Report on Predictive Maintenance Technology
for Equipment by Vibration Measurement
Introduction of Network-based Predictive Maintenance Systemtechnologies, the market has begun to see a wide range

1. Introductionof services based on models that store and analyze data

The maintenance of facilities and equipment inon servers and clouds via networks. The systemmanufacturing plants is shifting from preventive

enables remote status confirmation and predictive

ma int ena nce a nd brea kdown ma int ena nce t omaintenance from the network. However, the reality isSummer 2021

"predictive maintenance." Preventive maintenance isthat at actual sites that our customers are facing, there

performed at regular intervals and timings, andare many cases where it is not possible or not allowed

corrective maintenance is done by maintenance andto upload sensing data to an external network or therepairs after a failure, loss of production capacity or

cloud server for various reasons. We have developed adefective products. In contrast, predictive maintenancesystem that allows them to proceed even at such sites

is maintenance, where signs of failure are detected inby predictive maintenance using a local network. Thisadvance and repairs and maintenance are performed atreport introduces the network-based predictive

the appropriate timing. We have also been developing amaintenance system and the predictive maintenance1）tablet-type abnormal vibration detection system . At

technology it incorporates.

sites where maintenance activities are performed byNo.41

patrolling, this system measures and analyzes on thespot the vibration state of the maintenance target
using sensors to confirm the chronological changes in

2. Overview of Predictive
Maintenance System

the state using charts and numerical values. In this2-1）System configuration

way, the system allows us to systematically implement

A schematic diagram of the network-based predictive

predictive maintenance, when an abnormality occurs,

maintenance system in operation is shown in Figure1.

by displaying according to the degree of abnormality.

The basic configuration consists of a "vibration sensor,"

This is a so-called portable predictive maintenance"sensor unit," and "predictive maintenance analysis

system, but with the advancement of sensing and IoT

program"（to be installed on a PC for use）. The sensorFigure1
13

Schematic diagram of the maintenance system

Product Introductions

and the sensor unit are connected with the supplied
low noise cable. The sensor unit and the program（PC）
are connected using a LAN network. The LAN can also
use an existing network. It is operational even with thisbasic configuration, making it easy to handle. Moreover,by increasing the number of sensor units with aswitching hub, it will also be possible to increase thenumber of maintenance targets and sensing points,
check their status from a networked location via a web
screen on another PC and receive alerts by e-mail(Pilot model)

Figure2

Magnetic bracket

Organic piezoelectric vibration sensors

according to the degree of abnormality of themaintenance target（a mail server to be separatelyrequired）.
2-2）Components
The following are the three components.
2-2-1）Organic piezoelectric vibration sensor

Summer 2021

We have developed a thin, compact, and lightweightvibration sensor that uses an organic piezoelectricelement2） that can be easily retrofitted. It is fixed byattaching it to the mating surface, but due to its
flexibility, it can be fixed to a slightly curved surface.

(Pilot model)

Figure3

Sensor unit

Figure5

Using a special magnetic bracket will also make itElimination ofbackgroundvibration

possible to fix it with magnetic force（Figure2）.

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2-2-2）Sensor units

2-2-3）Predictive maintenance analysis programs

Since each sensor unit is equipped with eight input

Octave analysis is conducted on vibration sensing

channels, all of which have built-in charge amplifiers,

data, based on which predictive maintenance ischarge output type sensors can be connected as they

performed by conducting "MT method（degree ofare. Therefore, in addition to the organic piezoelectric

abnormality analysis）" and "trend analysis（statevibration sensor mentioned above, commerciallyanalysis）" from the data. For each measurement point,

available vibration and acceleration sensors can also be

the "time waveform," "FFT analysis," "octave analysis,"used. In the sensor unit, the sensor signal is digitally"MT method," and "trend analysis" can be checked onconverted and transmitted from the LAN terminal to

the screen dashboard（Figure4）.

the PC on the analysis side through the network. The

It also has a function to differentiate data（Figure5）toLAN connection also allows for distributed placement

eliminate background vibration from the environmentof sensor units, making it possible to build a multi-point

as a disturbance signal. To check the current status ofsensing environment flexibly and cost-effectively

the maintenance target, the screen displays the status（Figure3）.

and alert level corresponding to the threshold value. Incase of an abnormality（every time the status changes
to Caution, Maintenance, or Danger）, an alert e-mail canbe sent to the maintenance staff.
14

Report on Predictive Maintenance Technology for Equipment by Vibration Measurement

Introduction of Network-based Predictive Maintenance System(Pilot model)

Figure4

Summer 2021
No.41

Predictive maintenance analysis programs

3. Predictive Maintenance Methods

abnormality will be captured through this kind ofTo perform predictive maintenance based on changes

be used in the analysis, the top five frequencies with thein the vibration state of the maintenance target, it ismaximum change in vibration intensity from the octave

necessary to capture the changes in the vibration state

analysis data and another one（one can be selectedof the maintenance target from sensing data. Thisarbitrarily）are automatically selected. Alternatively, allsystem uses the vibration data on which octaveof them can also be selected manually（Figure6）.

analysis of the state of vibration. For the frequencies toanalysis has been conducted. We have adopted thismethod because it is very compact compared to theamount of data in FFT analysis, which is widely usedin vibration analysis, and it is suitable for analysis using
the trend analysis and MT method described below.
3-1）Trend analysis（state analysis）
This method uses the vibration data of the maintenance

Figure6

Overview of trend analysis

target in normal（regular）operation as a reference toconduct the initial measurement. By using the3-2）MT method（degree of abnormality analysis）

difference value between the vibration data and theThe MT（Mahalanobis-Taguchi）method 3） is a patternsubsequent vibration measurement data to draw arecognition method that aims to determine normality

trend chart of how much the vibration intensity has

and abnormality based on the Mahalanobis distance. Itchanged/differed, it will be compared with theis widely used for various types of judgment, and sincethreshold value to capture signs of abnormality. It is a

it is simple and can easily detect the degree oftrend analysis characteristic to focus on relative

abnormality, it is incorporated into the predictive

changes rather than absolute changes, and signs ofmaintenance analysis program together with the trend

15

Product Introductions

analysis described above（Figure7）.

state and the degree of abnormality numerically has

This method captures the degree of abnormality by

improved the accuracy of its predictive maintenance.

creating a unit space from the vibration data obtained
when the maintenance target is operating normally（steady-state）and then calculating the Mahalanobis
distance（MT score）with the subsequent vibration as

4. Opportunities for Use andPre-verification Activities

data space. However, by preprocessing the data to

The maintenance targets assumed for this predictive

adjust the number of analysis dimensions, we havemaintenance system are pumps, motors, engines andfound that the time of appearance of the MT score

other machinery whose status can be sensed withindicates the degree of abnormality. The predictivevibration. However, it is essential to verify in advancemaintenance analysis program is equipped with thisat the customer's site whether or not predictivefunction to adjust the number of dimensions, making it

maintenance is possible for the maintenance target.

possible to detect signs of abnormality based on theTherefore, obtaining cooperation from the customers

MT score right before the failure of the maintenanceconsidering the introduction of the system, we ask

target or with enough time to spare before such anthem to allow us to make trial measurements inincident. Thus, the function can be used effectively byadvance. We also ask them to consider the possibility

the style of the customer's maintenance activities

of predictive maintenance and its cost-effectiveness（Figure8）.

after installation.

"MT method" are installed and conducted simultaneously
in this system. Capturing changes in the vibration

5. Specifications
The specifications of the organic piezoelectric
vibration sensor are shown in Table1, those of theSummer 2021

The abovementioned methods of "trend analysis" andsensor unit in Table2, and those of the predictive
maintenance analysis program in Table3. In actual
use, the number of units and the number of sensing
units will determine practical measurement settings
and the number of PCs in the predictive maintenance
Figure7Overview of MT method

analysis program. Thus, the frequency of datacollection and the scale of sensing will need to be
confirmed and planned with each customer.
＊The specification values are from the pilot model data and may
differ from the product.

Table1

Specification summary of Organic piezoelectric
vibration sensors

Element

Organic piezoelectric element（Thickness 1mm）

（Reference value）

Sensitivity

0.1pc/m/s（Reference value）

Frequency
range

0.4 〜 10,000 Hz（Reference value）

Dimensions

2

24×20×0.5mm（Reference value）

Cable

Low noize（3m standard）（Reference value）

Weightapprox. 1g（Cable not included） （Reference value）

Figure8 O
 verview of adjustment of analysis dimensions andpredictive timing
16No.41

Report on Predictive Maintenance Technology for Equipment by Vibration Measurement

Table2

Specification summary of Sensor unit

Input Ch number

8ch（Built-in charge amplifier）

A/D Conversion

24bit（8ch at the same time）

Sampling frequency

51.2kHz

Input frequency range

0.16Hz 〜 20kHz

Introduction of Network-based Predictive Maintenance System

6. Conclusion
This "network-based predictive maintenance system"has been developed based on the concept of a systemthat can be used immediately at the manufacturing

ProtocolUDP/IP

Ethernet

1ch, 10/100/1000Mbps, RJ45

site, and we hope that it will be considered as aTrigger

Photocoupler（Requires external power supply DC24V）

benchmark for installation. This system will be a part

Power supply

DC24V

Dimensions, Weight

W52×H126×D128mm, approx. 450g

of our VALQUA Predictive Maintenance System

Operating Environment

0-55℃, 95% relative humidity, non-condensing

Table3

 pecification summary of Predictive maintenance
Sanalysis programs

FFT analysis,
Octave analysis（1/1, 1/3, 1/6, 1/12, 1/24）
Trend analysis (state analysis),
Predictive
MT method (degree of abnormality analysis),maintenance function
Alert display and email delivery by threshold
Background signal removal, Measurement by trigger,
Other functions
Remote panel connection using a web browser［Example］approx. 160GB / year ※ 1
Storage capacity （Settings: 8ch, 1/3 octave analysis, 10-minuteof analysis datainterval measurement※ 1 When not saving the time waveform data）
OS: Windows10, Processor: Intel Core i5
Operatingenvironment
equivalent or higher
VALQUA Sensor Unit ※ 2（NI9234［National Instruments］, connectable）
Connectable unit ※ 2 The number of connected sensor units depends
on the measurement settings and network
environment, so advance planning is required.
Analysis function

product range, and we hope that it will contribute to
the maintenance of production sites in variousindustries.

7. References
1）Makoto Haneda（MOL Techno-Trade, Ltd.）, HisatakaSato, Tetsuya Komeda, Yasushi Aburatani: Valqua
Technology News, No.36, 3-6（2019）2）Y o s h i r o T a j i t s u , T e t s u y a K o m e d a : V a l q u aTechnology News, No.26, 12-17（2014）

Summer 2021

3）Masato Suzuki, Self-study: Signal Processing, Shuwa
System,（2017）

No.41

Hisataka Sato
Frontier Development Team,Advanced Technology Development Department,
Research and Development Division17