Mitsubishi Heavy Industries Technical Review Vol. 56 No. 2 (July 2019)
1Advancements of Predictive Diagnosis Systemfor Power Generation Engines
ICHIRO ISHIDA*1

OSAMU NAKAKITA*2

TAKAAKI TAKAHASHI*3

TAKASHI KADO*4KAZUYUKI WAKASUGI*5 JUNNOSUKE ANDOU*6

Mitsubishi Heavy Industries Engine & Turbocharger, Ltd. (MHIET) has been engaged indeveloping predictive diagnosis systems to further help customers realize the stable operation of
their power-generating facilities. When it comes to engines for power generation, the applicationof commonly-used predictive diagnosis techniques has been considered to be difficult, because thepower output of these engines is not constant owing to their operating patterns. We developed anew predictive diagnosis system by taking the following three approaches: “the statistical method(i.e., vector quantization clustering or VQC),” “big data analysis” and “the utilization ofknowledge from past abnormalities.” The developed system is now in operation. By the end of
2018, we obtained solid outcomes indicating a high likelihood of achieving a practically-sufficientcorrect prediction rate. The predictive diagnosis service will be made available commercially in
2019.

|1. Introduction
We remotely monitor more than 450 power generation engines from the Remote Monitoring
Center at the Sagamihara Machinery Works (Sagamihara RMC), which was established in 2014.
The initial purpose of the center was to support the quick recovery of the power generation
facilities of clients after the occurrence of abnormalities. However, to further contribute to thestable operation of such facilities, a commercially-available predictive diagnosis system has
become essential. Power generation engines are often used for private power generation, andtherefore in many cases, the operation is limited to weekday daytime high-load hours for theirfacilities while shutting down during the night and weekends. If an imminent abnormal event of theequipment can be accurately predicted 1 or 2 weeks before it happens, the equipment concerned
can be examined/repaired when it is placed out of operation such as on weekends, which then will
inflict no economic loss on the customer due to the down time. On the other hand, power
generation engines are also used to curb peak demand in many cases. As the power output isnaturally inconstant, the measurement values (temperature and pressure) obtained from each part ofthe engine can vary substantially according to the power output fluctuations. Althoughcommonly-used statistical techniques for predictive diagnosis such as the Mahalanobis-Taguchi(MT) method can capture subtle changes in measurement values, their direct application to power
generation engines is difficult because of the reasons mentioned above. We therefore developed anew predictive diagnosis system by taking the following three approaches: “the statistical method(i.e., VQC),” “big data analysis” and “the utilization of knowledge from past abnormalities.” Thedeveloped system is now in operation. Figure 1 is a configuration diagram of the entire system
including both remote monitoring and predictive diagnosis. This report summarizes each of thesubsystems of our predictive diagnosis system, and describes the challenges we face when
operating the system and our future direction.
*1

Manager, Engine Sales Department, Engine & Energy Division, Mitsubishi Heavy Industries Engine & Turbocharger, Ltd,*2

Deputy Director, Engine Technology Development Department, Engine & Energy Division, Mitsubishi Heavy Industries Engine &*3

Engine Service Department, Engine & Energy Division, Mitsubishi Heavy Industries Engine & Turbocharger, Ltd,Turbocharger, Ltd,
*4Managing Expert, Engine & Energy Division, Mitsubishi Heavy Industries Engine & Turbocharger, Ltd,*5

EPI Department, ICT Solution Headquarters, Mitsubishi Heavy Industries,Ltd.

*6Combustion Research Department, Research & Innovation Center, Mitsubishi Heavy Industries,Ltd.

Mitsubishi Heavy Industries Technical Review Vol. 56 No. 2 (July 2019)
2Figure 1

Configuration diagram of entire system|2. Statistical method (VQC)
Since December 2013, a VQC-based predictive diagnosis system has been in use for ourpower generation engines. Having been developed by Hitachi Power Solutions Co. Ltd. (HitachiPS), it is known as the Hitachi Power Anomaly Measure Pick-up System or “HiPAMPS.”* As ofFebruary 2019, we monitor 70 medium class engines with 500-2000 kW of output in Japan witheach being equipped with 40 sensors, as well as 40 large class engines with 5000-7500 kW ofoutput (31 units in Japan and 9 units overseas) with each being equipped with 100 sensors.
VQC is summarized in Figure 2. In VQC, machine learning of the sensor data under normal
conditions is performed. Using the deviation of monitoring data from the normal data groups as anindicator, whether the monitoring data is either “the same as usual” or “different from usual” will
be determined.”(1) The diagnosis is made while the engine is in operation. The “predictive failure”
will be reported as an imminent stage of engine failure, when the status of “different from usual”
continues for 30 minutes or longer.

Figure 2

Summary of VQC

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In the beginning of the operation of the system, predictive failures were reported almost
every day, but most of them did not lead to abnormal events (hereafter referred to as unwanted
reports). We therefore reviewed the system settings including the number of clusters by considering(1) the customer’s engine operation patterns, (2) engine type in use, (3) narrowing down ofabnormal events to be reported and (4) the removal of the effects caused by the seasonaltemperature fluctuations. After the reported predictive failures were screened accordingly by theservice personal, the issuance of unwanted reports was reduced to a frequency of once a week. As aresult, during the period between July and December 2018, about 30 imminent abnormal eventswere successfully reported, which include (1) decrease/increase in the cylinder outlet temperature,(2) decreased lubricant oil pressure, (3) decreased primary cooling water pressure, (4) increased
cooling water temperature, (5) increased pressure in the crankcase and (6) disconnection of sensors.
Of these 30 events, three had the potential to lead to an unscheduled engine shutdown. Our service
personnel were promptly informed of these events, thus successfully preventing unscheduled
engine shutdowns in all the three cases. The measurement data in two of these cases are shown inFigures 3 and 4.

Figure 3

Predictive failure report example 1

Figure 4

Predictive failure report example 2

Mitsubishi Heavy Industries Technical Review Vol. 56 No. 2 (July 2019)
4To assess the accuracy of predictive diagnosis, we examined the system in terms of thecondition of predictive failure reporting and the actual warnings issued for engine anomalies(including cases where the occurrence of the predicted failures was prevented by informing therelevant sites) during the period between July and December 2018. Specifically, we calculated therate of correct prediction (obtained by dividing the number of actual warnings issued among thereported predictive failures by the total number of reported predictive failures) and the rate ofoverlooked events (obtained by dividing the number of warnings issued without predictive failure
reports by the total number of actual warnings issued). The results indicated a correct prediction
rate of 55% and a rate of overlooked events of 15% under the condition of “diagnosis by the system+ screening by service personnel.” Please note that the results were obtained when the anomalies
concerned were limited to those of the engine itself.
To make the predictive diagnosis service commercially feasible, there are two challenges: (1)the realization of a higher correct prediction rate without screening by service personnel and (2) theprediction of failures that can be made beyond the engine itself.
In 2019, we will attempt to improve the correct prediction rate of this subsystem and lower
the rate of overlooked events through analysis of all the reported predictive failures using AI,machine learning and the automatic extraction of abnormal conditions.

|3. Big data analysis to estimate the probability of abnormal events
In 2013, the Technology & Innovation Headquarters of Mitsubishi Heavy Industries, Ltd.
(the current ICT Solution Headquarters and Research & Innovation Center) developed a data
mining technique by combining two methods (i.e., support vector machine (SVM) and decisiontree), which is summarized in Figure 5. Using this technique-based analytical system, we analyzed
seven years of big data obtained from 130 large class engines with 5000 kW of output containing
200 parameters (various types of hourly measured data). Regarding the selected three abnormal
events (i.e., startup failure, misfire and decrease in the primary cooling water pressure), therelationships were extracted to show the association with conditions during operation. Based on thepatterns leading to the abnormal events, we predicted (quantified) the abnormal events that werelikely to occur in the coming three months. When compared with the actual occurrences of such
events in the past six months, the rate of correct prediction (which is obtained by dividing thenumber of actual warnings issued among the reported predictive failures by the total number ofreported predictive failures) in the coming three months was approximately 30%. However, when
limiting the duration for prediction to the coming week, it turned out that the rate fell to 0-10%.

Figure 5

Outline of developed data mining technique

In 2017, the number of predictable abnormal event types was increased to 20. Setting theduration for prediction to the coming week, we set out to improve the system with a target rate ofcorrect prediction of 80% and a target rate of overlooked events of 20% (which is obtained by
dividing the number of abnormal events that actually occurred when no such events were predictedby the total number of abnormal events that actually occurred). The 20 types of predictable

Mitsubishi Heavy Industries Technical Review Vol. 56 No. 2 (July 2019)5abnormal events were those that can cause considerable damage to the engine or substantiallyaffect the duration of shutdown for repairs (e.g., bearing anomaly and abnormal exhaust
temperature), including the three events mentioned earlier. The following three major steps weretaken to improve the correct prediction rate: (1) improve the accuracy and quality of data for use,
(2) increase the density of measurement data and (3) add combustion data to the data sets forlearning. For (1), we removed false warnings and abnormal events during post-maintenance test
operations from the supervisory abnormal event data and corrected data errors/missing informationin the measurement data. It turned out that a problem was found in more than 10% of the data. With
respect to (2), the measurement data sampling cycle was changed from 1 hour to 1 minute.
Regarding (3), the combustion data including cylinder internal pressure for every combustion cycle
were conventionally stored in the on-site computers (130 units in total). As the volume of such data
was extremely large, it was difficult to transfer them to the central computer and use the data forthe predictive diagnosis system. Therefore, we preprocessed the data of each site and only thedownsized data were forwarded to the central computer, thus making the information usable by thepredictive diagnosis system. As a result, the rate of correct prediction was improved as shown in
Figure 6, which compares the system developed in 2013 (System 2013) with the system upgradedin 2017 (System 2017) under the same conditions. Specifically, using the data in 2014 and 2015 forlearning, the prediction results were checked with the abnormal event occurrences in 2016. Using
System 2017, the correct prediction rate in the coming week was increased to roughly 50%, when
considering the three abnormal events that are predictable by System 2013.

Figure 6 Rate of correct prediction after the system was upgradedIn 2018, to further improve the correct prediction rate, we (1) increased the density ofcombustion data by reviewing the preprocessing procedure and (2) assessed an ensemble using
models with different algorithms or data processing conditions. The latter is based on the conceptthat the accuracy is increased when the assessment is conducted simultaneously using segmented
data (those obtained during the engine’s rated operation, shutdown, etc.), rather than using all thedata in a single model. As a result, we expect to achieve a rate of correct prediction of about 70%
and a rate of overlooked events of about 30%. We will further improve the correct prediction rate
through the operation of the system.

|4. Utilization of knowledge from past abnormalities
It has been known that several abnormal events that may severely damage the engine (e.g.,main-bearing burnout and intake/exhaust valve damage) can be detected by devising a method toobserve changes in the tendencies of the exhaust temperature or compression pressure in thecylinder, and therefore the occurrence of these abnormal events has often been successfully
prevented. The example given in Figure 7 is a typical case. However, whether such data

Mitsubishi Heavy Industries Technical Review Vol. 56 No. 2 (July 2019)
6knowledge can be of use greatly depends on the data analysis and interpretation skills of service
personnel, resulting in inconsistent detection accuracy. Because even similar damage (no matterhow small the dissimilarity might be) tends to produce different data changes, such variations haveoften gone unnoticed. Since 2018, in an attempt to overcome this problem, we have beensystematizing the data on main-bearing burnout and intake/exhaust valve rod damage by utilizing
the accumulated knowledge coupled with machine learning (k-nearest neighbor algorithm). We
will continue to gather and systematize this knowledge.

Figure 7

An example of detected abnormal conditions in the intake/exhaust valve|5. Conclusion
Table 1 lists the characteristics and improvements of the predictive diagnosis system. As
each subsystem of the predictive diagnosis system has strengths and weaknesses, the effective useof these characteristics makes it possible for the subsystems to complement each other, enabling thepredictive diagnosis system to more accurately predict (high correct prediction rate) and minimize
the number of signs that go unnoticed (low rate of overlooked events).
As solid outcomes that indicate a high likelihood of achieving a practically-sufficient correct
prediction rate have been obtained by the developments so far, we will first make the predictive
diagnosis service commercially available for selected engine types in 2019. The price and service
ranges are currently being considered.
Table 1 Characteristics and improvements of predictive diagnosis system
Diagnostic
approachStrengths

Weaknesses andcountermeasures

Statistical method (VQC)“Something different fromusual” is expressed in theform of degree ofdeviation.

The signs of “imminentabnormal events” can bedetected from tiny changes
in data.
Screening by humans isrequired.
→ Apply AI andmachine learning

Big data analysis
The subsystem deduces “laws”
from the “conditions during past
operations” and “warnings
issued,” and estimates theprobability of occurrence basedon the degree of coincidencewith the “conditions of thecurrent operation.”With regard to the selected 20
abnormal events, the correct
prediction rate is relativelyhigh.
The rate of correct prediction is
not satisfactory at this point intime.
→ Make improvements
through the operation ofthe system

Knowledge utilization
Humans deduce “laws” fromthe “conditions during pastoperations” and “warnings
issued,” and estimate the degreeof imminence based on thedegree of coincidence with the“conditions of the currentoperation.”With regard to some of theselected abnormal events, thecorrect prediction rate is high.
• Successful detection depends
on judgement by humans.
→ Combine with machinelearning• The number of predictable
abnormal events is small.
→ Mathematize and makeuse of the existing
accumulated knowledge

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While further improving the accuracy of the predictive diagnosis system, we will also apply
this technology to the assessment of the remaining life of parts, which enables us to offer a moreadvanced service menu, operation and maintenance (O&M) support services, etc.
* “HiPAMPS” is a registered trademark of Hitachi PS.

References(1)

Tadashi S., et.al., An Anomaly Detection System for Advanced Maintenance Services，Hitachi Review
Vol.63 No.4(2014) p.178～182