［ Basic Technologies ］

Visualization of Electrical Facility
Conditions by Monitoring
Data-Analysis
Keywords

Osamu Toda

Infrastructure, Data analysis, Visualization, Anomaly detection, Early sign of failure

Abstract

We provide a cloud-based maintenance service of electric facilities for thepurpose of shifting to Condition Based Maintenance (CBM). As a function of thisservice, we developed a visualization function of electric facilities using monitoring data. Speciﬁc examples of visualization include: ( 1 ) the visualization of awide-area status of the equipment using alarm logs, ( 2 ) the detection of failure
signs of heat exchangers using monitoring data, and ( 3 ) the detection of rotatingmachinery errors. This function was developed based on monitoring data anddoes not limit target facilities. By utilizing the visualization function, it is possible to encourage a shift to CBM based on monitoring data.

1 Preface
We provide electrical equipment and systems
to public and industrial sectors in Japan and abroad.
In doing so, we have contributed in building social
infrastructures. In order to maintain quality and performance of these electrical facilities, periodic inspection and maintenance, or Time-based Maintenance(TBM), is conducted. There is a growing trend ofshifting to Condition-based Maintenance (CBM) for
economic and management efﬁciency. To apply
CBM to actual facilities, the technology and tools
used to diagnose health with high accuracy areessential. For this reason, various sensors areinstalled in electrical facilities. The technical development for collection, storage, and analysis ofvarious types and large volumes of measurement
data is in progress. There are situations, however,where the modiﬁcation of existing facilities is notas economical in terms of effectiveness by thisintroduction considering the capital costs. As ameasure to solve this challenge, we consider theeffective use of data (monitoring data) collectedby control and monitoring systems such as theexisting Programmable Logic Controller (PLC) andSupervisory Control And Data Acquisition (SCADA).
If it is possible to analyze singularly by the monitoring data, can to expand the scope of our application to CBM of existing facilities.
We developed and constructed an analysisand management function (visualization) based on

Visualization technology forelectrical facilityAnalysis ofexisting dataFig. 1

Data-Driven Visualization Technology for
Imaging of Facility Conditions

Data-driven visualization technology for imaging of facility conditions is shown.

monitoring data (1)(2) to implement CBM for existing
facilities. Fig. 1 shows a data-driven visualization
technology for the imaging of facility conditions. In
this paper, we introduce our programs for advanced
maintenance technologies for existing electricalfacilities.

2 Monitoring Target: Combined Heat& Power (CHP) System
Monitoring data from the CHP system operating at a particular facility is used as a veriﬁcationtest site. We utilized the monitoring data, “alarmlogs” and “measurement data”, used by the remotemonitoring service from our customer center. We
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FuelOutside
air

Prime
mover

Cooling
water

Generator

Lubrication
oilPowerAnomaly log

Anomaly 7

Driving energy
Heat energy
Heatexchanger

Fig. 2Anomaly 4
Heated water,
warm airConfiguration of the Target CHP System

The CHP system is composed of multiple equipment units. A
variety of matters (fuel, cooling energy, etc.) are transmitted
among these machines. The measured values of these matters
are used for the monitoring of conditions within the system andoperation.

2006

Fig. 3made a visualization of the monitoring data obtainedin the past with the cooperation of the related facility
management department. Fig. 2 shows the conﬁguration of the target CHP system. The facility structure is shown below. This system mainly consists ofmultiple equipment, mostly our supplied diesel
engine generator and heat exchanger. In the visualized design stage for this facility, there were alreadydozens of monitoring items (power, temperature,pressure, ﬂow rate) installed and operated in theexisting monitoring system. Although there aremany monitoring items, under the CHP system
where equipment is intricately interlinked in a complicated manner, it is difﬁcult to evaluate statusbased on rule-based judgment. For the CBM basedon monitoring data, high-precision visualization
based on data- driven analysis is therefore required.

3 Visualization
The case study of analysis and visualization ofmonitoring data is shown below.

3.1 Visualization Using Alarm Logs
The alarm logs in the monitoring data store
information of past anomalies that have occurred.
Contents are varied and it is difﬁcult to extract theregularity by visual inspection. By the overview
showing of our accumulated alarm logs on the monitoring display, we focused on the visualization of
the wide-area trend of abnormalities. This display
function is effective in making the facility status visible by organizing and integrating data.
Fig. 3 shows the visualization of the facility
status in the bubble chart. The bubble chart shows

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2008
2010
2012
2014
Year of anomaly occurrence (Year)

2016

Visualization of Facility Status in Bubble ChartAnomaly logs are classiﬁed for each anomaly, year of anomaly
occurrence, and frequency of occurrence so that the status of
the monitoring target can be clariﬁed.

the frequency of occurrence of each abnormal factor on one screen. The frequency of occurrence ofeach factor is expressed by the varying size of thebubble. Each event bubble is placed on a horizontal
axis with time as the unit. This chart objectively
visualizes the wide-area error factors (equipmentconditions) inherent to the equipment. Focusing on
the alarm logs, Abnormality 4 and 7 can be identiﬁed as chronic alarms. Focusing on the year of each
alarm, it is discovered that many alarms were issued
in 2007 and 2011. This is the result of the occurrence of sudden failures and special operation. In
this way, this function can clearly show the “individual characteristics” of the equipment that weregrasped by the maintenance staff through theirexperiences.

3.2 Early Sign of Failure in Heat Exchanger
Anomaly 4 and Anomaly 7 were revealed bylog visualization, alarms related to the heat exchanger. We therefore, selected several measurementdata related to the heat exchanger and tried to visualize them by analyzing the data and processing thesignals.
Fig. 4 and 5 show the results of the visualization of the heat exchanger. The results of the analysis were visualized as a scatter plot diagram basedon the daily monitoring data. The horizontal axis
represents the exhaust gas and the vertical axis
represents the feature values showing the state of
the cooling system. Comparing the normal opera-

tion day in Fig. 4 and the day before the failure in
Fig. 5, the relationship between the characteristicvalues of the cooling system and the exhaust gas
system is signiﬁcantly different in the operation/stop
process. It was, at this time, conﬁrmed from theactual record of the equipment that a failure due toa malfunction of the cooling system had chronicallyoccurred. Since the change started several weeks
before the day before the failure, the failure can bepredicted by daily comparative evaluation of thevisualized results. This failure is difﬁcult to detect bymonitoring changes in a single sample of data. By
combining and analyzing multiple existing monitoring data, we were able to visualize the equipment
status based on the collapse of the relationships
between items.

3.3 Detection of Rotor Malfunction
This method is a data-driven analysis and canbe used for visualization of the state of different target machines. Fig. 6 and 7 show the result of visualization of the rotating machine. This is a caseFeature values of cooling system

Operation
start

Feature values on the casing side

Operation
start

Operation
stop

Operationstop
Feature values of exhaust gas system

Fig. 4Visualization of Heat Exchanger(Normal Operation Days)

Regarding the operating conditions, the feature values of boththe cooling system and exhaust gas system are changed withtime. As a result, the scatter plot diagram shows a circular locus.

Feature values on the shaft sideFig. 6

Visualization of Rotating Machine(Normal Operation Days)

For a different monitor target system (rotating machine), the feature values of both the shaft side and the casing side arechanged with time. The scatter plot diagram shows an arcshaped locus.

Operation
stop

Operation
start

Feature values on casing side

Feature values of cooling system

Operation
start

Operationstop
Feature values of exhaust gas system

Fig. 5Visualization of Heat Exchanger(Day before Failure)On the previous day before a failure, feature values of the exhaust gas system were changed according to the status of operation. The feature values of the cooling system, however, did notchange. For this reason, the scatter plot diagram shows a
non-circular locus. Failure prediction is possible by comparing
the data with the data record on a normal operation day.

Feature values on shaft side

Fig. 7Visualization of Rotating Machine(Day of Anomaly)

On the day of the anomaly, a change appeared in the feature
values of the casing side. As a result, the scatter plot diagram
does not show an arc-shaped locus, but a locus different fromthat of a normal operation day. In this manner, evaluation of alocus acquired from the scatter plot diagram is very effective anduseful in monitoring the system conditions.

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study of detection of an abnormality of a rotatingmachine. The horizontal axis is the feature value on
the axis side and the vertical axis gives the featurevalue on the housing side. In Fig. 6, during the normal operation, on the day, the numerical value onboth the shaft side and the housing side shows agradual upward trend during starting and stopping
time. Fig. 7, on the abnormal day, however, theresponse of the vertical axis to the ascending process is the axial direction, changed and behaved differently than usual. It was recorded in the alarm log
that this anomaly occurred multiple times. In addition, even on a day when there was no record in thealarm log, similar behavior was detected by thevisualization. This visualization suggests that it ispossible to deal with abnormalities that were difﬁcult
to detect by already existing methods.

4 Postscript
We introduced a data-driven management
function that visualizes equipment status, signs offailure, and detects abnormalities from monitoringdata. This technology has been made into a service
as part of our cloud-based maintenance service.
With the spread of CBM in social infrastructures, we will continue to develop technologies
for advanced maintenance of equipment, improvement of operation efﬁciency, and reduction of on-siteworkloads.
・All product and company names mentioned in this paper are
the trademarks and/or service marks of their respective owners.
《References》( 1 ) Toda: “Visualization of SCADA monitoring data with the useof VMS”, Mathematical System User Conference 2017, 2017 (inJapanese)( 2 ) Toda: “Data analysis for an equipment maintenance of cogeneration system”, the Institute of Electrical Engineers of Japan,
System Study Panel, ST-19-012, 2019 (in Japanese)

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