bluuebunny/crossref_metadata_2025 huggingface dataset:
QUALITY OF SORGHUM (Sorghum bicolor)-CALOPO (Calopogonium mucunoides) SILAGE WITH ADDITIONAL OF ORGANIC LACTIC ACID
Authors: Asminaya Nur Santy
Published: March 2024

Sorghum is a forage that has low protein content, whereas calopo is a protein source feed. If sorghum and calopo are fermented, it makes it difficult to lower the pH so that the nutrient content is easily damaged. The aim of this study was to evaluate the physical and chemical quality of fermented silage in combination between sorghum (Sorghum bicolor) and calopo (Calopogonium mucunoides) which were added with different levels of lactic acid. The ratio of the combination of sorghum and calopo as ingredients for silage is 40: 60%. Silage is made in 1 liter silo which is fermented for 21 days. This study used a completely randomized design (CRD) with 5 treatments (control and 4 levels of lactic acid addition, namely 1, 2, 3 and 4%) and 4 replications. Parameters observed included color, aroma, texture, pH, dry matter (BK), Fleigh value (NF), crude fiber (CF), and crude protein (CP). The results showed that the increase in the percentage of lactic acid use showed good physical quality (brown yellow color, citrus aroma and crumb texture) of silage. Chemically, the higher the level of addition of lactic acid to the sorghum-calopo silage, the BK content increased from 17.84% to 20.53%, decreased pH from 4.93 to 4.0 and increased NF from 43.33 to 86.0. The conclusion in this study was the use of lactic acid up to 4% in the silage of the combination of sorghum and calopo still showed good quality as indicated by NF 86 and pH 4.00
Sifat Kuantitatif Ayam Joper yang Dipelihara Kelompok Peternak “Papua Mandiri” di Kabupaten Manokwari Propinsi Papua Barat
Authors: Rahayu Bernaddeta Wahyuni Irianti , Tethool Angelina Novita
Published: March 2024

Sifat kuantitatif yang dimiliki oleh ayam Joper seperti ukuran tubuh merupakan salah satu parameter penting dalam prog pemuliaan ternak khususnya untuk mengetahui tingkat pertumbuhannya.   Penelitian ini bertujuan untuk mengetahui produktivitas ayam yang telah dipelihara selama dua bulan, menggunakan hubungan antara bobot badan dengan ukuran-ukuran tubuh ayam Joper.  Penelitian menggunakan metode deskripsi dengan teknik observasi. Data yang diukur berupa ukuran kuantitatif. Sampel ayam ditentukan secara purposif sampling pada kedua lokasi penelitian sebanyak 66 ekor.  Data yang diperoleh dianalisa secara tabulasi dan korelasi regresi dengan menggunakan SPSS versi 25. Bobot badan ayam Joper umur 2 bulan menggambarkan nilai yang cukup baik, karena memenuhi standar bobot badan ayam, yaitu 1015,5 g. Bobot badan ayam jantan adalah 1113,94 g dan betina dalah 916,36 g. Nilai korelasi pearson dan R square tertinggi adalah korelasi dan regresi panjang kaki terhadap bobot badan, sehingga ukuran panjang kaki dapat digunakan untuk menduga bobot badan ayam Joper pada usia 2 bulan.
SUBSTITUSI KULIT PISANG GOROHO (Musa acuminafe, sp) FERMENTASI TERHADAP KUALITAS TELUR AYAM PETELUR
Authors: Djunu Sri Suryaningsih , Saleh Ellen J , Chuzaemi Siti , Djunaidi Irfan H. , Natsir M Halim
Published: March 2024

ABSTRAkPenelitian ini bertujuan untuk mengevaluasi pemberian pakan kulit pisang Goroho fermentasi sebagai substitusi jagung dengan taraf berbeda terhadap kualitas telur ayam petelur. Materi penelitian terdiri dari ayam petelur fase layer berumur 47 minggu. Pakan yang digunakan adalah pakan basal dan bahan pakan kulit pisang Goroho fermentasi. Metode penelitian menggunakan Rancangan Acak Lengkap (RAL) dengan 5 macam perlakuan dan 5 ulangan, terdiri atas P0 (pakan tanpa tepung kulit pisang Goroho fermentasi), P1 (pakan + kulit pisang Goroho fermentasi substitusi jagung 5%), P2 (pakan + kulit pisang Goroho fermentasi substitusi jagung 10%), P3 (pakan + kulit pisang Goroho fermentasi substitusi jagung 15%), P4 (pakan + kulit pisang Goroho fermentasi substitusi jagung 20%). Variabel penelitian yakni: indeks kuning telur, indeks putih telur, tebal kerabang telur, volume telur, warna kuning telur dan kolesterol kuning telur. Data yang diperoleh dianalisis dengan rancangan acak lengkap dan bila terdapat perbedaan yang nyata antar perlakuan dilanjutkan dengan uji jarak berganda Duncans. Hasil diperoleh bahwa substitusi kulit pisang Goroho terfermentasi inokulum Rhizopus oligosphorus dan Trichoderma viride optimal meningkatkan kualitas indeks kuning telur sebesar 2,22% (P1: 0,460), volume telur 9,80% (P2: 55,45 ml) dan menurunkan kadar kolesterol sebesar 21,14% (P3: 14,59).
ﺩﻭﺭ ﺍﻟﻧﻳﺭﺍﻥ ﻓﻲ ﺗﺻﻭﻳﺭ ﺍﻟﺷﺎﻋﺭ ﺍﻟـﺟﺎﻫـﻠﻲ ­ ﺍﻟـﺫﺍﺗﻳﺔ ٬ ﺍﻟـﻣﻭﺿﻭﻋﻳﺔ ٬ ﺍﻟـﻘﺭﻯ ­ ﺍﻧﻣﻭﺫﺟـﺎً ­
Authors: Raja' lazm ramdan
Published: March 2024

ﻛﺎﻨـت اﻟﻨـﺎر ﻤـن اﻟﻤﻛﺘﺸـﻔﺎت ﻋﻠـﻰ طرﻴـق اﻟﺼـدﻓﺔ اﻟﺘـﻲ ﻋﻤـﻝ اﻻﻨﺴـﺎن ﻋﻠـﻰ اﺴـﺘﺨداﻤﻬﺎ ﻓـﻲ

ﺘطوﻴر ﻤﺨﺘرﻋﺎت ﻤﻘﺼودة ٬ ﻓﻘد ﻛﺎﻨت اﻋﺠوﺒﺔ ﺨﻠﻘﻬﺎ اﷲ ﺴﺒﺤﺎﻨﻪ وﺘﻌﺎﻟﻰ ﻟﻼﻨﺴﺎن (١) .

 

ﻓﻤﻨـذ ان اﻛﺘﺸـﻔﻬﺎ أرﻫـﺎ ﻋﻨﻴﻔـﺔ ٬ ﺘﻘﺘـﻝ وﺘﻬﻠـك وﺘﺒﻴـد ٬ (٢) ورآﻫـﺎ اﻴﻀـﺎً اﻨﻴﺴـﺔ داﻓﺌـﺔ ﺘﻘﻬـر ﻋـدوﻩ

اﻟﻤﺨﻴـف وﻫـو اﻟظـﻼم ٬ وﻗـﺎﻝ اﺼـﺤﺎب اﻟﻛـﻼم : ( ان اﷲ ﻋـز وﺠـﻝ ﺠﻤـﻊ ﻓـﻲ اﻟﻨـﺎر اﻟﺤرﻛـﺔ واﻟﺤـ اررة

 

واﻟﻴﺒوﺴﺔ ٬ واﻟﻠطﺎﻓﺔ واﻟﻨور ٬ وﻫﻲ ﺘﻔﻌﻝ ﺒﻛﻝ ﺼورة ﻤن ﻫـذﻩ اﻟﺼـور ﻤـن ﺨـﻼﻝ ﻤـﺎ ﺘﻔﻌـﻝ ﺒـﺎﻻﺨرى ٬

 

ﻓﺎﻟﺤرﻛﺔ ﺘﻐﻠﻲ اﻻﺠﺴﺎم ٬ وﺒـﺎﻟﺤ اررة ﺘﺴـﺨن ٬ وﺒﺎﻟﻴﺒوﺴـﺔ ﺘﺠﻔـف ٬ وﺒﺎﻟﻠطﺎﻓـﺔ ﺘﻨﻔـذ ٬ وﺒـﺎﻟﻨور ﺘﻀـﻲء ﻤـﺎ

ﺤوﻟﻬﺎ ) (٣) .

 

وﻛﺎﻨت اﻟﻨﺎر ﻓﻲ اﻟﻤوروث اﻟﺴﺎﻤﻲ ﻴﻌدوﻨﻬﺎ ﻤطﻬ اًر ﻗوﻴﺎً ﻻﻨﻬـﺎ ﺘﻠـﺘﻬم ﺠﺴـد اﻻﻨﺴـﺎن اﻟﻔـﺎﻨﻲ ﻓـﻼ

ﻴﺒﻘـﻰ ﻤﻨـﻪ اﻻ روﺤـﻪ اﻻﻟﻬﻴـﺔ اﻟﺨﺎﻟـدة (٤) . ﻓـﻲ ﺤـﻴن ان اﻟوﺜﻨﻴـﺔ اﻟﻔﻴﻨﻴﻘﻴـﺔ ﻛﺎﻨـت ﺘﻘـدم اﻟﻀـﺤﺎﻴﺎ اﻟﺒﺸـرﻴﺔ

 

ﻟﻠﻨﺎر ﻤﺘﺨذﻴن اﻋز اﺒﻨﺎﺌﻬم ﻗ ارﺒﻴن ﻟﻨﺎر اﻵﻟﻬﺔ (٥) .

 

وﻓﻲ ﻋﺼر ﻤﺎ ﻗﺒﻝ اﻻﺴﻼم اﺘﺼﻝ اﻟﻌرب ﺒـﺎﻟﻤﺠوس ﻤـن اﻟﻔـرس ﻓـﻲ اﻟـﻴﻤن واﻟﺤﻴـرة واﻟﺒﺤـرﻴن

 

٬ ووﻗﻔوا ﻋﻠﻰ ﻤذاﻫﺒﻬم وﻤﻌﺘﻘـداﺘﻬم اﻟدﻴﻨﻴـﺔ وﻤﻨﻬـﺎ ﻋﺒـﺎدﺘﻬم ﻟﻠﻨـﺎر ٬ ﻓﺤﺎﻛـت ﺘﻠـك اﻟطواﺌـف اﻟﻌرﺒﻴـﺔ ﻓـﻲ

ﻋﺒﺎدﺘﻬم (٦) .

 

وﺸـﻌر ﻋﺒـﺎس ﺒـن ﻤـرداس اﻟﺴـﻠﻤﻲ ﻓﻴـﻪ ﻤـدح ﻟﻠرﺴـوﻝ (  ) ﺒﺄﻨـﻪ اطﻔـﺄ ﻨﻴـ ارن اﻟﻔـرس وذﻟـك ﻓـﻲ

 

ﻗوﻟﻪ :

 

وﻨورت ﺒﺎﻟﺒرﻫﺎن اﻤ ارً ﻤد ّﻤﺴﺎً

 

 

واطﻔﺄت ﺒﺎﻟﺒرﻫﺎن ﻨﺎ ارً ﻤﻀّرﻤﺎ (٧)

 

وﻋظم اﷲ ﺘﻌﺎﻟﻰ ﻤن ﺸﺄن اﻟﻨﺎر ﺒﺄن ﺠﻌﻠﻬﺎ ﺘﻨﺘﻘم ﻓﻲ اﻻﺨـرة ﻤـن اﻋداﺌـﻪ ﺠﻤـﻴﻌﻬم ٬ وﻨﻬـﻰ ان

 

ﻴﺤرق ﺒﻬﺎ ﺸـﻲء ﻤـن اﻟﻬـوام (٨) . ﻓﻘـﺎﻝ ﻋﻠـﻰ ﻟﺴـﺎن رﺴـوﻟﻪ اﻟﻛـرﻴم (  ) : (( ﻻ ﺘﻌـذﺒوا ﺒﻌـذاب اﷲ ))

 

(٩) . وﻗﺎﻝ اﷲ ﺘﻌﺎﻟﻰ ﻟﻠﺜﻘﻠﻴن [ ﻳ  ﺮ  ﺳ  ﻞُ ﻋ  ﻠَﻴ  ﻜُﻤ  ﺎﺷ  ﻮ  ﺍﻅٌ ﻣ  ﻦ ﻧﱠﺎﺭٍ ﻭ  ﻧُﺤ  ﺎﺱ  ﻓَﻠَﺎ ﺗَﻨﺘَﺼ  ﺮ  ﺍﻥِ * ﻓَﺒِﺄَﻱ  ﺁﻟَﺎﺀ ﺭ  ﺑ  ﻜُﻤ  ﺎ ﺗُﻜَﺬﱢﺑ  ﺎﻥِ

 

[ (١٠) .

 

وﻋظم ﺸﺄن اﻟﻨﺎر ﻟﻤﺎ اﻀﻴﻔت اﻟﻰ اﺴم اﻟﺠﻼﻟـﺔ ﻓﻘـﺎﻟوا : ﻨـﺎر اﷲ وﺴـﻌرﻩ وﻗـﺎﻟوا : ﻏﻀـب اﷲ

وﻟﻌﻨﺘﻪ ٬ وﺴﺨط اﷲ وﻏﻀﺒﻪ . وﻫﻤﺎ ﻨﺎرﻩ او اﻟوﻋﻴد ﺒﻨﺎرﻩ (١١) .
ﻓﻛﺭﺓ ﺍﻟﻣﺳﺭﺡ ﻓﻲ ﺍﻟﻔﺿﺎءﺍﺕ ﺍﻟﻣﻔﺗﻭﺣﺔ(ﺍﻟﻣﺩﻳﻧﺔ ﺍﻟﻣﺟﺗﻣﻊ) "ﺍﻷﻟﻌﺎﺏ ﺍﻟﺷﻌﺑﻳﺔ ﺍﻟﻌﺭﺍﻗﻳﺔ ﻧﻣﻭﺫﺟﺎً"
Authors: ﺩ. ﻫﻳﺛﻡ ﻋﺑﺩ ﺍﻟﺭﺯﺍﻕ
Published: March 2024

اﻟﻤﺸﻛﻠﺔ ﺘﻨطﻠق ﻤن ﺴؤاﻝ اﻻﺨﺘﻼف واﻟﺘواﻓق ﺒﻴن ﻓﻀﺎء ﻓﻛرة اﻟﻤﺴرح ﻛﻌﻤﻠﻴﺔ ﻤﺴﺘﻤرة ﻓﻲ

 

اﻟﻔﻀﺎءات اﻟﻤﻔﺘوﺤﺔ وﻓﻀﺎء اﻟﻌرض اﻟﻤﺴرﺤﻲ ﻛﺈﻨﺘﺎج ﻟﻪ ﺒداﻴﺔ وﻨﻬﺎﻴﺔ.

 

ارﺘﺒط اﻷوﻝ ﺒﺈﻨﺘﺎج اﻟﺴﻴﺎﺴﺔ اﻟﺜﻘﺎﻓﻴﺔ ﻟﻠﻀﺒط واﻟﺘوازن اﻻﺠﺘﻤﺎﻋﻲ ﺒﻴن اﻟﻔرد وﺒﻴﺌﺘﻪ اﻟﻤﺎدﻴﺔ

 

واﻟﺜﺎﻨﻲ ﺒﺎﻟﻤﻌﺎﻴﺸﺔ اﻟﻔﻛرﻴﺔ ﻟﺘﻤظﻬ ارت ﺘﻠك اﻟﻌﻼﻗﺔ.

ﺃﻫﻣﻳﺔ ﺍﻟﺑﺣﺙ:

 

ﻴﺸﻴر اﻟـﻰ أﻫـﻤﻴﺔ ﻓﻛرة اـﻟﻤﺴرحـ ﻛﺴﺘراﺘـﻴﺠﻴﺔ وﺘـﻛﻨﻴك وﺜﻴق اـﻟﺼﻠﺔ ﺒـﺤﻴﺎة اﻻـﻨﺴﺎنـ اﻟـﺨﺎﺼﺔ

 

واﻻﺠﺘﻤﺎﻋﻴﺔ ٬ وﺜﻴق اﻟﺼﻠﺔ ﺒﺘﺤوﻝ وﺘﻛﺸف ﻫوﻴﺔ اﻻﻨﺴﺎن وﺘﺠددﻩ.

ﻫﺩﻑ ﺍﻟﺑﺣﺙ:

 

ﻴـﻠﻔت ﻨـظر اـﻟـﺨطﺎبـ اـﻟـﺴﻴﺎﺴﻲ اـﻟـﻰ ﺘـﻛﻨﻴك وـﺴﺘ ارـﺘـﻴﺠﻴﺔ ﻓـﻛرةـ اـﻟـﻤﺴرحـ ﻓـﻲ ﻓـﻀﺎءـ اـﻟـﻤﺠﺘﻤﻊ

 

ﻟﺘطوﻴر وﺘﺠدﻴد ﻫوﻴﺔ اﻻﻨﺴﺎن وﻗدرﺘﻪ ﻋﻠﻰ اﻻﻨدﻤﺎج واﻻﻨﺘﺎج واﻟﺘﻨﻤﻴﺔ واﻻﺴﺘﺜﻤﺎر وﺘﻨظﻴم

 

ﻤﺴؤوﻟﻴﺔ اﻟﻌﻼﻗﺔ وﻀﺒطﻬﺎ ﺒﻴن اﻟﻔرد وﻤؤﺴﺴﺎت اﻟﻤﺠﺘﻤﻊ ﺒﻤﻌزﻝ ﻋن اﻟﻌﻼﻗﺔ اﻟﻘﺴرﻴﺔ او

 

اﻻﺴﺘﺒدادﻴﺔ.
A Systematic Methodology for Gearbox Health Assessment and Fault Classification
Authors: Hassan Al-Atat Hassan x , Siegel David , Lee Jay
Published: March 2024

A systematic methodology for gearbox health assessment and fault classification is developed and evaluated for 560 data sets of gearbox vibration data provided by the Prognostics and Health Management Society for the 2009 data challenge competition. A comprehensive set of signal processing and feature extraction methods are used to extract over 200 features, including features extracted from the raw time signal, time synchronous signal, wavelet decomposition signal, frequency domain spectrum, envelope spectrum, among others. A regime segmentation approach using the tachometer signal, a spectrum similarity metric, and gear mesh frequency peak information are used to segment the data by gear type, input shaft speed, and braking torque load. A health assessment method that finds the minimum feature vector sum in each regime is used to classify and find the 80 baseline healthy data sets. A fault diagnosis method based on a distance calculation from normal along with specific features correlated to different fault signatures is used to diagnosis specific faults. The fault diagnosis method is evaluated for the diagnosis of a gear tooth breakage; input shaft imbalance, bent shaft, bearing inner race defect, and bad key, and the method could be further extended for other faults as long as a set of features can be correlated with a known fault signature. Future work looks to further refine the distance calculation algorithm for fault diagnosis, as well as further evaluate other signal processing method such as the empirical mode decomposition to see if an improved set of features can be used to improve the fault diagnosis accuracy.
A Model-Based Prognostics Approach Applied to Pneumatic Valves
Authors: Daigle Matthew J. , Goebel Kai
Published: March 2024

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of  implementation, and support for uncertainty management. We develop a general modelbased prognostics methodology within a robust probabilistic framework using particle filters. As a case  study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive  simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
Model Based Approach for Identification of Gears and Bearings Failure Modes
Authors: Klein Renata , Rudyk Eduard , Masad Eyal , Issacharoff Moshe
Published: March 2024

This paper describes the algorithms that were used for analysis of the PHM‟09 gear-box. The purpose of the analysis was to detect and identify faults in various components of the gear-box. Each of the 560 vibration recordings presented a different set of faults, including distributed and localized gear faults, typical bearing faults and shaft faults. Each fault had to be pinpointed precisely. In the following sections we describe the algorithms used for finding faults in bearings, gears and shafts, and the conclusions that were reached. A special blend of pattern recognition and signal processing methods was applied. Bearings were analyzed using the orders representation of the envelope of a band pass filtered signal and an envelope of the de-phased signal. A special search algorithm was applied for bearing feature extraction. The diagnostics of the bearing failure modes was carried out automatically. Gears were analyzed using the order domains, the quefrency of orders, and the derivatives of the phase average.
Engine Oil Condition Monitoring Using High Temperature Integrated Ultrasonic Transducers
Authors: Wu Kuo-Ting , Kobayashi Makiko , Sun Zhigang , Jen Cheng-Kuei , Sammut Pierre , Bird Jeff , Galeote Brian , Mrad Nezih
Published: March 2024

The present work contains two parts. In the first part, high temperature integrated ultrasonic transducers (IUTs) made of thick piezoelectric composite films, were coated directly onto lubricant oil supply and sump lines of a modified CF700 turbojet engine. These piezoelectric films were fabricated using a sol-gel spray technology. By operating these IUTs in transmission mode, the amplitude and velocity of transmitted ultrasonic waves across the flow channel of the lubricant oil in supply and sump lines were measured during engine operation. Results have shown that the amplitude of the ultrasonic waves is sensitive to the presence of air bubbles in the oil and that the ultrasound velocity is linearly dependent on oil temperature. In the second part of the work, the sensitivity of ultrasound to engine lubricant oil degradation was investigated by using an ultrasonically equipped and thermally-controlled laboratory test cell and lubricant oils of different grades. The results have shown that at a given temperature, ultrasound velocity decreases with a decrease in oil viscosity. Based on the results obtained in both parts of the study, ultrasound velocity measurement is proposed for monitoring oil degradation and transient oil temperature variation, whereas ultrasound amplitude measurement is proposed for monitoring air bubble content.
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
Authors: TV Vishnu , Chavan Diksha , Malhotra Pankaj , Vig Lovekesh , Shroff Gautam
Published: March 2024

Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) inherent noise ispresent in the sensor readings. The two scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process, often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem and propose LSTM-OR: deep Long Short Term Memory (LSTM) network-based approach to learn the OR function. We show that LSTM-OR naturally allows for the incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effectiveapproach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on the publicly available turbofan engine benchmark datasets, we demonstrate that LSTMOR is at par with commonly used deep metric regressionbased approaches for RUL estimation when sufficient failed instances are available for training. Importantly, LSTM-OR outperforms these metric regression-based approaches in the practical scenario where failed training instances are scarce, but sufficient operational (censored) instances are additionally available. Furthermore, our uncertainty quantification approach yields high-quality predictive uncertainty estimateswhile also leading to improved RUL estimates compared to single best LSTM-OR models.
PHM, a Continuum: the Past, Present, and Future
Authors: Hess Andrew
Published: March 2024

This Keynote Presentation will explore PHM from the perspective of being a continuum of ever evolving and increasingly effective capabilities. Background experience, particularly in aerospace applications, will be reviewed going from basic diagnostic capabilities to the evolution of the Prognostics and Health Management (PHM) concept. The present state-of-the art of advanced diagnostic and prognostic capabilities; including physics of failure, actual life remaining and prediction models will be explored. Current predictive analytic capabilities will be discussed as they are being applied  to platform and component asset management and enterprise-wide solutions. The spread of the PHM concept to many new industry sectors and future applications will be identified. Some significant lessons learned during the evolution of PHM will be cited. The trajectory of future PHM capabilities and applications will be discussed including their use in smart manufacturing and human performance.
Condition Monitoring and Diagnostics of Nuclear Mechanical Components
Authors: Park Jin-Ho
Published: March 2024

Typically, there exist two pressure boundary systems in a PWR(Pressurized Water Reactor) NPP(Nuclear Power Plant). One is the reactor coolant system(RCS) called as the primary system or Nuclear Steam Supply System(NSSS). The other is balance of power(BOP) called as the secondary system. The key mechanical components of the primary system is mainly comprised of reactor pressure vessel(RPV), pressurizer(PZR), steam generator(SG), reactor coolant pump(RCP) and pipings. The NIMS(NSSS Integrity Monitoring System) has been developed to monitor and diagnose the structural safety of the NSSS components on an on-line basis. In this paper, the Korean NIMS developed by KAERI is introduced. It is comprised of four independent sub-systems such as IVMS(Internal Vibration Monitoring System), LPMS(Loose Part Monitoring System), ALMS(Acoustic Leakage Monitoring System), and RCPVMS(Reactor Coolant Pump Vibration Monitoring System). The IVMS has been developed for early detection of the degradation of the preload condition of the reactor internal components by measuring the change of vibratory modal frequencies of the core barrel assembly. The LPMS is to monitor the presence of a loosened or detached metallic object within the reactor coolant system using the vibration sensors installed on the surface of the system. The primary purpose of the ALMS is to monitor coolant leakage at the potential leak regions such as the reactor vessel, welded region in pipings, and valves, etc. The second purpose is to detect initiation of crack on the surface of the pressure boundary of the reactor coolant system. The RCPVMS is to provide diagnostic information for detecting such symptoms as the shaft crack, the RCP misalignment, rotor unbalance, and the abnormalities of support bearings.
Describing Problem for the PHMAP 2017 Data Challenge Competition and Suggestions for Future Study
Authors: Oh Hyunseok , Seo Yun Ho , Mo Min-Hwan , Kim Joo-Hyung
Published: March 2024

Pulverizers in a power plant are used to grind coal in the form of find power for combustion. To secure reliable operation, redundant pulverizers are installed in the power plant. If the current health condition and remaining useful life of a gearbox system in each pulverizer are estimated correctly, the pulverizers can be operated and maintained in a cost-effective manner. To this end, a Data Challenge Committee in the PHMAP 2017 conference is organized to run an open competition. This paper presents the original problem and observed facts as well as the list of winners of the Data Challenge Competition. We anticipate that this paper can be used as a reference in the development of a prognostic method that can predict the health conditions of the pulverizers accurately.
Maintenance Analytics, Industrial Data Science and Virtual Commissioning
Authors: Galar Diego
Published: March 2024

Industrial systems are complex with respect to technology and operations with involvement in a wide range of human actors, organizations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerized systems, such as computerized control systems, or advanced monitoring and diagnostic systems. Moreover, assets cannot compromise the safety of the users by applying operation and maintenance activities. Industry 4.0 is a term that describes the fourth generation of industrial activity which is  enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of “internet of things”. Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the wholesystem including the one from design and manufacturing which obviously contains the physical knowledge. Therefore the integration of asset information during the entire lifecycle is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown avoiding black swans and other unexpected or unknown asset behaviors.Moreover, the asset data are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, these new sets of information will add value to the individual data sources. This talk will discuss the possibilities that lie within applying the maintenance analytics concept by the means of virtualization i.e virtual commissioning of the assets through data fusion and integration from a systems perspective.
Health Monitoring and Vibratory Fault Prediction of Rotating Machinery
Authors: Han Qingkai , Zhang Hao , Zhai Jingyu
Published: March 2024

The major rotating machines such as large centrifugal or axial flow compressor, gas turbine and aero-engine are in the value chains of high-end and the core aspects of the industry factories, regarded as important embodiments of the national core competence in industry and high-technology development. The health monitoring and fault diagnosis and prediction, belonging to the technologies of prognosis and health management (PHM) are widely focused in recent years and developing constantly. The principles of health monitoring and vibratory fault prediction of rotating machinery are introduced in this paper. The dynamics of rotor system and structures are introduced, and the vibration problems of the rotating machine or structures are interpreted. The diagnosis and prediction of vibration faults happening on these machines commonly are given with examples of bearing faults of a turbine test-rig. At last, some important research tasks in future are prompted.
Condition Monitoring of Automotive Smart Systems utilizing Piezoresistive Stress Sensor
Authors: Wu Bulong , Han Bongtae
Published: March 2024

It is expected that advanced electronic components and smart systems would dictate the level of innovations in nearly all industrial sectors, including but not limited to automotive and other transport/logistics solutions,production equipment, energy and other infrastructure, etc. One such component is automotive electronic control unit (ECU), which controls the electrical system or subsystems in motor vehicles. In a conventional ECU, a protective metal case is used to ensure reliability under harsh environmental conditions. Recently, an epoxy molding compound (EMC) was adopted to replace the metal case. The EMC technology reduced the manufacturing cost significantly, yet the presence of a large amount of outer EMC increased the stresses of ECUs during transfer molding process and operations (Gromala, Fischer, Zoller, Andreescu, Duerr, Rapp & Wilde, 2013, Kim, Han, Yadur & Gromala, 2014). The long-term reliability assessment of this newly adopted manufacturing technology should be conducted to make the technology a more viable alternative to various ECUs.
New prognostics-based structural maintenance strategies for civil aircraft
Authors: Gogu Christian
Published: March 2024

Currently, structural maintenance of civil aircraft is based on the scheduled maintenance strategy, where all the aircraft of a same model are inspected according to a predetermined schedule. Embedding sensors in aeronautical structures allowing to monitor their structural health can also open up new strategies for more efficient maintenance planning. A first possibility is to move from scheduled maintenance to condition based maintenance, where the current condition of the aircraft triggers specific maintenance policies. A further step is to not only use the current state of the damage but to also predict future damage growth and determine the maintenance policy accordingly. Both condition based as well as prognostics based strategies can be carried out independently or can be coordinated with other, scheduled, non-structural maintenances, which leads to a total of four new possible maintenance strategies. An application of these maintenance strategies to fuselage panels of a short range commercial aircraft is presented and their effectiveness and cost-efficiency is compared between them and with that of traditional scheduled maintenance. We show that large savings can be achieved, particularly when large variabilities are present.
Multilayer Perceptron for Classification of Structural delamination and Transducers Debonding in Smart Composite Laminates
Authors: Khan Asif , Kim Heung Soo
Published: March 2024

This paper investigates the feasibility of multilayer perceptron (MLP) for the classification of structural delamination and transducers debonding in smart composite laminates. Structural vibration response is employed to extract the discriminative features for multiple damages. The dynamic model of the smart structure with inter-ply delaminations and partially debonded piezoelectric sensor and actuator is developed by incorporating improved layerwise theory, higher order electric potential field and finite element method. The developed model is solved in the time domain to obtain the transient response of the healthy and damaged structures through a surface bondedpiezoelectric sensor for random input excitations applied through a piezoelectric actuator. The input-output information is fed into a system identification algorithm to identify damage sensitive features for the healthy anddamaged state of the smart composite laminate. The discriminative features are classified through MLP in a supervised manner and its classification accuracy is evaluated in terms of true positive (TP) rate, false positive (FP) rate, precision and area under the receiver operating characteristic curve (ROC area).
Development of the Aircraft Structural Health Monitoring Equipment integrating Optical Sensor and Analog Sensor
Authors: Choi Kyeungsik , Kim Sangyong , Lee Cheoljoo , Yun Hansoo
Published: March 2024

The structure of the aircraft has made lots of effort to secure the safety, and the safety is remarkably secured through the lifetime management and the non-destructive inspection. However, periodic inspection requires much time and expense, and various monitoring techniques for aircraft structures are required, especially when the SHM technique is studied for composite structures that cannot use traditional lifetime management techniques. For performing this monitoring, the predeveloped analog sensor has a limit in durability and cable weight. Therefore, the demand for the monitoring equipment of the new technique has been increasing, and FBG and PZT sensor are used representatively. In this paper, we have developed a device that can acquire analog sensor (Accelerometer and Strain gage) and FBG sensor, and support interworking with the Air Data Bus (MIL-STD-1553B, Ethernet) for real-time analysis. Also, it satisfies various military environmental requirements for use in military aircraft. The equipment which is introduced in this paper is expected to be used for SHM and lifetime management.
Defect state and Severity Analysis Using the Discretized State Vectors
Authors: Baek Sujeong , Kim Duck-Young
Published: March 2024

The time series of sensor data for condition monitoring of a system is often characterized as very-short, intermittent, transient, highly nonlinear and non-stationary random signals, which hinders the straightforward pattern analysis. For discovering meaningful features from original sensor data, we transform continuous time series data into a set of contiguous discretized state vectors using a multivariate discretization approach. We then search for important patterns that are found only in the case of defective systems. We discuss how to measure the level of importance of each defect pattern and further how to assess the severity degree of a defective state. We consider that a defective state is more severe if various defect patterns are observed in the state. Likewise, if a particular defect pattern describes as many as defective states, the pattern will be treated as significant. The proposed procedure is applied to detecting defective car door trims that have the potential to generate small but irritating noises. We analyzed the datasets obtained from two different monitoring methods using a typical acoustic sensor array and acoustic emission sensors. Defective car door trims were efficiently identified with their severity degrees.
MCMC-based Efficient Maintenance Plan Decision
Authors: Shimada Junya , Sakajo Satoko
Published: March 2024

In recent years, it has been an essential policy to monitor real-time health states of facilities and determine when to perform maintenance in order to ensure the high operation  atio and improve work efficiency. In this paper, target facilities diagnose their own health states by analyzing timeseries sensor data and transmit warning data and failure data to the monitoring center. These data include date and time of occurrence and warning/failure code which identifies the factor. Utilizing these data, we propose an MCMC-based maintenance plan decision to reduce the failures and the workloads. Firstly, state-based warning patterns which are composed of several warning codes are extracted. At that time, to avoid the state explosion, only warning patterns which are closely related to failure occurrence are extracted based on the time interval from warning states to failure state. Secondly, warning patterns are modeled based on N-th order Markov model. Finally, maintenance plan is decided based on failure probability. Experiment to evaluate whether the facilities can be maintained before failure occurs proved that this approach could actually reduce the number of failures and the frequency of dispatches of maintenance workers.
A Study on Noise Reduction of PTO Gear-box for Driving Pump of Large Excavator
Authors: Yoo Han-Ju , Lee Jong-Jik , Lee Yong-Bum , Choi Byung-Oh , Kim Tae-Seok
Published: March 2024

A PTO gearbox ("gearbox") is designed to be mounted on a construction vehicle to convey power from the engine to multiple pumps. The gearbox uses oil bath lubrication, which often experiences a disruption in the supply oflubricant inside the gearbox. Poor lubrication in any gearing system is a major cause of a drop in its efficiency, which is often displayed in the form of heat or noise. This study describes a technology that can reduce noise by making splash lubricant inside the gearbox fall down along the wall like a curtain and measures its performance through comparison. Measurement of noise was made under no-load condition and load condition. For the former, measurement was made while the servo motor was used for driving to minimize noise from the equipment. For the load test, the noise from the equipment was unavoidable because an electrical dynamometer had to be used to control the load, but a comparison was made against the measured noise to determine where there had been any impact. If any impact was identified, a correction would be applied for the noise measured.
Bayesian inferences of damage index and damage growth model based on on-site measurement data of steam turbine
Authors: Choi Woosung , Oh Hyunseok , Youn Byeng D. , Kim Namho
Published: March 2024

Accurate prediction of remaining useful life (RUL) of plant turbine is a major scientific challenge for effective operation and maintenance in the power plant industry. This paper proposes an RUL prediction methodology that incorporates a damage index into the damage growth model. Bayesian inference technique is used to estimate the probability distribution of a damage index from on-site hardness measurements with uncertainties. A Bayesian approach to the damage growth model is proposed for aged steam turbines and the predictive distribution of the damage index is estimated using its mean and standard deviation. As a case study, real steam turbines in power  plants were used to demonstrate the effectiveness of the proposed Bayesian approach. The results from the proposed damage growth model can be used to predict the RULs of the steam turbines of power plants regardless of load types (peak-load or base-load) of the power plant.
A Statistical Analysis on Modeling Uncertainty Through Crack Initiation Tests
Authors: Park Jae Phil , Park Chanseok , Bahn Chi Bum
Published: March 2024

Because a large time spread in most crack initiation tests makes it a daunting task to predict the initiation time of cracking, a probabilistic model, such as the Weibull distribution, has been usually employed to model it. In thiscase, although it might be anticipated to develop a more reliable cracking model under ideal cracking test conditions (e.g., large number of specimen, narrow censoring interval, etc.), it is not straightforward to quantitatively assess the effects of these experimental conditions on model estimation uncertainty. Therefore, we studied the effects of some key experimental conditions on estimation uncertainties of the Weibull parameters through the Monte Carlo simulations. Simulation results suggested that the estimated scale parameter would be more reliable than the estimated shape parameter from the tests. It was also shown that increasing the number of specimen would be more efficient to reduce the uncertainty of estimators than the more frequent censoring.
A Modelling Approach to Monitor Friction within Electromechanical Actuator Ballscrews using Motor Current
Authors: Hussain Yameen M. , Burrow Stephen , Henson Leigh , Keogh Patrick
Published: March 2024

A modelling approach to monitor ballscrew friction within Electromechanical Actuators (EMA) using motor current is presented along with subsequent fault diagnostics using classification of simulated data for healthy, degrading and faulty states. An approach was used where a baseline linear EMA system was modelled to a high level of detail. The modelling involved emphasis on the Permanent Magnet Synchronous Motor (PMSM) where a greater understanding of the drivetrain could be achieved. The PMSM was modelled using ‘dq axis’ transformation theory. The mechanical elements of the EMA were also modelled to include non-linear characteristics. Interaction between the ball and nut, and ball and screw are considered the main source of friction within the ballscrew, hence sliding velocities in these contact areas were used to calculate velocity dependent friction using the Stribeck friction model. Contact angles between ball and nut, and ball and screw, and mechanical efficiencies were varied to analyse the effect on the torque producing current for healthy, degrading and faulty conditions. The simulated data was trained for each condition for classification using a k-Nearest Neighbour (k-NN) algorithm. The first part of the analysis revealed that ballscrew degradation should be detectable using motor current by monitoring changes to the torque producing q-axis current for each failure state in the ballscrew damage model. External load disturbances were also modelled since they could cause fluctuations to the q-axis currents thus making itdifficult to isolate deteriorations to the ballscrew. The simulated datasets were processed for classification as training data using the k-NN algorithm where a classification accuracy of ~74% was achieved. Overall, the in-depthmodelling of the EMA system presented a comprehensive approach to monitoring ballscrew friction through use of motor current analysis from different test cases. It is proposed that employing a hybrid approach (combination of model based and data driven techniques) to fault diagnostics can further improve the classification accuracy.
Abnormal Sound Detection for Rotary Parts in Noisy Environment by One-class SVM and Non-negative Matrix Factorization
Authors: Kitamura Taichi , Takeishi Naoya , Yairi Takehisa , Hori Koichi
Published: March 2024

In this paper, we introduce a data-driven method for detecting abnormal sound from rotary machines, which is due to small scratches on the surface of bearings, in a manufacturing plant. Since it is difficult to obtain a sufficient amount of anomalous data beforehand, we assume only normal data are available for training a model. The challenge in our situation is the presence of high-level ambient noise, which makes detecting small anomalous sound very hard. In the proposed method, feature vectors are extracted by applying short-time Fourier transform, and one-class SVM is trained on normal data and used to discriminate normal and anomalous data. In addition, ambient noise is removed using nonnegative matrix factorization before extracting features to overcome the problem of noise superimposition and to improve the discrimination precision. We show the results of an experiment using actual sound data obtained from rotary machines with ambient noise.
Perspectives on using deep learning for system health management
Authors: Khan Samir , Yairi Takehisa
Published: March 2024

Having a robust health management and diagnostic strategy is an important part of a system’s operational life cycle as it can be used to detect anomalies, analyze faults/failures and predict the remaining useful life of components. By utilizing condition data and on-site feedback, data models can be trained using machine learning and statistical concepts. Once trained, the logic for data processing can be embedded on onboard controllers whilst enabling real-time health assessment and analysis. More recently, deep learning has gained increasing attention due to its potential advantages with data classification and feature extraction problems. It is an evolving research area and hence its use for aerospace maintenance applications must be researched if it can be used to increase overall system resilience or potential cost benefits for maintenance, repair, and overhaul activities. This paper focuses on investigating the application of deep learning for system health management, therefore incorporating reliable redundancy at the adequate point in the system. Deep learning is discussed, some recent developments are reviewed to clarify potential applications, after which some research issues relating to their realization are highlighted.
Reliable Faults Diagnosis of Roller Bearings using Hybrid Feature Models and Improved Multiclass Support Vector Machines with Classifiers Discriminant Analysis
Authors: Islam M M Manjurul , Kim Jong-Myon
Published: March 2024

In this paper, a reliable multi-fault diagnosis scheme based on hybrid feature models and improved one-against-all multiclass support vector machines (OAA-SVM) is proposed. First, distinct feature extraction methods are simultaneously applied on an acoustic emission (AE) signal to detect unique fault conditions about bearing defects. While a high dimensional features vector is further utilized with standard OAA-SVM classifier for classification, such method ignores individual classifier competence when results from multiple classes are agglomerated for making a final decision and therefore yields undecided and overlapped feature spaces where classification accuracy is severely degraded. To solve this unreliability problem, this study develops a dynamic reliability measure (DReM) technique for individual SVM in the OAA framework. This DReMaccounts for the spatial variation of the classifier’s performance by finding the local neighbor region of a test sample in the training sample space. Experiment results demonstrate that the proposed, OAA-SVM with DReM, classifier method outperforms the three state-ofthe-arts.
Unsupervised Fault Diagnosis of Journal Bearing Rotor System with Heterogeneous Data
Authors: Oh Hyunseok , Jeon Byung Chul , Jung Joon Ha , Youn Byeng D.
Published: March 2024

In general, it is extremely difficult to obtain failure data of real systems in the field such as power plant rotor systems. To accommodate the dearth of field failure data, conventional approaches employ data collected from a testbed that emulates the normal and faulty conditions of the real systems. Nevertheless, it is obvious that approaches developed with failure data solely collected from a testbed may not be ideal to diagnose the real systems. To this end, this paper proposes a unsupervised fault diagnostic approach for journal bearing systems that incorporates heterogeneous data from the testbed and real field systems. To demonstrate the validity of the proposed approach, a case study is conducted with the RK4 rotor kit and the power plant journal bearing system. The combination of vibration image generation with deep learning helps us use data from systems with an identical working principle but different scales. We anticipate that, by incorporating the heterogeneous data, the proposed approach can diagnose the conditions of actual journal bearing systems in the field more accurately.
A Reliable Technique for Remaining Useful Life (RUL) Estimation of Rolling Element Bearings using Dynamic Regression Models
Authors: Ahmad Wasim , Khan Sheraz Ali , Islam M M Manjurul , Kim Jong-Myon
Published: March 2024

Induction motors most often fail due to faults in the rolling element bearings. Sudden failures in a system result in long unscheduled downtimes, which cause huge economic losses. Prediction of imminent failures and estimation of the remaining useful life (RUL) of a bearing is essential for scheduling prior maintenance and avoiding abrupt shutdowns of critical systems. This paper presents a hybrid prognostics technique for rolling element bearings that utilizes dynamic regression models, which are updated recursively, to estimate the evolving trend in a bearing’s health indicator. These models are then used to predict the future value of the bearing health indicator and estimate the RUL of the bearing. The proposed algorithm is tested on the bearing prognostics data from the Center for Intelligent Maintenance Systems (IMS). Experimental results demonstrate excellent prognostic performance and bearing’s RUL estimates within the specified tolerance bounds by effectively determining the time to start prediction (TSP) and dynamically calibrating the models to adopt to the evolving behavior of the bearing health indicator.
Deep Stack Dictionary learning for Fault Diagnosis of Rotating Machinery
Authors: Jiao Jinyang , Zhao Ming , Lin Jing
Published: March 2024

Effective feature extraction of rotating machinery has been a hot topic in the prognosis and health management. However, it is a challenging problem to extract periodic impulses under heavy background noise and other interference. In the last decade, deep learning and dictionary learning have been promising methods to extract feature information, which have made great achievement in the field of image, video denoising, etc. In this paper, via fusing the deep learning with dictionary learning, an algorithm called deep stack dictionary learning is proposed. This algorithm is trained in a layer-wise greedy manner so as to suppress the noise and highlight periodic impulses.
Application of Hidden Markov Model to Fault Diagnostics of Glass De-chuck System
Authors: Choi Hwanoh , Noh Kyoungrae , Yun Sangmun , Kim Daewhan
Published: March 2024

De-chuck operation which attaches object to glues and detaches object from glues has been used in production line to convey materials in vacuum state. The de-chuck operation for glasses or wafers in production line sometimes causes damages on product in detaching it from glues. Since most of failures in de-chuck operation results from abnormal states of glues such as aged states or strong adhesive states, diagnostics of abnormal glues and a timely replacement of glues are the issues of the de-chuck system. In this study, a testbed simulated de-chuck system is devised and a hidden Markov model (HMM) is applied for diagnostics of the problematic glues in de-chuck system of a testbed. As a result, it was measured quantitatively to the rate of accuracy detecting the area of abnormal glues including aged stateglue and strong adhesive state-glue, and the maximum detection accuracy rate was approximately 82%.
Combustion and Resilient Mounting Condition Diagnostics thru Structural Vibration Monitoring of a Diesel Generator Set
Authors: Barro Ronald D , Lee Don Chool , Son Seok Man , Kim Hee Soo
Published: March 2024

Diesel engine real time condition can normally be depicted by its operational parameters. Condition management, on the other hand, can keep track of the machines’ status on a long term approach utilizing vibration amplitude and resonance. The mounting support condition along with cylinder combustion pressure excitation influences the diesel engine structural vibration dynamics that may lead to component damage and misalignment. As such, engine mounting condition-based monitoring as a tool can be applied to continuously check on its damping limitations brought by material deterioration. In this paper, a global vibration measurement in accordance  with ISO 13373-1 with the measuring points and direction done in accordance with ISO8528-9 was carried on a large diesel generator set installed in a power plant. The test engine is coupled on a generator and fitted with a torsional damper. Vibration analysis established the occurrence of engine structural vibration phenomena at lower frequency range (<200Hz). It was also confirmed that the actual state of the resilient mount can be verified by the structural vibration natural frequency of the engine.
Bearing wear prognosis based on Hidden Markov Model
Authors: Hong Jung Ryeol , Yoo Hong Hee
Published: March 2024

Condition-based maintenance (CBM) is a maintenance program that recommends maintenance decisions based on the information collected through condition monitoring. There are two substantial factors to support CBM, machine fault diagnosis and machine condition prognosis. In this paper deal with an estimation of the Remaining Useful Life of bearing based on the Hidden Markov Models. The Prognostic process is done in two phase: a learning phase and evaluation phase. In first process, the sensors’ data are processed in order to extract appropriate features, which are used as inputs of learning HMM. During second phase the extracted features are continuously injected to the obtained model to represents the current health state of mechanism system and to estimate its remaining useful life. The proposed method is tested on bearing wearing test of Rotor kit RK4.
Development of Easy Maintenance Assistance Solution (EMAS) for Gas Turbine
Authors: Ki Jayoung , Kang Myoungcheol
Published: March 2024

The solution was developed for the maintenance decision support of combined cycle power plant gas turbine. The developed solution provides the calculated result of optimal overhaul interval through the following modules: Overhaul Interval Prediction, Real Time Performance Monitoring, Model-Based Diagnostics, Performance Trend Analysis, Compressor Washing Period Management, and Blade Path Temperature Analysis. Model-Based Diagnostics module analyzed the differences between the data of MHI501G gas turbine performance model and the online measurement. Gas turbine performance model can be modified by the type of gas turbine of each combined cycle power plant. Compressor washing management module suggests the optimal point of balancing between the compressor performance and the maintenance cost. The predicted results of compressor washing period and overhaul period are able to support the operators in combined cycle power plant to make a proper decision of maintenance task. The developed solution was applied to MHI501G gas turbine and is, in present, on the process of field test at Gunsan combined cycle power plant, South Korea.