Contribution

The Current Situation of Condition Monitoring and
Diagnosis of Machine Systems

Tsuyoshi INOUE
Ph.D. Professor, Department of Mechanical Systems Engineering, Graduate School of Engineering, Nagoya University

Recently, condition monitoring and diagnosis technologies for machine systems havebeen rapidly developing and attracting attention. This paper first reviews the recent
trends in condition monitoring and diagnosis of mechanical systems, referring to thestandardization activities of ISO/TC 108/SC 5, in which the author has been involved formany years. Subsequently, this paper introduces the recent trends in asset management andcondition monitoring, condition monitoring and diagnosis of wind power generation, IoT for predictive maintenance,and condition monitoring and diagnosis using 1DCAE as the latest topics. Finally, some topics regarding condition
monitoring and diagnosis of machine systems and bearings at domestic conferences in the past few years have been
introduced.

1. Introduction
I have been involved in ISO standardization
activities for ISO/TC 108/SC 5 Condition Monitoringand Diagnostics of Mechanical Systems since 2004.
I will first briefly review the standardization activities
of ISO/TC 108/SC 5 and then give an overview ofrecent developments in condition monitoring anddiagnostics of mechanical systems. The first half ofthe paper (Chapters 2 and 3) is mainly based on acommentary article1) written and published in thejournal “Nondestructive Inspection” in 2020, with an
update based on information from the last two years.
The latter half of the paper (Chapters 4 to 7)
will cover recent trends in asset management andcondition monitoring, condition monitoring anddiagnostics for wind power generation, IoT for
predictive maintenance, and condition monitoring anddiagnostics using 1DCAE. Finally, we will introduce
some content from presentations at domestic
academic conferences in the past few years that relate
to condition monitoring and diagnostics of bearings.

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NTN TECHNICAL REVIEW No.89

2. International Trends in Condition Monitoringand Diagnostics of Machine Systems1)
2.1 Scope of ISO TC 108/SC 5
ISO/TC 108/SC 5 is officially called “Condition
monitoring and diagnostics of machine systems”. The
purpose and scope of SC 5 provided on the website2)
are as shown below.
Standardization of the procedures, processes andequipment requirements uniquely related to theactivity of condition monitoring and diagnostics ofmachine systems is an important part of all programswith selected physical parameters associated with
an operating machine system are periodically or
continuously monitored, measured and recorded forthe interim purpose of reducing, analyzing, comparing
and displaying the data and information obtained
to support decisions related to the operation andmaintenance of the machine system．
2.2 Working Group for ISO TC 108/SC 5
The working groups (WGs) established in ISO/TC
108/SC 5 as of September 2022 are listed in Table 1
and introduced individually below.
AG E: This is a working group for discussing theoverall strategic plan for SC 5. Here, the overall
direction of international standardization of SC 5, how
to proceed with the work of each working group, andthe establishment and dissolution of working groups
are examined and discussed. The initial draft of thepreliminary work item (PWI), which assumes that a
new working group will be established in the future, isalso considered here.

Table 1 Working Group2) of ISO/TC 108/SC 5(as of September 2022)
WG

Technical field

AG E

Strategic planning

WG 7

Training and accreditation in the field ofcondition monitoring and diagnostics

WG 11

Thermal imaging

WG 16

Condition monitoring anddiagnostics of wind turbines

WG 17

Condition monitoring anddiagnostics applications

WG 18

Condition monitoring management

WG 7: This working group focuses on the training
and certification of condition monitoring technicians.
It has developed a series of standards for training andcertification. In addition to ISO 18436-1:2021, which
covers the entire range, it has published standards
for condition monitoring technicians for individual
technologies which include ISO 18436-2:2014
Vibration condition monitoring and diagnostics, ISO
18436-4:2014 Field lubricant analysis, ISO 184365:2012 Lubricant laboratory technician/analyst, ISO
18436-6:2021 Acoustic emission, ISO 18436-7:2014
Thermography, and ISO 18436-8:2013 Ultrasound. In
Japan, certification of machinery condition monitoring
technicians based on these 18436 series, has been inpreviously in place, details of which are described in
Chapter 3.
WG 11 Thermal Imaging: This working groupfocuses on condition monitoring and diagnostic
techniques using thermal imaging. It has published
ISO standards for general procedures in ISO 184341:2008 and for interpretation of images in ISO 184342:2019, where images of bearing anomaly detection
are also presented. It is currently considering thestandardization of optical gas imaging. In addition
to WG11, which focuses on thermal imaging, whichwere previously in many other working groups on
condition monitoring and diagnostic techniques(WG3 Performance monitoring and diagnostics,
WG4 Tribology, WG10 Condition monitoring anddiagnostics of electrical equipment, WG14 Acoustic
techniques, WG15 Ultrasound diagnostics, etc.).
However, each of these has finished publishing the
ISO standards that are currently required and has
therefore been disbanded. Information on these
disbanded working groups is shown in Table 2. If
the need arises for a new ISO standard in the same
field, or if a major revision of the current ISO standard
becomes necessary, we plan to reopen the Working
Groups.

WG 16 Wind turbines, WG 17 Applications: These
working groups focus on condition monitoring anddiagnostics for individual machines. WG16 hascompleted the standardization of ISO 16079-1:
2017 General Guidelines and ISO 16079-2: 2020
Monitoring the drivetrain. The latter describes themonitoring and diagnosis of bearings and gears forwind turbines. As its name suggests, WG17 covers
a wide range of applications. It has published thestandard ISO 19283:2020 Hydroelectric generating
units and has started the standardization ofreciprocating compressors.
WG 18 Asset Management: This is a new working
group launched in 2019 that, as of September
2022, is the most active. The condition monitoringand diagnostics of mechanical systems havetraditionally been done mainly by the engineers who
directly handle machines. However, the need forstandardization that also incorporates the perspectiveof asset management systems, has been raised at
SC 5 international meetings (see Section 2.3) for the
past 10 years. Now, under the strong leadership of thecurrent SC5 chairman, the working group has begun
studying and vigorously promoting the standardizationof asset management systems with a specific focus onmechanical systems. The standards being examinedare (1) accreditation of condition monitoring
managers, (2) accreditation of condition monitoring
auditors, and (3) physical asset management.
2.3 International meetings for ISO TC 108/SC 5
In ISO standardization, international meetings
play an important role as forums for reviewing andreflecting ballot comments on draft standardization
texts and for considering the next standards. The firstinternational meeting of SC 5 was held in 1994, and23 meetings were held up to 2019. Table 3 shows
the years and venues of the conference. The 24th
meeting was originally scheduled to be held in
New York in September 2020 but was postponed(and subsequently canceled) for the first time due toglobal restrictions on international travel arising fromthe COVID-19 pandemic that began in March 2020. It
was held later in May 2022 as the first online meeting.
In 2023, with the pandemic now largely under control,
Japan will host the meeting in Kyoto, which will be thefirst in-person meeting in four years.

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The Current Situation of Condition Monitoring and Diagnosis of Machine Systems
Table 2 Working groups established in SC 51) 2)

Table 3 List of SC 5 International Conferences1)

WG

Technical field

1st

1994

Swansea

UK

2nd

1995

LondonUK

AG A

Vibration condition monitoring procedures andinstrumentation used for diagnostics

3rd

1996

SydneyAU

WG 1

Terminology

4th

1997

Berlin

DE

5th

1998

TasmaniaAU

WG 2

Data interpretation and diagnostics techniques

6th

1999

Copenhagen

DK

WG 3

Performance monitoring and diagnostics

7th

2000

Nanjing

CN

WG 4

Tribology-based monitoring and diagnostics

8th

2001

ViennaAS

WG 5

Prognostics

9th

2002

Minden, NV

USA

10th

2003

Paris

FR

JWG 6

Formats and methods for communicating,presenting and displaying relevant informationand data

11th

2004

London

UK

12th

2005

Dania Beach, FL

USA

WG 8

Condition monitoring and diagnostics ofmachines

13th

2007

Prague

CZ

14th

2008

Kyoto

JP

WG 10

Condition monitoring and diagnostics ofelectrical equipment

15th

2009

Copenhagen

DK

16th

2010

LondonUK

WG 14

Acoustic techniques (Acoustic emissions)

17th

2011

SydneyAU

WG 15

Ultrasound

18th

2013

Berlin

DE

19th

2014

Paris

FR

20th

2016

Sydney

AU

21st

2017

London

UK

22nd

2018

Helsinki

FI

23rd

2019

Copenhagen

DK

24th

2022

Online25th

2023

Kyoto (scheduled)

For these working groups, items to be discussed toconsider future projects or new areas of work are includedin each yearʼs meeting. These are examined, along withconsidering the resumption of activities.

JP

2.4 ISO TC 108/SC 5: Status of activities andoutlook
The latest SC 5 activities can be found on the ISO
Website. The current chairman is Mr. L. Hitchcock(SA), the fourth chairman. The secretary is Mr. A.
Rashid (SA). As of September 2022, the P-members(Participation members) consisted of 23 countries
and regions, while O-members (Observer members)consisted of 12 countries and regions.
Fig. 1 shows the status of SC 5 standards as of
September 2022. SC 5 has published 28 international
standards. Five standards are in the process of being
established or revised.

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Fig. 1 Standardization Activities as of September 2022
SC 5 began with the concept of “Condition
Monitoring and Diagnostics.” As the work ofstandardization progressed from 1994 to around2011, there was an overall shared recognition among
Japan, the United States, Europe, and other countries
in terms of the background, needs, and objectives ofthis concept. However, around 2012, while the original
concept was retained, (the background, needs, andobjectives of condition monitoring and diagnostics
as recognized and advocated by the SC5 members in
Europe and the United States) gradually expanded.
They have long recognized the need for and value ofcondition monitoring and diagnostics standards thatshould be used by engineers and management froman asset management standpoint. Now that the firstround of standardization of the basic technologies
of SC 5 has been finished, the activities of SC 5
show signs of a shift toward such new standards forcondition monitoring and diagnostics.
As of September 2022, as shown in Table 1, SC
5 has the following active working groups: Training
and Accreditation WG 7, Thermal Imaging WG 11,Wind Turbines WG 16, Applied Technology WG 17,
and Asset Management WG 18. As mentioned in theprevious section, the WG on foundational technologies(currently WG 11) is to be disbanded after completing
standardization. The future activities of SC 5 will
mainly take two directions: applied technology (WG16
and 17) and asset management (WG18). In particular,
the current chairman of SC 5 has strongly indicatedthe latter direction based on the aforementioned
background. This shift has been clear in the past
three international meetings, as SC 5 currently has
liaisons with IEC TC 56 Dependability and ISO/TC 251
Asset management in the opposite direction. WG18
is expected to be the center of SC 5 activities shortly.
However, TC 108/SC 5 has not yet reached a mutual
understanding or recognition concerning the asset
management of mechanical systems proposed by TC
108/SC 5 concerning the 55000 series, which is the
major ISO standard for asset management. It is also
clear that TC 108/SC 5 itself has yet to define theideal state of condition monitoring management formechanical systems.
In Japan, the needs and expectations for asset
management of mechanical systems are alreadyincreasing, and one can sense that interest in theactivities of SC 5/WG18 is growing rapidly. On theother hand, the ISO/TC 108/SC 5 National Committee
does not yet have a fully shared understanding of
the background, needs, and expanding objectives ofcondition monitoring and diagnostics from an asset
management perspective. The start of considerationof several ISO standards in asset management(WG 18) was approved at the 23rd International
Conference (Copenhagen) and will be considered atthe 25th Kyoto Conference. For the above reasons,the current ISO/TC 108/SC 5 National Committee
may not be able to adequately respond to thesestandardizations. To address this, the ISO/TC 108/SC
5 National Committee has recently started to engage
with the Japan Association of Asset Management(JAAM) and is preparing to start mutual participationin committee activities. The committee is also
considering adding asset management experts asmembers if necessary or restructuring the structure of
the ISO/TC 108/SC 5 National Committee. We hopethat experts from related companies in Japan who
are interested in asset management for condition
monitoring of mechanical systems will participate
in the ISO/TC 108/SC 5 National Committee orinternational meetings and share their views.

3. Engineer Accreditation Programs andCourses1)
3.1 Machine Condition Monitoring Qualification
Accreditation Project
To evaluate the technical competence of individuals
in a globally consistent and objective manner,
accreditation of machinery condition monitoring anddiagnostics technicians is conducted based on the ISO
18436 series (qualification of machinery condition
monitoring and diagnostics technicians) developedby ISO/TC 108/SC 5/WG 7.1) In Japan, accreditation
has been implemented for vibration, tribology, andthermography.
ISO 18436-2 (vibration) was published first in
2003. The ISO 18436 standard series continued insteady succession with ISO 18436-4 (field lubricant
analysis), ISO 18436-6 (acoustic emissions) and ISO
18436-7 (thermography). ISO 18436-5 (laboratory
lubricant analysis) followed in 2012 and ISO 184368 (ultrasound) was published in 2013. It should be
noted that in ISO 18436-2 (vibration) the overall field
is divided into categories Ⅰ through Ⅳ (category Ⅳ
is recognized as equivalent to a professional engineer
or doctor of Engineering), whereas all other fields
are divided into categories Ⅰ through Ⅲ. In addition,although the redefinition of vibration as Category Ⅰ
to Ⅲ has been proposed and periodically consideredat SC 5 international meetings, for the sake of clarityand ease of understanding of the overall categories,
the original category system has been maintained due
to opposition from countries that have already been
implementing the system for nearly 20 years.
The accreditation system for the qualificationof technicians who perform condition monitoring

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The Current Situation of Condition Monitoring and Diagnosis of Machine Systems
and diagnostics of machines (vibration) has been
implemented by the Japan Society of MechanicalEngineers (JSME3)) since June 2004. A total of 5 472
persons have been certified by the 37th examination
to be held in August 2022. In Category Ⅰ, the numberof qualified personnel is 819. In Category Ⅱ, it is 4 557.
In Category Ⅲ it is 434. On average, around 10 people
receive accreditation in Category Ⅰ, 100 in Category
Ⅱ, and 20 to 30 in Category Ⅲ. In addition, 41 people
have received accreditation in Category Ⅳ, which is
positioned as equivalent to a Doctor of Engineering or
a professional engineer. This shows the high level ofengineers involved in vibration technology in Japan. It
is hoped that those who aspire to work internationallyas engineers, will take up the challenge of Category Ⅲ
and Ⅳ.
The accreditation system for the qualificationof technicians who perform condition monitoringand diagnostics of machines (tribology) has been
implemented for Categories Ⅰ-Ⅲ since FY2009
through joint certification by JSME3) and the JapaneseSociety of Tribologists (JAST). As of September 2022, a
total of 1 421 persons have been certified. (In Category
Ⅰ, the number of qualified personnel is 1 176. In
Category Ⅱ, it is 234, and in Category Ⅲ it is 11.)
The qualification examinations for technicians who
perform condition monitoring and diagnostics ofmachines (thermography) have been conducted by
the Japanese Society for Non-Destructive Inspection(JSNDI) for Category Ⅰ since FY2016 and forCategory Ⅱ since FY2018. A total of 206 people (173
and 33 for Categories Ⅰ and Ⅱ, respectively) havepassed the examination as of September 2022.
JSNDI (Certification Division for CM Engineers)and JSME (Japan Society for Machine Monitoring
Engineers) have formed a partnership for
administering the accreditation of condition
monitoring4). For example, JSME sponsors meetingsof the Condition Monitoring and Vibration Diagnostics
Engineer Community. It provides a forum fordisseminating information on condition monitoring
and for exchange among qualified engineers, including
JSNDIʼs qualified CM engineers and other relatedparties4).
3.2 Workshop on condition monitoring anddiagnostics techniques
The ISO/TC 108/SC 5 National Committeeorganizes and holds a seminar entitled “A Must for
Global Engineers! Monitoring and Diagnostics of
Mechanical Systems, Fundamentals, Practical KnowHow, Application Examples and Standards” (hosted
by JSME). The content includes an introduction of
the SC5 standards and engineer accreditation for
condition monitoring, diagnostics using vibration,
lubricant analysis, thermal image analysis, introductionof basic technologies for acoustic emissions (AE),diagnosis of large rotating machinery, diagnosis ofwind turbines (mainly bearings), current indication
diagnosis (added from 2019), and comprehensive
diagnosis (added from 2021). The first seminar washeld in Tokyo in October 2007 (30 participants). The

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second seminar was held in Osaka in October 2007
(15 participants). After a hiatus following the above
two seminars, the third seminar was held in Tokyo in
September 2016 (29 participants). The fourth seminar
was held in September 2018 (44 participants), andthe fifth in September 2019 (53 participants).
A book was written based on the contents of the
5th seminar. Entitled “Condition Monitoring and
Diagnostic Techniques for Mechanical Systems”, it
was published in June 2021 (edited by Inoue andHyodo, Japan Society of Mechanical Engineers,
Corona Publishing).5) The 6th seminar was held online
in November 2021 (150 participants) using this bookas the textbook. In fiscal 2022, this workshop was
expanded and held in two rounds in November, one
for beginners and the other for intermediate-level
students. The last three seminars have been fully
booked, highlighting the growing need for and interestin this technology with the arrival of the IoT (Internetof Things) era. We will continue to educate people
about this field and introduce the activities of the ISO/TC 108/SC 5 National Committee. I hope that throughthese seminars and publications, people will learn thefundamentals of technology in this field.

4. Condition Monitoring and AssetManagement6)-14)
4.1 Europe: Trends in IoT and Asset Management
Ten years have passed since IoT and Industry
4.0 were proposed in the 2010s, and these terms
and concepts have spread widely. In Europe
and the United States, these technologies have
evolved through deep integration with equipment
diagnostics and asset management. This year, Kawaisummarized equipment diagnostics and IoT in Europe
in comparison with Japan as follows.6) In Japan,
discussions tend to focus on the use of data obtained
from equipment in a single factory or company. In
contrast, in Europe, the European Integrated Data
Infrastructure Project GAIA-X represents the European
Data Economy Area. In recognition of the strategic
utility of data, GAIA-X integrates various cloudservices of companies on a single system, enabling
interoperability through a standardized authentication
mechanism that facilitates data exchange acrossindustries (Data Economy Area).
It is expected that manufacturers will evolve to amodel of providing services to customers. Globally,
the customerʼs usage environment will become
a complex system environment that includes not
only their own products but also those of othercompanies.8) Japan will be no exception to this trend.
As a result, manufacturers will be required to manage
the entire life cycle of the userʼs usage environment
as they provide services. It is thus expected that
manufacturers will need to collect data generated
from the entire userʼs usage environment, including
competitorsʼ products. In particular, sharing andutilizing data from the operation and maintenancephases, which have not been visualized until now,
will become a new and important area of researchand development. It is clear that data collaboration
involving users will be key.8)
Let us briefly touch on the report “Applying theInternet of Things (IoT) to Manufacturing” 6) 9) which
Siemens has compiled on Industrial IoT (IIoT), thecollection and centralization of large amounts ofmachine data from industrial sites. Fig. 2 shows the
major use cases that manufacturers can implement
to maximize the added value and ROI (return on
investment) to their businesses, modeled in six levels
of IIoT maturity. It is recommended that the IIoT
be introduced at the basic level, which is relatively
easy to handle, and then gradually expanded toachieve greater value. It is interesting to note thatthe first asset management and asset maintenanceare specifically listed as condition monitoring,asset performance management, and predictive
maintenance. We will give an overview of these here.
Asset management condition monitoring uses
a centralized IoT system to monitor specificparameters (temperature, vibration, pressure, etc.)
and key performance indicators (KPIs). It tracks theoperational status of all connected assets and, shoulda problem occur, takes corrective action before that
asset fails, thereby maximizing uptime of criticalassets. In this way, condition monitoring ensurestransparency into the health and performance ofassets at locations around the world.
In asset performance management, KPIs areused to monitor and track the condition andstatus of machines and identify machines that are
not performing adequately in terms of efficiency
and productivity. In addition, asset performance
management applications using IoT can improve
performance by making changes to production linesthat deviate from optimal operating conditions. In thisway, production is accelerated, and positive effects
spill over to resource allocation, time-to-market, andcustomer satisfaction. In addition, KPIs are fine-tuned
each time to identify machine performance moreaccurately, and machines are continuously adjusted
based on real-time data to improve performance. In
this way, asset performance management improves
overall equipment and corporate profits.
Predictive maintenance in asset maintenancedynamically collects and analyzes machine healthand performance data. It then identifies parts thathave reached key thresholds, determines when
maintenance or replacement is required, and performs
maintenance only when necessary. This eliminates
the need for scheduled maintenance and significantly
reduces unscheduled maintenance. As a result,predictive maintenance can reduce unscheduled
downtime, lower maintenance costs, improve quality,
productivity, asset uptime, availability, and production,and extend the service life of machines. For more
information, including other topics of interest, pleaserefer to the report.

Fig. 2 Siemens Digital Maturity Model6) 9)
Kawai stated that when he attended COMADEM(Condition Monitoring and Diagnostic Engineering
Management),10) a traditional international conferencein the field of condition monitoring and diagnosis,
he noticed that many of the presentations by Asian
researchers were on individual diagnostic techniques,
while those by Western researchers were mostly
about asset management.6) This shows how in Europe
and the U.S., research has already expanded in scope
to include not only equipment management using
diagnostics of anomalies, but also how to realize
managerial cost reductions based on such data, taking
into consideration the costs of installing diagnostics
equipment and conducting diagnostics necessary
for that purpose. This point is consistent with thetrend of thinking among Western experts, which
the author himself has experienced and felt throughthe standardization activities of ISO/TC 108/SC 5.
Japanese engineers and researchers must quickly
recognize this global trend so that they can respond
appropriately.
4.2 Trends in Japan: Maintenance Optimum
Strategic Management System (MOSMS)11)-14)
In Japan, the Japan Institute of Plant Maintenance(JIPM) has been continuously and actively involvedin equipment management. Fig. 3 shows the historyof facilities management in Japan, based on the
introduction material of the association11). Facilities
management technology continues to expand, as canbe seen in the background and history of MOSMS,
which integrates these technologies and incorporates
asset management.

Fig. 3 History of facility maintenance in Japan11)

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The Current Situation of Condition Monitoring and Diagnosis of Machine Systems■ Estimated number of employees(manufacturing and maintenance, thousands)
Manufacturing
5 000

4 887 4 792

Maintenance
4 577

808
4 235 4 386

610

4 000

4 365
629
3 739

3 000
2 000

474

527
388

796

900
700

4 067

514

(From maintenance performance survey)

2010 2011 2012 2013 2014 2015 2016 2017
Manufacturing
departments

this way, the system aims for to align maintenancemanagement, which aims to balance risk and cost,with planned maintenance using technology forcontinuous improvement.

500
300

Maintenancedepartments

Fig. 4 Number of employees in manufacturingand maintenance departments11)
Fig. 4 shows the number of employees in the
manufacturing and maintenance departments.11) The
results show that the introduction of new equipment
has recently decreased. As this has happened, there
has been a growing shift toward maintaining andoperating existing equipment as well as possible.
Therefore, it can be said we are in the age of
maintenance.
Given this trend, in 2006, JIPM proposed and started
the Maintenance Optimum Strategic Management
System (MOSMS).12) 13) Its aim was to build amaintenance system that matched management
theory and technology theory through a consistentconcept for dealing both with loss (issues that havealready occurred) and risk (possibility that such issues
may occur in the future). This system clarifies therelationship between the PDCA cycle of management
and the PDCA cycle of maintenance (Fig. 5). In
particular, it emphasizes that in order for maintenanceto be integrated with management, a plan must be
created that is logical from a management perspective
and it must be ensured that maintenance follows theplan (maintenance management).13) It is necessary to
fully align management and maintenance to create a
grand design for maintenance (a maintenance planoffering maximum advantage from a management
perspective). MOSMS is a “made in Japan” systemthat has been developed for this very purpose.
MOSMS lists P (productivity), Q (quality), C (cost),
D (delivery time), S (safety), M (motivation), andE (environment) as loss/risk categories, aiming
to minimize these loss/risk categories as a resultof facility management. The target group is the
middle rank (managerial level), which is the sameas the target group for asset management in ISO/TC 108/SC 5/WG 18 described in the previous
section. To link management and maintenance, as
shown in Fig. 5, loss/risk management and facility
maintenance strategies are incorporated into the P(plan) of the management PDCA cycle. The D (do)of the management PDCA cycle and the P (plan) of
the MOSMS PDCA cycle are linked with each other,
the C (check) and A (act) of both PDCA cycles arealso linked through evaluation of each other.14) In

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Fig. 5 Linking management and conservation
through MOSMS14)

5. Current status of wind power
generation and trends in condition
monitoring and diagnostics15)-21)
Let us look at wind power generation systems, oneof the main systems subject to condition monitoringand diagnostics, and trends in this area.
5.1 Wind power producer trends15) 16)
The following is a summary of a report issued by
Accenture15) 16) this fiscal year. Over the past decade,efforts to reduce the Levelized Cost of Electricity(LCOE) of onshore and offshore wind power have
expanded and intensified. This has ushered in a digital
revolution in the wind power industry. Global onshore
wind power generation capacity has risen sharply
over the past decade, from 178 GW in 2010 to 594
GW in 2019. Offshore wind power is also growing
rapidly, reaching 29 GW of global capacity in 2019.
Fig. 6 shows global offshore wind power capacity andprojected short-term growth rates. The power market
is investing heavily in offshore wind power, whichcurrently accounts for only about 5 % of global wind
power capacity, but with the scale and growth rate of
the project, some predict that offshore wind power
capacity will nearly triple over the next five years.

Fig. 6 Global Offshore Wind Power Capacity andProjected Short-Term Growth15)

5.2 Condition monitoring and diagnostics of windturbines15) 16)
In the wind power business, LCOE has been
improved by increasing turbine size (from 2 MW
to 12 MW in 10 years in the UK16)), reducing
capital investment per MW, and increasing facility
utilization (from 30 % to over 40 % in the UK16)).
Accordingly, operation and maintenance (Operation& Maintenance, O&M) costs in the LCOE of wind
power plants have gradually increased in relative
terms and now exceed one-third of the life cycle
costs. As a result, there has recently been a growinginterest in achieving productivity gains and cost
reductions for O&M processes, and digital technology
has been identified as one of the key determinants of
its success. Accenture has conducted a survey of 11
leading onshore and offshore wind energy companies
and identified six key O&M use cases. The results ofthe study on the impact of advanced digital tools on
process improvement for these six key O&M use cases
are shown in Fig. 7.

Fig. 7 Degree of impact of advanced digitaltechnologies on key O&M use cases andbreakdown of surveyed companiesʼ ratings15)
Three of the six major O&M use cases are related
to condition monitoring. Applying advanced digital
tools to these three cases offers very high potential
for improvement. The following sections provide an
overview of three of these use cases individually.
• Alerts, Alarms, Warnings, and Faults: Types ofalerts and faults are analyzed to identify trends
and patterns for equipment condition analysis,
work prioritization, etc. These are incorporated
into response time guidelines to minimize loss ofgeneration and cost. The control room monitors
alarms and warnings and determines the response.
The performance engineering team takes chargeof the cause and relevance of alarms and faults,
while the asset management team and O&M team
identify follow-up actions and prioritize responses.
• Troubleshooting and minor corrective maintenance(CM): The process of investigating and repairing
incidents remotely or in the field to reduce

downtime and increase power generation. An
operations team monitors plant performanceand remotely investigates reported incidents,
coordinates field inspections, and handles
subsequent activities based on work instructions.
• Failure prediction and major component
replacement: Condition monitoring tools andalgorithms are used to understand the risk ofmajor component failure within a pre-specified
time frame. Setting up an optimally timed
major component replacement process reduces
downtime and maintenance activities, resulting incost savings and increased power generation.
In addition, this study comprehensively analyzes thecases above and clarifies the following key findings
related to the current role, potential and challenges ofdigital technology in O&M.15) 16)
Most of the companies surveyed see digitaltechnology as having an important role in fault
prediction, alerts and warnings, and trouble shooting.
All companies have implemented appropriate custombuilt solutions, and many of them would like todevelop further functionality. In this regard, a business
opportunity has arisen for third-party companies toprovide O&M insight services that power producers
can implement in their current environments.
• Integrating data of all types throughout the O&M
process enables the addition of rich contexts toanalytics to create added value. However, becauseof the low quality of data and the lack of dataengineering skills, few companies have achieved
this.
• Many companies are willing to improve their O&M
processes by learning from past activities andinformation, such as alert responses and service
records. In reality, however, the lack of time and ofeasy-to-use digital tools makes this difficult to do.
• As for data, data owners, such as owners, power
generators, and OEMs (or O&M service providers),
are reluctant to share the data and the insights
derived from it. Therefore, third-party companies
need to clearly define the ownership of the dataand consider and provide a partnership model forjointly developing software and tools.
Going forward, based on the abovementioned
recognition, advanced digital technologies will be
used to devise and build mechanisms for successful
data sharing. There will be a rapid global shift toachieve increased productivity and cost reduction for
the O&M process of wind power generation. In thisregard, condition monitoring and diagnostics will play
an important role.

NTN TECHNICAL REVIEW No.89

9

BladeControl andpower electronics
Main shaft / bearings systems
Main
shaft
Hub

Nacelle

0.60
0.50
0.40
0.30
0.20
0.10
0.00

Fig. 9 Trends in wind turbine failure andaccident rates17)
Table 4 Breakdown of Failure and Accident Factors(FY 2016)17)
Failure andaccident
factors

Breakdown offactors

Number ofoccurrences

Percentage
of total

Strong winds

0

0.0 %

Lightning

34

9.1 %

Air turbulence

19

5.1 %

Low
temperatures/
freezing

0

0.0 %Flooding

7

1.9 %

Other

12

3.2 %

Design flaw

3

0.8 %

Manufacturing
flaw

17

4.5 %

Construction
flaw

13

3.5 %

Human factor

Inadequate
maintenance

23

6.1 %

SystemfailureSystem failure

17

4.5 %

Under
investigation

6

1.6 %

Unable toidentify

108

28.9 %

Other*

115

30.7 %

374

100 %Naturalphenomenon

PitchsystemTower

YawsystemGear box

Mechanicalbrake

Oil hydraulics/
Powergenerator cooling system

Fig. 8 Example of windmill equipment configuration

17)

Fig. 9 shows the trends in the failure and accident
rates for the wind turbines cooperating with thesurvey (the same survey method has been in placesince around 2012, and the rates have remained
almost constant.)17) Table 4 shows a breakdown of
the causes of failures and accidents in fiscal 2016.
Among the factors related to condition monitoringand diagnosis, “inadequate maintenance” (6.1 %), a“human factor”, was relatively common. Comparedto past surveys, the “Other” category in the “Cause
unknown/other” category, which is related to condition
monitoring and diagnosis, has increased significantly.
This “Other” category was mainly reported as longterm deterioration. The same trend has continued inrecent years, indicating an increase in the number of
failures and accidents due to the deterioration of windturbines after a certain number of years.

10

0.70

20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16

5.3 Wind Power Condition Monitoring and
Diagnostics Trends in Japan(from the NEDO Report)17)
In Japan, a few years ago, NEDO summarized its
research results in “R&D of Technologies for Wind
Power and Other Natural Energies, R&D for Advanced
Practical Use of Wind Turbine, smart maintenancetechnology R&D (analysis) (fatigue prediction, etc.).”17)
The following is a brief overview of the research
results.
Fig. 8 shows an example of a basic wind turbine
component configuration. In this report, to contributeto the development of maintenance methods
for highly efficient rotating (drive train, control
equipment) and non-rotating (tower, etc.) systems,
data was collected on wind turbine failures andaccidents, the circumstances and causes of theiroccurrence was analyzed, along with analyzing
maintenance methods. The report also summarizes
the maintenance methods of existing wind turbines inJapan and overseas, as well as the measures taken in
response to failure accidents.

Failure/accident rate (times/year/unit)

The Current Situation of Condition Monitoring and Diagnosis of Machine Systems

NTN TECHNICAL REVIEW No.89

Failure inwind turbine

Unknowncause/Other

Total* “Other” of “Cause unknown/other” is mainly reported as“long-term deterioration”

Other Over all Blade Hub 0.5 %System 16.5 % 2.6 % 7.1 %Aerodynamic brakelinkage device
0.7 %
2.8 %
Mechanical brake
Joint
1.6 %
1.9 %
Pitch control unit
11.5 %
Foundation
Main shaft /0.5%
bearings
Sensors
1.2 %4.2 %Gear box
7.8 %Oil hydraulics
Power
4.0 %
generator
Electrical
Control
Yaw 2.8 %Windequipment
unit
anemometer device
17.9 %
12.2 %
0.2 %4.0 %5.4 Condition monitoring technology for windturbines17)
5.4.1 SCADA and wind turbine CMS

Fig. 10 Summary of wind turbine failures andaccidents by site of occurrence (FY2016)17)
Fig. 10 shows a summary of wind turbine failures
and accidents by site (FY2016). The most common
failure/accident categories were “electrical unit” (17.9%), “other” (16.5 %), “control unit” (12.2 %), and “pitch
control unit” (11.5 %). In the evaluation by operation
period, “blades”, “pitch control unit”, “gearbox”,“control unit”, and “electrical unit” accounted for a
relatively high number of failures of wind turbines that
had been in operation for “10 years or longer”. In mostcases, the longer the operation period, the greater
the number of failures. In the past survey results, thenumber of failures and accidents tended to increase
with the length of operation period for parts withmechanical drive mechanisms such as the pitch
control device, gearbox, and yaw device. In contrast,
the number of failures and accidents tended to behigher for blades, generators, electrical devices, andcontrol devices regardless of the length of operation
period. This is because the former are mechanicaldevices, while the latter are electrical devices.
Mechanical fatigue (including the effect of turbulence)is thought to be the main cause of the former failures,
while lightning strikes are often the cause of the latter.
The NEDO report also mentions that the data owners
are not willing to share their data. The same trend canbe seen in the situation in Europe described in theprevious section. In addition, a serious shortage ofengineers who can work on O&M of wind turbines has
been reported worldwide.

Fig. 11 SCADA and condition monitoring (CMS)
system for wind turbines17)
Fig. 11 shows the supervisory control and dataacquisition (SCADA) system and condition monitoring
system (CMS), which are the core systems forwind turbine operation and control. The actual
relationship between CMS and SCADA is organized
as an integrated image. Together with the operational
status data of each device collected by the CMS,
Operational data obtained by the SCADA system is
indispensable for evaluating the condition of windturbine components and equipment. As for thenecessity of CMS for wind turbines, the failure rateof gearboxes, generators, and converters, which aremajor components of wind turbines, is reported to behigher than those of the same type and capacity usedin other industries. As the output of a wind turbine
fluctuates with wind conditions, the load constantly
changes due to turbulence and blade position anddeformation. In addition to these load fluctuations,wind turbines with higher output and larger sizes
tend to have lighter equipment components, which
can increase the probability of failure and accelerate
fatigue. It is therefore important to constantly monitor
the equipment condition (load and response values)
to detect and address anomalies as early as possible(application of condition-based maintenance). It isalso important to use digital technology to evaluate
and predict the impact of load fluctuations on theequipment and to take measures for operation andmaintenance in advance (remaining service life
evaluation).

NTN TECHNICAL REVIEW No.89

11

The Current Situation of Condition Monitoring and Diagnosis of Machine Systems
5.4.2 CMS technology for wind turbines
The failure modes of wind turbines and diagnostic
techniques are shown in Fig. 12. Most of themonitoring technologies applied to wind turbine
CMS are based on existing condition monitoring
technologies. The CMS of wind turbines ischaracterized by the high need for online monitoring
because the operation and maintenance of many
wind turbines must be performed at a distance fromthe site. Table 5 shows the results of a survey ofdetection technologies in the current wind turbine
CMS in terms of online monitoring and diagnostic
effectiveness. While the development and applicationof new CMS technology specifically for wind turbines
is not necessary, given the shortage of real-world
examples there are major obstacles to wind turbine
CMS in terms of providing qualified failure diagnosis
through online monitoring.

12

Table 5 Anomaly detection technologies, target sites,
and characteristics of wind turbines17)
No

CM technology

cost

onlinecommercial

failurediagnosis

track
recordwind turbine
components

1

thermocouple

low

○

×

in usebearing
generatorsconverter
nacelletransformer

2

particlecounter
methodlow○×in usegearboxbearing3

vibration
analysis

low

○

○

in usemain shaft
main shaft
bearings
gearboxgenerator
nacelletower
foundation

4

ultrasonic
exploration

low tomedium○×

undertesting

tower
blade

5

electricalsignal(e.g., discharge
measurement)

low

○

×

in usepower
generator

6

vibroacoustic
analysismedium○××

blade shaft
bearing
gearboxgenerator

7

lubricating oil
component
analysis
methodmediumto high×○×

gearboxbearing8

acoustic
emission

high

○

×

×

blade shaft
bearing
gearboxgenerator
tower

9

twisting
vibration(encoderbased)

low

○

×

under
testing

shaft
gearboxFig. 12 Failure modes at general plants and applied
condition monitoring techniques used forwind turbine CMS17)

10

optical fiber
strain gages

extremely
high

○

×

in useblade

5.4.3 Condition monitoring and prediction of main
bearings for wind turbines
As the main bearings of wind turbines are used
under low-speed conditions, vibration caused bydamage is itself small. In laboratory tests, it is possible
to detect changes in vibration levels even in the very
early stages of damage. In actual wind turbines,
however, peripheral equipment generates vibration
at a level that can bring severe damage to the main
bearings. Transmission of this vibration from theperiphery hinders the detection of damage to themain bearings. A key task in detecting damage tothe main bearing is to improve the signal-to-noise
ratio by suppressing this vibrational disturbance. To
address this issue, this report17) develops a detection
method that highly integrates frequency band
splitting, a physical method, and machine learningalgorithms, a statistical method. As shown in Fig. 13,
we have demonstrated that the method can be used
to properly detect and diagnose initial damage to themain bearing.

11

thermography

extremely
high

○

×

×

blade shaft
main shaft
bearings
gearboxgenerator
converter
nacelletransformer

12

shaft torquemeasurement

extremely
high

○

×

under
testing

blade shaft
main shaft
bearings

13

shock pulse
method

low

○

×

×

bearing
gearboxNTN TECHNICAL REVIEW No.89

The same report17) also attempted to construct amethod for predicting the residual life of wind turbine
main bearings. By proposing an equation relating
the equivalent distance between wind turbines andthe load coefficient that reflects the effect of wakes(areas of lower velocity than the main flow that
occur downstream of objects placed in the flow), it ispossible to evaluate the bearing-rated life as shown in
Table 6.

Fig. 13 Damage detection of the main bearing of awind turbine17)
Table 6 Predicted and actual comparison of rated life17)WindturbineDistance
between
Actualequivalent
rated life
wind
(years)turbines(km)

Load
coefficient

Predicted
rated life
(years)Unit 4

16

0.3

1.55

14

Unit 5

17

0.3

1.51

16

Unit 19

10

0.2

1.72

9

Unit 20

8

0.2

1.75

8

5.4.4 Wind Turbine Tower Anomaly Detection
On March 12, 2013, a wind turbine nacelle fell fromthe top of the wind turbine support tower at windturbine No. 3 on the Taikoyama wind farm in Japan.
This was due to a fatigue fracture of the tower casing
just below the flange. Investigation of the accident
revealed that the cause of the fatigue fracture wasa stress concentration in the tower casing due todamage to the tower top bolt installed in the flange at
the top of the tower. Similar past accidents occurring
as anomalies characteristic of wind turbines include:a blade fallout in Falkenberg, Sweden, in 2009; an
accident at the Lemnhult wind farm south of Vetlanda,
Sweden, in December 2015; and an accident at the
Auwahi wind farm on Ulupalakua Ranch on the south
coast of Maui, Hawaii, USA in October 2016. In thecase of the collapse accident at the Taikoyama wind
farm, no anomalies were detected during routine
inspections before the accident, and fatigue fracture
occurred in a short period. In response to this accident,
a method was developed for detecting anomalies in

0.010
0.00800.00600.00400.00200.0

080 120 170 420 680
Axial force (Nm)(a) Torque change

Mahalanobis Distance(b) Diagnostic results for Unit 4 on the Unit 5 model.

Mahalanobis Distance(a) Diagnostic results for Unit 5 on the Unit 20 model.

tower top bolts at an early stage and evaluating axial
force.17) The MT system (Maharonobis-Taguchi) T
method (3), a type of multivariate analysis method,
was used to detect abnormal bolts. The results of
the analysis using the second-order derivative of thestrain variation are shown in Fig. 14. When the torquewas 680 N-m, the value exceeded the threshold value,indicating that it was possible to determine whether
the bolt was abnormal or normal, and the accuracyof the determination could be maintained even when
the rotor orientation changed (from -45°to 45°).

0.010
0.00800.00600.00400.00200.0-45 -22.5 0 22.5 45
Rotor load position( °)(b) Rotor orientation

Fig. 14 Early anomaly detection method for windturbine tower top bolts by Mahalanobis
distance17)
5.4.5 Example of standard for wind turbine CMS17) 18)
Here we will introduce the standard for windturbine CMS using the Guideline for the Certificationof Condition Monitoring Systems for Wind Turbines
Edition 201318) published by GL (Germanischer Lloyd)
as an example. The scope and objects of application of
the CMS for wind turbines are mainly the drive train,main bearings, gearboxes, and generators. In addition
to these, the CMS also covers the tower, blades,
main bearings, gearbox lubrication oil, foundations,and other parts of the wind turbine. Monitoringdata for wind power generation equipment include
load (power generation or torque), shaft speed,
temperature in the nacelle, bearing temperature,generator winding temperature, oil temperature, andoil pressure. Other environmental data, such as windspeed, wind direction, and outside temperature, arealso required to be measured when available.
Table 4.1 Ref.18) shows the number of sensors
required for a wind turbine CMS. It states that at leastsix sensors are required to monitor the vibration stateof the drive train of a wind turbine equipped with agearbox. The required frequency bandwidth starts
from fairly low speeds and has a wide range. Table 4.2
in Ref.18) shows a list of methods for using the signals
acquired from each sensor in a wind turbine CMS. It
requires the application of various analysis methods
for each site in order to deal with frequencies,
vibration modes, and corresponding anomaly modes.

NTN TECHNICAL REVIEW No.89

13

The Current Situation of Condition Monitoring and Diagnosis of Machine Systems
6. Predictive Maintenance and IoT19)
This is a citation from the article “IoT for Predictive
Maintenance” compiled by Mr. Sako, a long-timemember of the ISO/TC 108/SC 5 National Committee,
in 202019).
6.1 Key Points for Building a Predictive Maintenance
IoT System
Predictive maintenance is also called conditionbased maintenance or condition monitoring
maintenance. It is a maintenance method that uses
diagnostics techniques to measure and monitor thecondition of facilities and equipment to understandor predict the degree of deterioration and takeappropriate maintenance measures. The key points of
IoT include:• selection of sensors for detecting anomalies,• selection of optimal signal processing methods andanalysis methods,• determination of information collection methods,• determination of monitoring methods,• and automation of analysis and diagnosis.
Several development items are needed to promote
IoT for predictive maintenance. Here, we take“visualization of equipment conditions (as quantitativevalues)” as an example. Technological development
for visualization of facility conditions (as quantitativevalues) includes expansion of target facilities foranomaly detection, improvement of detection
accuracy, and more efficient and easier detection. To
this end, it is important to first clarify the equipment
to be monitored, the failure mode, the requiredprediction level (lead time), the difficulty of stopping
the equipment, and the time when the equipment canbe stopped. At the same time, it is necessary to clarify
the measurement location and available sensor types.
The procedure includes identifying a sensing
method capable of detecting anomalies throughlaboratory testing and determining evaluation indicesand threshold values for good or bad judgment
through field testing using the same method. Table 7
shows the application status of the diagnostic
techniques developed by Sako et al. for rotating
equipment19). The diagnostic techniques have been
comprehensively applied to a wide range of objects.
In his contribution19), Sako further describes indetail the diagnostic technologies for rolling bearings,
plain bearings, and motor current symptom analysis(MCSA) for the IoT of predictive maintenance. Here,we will focus on “diagnostic technology for rolling
bearings”.
6.2 Diagnostics techniques for rolling bearings forpredictive maintenanceAs shown in Fig. 15, rolling bearing anomalies
generate vibration and sound in various frequency
ranges19). These can be roughly classified into two
categories: structure-borne, such as vibration and AE(acoustic emission), and airborne, such as acoustic.
Sound is further divided into audible sounds andinaudible sounds above 20 kHz.

14

NTN TECHNICAL REVIEW No.89

6.2.1 Diagnostics using the vibration method19)
One of the signals used to diagnose anomalies
in rolling bearings is vibration acceleration. This
acceleration is further divided into two maincategories: acceleration in the high-frequency
range of 10 k to 30 kHz and acceleration in the lowfrequency range of 1 k to 10 kHz.
Fig. 16 shows an example of bearing diagnostic
results from the field. This is the acceleration
spectrum of a 6310 deep groove ball bearing whenfalse brinelling occurred in the inner ring due topropagating vibration from adjacent equipment.
As shown in Fig. 15, a frequency of 14 KHz was
generated in the zone indicated as high-frequency
acceleration. This is an example of how even a small
flaw could be detected by acceleration in the highfrequency range.
Fig. 17 compares the trends of acceleration values
in the high-frequency range for the six cases that
resulted in bearing failure. The average value in thenormal state is set to 1.0. The vertical axis shows theratio of the acceleration to the normal value, and thehorizontal axis shows the time elapsed from the time
when the condition began to change. Each bearingshows an exponential upward curve. It took three to
six months to reach the relative standard of caution(4.0 times), and about six months for the anomaly toarise (8.0 times). When the bearings were replaced
after about six months, small defects were observed.
It is explained that the variations in the degradation
speed of each bearing relates to differences inlubrication conditions and bearing load.
As deterioration progresses from this state, a lowfrequency acceleration spectrum in the 2 k to 3 kHz
range is generated in addition to the high-frequency
acceleration. This is the natural frequency of the
bearing outer ring. The explanation for this is thatthe progressive deterioration of the bearing results
in a higher excitation force, which vibrates the entire
bearing and generates natural frequencies. The trendof acceleration values in the low-frequency range wasalso investigated in four cases.19) As with the highfrequency range shown in Fig. 17, an exponential
upward curve was drawn, leading to bearing
replacement within approximately one to threemonths after the change was observed.
Furthermore, as bearing deterioration progresses,changes in vibration velocity also occur. A comparisonof velocity trends in seven cases of bearing failure due
to increased velocity shows an exponential increase
curve similar to that in the high-frequency range. Thechange in velocity value is so rapid, however, about
one or two weeks after the vibration velocity begins
to increase, the bearing can be said to have reacheda terminally degraded state that requires it to be
replaced.19)
The conclusion is that although the residual life ofbearings can be predicted in several frequency ranges,
monitoring using acceleration in the high-frequency
range may be essential for critical facilities wherecontinuous operation must not be interrupted.

Table 7 Application of Diagnostic Techniques toRotating Equipment19)

mediumand highspeed
rotationslidingbearing

gear

coupling
belt

acoustics
AE

audiblerange

ultrasoundelectric
current(MCSA)

×

○

○

△

○

○

○

○

poor
lubrication

×

○

○

○

○

○

○

○

wear

×

○

○

○

○

○

○

○

looseness/rattle

×

○

○

×

○

○

○

○
○

seizing

×

○

○

○

○

○

○

fatiguespalling

×

×

○

×

○

×

○

poor
lubrication

×

×

○

△

○

×

○

wear

×

×

○

×

○

×

○

looseness/rattle

×

×

×

×

○

×

○

seizing

×

×

○

△

○

×

blowers,
pumps,
compressors,
motors, etc.

○

seizing×○ :Kepstrum

○

○

△

×

○ :Kepstrum

○

○

△

fatigue×○ :Kepstrum

○

×

△

contamination
by foreign
matter×○ :Kepstrum

○

×

△

oil whirl

○

×

×

×

×

×

×

oil whip

○

×

×

×

×

×

×

pitching

△

○

○

○

○

○

○

○

spalling

△

○

○

○

○

○

○

○

scratching

△

○

○

○

○

○

○

○

meshinganomaly

△

○

×

×

○

○

○

○

misalignment

○

×

×

×

×

×

×

○

slack

○

×

×

×

×

×

×

○

overstretching

×

×

×

△

×

×

×

○

imbalance

○

×

×

×

×

×

×

○

rotating stall

○

×

×

×

×

×

×
×

turbines,
compressors,dieselengines, etc.

surging

○

×

×

×

×

×

cavitation

×

○

×

×

○

○

contact

×○ :Kepstrum

×

×

○

○

○

bend

○

×

×

×

×

×

×

eccentricity

○

×

×

×

×

×

×

• AMD
speed
criteria• AMD
rollingbearing
criteria• Noisereduction
methodfor inverter
motors• SOAP
method,ferrographymethod• Lighttransmission
method(MD-410L:
currently
discontinued)

Remarks

applicableequipment

agitators,
extruders,rolls, paper
machines, etc.

rubbing

blade

axis

lubricanttemperature
analysisfatiguespalling

rollingbearing
low
speed
rotation(severalhundredrpm orless)vibration
vibration vibration
velocity acceleration

Sensoryobservation
Structuralpropagation

20

30

50

70

100 200 300 500 700 1k

reduction
gears, speed
increasers,
etc.

3k

5k

7k

blowers,
pumps, etc.
ventilator
ventilator

○

pump

×

screw
compressor

10k 20k 30k 50k 70k 100k 200k 300k 500k 700k 1M

Audible range (hearing with auscultation rod)
Vibration velocity

Low range
acceleration

Inaudible range
High frequency
acceleration(Brg acceleration)

Audible acoustics
Rattling vibration due to bearing flaws (4)y=e0.0006x

8.00

⑥

y=e0.0003xy=e0.0004x

7.00
6.00

⑤

5.00

③

① ②

④

4.00
3.00
2.00

0

1 000 2 000 3 000 4 000 5 000 6 000 7 000 8 000

Elapsed time from change point Hr

Fig. 17 Bearing degradation curve at high frequency
range acceleration19)

Shaking detection by palpation

Airborne
propagation
Rollingbearing
acoustics

2k

9.00

6 months

1.00

Frequency Hz
10

3 months

10.00

0.00

• Application
to equipment
for whichvibration isdifficult tomeasure(cannedpumps,
axial flow
fans, etc.)•
Measurement
at power
supply panels• Non-contact
measurement• Operation
for moving
equipment(LM Guide,etc.)• Thermal
imaging
cameraFig. 16 Acceleration spectrum during false brinelling19)

Multiplier to normal

type ofanomaly(example.)machineelement

Acoustic emission(AE)

Ultrasonicacoustics
Ultra soundNatural frequency of bearing outer ring (3) Contact noise between bearing steel (2) Crack propagation sound of bearing steel (1)Fig. 15 Vibration and acoustics generated by rolling
bearings19)

6.2.2 Diagnosis by AE method19)
The AE method is the method that enables theearliest detection of anomalies in rolling bearings.
Fig. 18 shows the results of an accelerated life test at
750 rpm. The horizontal axis shows the test time, andthe vertical axis shows the event rate for both AE andacceleration of vibration (ACC). AE captures rolling
bearing anomalies earlier than the high-frequency
accelerations shown in Fig. 17. The explanation
given19) for this is that fatigue causes spalling on the
bearing steel from its internal starting point, andas it progresses to the surface of the raceway, AE
occurs at the time of the spalling occurring whereas
acceleration occurs after the rolling elements havebegun to collide with the spalling on the orbital planesurface. In the case of spalling with a surface causesuch as that due to oil film rupture or indentation, thetwo occur almost simultaneously.
Fig. 19 compares the AE spectra of the exfoliation
propagation and slit cracks. The AE spectrum of the
exfoliation propagation has a prominent frequency
component between 100 k and 500 kHz, whereas the
AE spectrum of the slit flaw has almost no component
above 100 kHz. Thus, AE can also capture thedifference in anomaly modes of rolling bearings in theearly stage.
The AE method is also superior in the low-speed
speed range. In the low-speed range, where theproduct of bearing bore diameter d (mm) androtational speed N (rpm) (dN value) is less than 2.0
× 104, the shock signal level of vibration acceleration
decreases and is buried in the noise level, making
it generally difficult to distinguish between normal
and abnormal conditions. In contrast, the AE method

NTN TECHNICAL REVIEW No.89

15

The Current Situation of Condition Monitoring and Diagnosis of Machine Systems
is an effective measurement method because it isindependent of rotation speed.

event rate Pieces/10 min

10 000

ACC
AE

1 000
100
10
1
0

0

100

710

810

910

Time min

1 010 1 100

θ dB

At the time ofdelamination
progress-36 dB-72 dB

0500 KHz

Frequency

Amplitude

Amplitude

Fig. 18 Accelerated life test results and diagnosis by AE19)

1 MHz

θ dB

time ofslit flaw-36 dB-72 dB

0500 KHz

Frequency

1 MHz

Fig. 19 AE spectra during exfoliation propagationand slit flaw19)
6.2.3 Other19)
The AE method requires a couplant to be applied
between the equipment surface and the sensor
surface. It is difficult to obtain a stable signal in cases
where this is strongly affected by unevenness orcurvature of the installation surface. For equipmentthat has conventionally been difficult to diagnose
due to such drawbacks, the authors have developed
a non-contact detection method using ultrasonicmicrophones. Using 20 k to 100 kHz ultrasonicacoustics, it has been shown that bearing separation
anomalies can be detected in the same way as with
the AE method, even at low rotational speeds of65 rpm. The ultrasonic acoustic method was applied
to detect rolling contact fatigue exfoliation of roll
support bearings in a solvent gas environment where
AE could not be used due to couplant leakage. In
addition, AE is also used to diagnose abnormalities in
low-speed rotating equipment, such as contact withthe extruder body, pitching of reduction gear gears,
and bearing rattling in agitators19).

7. Trends in Condition Monitoring and
Diagnostics Using 1DCAE
Kawai, a leading domestic expert who has been
engaged in condition monitoring and diagnosis ofmachinery for many years in Japan, provides anintroduction “On Diagnostic Methods for Equipment
Using Physical Models – Application to Digital Twins”21)
in the opening article of the 2022 JSME Dynamics,

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NTN TECHNICAL REVIEW No.89

Measurement and Control Division Newsletter. In
this article, Mr. Kawai summarizes the process ofincorporating 1DCAE software into the equipment
diagnosis that he has been working on for many years.
1DCAE is a generic term for CAE methods and toolsthat have been rapidly spreading and developing inrecent years and their design support concepts.22) It
can be applied from the upstream stage of productdevelopment because it accurately captures thefundamental nature of the target. It can be expressedin a simple and clear format and can be used forevaluation and analysis. The article explains how
modeling the target system with 1DCAE software (inthe case of this data, Modelica is used) is useful forequipment diagnosis, and shows examples of system
and anomaly modeling (Table 8) that Mr. Kawai has
been working on.
As a concrete example, the model of anelectromagnetic brake is shown. It is explained thata system such as an electromagnetic brake, which
combines complex physical phenomena consisting ofdynamics, magnetic circuits, and electric circuits, canbe easily modeled by the 1DCAE software (Fig. 20).
Here, it is explained that the wear of the armature
increases the clearance between it and the plate, andthat the resulting effects on other physical quantities
can be simulated in a variety of simple ways. Fig. 21
shows the effect of the clearance on the time variationof the current at the start of energization. In thesame example, he states that the simulation andexperimental results align very well. Thus, he argues,
modeling the system and anomalies with 1DCAE
software makes it easy to analyze the system behavior
when anomalies occur in the system. The interested
reader is advised to view the material, which isavailable free of charge from the departmentʼs
website.
Some examples of modeling of mechanical systems
using 1DCAE software are shown below. Kashiwase etal.23) modeled a capacitor motor using the magnetic
circuit method and conducted an analysis using 1DCAE
software (Modelica). A coupled system (Fig. 22)
connecting an electric motor model and a rotating
shaft model was used to perform a coupled simulation(Fig. 23) relating the vibration of the rotating shaft
and the current of the power supply. This is expected
to enable more detailed diagnosis of the electricmotor.
Furthermore, Kashiwase et al. constructed a basicmodel for three-phase induction motors, which arewidely used in plants, based on the magnetic circuit
method, which consists of a stator, rotor, air gap, andother components, and a model that can be coupledwith a rotating shaft24). The validity of the modelusing 1DCAE software was verified by comparing its
electromagnetic behavior with FEM analysis, and theresults showed promise for simulation application toelectric motor diagnosis. Fig. 24 shows the winding
currents in each phase. Although the pulsation of the
1DCAE model (Modelica) is larger, it was confirmedthat the overall behavior can be reproduced,
according to the report.

Table 8 System and Anomaly Modeling Examples21)

control valve

biting, sensor offset

compressor

leakagecogeneration systemdecrease in efficiency, clogging,and heat transfer coefficient due
to debris

crankshaft

bearing rattling

rotational axis
systemrotor disproportion, bearing flaw,
bearing rattlingcoupling

misalignment, miscoupling

journal bearings

wear and its evolution(residual life estimation)screw compressor

bearing rattling, leakage

electromagnetic
brake

wear

gear mechanism

gear wear

3D printerfailure of fan for cooling(control in case of anomaly)

1.2
1.0

Electric current [A]anomaly

0.8

0.2 mm
0.3 mm
0.5 mm0.75 mm

0.6
0.4
0.2
0.0
0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80

Time [s]experiment
1.2
1.0

Electric current [A]target system

0.8

0.2 mm
0.3 mm
0.5 mm0.75 mm

0.6
0.4
0.2
0.0
0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80

Time [s]simulation

Fig. 21 Current change at the start of energization(effect of clearance)21)

energized state
dynamics model

magnetic circuit model

electric circuit modelFig. 20 Electromagnetic brake model21)
Fig. 22 Modelica model of motor and shaft system23)

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The Current Situation of Condition Monitoring and Diagnosis of Machine Systems

8. Research Trends in Condition Monitoringand Diagnosis of Bearings in Japan

Displacement of Motor Shaft
Displacement [m]5.0E-13
2.5E-13-0.0E+00-2.5E-13-5.0E-13

Time [s]Spectrum

Amplitude

1.2E-13
1.0E-138.0E-14
6.0E-14
4.0E-142.0E-140.0E+00
050

100 150 200 250 300 350 400

Frequency [Hz]

8
6
4
2
0
-2
-4
-6
-8

lu

lv

lw

Current [A]Current [A]Fig. 23 Vibration analysis results of motor and shaft
system23)

00.005 0.01 0.015 0.02

Time [s]

8
6
4
2
0
-2
-4
-6
-8

lu

0

lv

lw

0.005 0.01 0.015 0.02

Time [s]Fig. 24 Stator current (unsteady) (1DCAE and FEM)24)
Another study25) modeled gear pairs and gear
tooth wear, which are typical power transmission
mechanisms, using 1DCAE software (Modelica).
The model was used to estimate how the vibration
characteristics of the gear pair changed due to wearon the tooth flanks, and also to verify the results using
actual equipment.
In this regard, modeling of mechanical systems
using 1DCAE software has been used extensively
in recent years. The results of 1DCAE modeling ofvarious mechanical systems were presented in across-divisional OS organized at the 2022 annual
conference of the Japan Society of MechanicalEngineers (JSME). As mentioned above, the use of
1DCAE software as a modeling method for condition
monitoring and diagnosis of mechanical systems is
expected to advance further.

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NTN TECHNICAL REVIEW No.89

8.1 National Scientific Conference on Condition
Monitoring and Diagnosis
Finally, I would like to review research trends in thisfield in Japan. In Japan, the representative academic
societies for this field are the Japan Society of
Mechanical Engineers, the Japanese Society for NonDestructive Inspection, the Society of Plant Engineers
Japan, and the Japanese Society of Tribologists.
Among the conferences held by these academic
societies are the Dynamics and Design Conferenceof the Dynamics, Measurement and Control Divisionof the Japan Society of Mechanical Engineers20) andthe Symposium on Evaluation and Diagnosis, which
is held jointly by the Dynamics, Measurement andControl Division of the Japan Society of MechanicalEngineers, the Society of Plant Engineers Japan,
and the Japanese Society of Tribologists. Numerous
condition monitoring and diagnostics research results
are published every year.
The following is a list of articles and papers
presented at these conferences over the past year or
two, focusing on bearing-related topics.
8.2 Examples of AI-based diagnostic techniques
In the field of bearing anomaly diagnosis, there havebeen recent studies on the use of AI technology for
intelligent and automated diagnostics techniques.
Deep learning, with its high feature extraction
capability has been garnering attention in theequipment diagnosis field in particular. In Japan,
Maeda et al. have been working on AI-based bearing
diagnosis. In one recent example, after removing
noise from vibration signals measured for bearing
diagnostics using a statistical information filter, thespectrum of the vibration signal was converted into aquadratic array (equivalent to image information). The
method automatically performed feature extraction
and state classification using a convolutional neuralnetwork (CNN), which is a type of deep learning26).
The results of various validation tests showed thatthe proposed method can achieve highly accurate
automatic diagnostics of bearings even in noisy
environments.
8.3 Examples of lubricant film evaluation techniques
Takeda et al. have verified the effect of differencesin lubricant composition on bearing life by using athrust ball bearing operating life test machine. The
test observed the lifetime until the thrust ball bearing
actually failed as a test piece. In addition, the effectof lubricant degradation on bearing life has also beenverified by observing the thickness of the lubricant
film after bearing failure in new oil and life tests using
a lubricant film visualization system. Recently, as a
new oil film evaluation method, an ECR (ElectricalContact Resistance) observation circuit was attachedThrice ACC (convenient method)
1

Rep. ACC

to the lubricant film visualization system (Fig. 25) toelectrically evaluate and observe the oil film condition,and the observation principle and results werereported27).

0.8

Twice ACC
(usual method)

0.6
0.2
0

Thrice ACC(usual method)

Once ACC(usual method)

0.4

0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15Time [hr]Fig. 26 Analysis of URIs with low signal-to-noise
ratios using a simplified method to improve
periodicity extraction of rolling behavior28)

8.4 Examples of diagnostic techniques using
ultrasound and acoustics
Wakabayashi et al. reported the efficacy of theultrasonic echo method for condition monitoringof bearings. They studied the early detection of
anomalies in oil-lubricated bearings by applying
autocorrelation analysis to the time variation ofultrasonic reflection intensity (URI). The higher theperiodicity of the URI waveform, the better the
rolling behavior of the bearing. Research has also
been conducted recently into the applicability of
the ultrasonic echo method to a grease-lubricatedbearing (6210 deep groove ball bearing). It was
shown that the method can detect rolling behavior
inside the bearing during operation and grease
removal and replenishment on the orbital plane in theactual working condition by performing two analyses
based on autocorrelation for the time variation of
the URI. In addition, methods were investigated forextracting periodicity of rolling behavior in URIs withlow signal-to-noise ratio (S/N ratio), which is often
obtained when diagnosing abnormalities in greaselubricated bearings by ultrasonic echo method. A
comprehensive method and a simplified method weredeveloped as methods to improve multiple analyses
by autocorrelation, and it was clarified that they wereeffective (Fig. 26)28).

1

Amplitude [V]Fig. 25 Equipment for electrical evaluation andobservation of bearing oil film condition27)

For highly accurate acoustic diagnosis of bearings,
Takada has proposed a new method for improving thesignal-to-noise ratio. Recently, he applied this method
to failure detection of rolling bearings and confirmed
its effectiveness.29) In particular, the effectivenessof this method was demonstrated by applying it tooperating conditions with low dN values, which are
generally considered difficult to diagnose (Fig. 27).
Furthermore, the S/N ratio improvement method using
an adaptive line enhancer, which is one of the adaptive
signal processing methods, was used as a subject forcomparison, which shows that the proposed method
was effective.

: Applied Data
: Not Applied Data

0.5
0
-0.5
-1

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5Time [s]Fig. 27 Time series data obtained by applying the
proposed method and an anti-aliasing filter(low-pass filter with a cutoff frequency of
40 [kHz]) to the measured rotating noise of abearing with outer ring failure29)
8.5 Examples of data collection techniques for
initial failure processes
Conventional research into the diagnostics ofbearings often involves scratching the inner orouter rings beforehand. In actual bearing condition
diagnostics, however, it is necessary to detect theearliest signs of any anomalies that could eventuallylead to failure. Inoue et al. focused on this point.
With the aim of collecting bearing data from thenormal state to the occurrence of failure in a timeefficient manner, they created a rotating device with avibratory mechanism that can apply displacement of
any frequency to the rotating shaft. They conductedvibration experiments on deep groove ball bearings as
the targets for inducing failure30). Failure progression

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The Current Situation of Condition Monitoring and Diagnosis of Machine Systems
was evaluated using acceleration data from the normal
state to the occurrence of initial defects (Fig. 28).
The degree of failure to the actual bearing was thenconfirmed by cut-off disassembly inspection.

Acceleration [m/s2]

0.20
0.15
0.10
0.05
0.00

0

10

20

0

10

20

30

40

50

60

70

30

40

50

60

70

Frequency [Hz]Acceleration [m/s2]

0.20
0.15
0.10
0.05
0.00

Frequency [Hz]Fig. 28 Envelope spectrum of acceleration signal(upper) before vibration, (lower) after
vibration30)

9. Conclusion
In this article, entitled “Trends in Condition
Monitoring and Diagnosis of Mechanical Systems”,
the author first describes the recent activities andtrends in ISO/TC 108/SC 5 condition monitoring anddiagnosis of mechanical systems with which he isinvolved1). Those interested to know more about thehistorical background to this subject may refer to theseries of documents 31)-34) compiled by the TC 108/SC 5 National Committee Chairman at that time.
Going forward, the international standardizationof condition monitoring and diagnosis of bearings
is expected to continue. It will develop along withthe standardization of condition monitoring asset
management in connection with the standardizationof individual machines. We look forward to continued
cooperation from all parties concerned.
Next, the status of condition monitoring and asset
management in Japan and overseas is described,
with a focus on the IoT. This field is expected to be
one of the biggest areas of development globally inthe field of condition monitoring and diagnosis. The
paper describes IoT-based condition monitoring anddiagnostics and predictive maintenance in wind power
generation projects. With the wind power generation
business undergoing full-fledged development inJapan with the installation of large floating offshore
wind turbines, condition monitoring techniques thatinvolve linkage to a digital twin are being developed.

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NTN TECHNICAL REVIEW No.89

We look forward to seeing the development of thesefields in Japan.
In addition, the author also focuses on the trendof condition monitoring and diagnosis using 1DCAE
modeling of target machines in relation to thecondition monitoring technology using the digitaltwin described above. This field is also being actively
researched in Japan, and its future trends will beworth watching.
Finally, the research and publication trends
in the field of bearing condition monitoring anddiagnostics in Japan are described. Recently, thepublished proceedings of academic conferences
have tended to be extremely short. This makes it
necessary to actually visit the conference and listen
to live presentations. In the wake of social distancing
measures adopted during the COVID-19 pandemic,
academic conferences are adopting hybrid formats of
online and face-to-face meetings. I hope that there
will be a resumption in people visiting academic
conferences, exploring the latest trends, and engaging
in lively exchanges with researchers and technicians invarious fields.
The above is an introduction to the trends incondition monitoring and diagnostics of mechanicalsystems in the direction of this authorʼs interest andconcern, which it is hoped may be of some reference.
The author wishes to thank the Japanese Society for
Non-Destructive Inspection for granting permission to
include the citation in the first half of this paper1). For
providing information in the latter part of this paper,
I would like to express my gratitude to Mr. Hitoshi
Sakakida, ISO/TC 108/SC 5 committee member andformerly of Toshiba Corporation, Mr. Keita Ishimitsu
of JERA Co., Inc, Mr. Takashi Sako, formerly of Asahi
Kasei Corporation, to the Japan Institute of Plant
Maintenance, and to Professor Tadao Kawai of Osaka
Metropolitan University.
References
1) Tsuyoshi Inoue and Koji Hyodo, History and Current
Situation of ISO Standards on Condition Monitoringand Diagnostics of Machines and Activity of ISO/TC 108/SC 5 (Special issue on trends in condition
monitoring and diagnosis technology), Journal of theJapanese Society for Non-destructive Inspection, Vol.
69, No. 9 Sep, (2020) 442-448.
2) ISO/TC 108/SC 5 Condition monitoring anddiagnostics of machine systems website, https://www.iso.org/committee/51538.html
3) The Japan Society of Mechanical Engineers (JSME)
Certification Program for Machine Condition
Monitoring (Vibration, Tribology)
https://www.jsme.or.jp/jotaiweb/
4) Japanese Society for Non-Destructive Inspection
Certification System for Machine Condition
Monitoring and Diagnosis Engineers (Thermography)
http://www.jsndi.jp/quali_cation/index12n.html
5) Tsuyoshi Inoue and Koji Hyodo, editors, The
Japan Society of Mechanical Engineers, Condition
Monitoring and Diagnostic Techniques for Mechanical
Systems, Corona, 2021.

6)

T. Kawai, Equipment Diagnosis in the Continuing IoTEra, Journal of Economic Maintenance Tribology, No.
685, (2022) 26-29.
7) What is GAIA-X, a European cloud data infrastructure
initiative involving GAFAM, Fintech Journal, SB
Creative, 2021/04
https://www.sbbit.jp/article/cont1/56622
8) GAIA-X and Catena-X: The Rise of a Huge Ecosystemthrough Impact Data Linkage̶DX in Manufacturing:
For Promoting Servitization, Nomura ResearchInstitute, Column, 2022/07.
https://www.nri.com/jp/knowledge/blog/lst/2022/iis/
fujino/0727_1
9) Applying the Internet of Things to manufacturing 8
IoT use cases to boost ROI (IoT (Internet of Things)
adoption in the manufacturing industry, 8 IoT use
cases to improve ROI), Siemens website
https://resources.sw.siemens.com/ja-JP/e-book-8industrial-iot-use-cases-for-manufacturing-2
10) COMADEM (Condition Monitoring and Diagnostic
Engineering Management)
http://www.comadem.com/
11) Japan Institute of Plant Maintenance12) Strategic Maintenance Management System(MOSMS), Japan Institute of Plant Maintenancehttps://www.jipm.or.jp/report/?ca=113) Maintenance Science for Management, Japan
Institute of Plant Maintenance14) Hybrid of “Field Capability” and “MaintenanceManagement Capability”, Utilization of MOSMS“MOSMS”, Japan Institute of Plant Maintenance, 200815) Survey on the use of digital technology in wind power
generation, jointly conducted with The Offshore
Renewable Energy (ORE), Accenture and ORE
Catapult, UK
https://www.accenture.com/_acnmedia/PDF-151/
Accenture-210164-ORE-WindCatapult-POV-LCS-JP.
pdf16) Using Digital Technology in Wind Power Generation
Learning from Overseas Case Studies, Wind Power
Generation / Regional Industry Development andthe Use of Digital Technology (Online Seminar),
Accenture, May 13, 2022.
17) New Energy and Industrial Technology Development
Organization (NEDO), FY2013-FY2017 Result Report,
Research and Development of Wind Power andOther Renewable Energy Technologies, Research andDevelopment of Advanced Practical Application ofWind Power Generation, Research and Developmentof Smart Maintenance Technology (Analysis) (Fatigue
Prediction, etc.), February 2018.
18) Service Specification DNVGL-SE-0439, Certificationof Condition Monitoring, DNV• GL 2016, GL(Germanischer Lloyd)19) Takashi Sako, On IoT for predictive maintenance,
Plant engineer: Technology for new generationengineers & Information Magazine, Japan Institute of
Plant Maintenance, 52-4, (2020) 32-45
20) The Japan Society of Mechanical Engineers (JSME)
Dynamics, Measurement and Control Division web
page
https://www.jsme.or.jp/dmc/

21) Tadao Kawai, on a diagnostic method of equipmentusing physical models - Development to digitaltwin, Newsletter of the Japan Society of MechanicalEngineers, Dynamics, Measurement and Control
Division, No. 70 (July 2022) (available from thedivision website).
22) Koichi Ohtomi, and Takehiro Hato, Design Innovation
Applying 1DCAE, Toshiba Review Vol. 67. No. 7,(2012)
23) Shoichi Kashiwase and Kenji Ozaki, “Development of a
Model-Based Diagnostic Method for Electric Motors”,
Dynamics and Design Conference 2021, September
2021, 442.
24) Shoichi Kashiwase, Kenji Ozaki, Hiroaki Makino,
Development of a Model-Based Diagnostic Method
for Electric Motors, 19th Symposium on Evaluation
and Diagnosis, 208, 2021
25) Tadao Kawai, Yoshihiro Yamamoto, Tatsuro Ishibashi,
Estimation of Gear Wear Using a Physical Model,
Dynamics and Design Conference 2022, September
2022, 239
26) Linghe Maeda, Haihong Tang, Shanpeng Chen, Keishi
Mori, Yuji Yonekura, Intelligent State Diagnosis of
Bearings Using Statistical Information Filter andDeep Learning, 19th Symposium on Evaluation andDiagnosis, 106, 2021
27) Yusuke Takeda, Noriaki Satonaga, Masaya Kano,
Takashi Watanabe, Tomoyuki Sonoda, An Evaluation
and Observation Method of Oil Film Condition
by Electrical Contact Resistance Method, 19th
Symposium on Evaluation and Diagnosis, 210, 2021
28) Toshiaki Wakabayashi, Takahiro Nakatsu, Hideki
Yamasaki, Application of Ultrasonic Echo Techniqueto Diagnostic Evaluation of Grease Lubricated Bearing- Method of Detecting Periodicity in Rolling Behavior-,
19th Symposium on Evaluation and Diagnosis, 211,
2021
29) Hirotomo Takada, Hiromitsu Ohta, Shuji Miyazaki,
Daisuke Matsuo, Mirai Ohmori, Satoshi Shimizu,
High Precision Acoustic Diagnosis Method for LowSpeed Rolling Element Bearing, 19th Symposium on
Evaluation and Diagnosis, 202, 2021
30) Yuto Inoue, Tsuyoshi Inoue, Promotion Controlof Rolling Bearing Failure, 19th Symposium on
Evaluation and Diagnosis, 203, 2021
31) Hitoshi Sakakida, Takuzo Iwatsubo, The trend of
ISO/TC108/SC5 Mechanical Vibration and ShockCondition Monitoring and Diagnostics of Machines,
Proceedings of the 1st Symposium on Evaluation andDiagnosis, (2002) 32-36.
32) Hitoshi Sakakida, The trend of ISO/TC108/SC5
Condition Monitoring and Diagnostics of Machines,
Proceedings of the 5th Symposium on Evaluation andDiagnosis, (2006) 7-11.
33) Hitoshi Sakakida, The trend of ISO/TC108/SC5
Condition Monitoring and Diagnostics of Machines,
Newsletter of the Japan Society of MechanicalEngineers, (2007) 1.
34) Tsuyoshi Inoue, Present situation and trend of the ISO
standards for condition monitoring and diagnostics
of machines (TC108/SC5), Turbomachinery 39(5),(2011) 304-312.

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Author Profile

Tsuyoshi Inoue
Ph.D. Professor, Department of Mechanical Systems Engineering, Graduate School of Engineering, Nagoya University

Academic and professional background
1993
Completed masterʼs program in Electronic and Mechanical Engineering, Graduate
School of Engineering, Nagoya University
1993-1995
Okuma Corporation
1995-2001
Assistant Professor, Faculty of Engineering, Nagoya University
2000
Doctor of Engineering (Nagoya University)
2001-2005
Lecturer, Graduate School of Engineering, Nagoya University
2004-2012
ISO / TC108 / SC5 National Committee Secretary
2005-2012
Assistant Professor, Graduate School of Engineering, Nagoya University(renamed Associate Professor in 2007)2012 - Present Professor, Graduate School of Engineering, Nagoya University
2012 - Present Chair, ISO / TC108 / SC5 National Committee
2013 - Present Chairman, Multibody Dynamics Council
2017 - Present ASME Journal of Computational and Nonlinear Dynamics Associate Editor
2018 - Present Director, Creation Plaza, School of Engineering, Nagoya University
2018 - Present Delegate, Turbomachinery Society of Japan
2021 - Present Vice-Chairman, Standard Project Committee, Japan Society of Mechanical Engineers
2022
Head of the Dynamics, Measurement and Control Division, Japan Society of MechanicalEngineers
Specialties• Mechanical mechanics, vibration engineering, nonlinear dynamics, vibration control• Explanation and use of vibration phenomena, in particular those caused by nonlinearity
Academic societies
Japan Society of Mechanical Engineers, Turbomachinery Society of Japan, Japan Society for Design
Engineering
The Japanese Society of Tribologists, ASME
Major awards and commendations
Fiscal 2000
Japan Society of Mechanical Engineers Japan Society of Mechanical Engineers Award
for Encouragement of ResearchFiscal 2006
The Japan Society of Mechanical Engineers, Dynamics, Measurement and Control
Division, Division Contribution Award
Fiscal 2013
Turbomachinery Society of Japan Award (Technology Award)
Fiscal 2016
Turbomachinery Society of Japan Award (Best Paper Award)
Fiscal 2018
Project Award, Tokai Branch, Japan Society of Mechanical Engineers
Fiscal 2019
Turbomachinery Society of Japan Award (Best Paper Award)
Fiscal 2020
Tokai Branch Distinguished Service Award, Japan Society of Mechanical Engineers
Fiscal 2020
25th Engineering Education Award, Japan Society for Engineering Education
Fiscal 2021
Turbomachinery Society of Japan Award (Best Paper Award)
Fiscal 2022
Japan Society of Mechanical Engineers (JSME) Medal for Outstanding Paper（2 papers）
Fiscal 2022
The Japan Society of Mechanical Engineers (JSME) Standard Project Award
Contribution Award

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