Structual Simulation: A Case Study on a Collapse
Accedent in a Water Pipe Bridge in Japan
Kosuke Kinoshita1, Yoshiyuki Yajima1, Takahiro Kumura1, and Mayuko Nishio2
1. NEC, 2. University of Tsukuba

OS06-04

OS04-2
PHM for Commercial Airplanes 2
Chair: Masaru (Ken) Nishiwaki(ANA)

OS05-2Evolving Smart Manufacturing System bydesign and implementation of PHM 2
Chair: Yutaka Oiwa
(National Institute of Advanced Industrial Science and Technology)

OS12-1
Advances in PHM for Tribology 1
Chair: Tomomi Honda(University of Fukui)OS01-2
Prognostic Discriminator under Uncertainty 2
Chair: Takato Yasuno(Yachiyo Engineering, Co.Ltd)

OS06-2
PHM Applications for Urban Infrastructure
Resilience and Maintenance 2
Chair: Ken Ueno (Toshiba)

OS04-05

OS05-05

Anomaly Detection in Air Handling Units using Motor Current
Signal Imaging for Belt Looseness Detection
Sung Hyun Yun1, Wonho Jung1, Daeguen Lim1, and Yonghwa Park1
1. Korea Advanced Institute of Science and Technology

Coffee BreakModel Based and Big data enabled Predictive
Maintenance Capability Development Experience
Mark Mazarek1, Darren Macer1, and Changzhou Wang1
1. Boeing

Integration of health monitoring of cutting tools andproduction scheduling in smart factoryOS12-01

A feature Selection Method for Machine Tool Wear
Diagnosis
Yuji Homma1
1. Mitsubishi Electric

OS01-05

OS06-05

Hitoshi Komoto1, German Herrera1, Jonny Herwan1,
and Yoshiyuki Furukawa1

OS04-06

OS05-06

OS12-02

OS01-06

OS06-07

Masaki Goto1, Tsuyoshi Inoue1, Akira Heya1, Keiichi Katayama2,
Shogo Kimura2, Shigeyuki Tomimatsu2, and Shota Yabui3

Masato Tsuchiya1, Ken Miyamoto1, Takashi Ota1,
and Yasushi Sugama1

Development of a method for detecting V-belt looseness
Hiroshi Inoue1, Ken Hirakida1, Makoto Kanemaru1,
and Peng Chen2
1. Mitsubishi Electric, 2. Mie University
OS06-08

1. National Institute of Advanced Industrial Science and Technology

Imbalanced Anomaly Detection Using Augmented Deeper
FCDDs for Wooden Sleeper Deterioration Prognostics
Takato Yasuno1, Junichiro Fujii1, and Masahiro Okano1
1. Yachiyo Engineering Co., Ltd.

Fleet Monitoring as a means of Operationalizing PHM
Gururaj Saralaya1
1. Collins Aerospace

Advancing Predictive Maintenance: A Study of Domain Adaptation
for Fault Identification in Gearbox Components
Shinya Tsuruta1, Koji Wakimoto1, Takaaki Nakamura1, Shahin Siahpour2,
Marcella Miller2, John Taco2, and Jay Lee3
1. Mitsubishi Electric, 2. University of Cincinnati, 3. University of Maryland

Modeling of Journal Bearings for Wear Diagnosis and
Its Verification Using SVM

OS04-07

OS05-07OS12-03

OS01-07

Osamu Yoshimatsu1, Keiichiro Taguchi1, Yoshihiro Sato1,
and Takehisa Yairi2

Yuta Shirakawa1, Satoshi Ito1, Reiko Noda1, Naoto Yoshitani2,Masahide Wake1, and Honoka Takano1

Lessons Learned from Aircraft Component Failure
Prediction using Full Flight Sensor Data
Changzhou Wang1, Darren Puigh1, Audrey Lei1, Wei Guo1,
Jun Yuan1, and Mark Mazarek1

1. BoeingOS04-08

Effect of Aircraft Health Management on Aircraft Maintenance
Program Development by Aircraft Manufacturer
Takuro Koizumi1, and Nozomu Kogiso1
1. Osaka Metropolitan University

Building the health monitoring and fault diagnosis models forstamping press
Yuan-Jen Chang1, Lin-Jie Chen1, Yuta Tu2, Hung-Pin Yang2, and Chen-Kang Lee3
1. Feng Chia University, 2. Chin Fong Machine Industrial Co., LTD,
3. University of California San Diego

OS05-08

An Easy-to-Use and Customizable Data Science Tool
for Predictive Maintenance in Manufacturing
Naoki Sugawara1
1. Mitsubishi Electric

1. Nagoya University, 2. DMW, 3. Tokyo City University
Size Estimation of Flaking in Rolling Bearings Using
Deep Learning with Explainability
1. NSK, 2. University of Tokyo

Field study toward anomaly road damage detection withdrive recorder
1. Mitsubishi Electric

Automating daily inspection for Expressways using
anomaly detection model

1. Toshiba, 2. Central Nippon Expressway Company Limited

A new approach to multivariate statistical process control andits application to wastewater treatment process monitoring
Osamu Yamanaka1, and Yukio Hiraoka1
1. Toshiba Infrastructure Systems & Solutions

Few-Shot Learning for Full Ceramic Bearing Fault
Diagnosis with Acoustic Emission Signals
David He1, Miao He2, and Alessandro Taffari1
1. University of Illinois at Chicago, 2. Siemens Technology

OS12-04

Expert Knowledge Transfer from CAE Models to CNN
Models Using Enhanced Adversarial Domain Adaptation
Minseok Choi1, Dongmin Lee1, Mikyung Hwang1, Dongwhi Yoo1,
and Hyunseok Oh1

1. Gwangju Institute of Science and Technology

Coffee Break

OS04-11

The role of visualizations in Ascentia, analytics-based
aircraft maintenanceArman Haidari1
1. Collins Aerospace

OS05-3Evolving Smart Manufacturing System
by design and implementation of PHM 3
Chair: Koji Wakimoto (Mitsubishi Electric)
OS05-09

Design of a framework for demand forecasting using time series
decomposition-based approach
Hazuki Shibayama1, Aya Ishigaki1, Takasumi Kobe2, Takafumi Ueda3,
Daichi Arimizu3, and Takaaki Nakamura3
1. Tokyo University of Science, 2. Deloitte Tohmatsu Consulting LLC, 3. Mitsubishi Electric

OS12-2
Advances in PHM for Tribology 2
Chair: Osamu Yoshimatsu(NSK)
OS12-05

Acoustic Signal based Non-Contact Ball Bearing Fault
Diagnosis Using Adaptive Wavelet De-Noising
Wonho Jung1, Sung-Hyun Yun1, and Yong-Hwa Park1
1. Korea Advanced Institute of Science and Technology

OS01-3
Prognostic Discriminator under Uncertainty
Chair: Yasutaka Narazaki 3(Zhejiang University)

OS06-3OS01-08

OS06-09

Digital Twin of Built Structures assisted by Computer Vision
Techniques: Overview and Preliminary Results
Yasutaka Narazaki1
1. Zhejiang University

PHM Applications for Urban Infrastructure
Resilience and Maintenance 3
Chair: Tomoyuki Iwawaki(Mitsubishi Electric)
Semi-supervised machine learning for motor eccentricity
fault diagnosis
Bingnan Wang1, Shen Zhang2, Hiroshi Inoue3, and Makoto Kanemaru3
1. Mitsubishi Electric Research Laboratories, 2. Georgia Institute of Technology, 3. Mitsubishi Electric

OS04-09

OS05-10OS12-06

Time Shifting Data Augmentation to Alleviate Class-Imbalance
Problem for Cross-Domain Bearing Fault Diagnosis
Wonho Jung1, Sung-Hyun Yun1, and Yong-Hwa Park1
1. Korea Advanced Institute of Science and Technology

Digital Twin for condition based maintenance within a railway
infrastructure testing lab
Antonio J. Guillén López1, Juan Fco. Gómez Fernández1, Pedro Urda1,
Jose Luis Escalona1, Adolfo Crespo Márquez1, and Fernando Olivencia1
1. Universidad de Sevill

OS01-09

OS06-10OS04-10OS05-11OS12-07

OS01-10OS06-11Big data platform as a foundation of airlines predictive
and health monitoring
William Bernard1
1. Airbus
Deep dive into Prediction with AVIATAR with in-service
examples from Airlines
Mia Witzig1
1. Lufthansa Technik
OS04-12A New Challenge in Predictive Maintenance Analysis
for Aircraft
Takashi Takemura1
1. JAL

Prediction of Remaining Life of Turbo Pump inducer
for Spacecraft Using Cumulative
Hatsuo Mori1, and Makoto Imamura2
1. IHI, 2. Tokai University
Deep Learning/Machine Learning Techniques for Vibration Condition
Monitoring of Major Facilities in Automobile Assembly/Painting Plants
Gun Sik Kim1, Deog Hyeon Kim1, Jin Woo Park1,
and Ju Heon Hwang1
1. Hyundai Motors

OS05-12A case study of applying power consumption optimization
in EAF using time series prediction
Jaehyuk Lee1, Songhwan Kim1, Boseon Yoo1, and Jaesik Choi1
1. ineeji

A quantitative analysis of domain discrepancies between generalspeed and very low-speed bearings and its applications to fault
diagnosis of very low-speed bearings using transfer learning
Seungyun Lee1, Sungjong Kim1, Su J. Kim2, Jiwon Lee1, Heonjun Yoon3, and Byeng D. Youn1
1. Seoul National University, 2. OnePredict, 3. Soongsil University

Efficient Inspection of Civil Engineering Structures forRailways and Roads Using Images and GNSS
Tomoya Abe1
1. Nakashacreative.co

OS12-08

Extracting Broken-Rotor-Bar Fault Signature of Varying
Speed Induction Motors
Dehong Liu1, Anantaram Varatharajan1, and Abraham Goldsmith1
1. Mitsubishi Electric Research Laboratories
Fault detection of rotor bars in inverter-fed induction motors
based on current signature analysis technique
Tomoyuki Iwawaki1, Makoto Kanemaru1, Yuto Yasuhara1, and Toshihiko Miyauchi1
1. Mitsubishi Electric
OS06-12

On-site degradation diagnosis method for the antioxidants
by the colorimetric analysis of membrane patches
Tomomi Honda1
1. University of Fukui

Data-driven pump performance analysis using online monitoring data
accumulated with supervisory control and data acquisition system
Ryo Namba1, Hiroyuki Hokari1, and Hideaki Komine1
1. Toshiba Infrastructure Systems & Solutions

Coffee Break
OS04-4
PHM for Commercial Airplanes 4
Chair: Masaru (Ken) Nishiwaki
(ANA)

17:40
17:50
18:00
18:10

OS06-03

Study on the Estimation of Concrete Defects Volume
on Dam Body Surface
Akira Ishii1, Hiroaki Sugawara1, and Masazumi Amakata1
1. Yachiyo Engineering Co., Ltd.

R01-04

Anomaly detection for yield improvement in glass production Advanced Weibull Modelling with Outliers
Haruo Yonemori1, Kenichi Arai1, Hironobu Yamamichi1, Yipeng Pang1, Guoqiang Hu1, and Sungin Cho1
1. Nanyang Technological University, 2. SP PowerGrid
Ichiro Sakata1, and Makoto Imamura2
1. AGC Inc., 2. Tokai University

17:20

17:30

Predictive Maintenance for station equipment andApplications for the Space field
Ryoma Yokoyama1, Ryosuke Kono1, Atsushi Matsuda1, and Yohei Shiomi1
1. West Japan Railway

OS05-04

MLOps for PHM systems
Mikael Yemane1
1. Collins Aerospace

OS04-3
PHM for Commercial Airplanes 3
Chair: Junichiro Kaya
(ANA)

16

Observation maintenance for bridges using early
detection of deterioration progress
Hitoshi Ito1, and Toshiaki Mizobuchi1
1. Yachiyo Engineering Co., Ltd., 2. Hosei University

Anomaly Sign Detection for Automatic Ticket Gates
by Histogram Limitation Method
Ken Ueno1, Shigeru Maya1, and Kiyoku Endo2
1. Toshiba, 2. Toshiba Automation Systems Service

OS04-04

15:40
15:50

16:00
16:10
16:20
16:30

OS06-1
PHM Applications for Urban Infrastructure
Resilience and Maintenance 1
Chair: Ken Ueno (Toshiba)

Lunch

14:10

14:30
14:40
14:50
15:00
15:10
15:20
15:30

OS01-1
Prognostic Discriminator under Uncertainty 1
Chair: Takahiro Saitoh(Gunma University)

2F Lobby

Prof. Yuichi Tsuda (JAXA)

11:50
12:00
12:10
12:20
12:30

12:50

Room
E

Keynote1 (Hall)
Asteroid Sample Return Mission “Hayabusa2” – A Successful
Example of Large-Scale Scientific Project Management

12:40

Room
D

Opening Ceremony (Hall)

10:30
10:40
10:50
11:00

14:20

14

Room
A

OS12-3
Advances in PHM for Tribology 3
Chair: Osamu Yoshimatsu
(NSK)

R02-1
Smart Monitoring/Maintenance/Operation 1
Chair: Tadahiro Shibutani(Yokohama National University)

Coffee Break

OS04-13

Prognostic is nothing without fulfillment – Set-up of an holistic
fleet management and integration into daily processes
Sebastian Lang1, and Mia Witzig1
1. Lufthansa Technik
OS04-14

Anomaly Detection in Airliner Centrifugal Compressor using sensor
data during the climb phase
Sadanari Shigetomi1, Makoto Imamura2, Naoya Kaido1, Makoto Taniguchi1,
Masaru Nishiwaki1, and Junichiro Kaya1
1. ANA, 2. Tokai University

Panel Session 1
Full Flight Sensor Data sharing within
the stakeholders of PHM
Organizer: Masaru (Ken) Nishiwaki (ANA)
Panelists:
1. Darren Macer (Boeing)
2. Gavin Chew (Collins Aerospace)
3. Sebastian Lang (Lufthansa Technik)
4. Manabu Tono (ANA)

OS12-09

Non-destructive Diagnostics for Rolling Bearings
by Eddy Current Testing
Daisuke Kobayashi1, Koichiro Ono1, Masahide Natori1, and Hiroki Komata1
1. NSK

R02-01

Energy Saving Structural Health Monitoring Using
Semi-Active Identification
Yushin Hara1, Tianyi Tang1, Keisuke Otsuka1, and Kanjuro Makihara1
1. Tohoku University
R02-02OS06-4

PHM Applications for Urban Infrastructure
Resilience and Maintenance 4
Chair: Tomoyuki Iwawaki(Mitsubishi Electric)

OS06-13

Localization of partial discharges in oil-filledtransformers using acoustic emission signals
Yasutomo Otake1, and Kunihiko Tajiri1
1. Mitsubishi Electric
OS06-14

Physical reservoir-based health monitoring ofa structure with nonlinear attachments
Arata Masuda1, and Konosuke Takashima1
1. Kyoto Institute of Technology

AI image analysis technologies for efficient water pipe inspection
Ying Piao1, Hiroshi Sukegawa1, Kenji Kimiyama1, Kensuke Nakamura1,
Toshiharu Sugino1, Takaharu Kunizane2, and Akira Koizumi2
1. Toshiba Infrastructure Systems & Solutions, 2. Tokyo Metropolitan University

R02-03OS06-15

R02-04OS06-16

Advanced condition-based monitoring of CFRP under multiple
impacts using monte carlo based prognostics and real-time
self-sensing data
In Yong Lee1, So Young Oh1, Juhyeong Jang1, and Young-Bin Park1
1. Ulsan National Institute of Science and Technology

Deep Neural Network Anomaly Detection and Statistical
Estimation of High Pressure Liquefied Natural Gas Pipe
Dabin Yang1, and Jongsoo Lee1
1. Yonsei University

Prediction of impact information of composites laminates Machine learning model for detecting hydrogen leakage fromhydrogen pipeline using physical modelingconsidering the practicality
Yuki Suzuki1, Jo Nakayama1, Tomoya Suzuki1, Tomoya Soma2,
Saki Hasebe1, Ryo Higuchi1, Tomohiro Yokozeki1, and Shin-ichi Takeda2
Yu-ichiro Izato1, and Atusmi Miyake1
1. University of Tokyo, 2. Japan Aerospace Exploration Agency
1. Yokohama National University, 2. NEC

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Oral Sessions
Sep. 13

Time/Date
JST(UTC+9)

9

10

11

Room
A

Hall

Room
B

Room
C

Room
D

Room
E

9:00
9:10
9:20

2F Lobby

PosterSession 1

9:30
9:40
9:50
10:00

Keynote 2 (Hall)

10:10
10:20
10:30
10:40

Industrial AI-Augmented Prognostics
for Highly Connected and Complex Industrial Systems
Prof. Jay Lee (University of Maryland College Park)

10:50
11:00
11:10
11:20

Sponsor Talk 2 (Hall)

11:30
11:40
11:50
12:00

Keynote 3 (Hall)
How is Industrial AI Transforming Manufacturing Industry?
Prof. Byeng Dong Youn (Seoul National University, and OnePredict Inc.)

12:10

12

13

12:20
12:30
12:40

Lunch

12:50
13:00
13:10

R01-2
OS02-1
New trends and challenges in aerospace PHM 1
PHM for Electric Power Systems 2
Chair: Nozomu Kogiso
Chair:Byeng Dong Youn
(Seoul National University, and OnePredict Inc.)(Osaka Metropolitan University)

13:20

13

14

13:30
13:40
13:50
14:00
14:10
14:20
14:30

OS07-1
Autonomous decision making undercomplex conditions or situations 1
Chair: Masanao Natsumeda (NEC)R02-2
Smart Monitoring/Maintenance/Operation 2
Chair: Eric Bechhoefer(GPMS Inc.)

R03-1
Machine Learning/Deep Learning for PHM 1
Chair: Atsuhiro Takasu(National Institute of Informatics)

OS07-01

R02-05

R03-01

R01-05Detection and Diagnostic with Random Forest Classifier (RFC)
to Improve the Maintenance Management System in SteamBoiler of Power Plant
Ghiffari Awliya Muhammad Ashfania1, Tarwaji Warsokusumo2, Toni Prahasto1, and Achmad Widodo1
1. Diponegoro University, 2. PT PLN Indonesia Power

OS02-01

R01-06
Deep Learning-Enabled Statistical Model Estimation for Power
Transformers under Censoring and Truncation Constraints

OS02-02

A Health Index for Satellite System Based on
Characteristics of Telemetry Data
Shun Katsube1, and Hironori Sahara1
1.Tokyo Metropolitan University

OS07-02
Deep Learning Approach for Operational Transfer Path Analysis:
Case Study of Electric Vehicles
Jeongmin Oh1, Donghwi Yoo1, Hyunseok Oh1, Yong Hyun Ryu2,
Kyung-Woo Lee2, and Dae-Un Sung2
1. Gwangju Institute of Science and Technology, 2. Hyundai Motor Company

R02-06

Statistical Analysis and Runtime Monitoring for an
AI-based Autonomous Centerline Tracking System
Yuning He1, and Johann Schumann1
1. NASA

Unsupervised Anomaly Detection in Marine Diesel Engines using
Transformer Neural Networks and Residual Analysis
Qin Liang1, 2, Knut Erik Knutsen1, Erik Vanem1, Houxiang Zhang2,
and Vilmar Æsøy2
1. DNV, 2. Norwegian University of Science and Technology

OS02-03OS07-03

R02-07

R03-03

Shota Iino1, Hideki Nomoto1, Takashi Fukui2, Sayaka Ishizawa2, Miki Sasaki2,
Yohei Yagisawa2, Takayuki Hirose1, Yasutaka Michiura1, and Hiroharu Shibayama3

Jun Kawahara1, Chuta Yamaoka1, Takehiro Ito2, Akira Suzuki2, Daisuke Iioka3, Problem in AS/RS
Shuhei Sugimura4, Seiya Goto4, and Takayuki Tanabe4
Kuniaki Satori1, and Nobuyuki Yoshikawa1
1. Kyoto University, 2. Tohoku University, 3. Chubu University, 4. MEIDENSHA 1. Mitsubishi Electric

Prognostics in Highly Accelerated Limit Testing Using
Deep Learning Data Analysis
Tadahiro Shibutani1, Yuki Terauchi1, and Yosuke Kikuchi1
1. Yokohama National University

OS07-04

R03-04

Jiaxiang Cheng1, Sungin Cho2, Yap Peng Tan1, and Guoqiang Hu1
1. Nanyang Technological University, 2. Singapore Power

R01-07Long-Term Preventive Failure Mitigation Strategy For
Transformers Based on Markov Method
Jianzheng Wang1, Guoqiang Hu1, and Sungin Cho2
1. Nanyang Technological University, 2. SP Group

Data-driven satellite monitoring method applicable tovarious telemetry

Noriyasu Omata1, Seiji Tsutsumi1, Masaharu Abe2, and Iku Shinohara1

1. Japan Aerospace Exploration Agency, 2. Ryoyu Systems

Systemic symptom detection in telemetry of ISS with explainability
using FRAM and SpecTRM

1. Japan Manned Space Systems Corporation, 2. Japan NUS Co., Ltd, 3. Space Engineering Development Co.,Ltd.

R01-08

Explainable multimodal learning for predictive
maintenance of steam generators

A study on self-diagnosis/prediction technology for
Lidar sensor of autonomous vehicles
Jaewook Lee1, and Jongsoo Lee1
1. Yonsei University

Algorithmic Study for Power Restoration in Electrical Distribution Networks Quantum Optimization for Location Assignment

A study on the intent-based system design automation
method for fault detection and fault tolerance

Duc An Nguyen1, Sagar Jose1, Thi Phuong Khanh Nguyen1, and Kamal Medjaher1

Takayuki Kuroda1, Toshiki Watanabe1, and Tatsuya Fukuda1

16

17

Unsupervised Retrieval Based Multivariate Time Series Anomaly
Detection and Diagnosis with Deep Binary Coding Models
Takehiko Mizoguchi1, Yuji Kobayashi1, and Yasuhiro Ajiro1
1. NECOS08

OS02-2
Technologies and methods for achieving Zero
New trends and challenges in aerospace PHM 2
Defect Manufacturing in the era of Industry 4.0 / 5.0
Chair: Seiji Tsutsumi
Chair: Foivos Psarommatis (University of Oslo),(Japan Aerospace Exploration Agency)
and Samir Khan (University of Tokyo)

R02-3
OS07-2Autonomous decision making under complex Smart Monitoring/Maintenance/Operation 3
Chair: David Weihua He
conditions or situations 2
Chair: Masanao Natsumeda (NEC)(University of Illinois at Chicago)

OS09

15:00
15:10

OS08-01OS07-05

OS09-01

15:20
15:30

OS08-02

OS02-06

Hyun Joon Lee1, Jae Won Jang1, Hyung Jun Park1, Kyoung Rae Noh2,
Sangchul Lee1, and Joo-Ho Choi1
1. Korea Aerospace University, 2. LG Electronics

Shota Iino1, Hideki Nomoto1, Takayuki Hirose1, Yasutaka Michiura1,
Go Fujii2, and Takashi Uchiyama2
1. Japan Manned Space Systems Corporation, 2. Japan Aerospace Exploration Agency

15:40
15:50
16:00
16:10
16:20
16:30

OS08-03

Coffee BreakOS02-05

Applications of Active Learning in Predictive Maintenance Study on Fault Diagnosis in a Spacecraft Propulsion System
Navid Zaman1, You Jung Jun1, Yan Li1, and Daniel Chan1 Kazushi Adachi1, Samir Khan1, Kohji Tominaga2, Noriyasu Omata2,
Seiji Tsutsumi2, and Taiichi Nagata2
1. PHM Technology
1. University of Tokyo, 2. Japan Aerospace Exploration Agency
Development of demonstration system for fault diagnosisof rotating equipments using RK4 test rig

A Time Series Anomaly Detection Method Using Image
Encoding Techniques
Ryo Sakurai1, and Takehisa Yairi1
1. University of Tokyo

Anomaly detection of propulsion system of spacecrafts
with Light GBM

OS02-07

Development of a space exploration rover digital twin
for damage detection
Lucio Pinello1, Marco Giglio1, Claudio Sbarufatti1, Francesco Cadini1,
and Giuseppe Francesco De Luca2
1. Politecnico di Milano, 2. Agenzia Spaziale Italiana

Nonlinear Model Predictive Control using Neural ODE
Replicas of Dynamic Simulators

Shumpei Kubosawa1, 2, Takashi Onishi1, 2,
and Yoshimasa Tsuruoka1, 3
1. National Institute of Advanced Industrial Science and Technology, 2. NEC, 3. The University of TokyoMin Joo Kim1, Yun Hee Kim1, Sun Geu Chae1, and Suk Joo Bae1
1. Hanyang University

R02-09Online fault detection for industrial processes throughKalman filter
Wenyi Liu1, and Takehisa Yairi1
1. University of Tokyo

OS07-06

A two-step detection method for actuator and sensor failures basedon self-repairing PID control
Masayoshi Hara1, and Masanori Takahashi1
1. Oita University

R02-10Abnormal Detection Using Two-Stage Method in
Combined Power Plant
Yun Hee Kim1, Suk Joo Bae1, SangWoo Ha1,
Shengxin Jin1, Moon Won Lim1, Sungyong Chang2,
HaeSu Kang2, Gyusang Park2, and Milee Joo2
1. Hanyang University, 2. Korea Electric Power Corporation

OS07-07

R02-11An Experimental Study of the Effect of Patrol Inspection Strategy
for Improving Detection Rate of Abnormality of Industrial Plants
Akio Gofuku1, and Riku Kishimoto1
1. Okayama University

Withdraw
OS02-08
OS08-04
Construction and evaluation of an anomaly detection system using
Early Detection of Lithium-ion Battery Degradation Leveraging System Invariant Analysis Technology(SIAT) for sound data
Conditional Denoising VAEs for Dynamic Noise Estimation

Event detection in a noisy time series data using static
smoothening & gradient variation analytics
Saravanaram T1, Unnat Mankad1, Simi Madhavan Karatha1,
Carmine Allegorico1, and Arati Halaki2
1. Baker Hughes, 2. Wissen Infortech

AI based PHM for Power Electronics
components and systems
Chair: Madhav Mishra (RISE Research Institutes of Sweden)
RUL Estimation for Package Failure of Power Electronic
Devices Using Integral Mean of Precursor Signal
Zhonghai Lu1, and Chao Guo1, Pol Ghesquiere2, Kai Kriegel2,
and Gerhard Mitic2
1. KTH Royal Institute of Technology, 2. Siemens
OS09-03

A Simple Hybrid Model for Estimating Remaining UsefulLife of SiC MOSFETs in Power Cycling Experiments
Mattias P. Eng1, Andreas Lövberg1, Maciej Misiorny2,
Wilhelm Söderkvist Vermelin1, and Klas Brinkfeldt1
1. RISE Research Institutes of Sweden, 2. QRTECH AB

OS09-04

Damage-Based Lifetime Modeling for Power Electronic
Devices
Chao Guo1, and Zhonghai Lu1
1. KTH Royal Institute of Technology

R02-12

Improving Anomalous Sound Detection by DistanceMatrix-Based Visualization of Measurement Flaws
Nobuaki Tanaka1, Takeru Shiraga1, and Yusuke Itani1
1. Mitsubishi ElectricTomoya Soma1, and Akiko Sasaki1
1. NEC

Coffee BreakR05
PHM for Motors
Chair: Arata Masuda(Kyoto Institute of Technology)

16:40
16:50

R04-01

17:00
17:10
17:20
17:30

R04-02

R05-02

R04-03

R05-03

17:40
17:50

16

Automatic Generation of Seven-Segment Display Imagefor Machine-Learning-Based Digital Meter Reading
Kota Gushima1, and Takahiro Kashima1
1. Mitsubishi Electric

R03-02

1. NECR04
PHM for Power Electronics/Battery
Chair: Byeng Dong Youn
(Seoul National University, and OnePredict Inc.)

16

R02-08

Assessing the Performance of Transformer forTime Series Anomaly Detection
Takuto Nakashima1, and Takehisa Yairi1
1. University of Tokyo

1. Ecole Nationale d'Ingénieurs de Tarbes

14:40
14:50

15

Tacholess instantaneous speed estimation considering thecharacteristics of vibration harmonic
Jinoh Yoo, Jongmin Park1, Taehyung Kim1, Jong Moon Ha2,
and Byeng Dong Youn1
1. Seoul National University, 2. Korea Research Institute of Standards and Science

An Enhanced Model-Based Algorithm for Early Internal
Short Circuit Detection in Lithium-Ion Batteries
Yiqi Jia1, Lorenzo Brancato1, Marco Giglio1, and Francesco Cadini1
1. Politecnico di Milano

R05-01Analysis of Diagnostic Capabilities for Degradation of Brushless
Direct Current Motors Depending on Varying Simulation Data
Max Weigert1
1. Technical University of Darmstadt

Multiphysics-informed DeepONet of a lithium-ion battery A multi-periodicity and multi-scale network for motor
to predict thermal runaway
fault diagnosis
Jinho Jeong1, Eunji Kwak1, Jun-Hyeong Kim1, and Ki-Yong Oh1 Pengcheng Xia1, Kaiwen Zhang1, Yixiang Huang1, and Chengliang Liu1
1. Shanghai Jiao Tong University
1. Hanyang University

R03-2
Machine Learning/Deep Learning for PHM 2
Chair: Naoya Takeishi(University of Tokyo)

R02-4
Smart Monitoring/Maintenance/Operation 4
Chair: Yixiang Huang(Shanghai Jiao Tong University)

DC01
Data Challenge
Chair: Satoshi Suzuki(ISID)

R03-06

R02-13

To Trust or Not: Towards Efficient Uncertainty Quantificationfor Stochastic Shapley Explanations
Joseph Cohen1, Eunshin Byon1, and Xun Huan1
1. University of Michigan

A Reliability-Centered Maintenance Framework forDistribution Grids Based on Fault-Tree Analysis
Ting Wang1, Guoqiang Hu1, and Sungin Cho1
1. Nanyang Technological University, 2. SP Group

R03-07R02-14

D01-02

P01-14A Signal Pre-processing Method for Condition Monitoring basedon Vibration Signals from On-Site Manipulators
Hea-Ryeon Seo1, Geonhwi Lee1, Gun Sik Kim2, Jae Min Lee2,
Deog Hyeon Kim2, Jin Woo Park2, and Hae-Jin Choi1
1. Chung Ang University, 2. Hyundai Motor Group
R02-16

Hybrid Approach of Xgboost and Rule-based Model for Fault Detectionand Severity Estimation in Spacecraft Propulsion System
Sang Kyung Lee1, Jiwon Lee1, Seungyun Lee1, Bongmo Kim1,
Yong Chae Kim1, Jinwook Lee1, and Byeng D. Youn1, 2
1. Seoul National University, 2. OnePredict Inc.

Analysis of the Effect of the Data Heterogeneity inFederated Fault Identification
Zahra Taghiyarrenani1, Slawomir Nowaczyk1, and Sepideh Pashami1
1. Halmstad university

A Concept of Condition Monitoring for AC-DC Converter Anomaly data synthesis and detection via domain
Output Capacitors via Discriminative Features
randomization
Akeem Bayo Kareem1, and Jang Wook Hur1
Joonha Jun1, and Jongsoo Lee1
1. Kumoh National Institute of Technology
1. Yonsei University
Withdraw
R02-15

Optimal warranty policy for a system considering a
failure interaction
Minjae Park1, Bermawi K. Iskandar2, and Dong Ho Park3
1. Hongik University, 2. Bandung Institute of Technology, 3. Hallym University

A Comparative Study of K-Means Clustering and a Novel Ranking
Algorithm for Risk Priority Number Analysis in FMECA
Jiaxiang Cheng1, Sungin Cho2, Yap Peng Tan, and Guoqiang Hu1
1. Nanyang Technological University, 2. Singapore PowerSound-Dr: Reliable Sound Dataset and Baseline Artificial
Intelligence System for Respiratory Illnesses
Van Truong Hoang1, Quang Nguyen2, Quoc Cuong Nguyen1,
Xuan Phong Nguyen1, and Hoang Nguyen3
1. FPT Software, 2. Vietnam National University, 3. University College Cork

D01-01

Dataset generation based on 1D-CAE modeling for fault diagnostics
in a spacecraft propulsion system
Kohji Tominaga1, Yu Daimon1, Masao Toyama1, Kazushi Adachi2,
Seiji Tsutsumi1, Noriyasu Omata1, and Taiichi Nagata1
1. Japan Aerospace Exploration Agency, 2. University of Tokyo

PHM for Spacecraft Propulsion Systems: SimilarityBased models and Physics-Inspired Features
Takanobu Minami1, 2, and Jay Lee1
1. University of Maryland Collage Park, 2. Komatsu Ltd.
D01-03

D01-04

Anomaly Detection in Spacecraft Propulsion System using
Time Series Classification based on K-NN
Yoshiki Kato1, Taku Kato1, and Tsubasa Tanaka1
1. Mitsubishi Electric

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Oral Sessions
Sep. 14

Time/Date
JST(UTC+9)

9

10

11

Hall

Room
D

Room
E

2F Lobby

Keynote 4 (Hall)

10:10
10:20
10:30
10:40

Prediction of the Movement of a Landslide for the Early Warning System
Against Disaster in Japan
Prof. Katsuo Sasahara (Kochi University)

10:50
11:00
11:10

11:20
11:30
11:40

R06
PHM for Device Monitoring
Chair: Makoto Imamura(Tokai University)
R06-01

Quantum Computing meets PHM

Failure prediction of hard disk drives in redundant
arrays using disk-level performance metrics

1. Noriaki Shimada (IBM Quantum/IBM Research) Masanao Natsumeda1
1. NEC
2. Nobuyuki Yoshikawa (Mitsubishi Electric)
R06-02
3. Takao Tomono (Toppan Inc.)

Automatic detection of hardware failures in an air
quality measuring station with low cost sensors
Sylvain Poupry1, Kamal Medjaher1, and Cédrick Béler1
1. ENIT-LGP

12:00
12:10
12:20

R06-03

12:30
12:40

Sheriff Murtala1, Soojung Hur1, and Yongwan Park1

Study on the intermittent fault mechanism of electromagnetic
relay under complex environmental stress
Jingzhong Ye1, Jing Qiu1, Guanjun Liu1, and Kehong Lyu1
1. National University of Defense Technology
R06-04

A Simplified Framework for Fault Prediction in Radar
Transmitter based on Vector Autoregression Model
1. Yeungnam University

12:50
13:00

13

Room
C

PosterSession 2

9:30
9:40
9:50
10:00

11:50

12

Room
B

9:00
9:10
9:20

Panel Session 2
Organizer: Ken Ueno (Toshiba),
Seiji Tsutsumi(Japan Aerospace Exploration Agency)

11

Room
A

OS02-3
New trends and challenges in aerospace PHM 3
Chair: Samir Khan (University of Tokyo)
OS02-09

R07-1
RUL Prediction 1
Chair: Yuantao Fan(Halmstad University)

R08
PHM for Rotary Machines
Chair: Xiaomo Jiang(Dalian University of Technology)

R03-3
Machine Learning/Deep Learning for PHM 3
Chair: Francesco Cadini(Politecnico di Milano)

Withdraw
R07-01

R08-01

Multi-Label Fault Diagnosis of Rotary Machine via Domain Adversarial
Neural Network-Based Domain Generalization Targeting High Range
Rotating Speed
Sukeun Hong1, and Jongsoo Lee1
1. Yonsei University

R03-08Estimation of Remaining Useful Life of Complex Systems
Based on Autoencoder-Based Latent Variable Extraction
Sungeu Chae1, and Sukjoo Bae1
1. Hanyang University

OS02-10
Improvement in Identification Accuracy of a Failure Diagnostic
System for a Reusable Rocket Engine
Fumihisa Nagashima1, Hatsuo Mori1, Yasuhiro Ishikawa1,
Masaki Sato2, and Tomoyuki Hashimoto2
1. IHI, 2. Japan Aerospace Exploration Agency
OS02-11
Data-Driven Prognostics and Diagnostics of Industrial
Machinery- A Turbofan Engine Case Study
Russell Graves1, Peeyush Pankaj1, Vineet J Kuruvilla1,
Rachel Johnson1, and Michio Inoue1
1. MathWorks

R07-02

R08-02

R03-09

R07-03

R08-03OS02-12
Demonstration of model-based real-time anomaly detection
in a JAXA 6.5m×5.5m low-speed wind tunnel
Shotaro Hamato1, Seiji Tsutsumi1, Hirotaka Yamashita1,
Tatsuro Shiohara1, Tomonari Hirotani1, and Hiroyuki Kato1
1. Japan Aerospace Exploration Agency

R07-04

R08-04

Resilient Operation Planning for CubeSat Using
Reinforcement Learning
Shuntaro Kuroiwa1, and Nozomu Kogiso1

1. Osaka Metropolitan University

Fusion with Joint Distribution and Adversarial Networks: A New
Transfer Learning Approach for Intelligent Fault Diagnosis
Xueyi Li1, Tianyu Yu1, David He2, Zhijie Xie1, and Xiangwei3
1. Northeast Forestry University, 2. University of Illinois at Chicago,
3. Northeast University

Ensemble Probabilistic Neural Networks for RUL Prediction Sequential Domain Adaptation for Fault Diagnosis in Rotating Machinery Elastic wave field neural networks for structural health monitoring:Abhishek Srinivasan1,2, Juan Carlos Andresen2,
Yong Chae Kim1, Jin Uk Ko1, Jinwook Lee1, Taehun Kim1,
an analytical and numerical study of multiple neurons
and Anders Holst3
Joon Ha Jung2, and Byeng D. Youn1
Arata Masuda1, and Konosuke Takashima1
1. KTH, 2. Scania, 3. RISE
1. Seoul National University, 2. Ajou University
1. Kyoto Institute of Technology
A Simple Remaining Useful Life Algorithm Using the Quadratic An improved OAKR approach to condition monitoring
Eric Bechhoefer1, Nobuhiro Fujiki2, Yusuke Kitsutaka2, of rotating machinery
Sotaro Tsukamoto2, and Akio Usui2
Kexin Zhang1, and Xiaomo Jiang1
1. GPMS International, 2. Toho Technology
1. Dalian University of Technology

Prognosis using Bayesian Method by Incorporating
Physical Constraints
Hyung Jun Park1, Nam Ho Kim2, and Joo-Ho Choi1
1. Korea Aerospace University, 2. University of FloridaR03-10

Fuzzy-membership-based labeling – a new labeling method for bothclassification task and regression task
Diwang Ruan1, Zhaorong Li1, Yuheng Wu1, Jianping Yan2,
and Clemens Gühmann1
1. Technische Universität Berlin, 2. Zhejiang University

R03-05Observation and Prediction of Instability due to RD Fluid Force in
Rotating Machinery by Operational Modal Analysis
Daiki Goto1, Tsuyoshi Inoue1, Akira Heya1, Shogo Kimura1,
Shinsaku Nakamura2, and Yusuke Watanabe2
1. Nagoya University, 2. Ebara

Novel Ensemble Domain Adaptation Methodology for Enhanced
Multi-class Fault Diagnosis of Highly-Connected Fleet of Assets
Takanobu Minami1, Alexander Suer2, Pradeep Kundu3,
Shahin Siahpour3 and Jay Lee2
1. University of Maryland, 2. University of Cincinnati, 3. KU Leuven

R02-5
Smart Monitoring/Maintenance/Operation 5
Chair: Ken Ueno
(Toshiba)

Chair: Hyunseok Oh(Gwangju Institute of Science and Technology)

Lunch

13:10
13:20
13:30
Panel Session 3
Organizer: Michio Inoue(MathWorks)

13
14

13:40
13:50
14:00
14:10
14:20
14:30
14:40
14:50
15:00
15:10

15

16

17

15:20
15:30
15:40
15:50
16:00
16:10

Predictive Maintenance Challenges andSolutions: Vendor Insights
1. Byeng Dong Youn (OnePredict Inc.)
2. Chris Stecki (PHM Technology)
3. Eric Bechhoefer (GPMS Inc.)
4. Rachel Johnson (MathWorks)

R09
PHM for Turbines
Chair: David Weihua He(University of Illinois at Chicago)

R07-2
RUL Prediction 2
Chair: Joo-Ho Choi
(Korea Aerospace University)

R10
Digital Twin for PHM

R09-01

R07-05

R02-17

R10-01

R09-02

R07-06

R02-18Gabriel Davidyan1, Jacob Bortman2, and Ron.S Kenett3
1. Israel Railway, 2. Ben-Gurion University of the Negev,
3. Samuel Neaman Institute for National Policy Research
R10-02

Abdallah Alabdallah1, Thorsteinn Rognvaldsson1, Yuantao Fan1,
Sepideh Pashami1, and Mattias Ohlsson1
1. Halmstad University

Mahmoud Rahat1, Zahra Kharazian2, Peyman Sheikholharam Mashhadi1, MTarwaji Warsokusumo1, Ghiffari Awliya Muhammad Ashfania2,
Toni Prahasto2, and Achmad Widodoi2
Thorsteinn Rögnvaldsson1, and Shamik Choudhuryi3
1. PT PLN Indonesia Power, 2. Diponegoro University
1. Halmstad University, 2. Stockholm University, 3. Volvo

A Physics Informed Machine Learning Approach for
Performance Degradation Monitoring of Gas Turbine
Yiyang Liu1, Xiaomo Jiang1, Xin Ge1, and Manman Wei1
1. Dalian University of Technology
Discovering Premature Replacements in Predictive
Maintenance Time-to-Event Data

R09-03

Trip Reduction in Turbo Machinery
Pranay Mathur1, Carlo Michelassi1, Simi Karatha1,
Gilda Pedoto1, and Miguel Gomez Alguacil1
1. Baker Hughes

Bayesian-based Component Lifetime Prediction Model
Using Workshop and Telematics Data
Seungyoung Park1, and Jihyun Lee1
1. HL Mando

Comprehensive Failure Diagnosis Model with
Degradation Indicators of Multiple Sensors
Jun Tominaga1, Shoya Kamiaka1, and Kohei Kuroda1
1. Daikin Industries

Development of an Operational Digital Twin of a Locomotive
Braking System Solenoid Valve for Fault Classification

Bridging the Gap: A Comparative Analysis of Regressive Remaining Useful Prescriptive Maintenance-Decision Analysis Through Energy Architecting a Digital Twin-Based Predictive Maintenance
System for Modelling Cable Joint Degradation
Life Prediction and Survival Analysis Methods for Predictive Maintenance Efficiency-Based Hybrid Prognostic and Diagnostic Model

Withdraw
R07-07

Raymon van Dinter1,2, Görkem Ekmekci2, Sander Rieken3,
Bedir Tekinerdogan4, and Cagatay Cata5
1. Wageningen University, 2. Sioux Technologies, 3. Alliander, 4. Wageningen University, 5. Qatar University

R02-19Explainable Condition Monitoring Identification for
Degradation-Trend Analysis and Fault Prognosis
Amine Atoui 1
1. University of Halmstad

Differential Diagnosis with Active Testing
Emile van Gerwen1, Leonardo Barbini1,
and Michael Borth2
1. TNO-ESI, 2. TNO

Coffee BreakKeynote 5 (Hall)
From Physics to Machine Learning and Back: Applications to Prognostics and Health Management
Prof. Olga Fink (EPFL, Lausanne)

Closing/Award Ceremony (Hall)

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Poster Sessions
Join us for our Poster Sessions!
Dates: Sep 13 (Wed) and Sep 14 (Thu)
Time: 9:10-9:50AM
Place: 2F Lobby
Posters will be displayed throughout the conference, but poster presenters will be present
for discussions and Q&A during the designated times mentioned above. Don’t miss thisopportunity to dive deep into their research, ask questions, and share insights.
For online attendees, posters and presentation videos will appear in Whova. Go to theposter sessions, and ask questions, and share insights to the poster presentation using
chat of Whova.

Sep. 13 9:00-12:00
No.

Title

Speaker

P01-01

Demonstration of Sensor Monitoring of Lubricants

Kyoko Kojima1
1. Hitachi, Ltd.

P01-02

Data-driven method for assessing slagging/fouling index in thermal power
plant using autoencoder-based covariate filtering and feature extraction

Seongjoon Kim, Geunseop Lee, and Seonggwan Son1
1. Chosun University

P01-03

Health Monitoring of Power Semiconductor Module Using Temperature
Sensitive Electrical Parameter

Guesuk Lee1, Sungsoon Choi1, Byongjin Ma1,
and Jemin Kim1
1. Korea Electronics Technology InstituteP01-04

Application of Model-Based Deep Reinforcement Learning Framework to
Thermal Power Plant Operation Considering Performance Change

Yutaka Watanabe1, and Takehisa Yairi2
1. Central Research Institute of Electric Power Industry,
2. University of Tokyo

P01-05

Survival Time Analysis of Turbocharger Devices in a Real Fleet of Volvo
Trucks: Incorporating Temporal Information from Condition Monitoring Sensors

Mahmoud Rahat1, and Peyman Mashhadi1
1. Halmstad University

P01-06

Simulation-driven Bearing Fault Diagnosis for Condition Monitoring
without Faulty Data

Iljeok Kim1, and Seungchul Lee1
1. Pohang University of Science and Technology

P01-07

Fully Unsupervised Defect Clustering using Adversarial Autoencoder
and Bayesian Mixture Model

Taewan Kim1, and Seungchul Lee1
1. Pohang University of Science and Technology

P01-08

Evaluation of Multi-Modal Learning for Predicting Coolant Pump
Failures in Heavy Duty Vehicles

Yuantao Fan1, Amine Atoui1, Slawomir Nowaczyk1,
and Thorsteinn Rognvaldsson1
1. Halmstad University

P01-13

Change Point-based Spatio-temporal Process Modeling of Image
Degradation for Manufacturing Process

Munwon Lim1, and Suk Joo Bae1
1. Hanyang University

P01-15

Deep Metric Learning for Anomaly Detection of Satellites from Irregularly
Sampled Light Curve

Jun Yoshida1, Ryosuke Togawa1, and Taichiro Sano1
1. NEC

R10-03

Configuration and Comparative Study of Prediction Models for Indoor
Air Quality

Geonhwi Lee1, Hea-Ryeon Seo1, and Hae-Jin Choi1
1. Chung Ang University

R06-04

A Simplified Framework for Fault Prediction in Radar Transmitter basedon Vector Autoregression Model

Sheriff Murtala1, Soojung Hur1, and Yongwan Park1
1. Yeungnam University

R07-05

Bayesian-based Component Lifetime Prediction Model Using Workshop
and Telematics Data

Seungyoung Park1, and Jihyun Lee1
1. HL Mando

OS06-06

Condition-based Maintenance of Brake Pads and Tires in Shared Vehicles
using Cloud-based Health monitoring and prognostics

Jeong Hae Lee1, Jaewook Oh1, Jeongwoo Lee2, Seungyoung Park2,
Jihyeon Lee2, and Namsu Kim1
1. Konkuk University, 2. HL Mando

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Asia Pacific Conference of the Prognostics and Health Management Society 2023

Poster Sessions
Poster session is held in 2F Lobby.

Sep. 14 9:00-12:00
No.

19

Title

Speaker

P01-09Spacecraft Propulsion System Diagnosis via MiniRocket: a resultof PHMAP 2023 Data Challenge

Kento Kotera1, and Akihiro Yamaguchi1
1. ToshibaP01-10High-Speed Feature Extraction and Real-Time Fault Diagnosis
Using Unscented Kalman Filter

Fumihisa Nagashima1, Hatsuo Mori1,
and Yasuhiro Ishikawa1
1. IHI

P01-11Sparse Modeling of Wavelet Features for Fault Classification andRegression in Spacecraft Propulsion Systems

Joseph Cohen1, Andreas Rauch1, Jennifer Li1,
Bernardo Modenesi1, James Boyko1, Yutong Wang1,
Eunshin Byon1, and Xun Huan1
1. University of Michigan

P01-12

Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes

Osarenren Kennedy Aimiyekagbon1, Amelie Bender1,
Alexander Löwen1, Lars Muth1, and Walter Sextro1
Paderborn University

P02-16

Bayesian Autoencoder with Uncertainty Quantification for Predicting
Premature Lithium-Ion Battery Degradation

Sungeu Chae1, Minjoo Kim1, Yunhee Kim1, and Suk Joo Bae1
1. Hanyang University

P02-17

On Explaining Anomaly Detection by Anomaly Score Attribution

Naoya Takeishi1
1. University of Tokyo

P02-18

Facility Risk Management at JAXA’ s Tanegashima Space Center | Flagship
Rocket Launch Site and Preventive Maintenance Using the Latest Technologies

Takahiro Akeda1
1. Japan Aerospace Exploration Agency

P02-19A Study on Hierarchical Defect Detection Algorithm for | Transparent Plastic
Bottles Based on YOLO

Young Woon Choi1, Hyun Seok Lee1, and Sang Won Lee1
1. Sungkyunkwan University

P02-20Fair Inventory Ordering Method for a Supply Chain Health Management

Jundai Koketsu1, and Aya Ishigaki1
1. Tokyo University of ScienceP02-21

Fleet Vehicle Component Lifetime Prediction Using Driver Input

Seungyoung Park1, and Jihyun Lee1
1. HL Mando

P02-22

Unpaired Image-to-Image Translation for Cable Vibration Detection from
Low-Visibility CCTV Images

Sunjoong Kim1, and Hun Lee1
1. University of Seoul

R02-09Online fault detection for industrial processes through Kalman filter

Wenyi Liu1, and Takehisa Yairi1
1. Univerisity of Tokyo

R02-16

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence
System for Respiratory Illnesses

Van Truong Hoang1, Quang Nguyen2, Quoc Cuong Nguyen1,
Xuan Phong Nguyen1, and Hoang Nguyen3
1. FPT Software, 2. Vietnam National University,
3. University College Cork

R05-04

System-Level Simulation of 120 kW Interior Permanent Magnet Synchronous Motor
Drive for Electric Vehicle Usage Under Various Types of Faults for Fault Diagnosis

Woyeong Kwon1, Jaewook Oh1, Inhyeok Hwang1,
and Namsu Kim1
1. Konkuk University

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Keynote Speakers
All the keynote speeches are held in Hall.
Keynote 1

Sep. 12, 10:20-11:20

Asteroid Sample Return Mission “Hayabusa2” – A Successful Example of Large-Scale Scientific
Project Management

Prof. Yuichi Tsuda

Institute of Space and Astronautical Science (ISAS), Japan Aerospace Exploration Agency (JAXA)

Keynote 2

Sep. 13, 10:00-11:00

Industrial AI-Augmented Prognostics for Highly Connected and Complex Industrial Systems

Prof. Jay Lee

Univ. of Maryland College Park

Keynote 3

Sep. 13, 11:20-12:20

How is industrial AI transforming manufacturing industry?

Prof. Byeng Dong Youn

Seoul National University and ONEPREDICT INC

20

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Keynote 4

Sep. 14, 10:00-11:00

Prediction of the movement of a landslide for the early warning system against disaster in Japan

Prof. Katsuo Sasahara
Kochi University

Keynote 5

Sep. 14, 15:00-16:00

From physics to machine learning and back: Applications to prognostics and health management

Prof. Olga Fink

Intelligent Maintenance and Operations Systems, EPFL, Lausanne

21

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Panel Sessions【Panel Session 1 】
Date: Sep. 12, 18:10-19:00
Location: Room A

Full Flight Sensor Data sharing within the stakeholders of PHM
Organizer: Masaru (ken) Nishiwaki (ANA)
Panelists:
1. Darren Macer (Boeing)*
2. Gavin Chew (Collins Aerospace)*
3. Sebastian Lang (Lufthansa Technik)*
4. Manabu Tono (ANA)*【Panel Session 2 】
Date: Sep. 14, 11:20-12:20
Location: Hall

Quantum Computing meets PHM
Organizer: Ken Ueno (Toshiba), Seiji Tsutsumi (JAXA)
Panelists:
1. Noriaki Shimada* (IBM Quantum/IBM Research)
2. Nobuyuki Yoshikawa* (Mitsubishi Electric)
3. Takao Tomono* (Toppan Inc.)【Panel Session 3 】
Date: Sep. 14, 13:40-14:40
Location: Hall

Predictive Maintenance Challenges and Solutions: Vendor Insights
Organizer: Michio Inoue (MathWorks)
Panelists:
1. Byeng Dong Youn* (OnePredict)
2. Chris Stecki* (PHM Technology)
3. Eric Bechhoefer* (GPMS Inc)
4. Rachel Johnson* (MathWorks)* Invited

22

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Exhibitions
Join Us at the Sponsor’s Exhibition
PHMAP23 is proud to present an exclusive exhibition, featuring the latest innovations
and research contributions from our esteemed sponsors. This year, we are thrilled toinvite you to explore the forefront of technology and engage with industry-leading
companies in our exhibition area.
Exhibition Details:
Date: September 12th, 12:40 PM to September 14th, 3:00 PM
Location: Exhibitions
Don't miss this unique opportunity to immerse yourself in a world of innovation andinspiration. Mark your calendar and make plans to join us at the exhibition.

Earth

Moon

Moon

Mars

Moon

Moon

Earth

Coffee23

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Welcome Reception and Baquet
A welcome reception & banquet will be held at the conference. We will pick you up from the nearby
Hitotsubashi Hall on Sep 11 and take you to the reception site.Please refer to the map on the next pagefor the location. The fee for this event is included in the conference registration fee. We look forward toseeing you there.
※The welcome reception may be held on September 14 for the farewell reception or alternative arrangements
depending on the weather conditions.

Welcome Reception
Please complete the registration by 16:30, and wait at the Room C and Room D. The bus will leave thevenue at 16:30-17:00. We will take you to the bus as soon as the bus arrives.
The welcome reception will be held on Monday, September 11, from 17:30 to 20:00 aboard a
Yakatabune, cruising along the picturesque Sumida River in Tokyo. Enjoy a delectable Japanese kaiseki
cuisine while taking in the enchanting beauty of Tokyo’ s night skyline and the traditional ambiance of theYakatabune. There is also the bus back to the venue.

Welcome Reception / Sep. 11 (Mon.), 17:30-20:00
YAKATABUNE HARUMIYA
4-6-1 Kachidoki, Chuo-ku, Tokyo
TEL: 03-3644-1344, FAX: 03-3644-5445,
http://yakatabune-tokyo.com/index.html#0401※ If you are not taking bus, please come directly to the venue referring the below map.
(No boarding YAKATABUNE after 17:30)

Chater Bus Stop (Reception & Banquet)

24

Welcome Reception: Sep. 11(Mon.), 17:30-20,30

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Banquet
The banquet will be held on Wednesday, September 13, from 19:00 to 21:00. in a vibrant beer garden
located in Shinjuku, one of Japan’ s busiest districts. Experience the lively atmosphere of the beer garden
and enjoy the unique ambiance while savoring delicious party food. The award ceremonies will be held
during the banquet. Please come to the banquet site by yourself using Toei Shinjuku Line. Check the maps
below. At the Shinjuku station, please exit from the gate of "Keio Shinsen Gate". You can see staffs there.

Banquet / Sep. 13 (Wed.), 19:00-21:00
Shinjuku BBQ beer-garden Hello
LUMINE 1-RF L Terrace, 1-1-5 Nishi-Shinjuku, Shinjuku-ku, Tokyo
TEL: 03-9694-7071,
https://hello-bbq.tokyo/※ Please come directly to the venue referring the below map and URL (Using the Toei Shinjuku Line).

Jimbocho station map

https://www.tokyometro.jp/lang_en/station/jimbocho/index.html

Shinjuku station map

https://www.kotsu.metro.tokyo.jp/eng/services/subway/stations/shinjuku.html#solid

Jimbocho Station

Banquet (From Hitotsubashi Hall to Jimbocho Sta.)

Banquet (From Toei Shinjyuku Line Shinjyuku Sta. to LUMINE 1)

25

Asia Pacific Conference of the Prognostics and Health Management Society 2023

Short course
PHM Asia Pacific 2023 offers one-day intensive short course on Sep. 11, 2023. The course will bedelivered in Japanese.
時間（講師）
10:00-16:30
日時：9/1110:00-12:00

テーマ

PHM 入門（今村）

機械学習を用いた予知保全

サマリー
・PHM とは・データ分析プロセス
・不確実性
・異常検知
・故障診断
・余寿命予測寿命分布の推定（寿命データの活用）

・寿命データとは，打ち切りデータ・パラメトリック推定( ワイブル分布など )・ノンパラメトリック推定( カプランマイヤー法など )13:00-15:00（今村）

劣化モデルに基づく余寿命予測（状態基準保全でのデータ活用）
時系列処理

15:00-16:30

リチウムイオン電池の基本事項（有馬）・類似度モデル・累積損傷モデル・状態空間モデル・周波数解析
・時系列データマイニング（Motif,Discord,Shaplet など )・他の電池と比較した特徴、主要材料、
これまでとこれからの用途
・資源的課題とリユース・リサイクルの動向
・重要な特性 ( 充電率、満充電容量、開回路電圧、内部抵抗 )リチウムイオン電池の劣化とその診断（予知保全）

・1/2 乗則・アレニウス則および劣化現象を構成する要素・交流インピーダンス法とバッテリー
マネジメントシステム
・非破壊劣化診断手法の体系分類

適応フィルタによるリチウムイオン
電池の残量推定・劣化診断の事例

26

・状態空間モデル・残量推定と満充電容量推定
・特性プロファイルの学習への拡張と充放電効率推定Asia Pacific Conference of the Prognostics and Health Management Society 2023

【講師】
有馬 理仁
2006 年 3 月東京工業大学大学院生命理工学研究科修士課程（分子生命科学専攻）修了。
同年 4 月大和製罐（株）入社。2021 年 3 月立命館大学大学院理工学研究科博士後期課程（電子システム専攻）修了．博士（工学）。現在，リチウムイオン蓄電池の評価試験・寿命劣化・
運用経済性に関する新規事業・研究開発に従事。（寿命データの活用）

今村 誠
1986 年三菱電機株式会社入社。自然言語インタフェース、製造業向け知識管理、Web マイニング等を経て、予知保全や生産性向上のためのデータ分析技術の研究・開発に従事。2016 年 4 月より東海大学
情報通信学部組込みソフトウェア工学科教授。予知保全関連の産学共同プロジェクトにも精力的に従事。

27

Asia Pacific Conference of the Prognostics and Health Management Society 2023