bluuebunny/crossref_metadata_2025 huggingface dataset:
PENGARUH CITRA MEREK DAN MOTIVASI BELANJA HEDONIS TERHADAP PEMBELIAN IMPULSIF ONLINE DI INSTAGRAM
Authors: Putri Andita Mirza , Abdurrahim Abi Anwar Umari
Published: November 2023

Adanya media sosial membuat wanita Indonesia mudah untuk mendapatkan informasi tentang global brand fast fashion ternama seperti Zara. Kemudahan ini membuat mereka terpicu untuk membeli baju Zara secara impulsif di platform online seperti Instagram. Tujuan dari penelitian ini adalah untuk meneliti apakah citra merek Zara di benak konsumen dan faktor – faktor yang membuat mereka termotivasi untuk belanja hedonis berpengaruh terhadap keinginan mereka untuk melakukan pembelian baju Zara secara impulsif di Instagram. Data penelitian diperoleh dengan menyebarkan kuesioner kepada 108 responden yang telah membeli 1 (satu) baju Zara di Instagram dalam waktu 1 tahun terakhir (April 2022 – April 2023). Hipotesis dalam penelitian ini diuji dengan uji bootstrapping di Smart PLS 4.0. Hasil pengolahan data menunjukkan bahwa citra merek tidak berpengaruh signifikan terhadap pembelian impulsif konsumen Zara di Instagram, tetapi motivasi belanja hedonis berpengaruh signifikan terhadap pembelian impulsif konsumen Zara di Instagram.
THE ROLE OF ENTREPRENEURIAL CHARACTERISTIC ON ENTREPRENEURIAL INTENTION
Authors: Wahyudono Denny Bernardus Kurnia
Published: November 2023

The Theory of Planned Behaviour put forward by Ajzen says that to influence entrepreneurial intentions, there are several factors, namely attitudes towards entrepreneurship, subjective norms, and perceived behavioural control. This study aims to analyse how the influence of entrepreneurial characteristics influences entrepreneurial intention. Derivative entrepreneurial characteristics include sociability, planning, leadership, innovation, and risk-taking. The sample of this study were Business School Students at Universitas Ciputra in Surabaya who had taken Entrepreneurship courses up to semester 6 of 190 respondents. The data that has been collected is processed using SEM-PLS. This study concludes that not all elements in entrepreneurial characteristics significantly influence students' entrepreneurial intentions. This is because many students still need clarification about planning the career path they will take in the future.
PENGARUH WORK-LIFE BALANCE TERHARAP WORK ENGAGEMENT PADA GIG WORKERS DENGAN WORK MEANINGFULNESS SEBAGAI VARIABEL INTERVENING (STUDI PADA MITRA PENGEMUDI STARTUP BISNIS TRANSPORTASI ONLINE)
Authors: Harianto Febriani , Herachwati Nuri
Published: November 2023

Abstrak

Penelitian ini memiliki tujuan untuk mengetahui pengaruh work-life balance terhadap work engagement melalui work meaningfulness sebagai mediasi pada gig workers, spesifik pada pengemudi ojek online di Indonesia khususnya di Surabaya dan sekitarnya. Objek penelitian dipilih karena fenomena profesi sebagai pengemudi ojek online di Indonesia berjumlah 2,7juta karena fleksibilitas yang ditawarkan dan terbukti mengurangi TPT (Tingkat Pengangguran Terpadu). Penelitian ini kuantitatif dengan pengumpulan data survey menggunakan kuisioner pada 491 orang pengemudi ojek online roda dua. Data diolah menggunakan structural equation modelling dengan software AMOS. Hasil yang didapatkan adalah work-life balance memiliki pengaruh pada work meaningfulness dan work engagement, work meaningfulness memiliki pengaruh pada work engagement, dan work meaningfulness dapat memediasi pengaruh antara work-life balance terhadap work engagement. Penelitian ini diharapkan menjadi tambahan literasi penelitian terhadap objek gig workers khususnya pengemudi ojek online karena gig workers bukanlah karyawan tetap dari suatu perusahaan.

 

Keywords : Work-Life Balance, Work Meaningfulness, Work Engagement, Gig Workers, Gig Economy, Ojek Online, Startup
INTEGRASI EKONOMI DAN SINKRONISASI SIKLUS BISNIS DI ASEAN 5+3
Authors: Tandjung Mochamad Onky Prahasetia , Heriqbaldi Unggul
Published: November 2023

Abstract. Economic integration in ASEAN5+3 is formed after the implementation of free trade in ASEAN. The purposed of ASEAN formation to create stable, welfare and competitive economy. One of the requirements to create the currency union will depend on the level of business cycles synchronization with other countries. The more similar of business cycles among countries the more possible to create a regime of collective exchange rate. The Objective of this research is to  find out the determinants factor potentially influence the occurrence of business cycles synchronization in ASEAN5+3. This research involves time series data 2002 – 2013 and the cross section data of eight ASEAN plus three countries by using dynamic panel instruments. The estimated result shows that the variable of trade intensity, finance integration, similarity of structure industry, coordination fiscal policy, coordination monetary policy and inflation effect significantly the business cycle synchronization.
 

Abstrak.  Integrasi ekonomi di ASEAN5+3 terbentuk setelah diterapkannya perdagangan bebas di ASEAN. Tujuan pembentukan ASEAN untuk menciptakan perekonomian yang stabil, sejahtera, dan kompetitif. Salah satu persyaratan untuk membentuk kesatuan mata uang akan bergantung pada tingkat sinkronisasi siklus bisnis dengan negara lain. Semakin mirip siklus bisnis antar negara, semakin besar kemungkinan terciptanya rezim nilai tukar kolektif. Tujuan penelitian ini adalah untuk mengetahui faktor determinan yang berpotensi mempengaruhi terjadinya sinkronisasi siklus bisnis di ASEAN5+3. Penelitian ini menggunakan data time series 2002 – 2013 dan data cross section delapan negara ASEAN plus tiga negara dengan menggunakan instrumen panel dinamis. Hasil estimasi menunjukkan bahwa variabel intensitas perdagangan, integrasi keuangan, kesamaan struktur industri, koordinasi kebijakan fiskal, koordinasi kebijakan moneter dan inflasi berpengaruh signifikan terhadap sinkronisasi siklus bisnis.
EFISIENSI BANK DAN RETURN SAHAM PADA SEKTOR PERBANKAN SELAMA PERIODE TAHUN 2013-2021
Authors: Hamzah Salman , Deliyana Sita
Published: November 2023

This study aims to comprehend the interaction between bank efficiency and its stock performance in Indonesia. The sample for this research consists of 31 banking companies listed on the Indonesia Stock Exchange (IDX) from the period of 2013 to 2021. To assess the level of bank efficiency, a non-parametric method, Data Envelopment Analysis (DEA) will be employed. Meanwhile, a panel data regression analysis will be utilized to investigate the influence of bank efficiency on the performance of Indonesian banks' stocks. The results of the DEA analysis indicate fluctuations in the level of bank efficiency from year to year, which could possibly be attributed to global economic conditions. Conversely, the regression analysis results show that the level of bank efficiency does not exhibit a significant relationship with stock returns. This suggests that efficiently operated banks do not always yield better stock performance in the stock market.
BANK NTB SYARIAH'S SOUNDNESS: A MULTI-METHOD ASSESSMENT USING CAMELS, RGEC, AND ALTMAN Z-SCORE
Authors: Syahrir Dimas Kenn , Hosen M. Nadratuzzaman , Wahyudi Ickhsanto
Published: November 2023

This paper endeavors to appraise the level of stability of Bank NTB Syariah during the time span of 2019 to 2021, by employing the CAMELS technique (which scrutinizes the bank's Capital, Assets, Management, Earnings, Liquidity, and Market Sensitivity), RGEC (which assesses the Risk Profile, Good Corporate Governance (GCG), Earnings, and Capital), as well as the Altman Z Score method (which analyzes the potential for bankruptcy). The data utilized for this research were procured through the application of documentation techniques and sourced from the financial statements and annual reports of Bank NTB Syariah, from 2019 to 2021. The data analysis technique utilized involved an evaluation of the bank's level of stability, utilizing a risk-based approach, encompassing the CAMELS, RGEC, and Altman Z Score factors. Based on the appraisal of the level of stability of PT. Bank NTB Syariah from 2019 to 2021, as evaluated by the comprehensive RGEC and CAMELS approach, the bank is deemed to be in a sound financial condition. However, the results of the bankruptcy prediction analysis, utilizing the Altman Z Score model, indicate that the financial state of Bank NTB Syariah in 2021 is situated in a nebulous area, albeit superior to its condition in 2020 and 2019, which indicated an indication of financial distress and potential bankruptcy.
PENGARUH LEVERAGE, TRANSFER PRICING TERHADAP AGRESIVITAS PAJAK PADA PERUSAHAAN SEKTOR TEKNOLOGI YANG TERDAFTAR DALAM BEI PADA TAHUN 2020-2022
Authors: Manullang Martha Vina , Karundeng Meidy Lieke
Published: November 2023

Banyak perusahaan berusaha mengoptimalkan keuntungan bisnis mereka dengan cara menurunkan kewajiban pajak yang seharusnya mereka bayar. Pada penelitian ini penulis mencoba meneliti pada perusahaan sektor teknologi yang tercatat di BEI tahun 2020-2022 untuk mengetahui pengaruh leverage, transfer pricing terhadap agresivitas pajak. Dengan menggunakan metode purposive sampling diperoleh jumlah sampel sebanyak 15 perusahaan, dan dari hasil uji SPSS versi 29 menunjukkan secara parsial, variabel leverage terhadap agresivitas pajak memiliki pengaruh negatif dan tidak signifikan (0.882 > 0.05) dan untuk variabel transfer pricing terhadap agresivitas pajak memiliki pengaruh negatif dan tidak signifikan (0.537 > 0.05).
PENGARUH TAX PLANNING, GOOD CORPORATE GOVERNANCE TERHADAP TAX AVOIDANCE PADA PERUSAHAAN SEKTOR ENERGI YANG TERDAFTAR DI BEI TAHUN 2019-2022
Authors: Sabatini Rolinda Esterisa , Simbolon Remista
Published: November 2023

Abstract.  This study was conducted by researchers to find out effect of tax planning, good corporate governance on tax avoidance. The company focused on energy sector listed on the IDX in 2019-2022. Purposive sampling is a technique used in sample determination, thus producing samples from 12 companies over a 4-year. Through the SPSS version 29, the results prove do not have significance value (0.140 > 0.05) from tax planning, audit commitee on tax avoidance. Partially, tax planning have an effect but insignificance (0.061 > 0.05) and audit committee have a negative effect but insignificance (0.569 > 0.05).

 

Abstrak. Penelitian yang dilakukan memiliki maksud untuk melihat tax planning, good corporate governance mempengaruhi tax avoidance. Penelitian dilakukan melalui pengumpulan laporan keuangan dari sektor energi yang terdaftar di BEI tahun 2019-2022. Purposive sampling merupakan teknik yang dipakai, maka sampel yang terseleksi berjumlah 12 perusahaan dalam 4 tahun. Berdasarkan SPSS versi 29, menunjukan hasil secara simultan dan terdapat pengaruh signifikan (0.140 > 0.05) dari penelitian untuk variabel tax planning, komite audit pada tax avoidance. Secara parsial, adanya pengaruh tapi tidak signifikan (0.061 > 0.05) dari tax planning pada tax avoidance dan menghasilkan pengaruh negatif dan tidak signifikan (0.569 > 0.05) dari komite audit pada tax avoidance
PENGARUH KEPEMIMPINAN TRANSFORMASIONAL DAN BUDAYA ORGANISASI TERHADAP KINERJA MELALUI MOTIVASI PADA PERAWAT DI RUMAH SAKIT TRIA DIPA JAKARTA
Authors: Irianti Antania Hanjani , Bernarto Innocentius
Published: November 2023

Tujuan penelitiani ini ialah untuk mengetahui pengaruh kepemimpinan transformasional dan budaya organisasi terhadap motivasi, dan pengaruh motivasi terhadap kinerja perawat. Metode untuk survey yang dilakukan pada penelitian ini menggunakan kuesioner. Penelitian ini merupakan penelitian sensus dengan jumlah perawat 78 responden di Rumah Sakit Tria Dipa Jakarta. Analisis data pada penelitian ini memakai pendekatan PLS-Structural Equation Modelling(SEM) menggunakan Software SMART PLS. Hasil penelitian ditemukan kepemimpinan transformasional berpengaruh positif terhadap motivasi, budaya organisasi berpengaruh positif terhadap motivasi dan motivasi berpengaruh positif terhadap kinerja perawat.

 

Kata kunci: Kepemimpinan Transformasional, Budaya Organisasi, Motivasi, Kinerja
EVALUASI PERENCANAAN DALAMPENGEMBANGAN ORGANISASI BERIMPLIKASI PENINGKATAN PEREKONOMIAN MASYARAKAT KEPULAUAN NIAS MELALUI KOPERASI KONSUMEN OSSEDA FAOLALA PEREMPUAN NIAS
Authors: Harefa Animan , Ndraha Ayler Beniah
Published: November 2023

Abstract. Evaluation is one of a series of activities to improve the quality and performance or productivity of an institution in implementing a program. Organizational Development (PO) is a long-term planning program and process that brings transformational change in organizational culture through the use/application of research and theories based on Information Technology and Science. The Osseda Faolala Perempuan Nias Consumer Cooperative has become a center for women's education & learning, especially for members of the Osseda Faolala Perempuan Nias Cooperative through various capacity building activities in the form of training (TOT), critical awareness discussions or courses aimed at increasing women's leadership potential so that they are able to involve themselves & become takers. decisions either in government agencies or non-government agencies. The obstacles faced by the Osseda Faolala Women's Nias Consumer Cooperative are caused by the Vision and Mission and the rule from year to year that membership is only women in accordance with the Vision and Mission that has been developed. It can be concluded that the problem is that it collides with the rules that have been established since time immemorial, especially the Vision and Mission that have been created and continue to be adopted to this day, however, they do not address the dynamics of the needs of the community, especially the Nias Islands. The results of this research are that the Osseda Faolala Perempuan Nias Cooperative has a good understanding of effectiveness, especially in the context of planning and evaluation. Their efforts to correct discrepancies between plans and results, as well as awareness of changes in the external environment, demonstrate a mature approach to managing challenges. A good understanding of challenges and changes, strong collaboration with stakeholders, a high level of member participation, and holding a scheduled Annual Membership Meeting every year are important elements that enable an organization to address and overcome challenges, maintain transparency, and achieve goals community economic development.
Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System
Authors: Aimiyekagbon Osarenren Kennedy , Lowen Alexander , Bender Amelie , Muth Lars , Sextro Walter
Published: November 2023

This paper presents a comprehensive study on diagnosing a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, specifically obtained as part of the Asia-Pacific PHM conference’s data challenge 2023. The objective of the challenge is to identify and diagnose known faults as well as unknown anomalies in the spacecraft’s propulsion system, which is critical for ensuring the spacecraft’s proper functionality and safety. To address this challenge, the proposed method follows a systematic approach of feature extraction, feature selection, and model development. The models employed in this study are kMeans clustering and decision trees combined to ensembles, enriched with expert knowledge. With the method presented, our team was capable of reaching high accuracy in identifying anomalies as well as diagnosing faults, resulting in attaining the seventh place with a score of 93.08 %.
monitoring method for detecting and localizing overheat, smoke and fire faults in wind turbine nacelle
Authors: Lee Minsoo , Do Eunchan , Oh Ki-Yong
Published: November 2023

This study presents a monitoring method that utilizes 3D object classification to accurately detect mechanical and electrical components of a wind turbine by combining a geometric and statistic feature extractor (GSFE) with a multiview approach. The proposed monitoring method also detect outlier after executing object detection to localize overheat faults in these components with fused Optical or Infrared/LiDAR measurements. The proposed method hasthree key characteristics. First, the proposed outlier detection allocates two extremes of normal and faulty clusters by using 2D object classification/detection model or measuring thestandard deviation of temperature with sensor fusing measurements. Specifically, the outlier detection with sensor fusing measurements extracts the position coordinates andtemperature data to localize overheat faults, effectively detecting an overheat component. Second, the GSFE utilizes a group sampling approach to extract the local geometric feature information from neighboring point clouds, aggregating normal vectors and standard deviation. This method ensures the high accuracy of object classification. Third, a multi-view approach focuses on updating local geometric and statistic features through a graph convolution network, improving the accuracy and robustness of object classification. The proposed outlier detection is verified through overheat/fire field tests. The effectiveness of the proposed 3D object classification method is also validated by using a virtual wind turbine nacelle CAD dataset and a public CAD dataset named ModelNet40. Consequently, the proposed method is practical and effective for monitoring a fire and overheat component because it can accurately detect critical components with only a few virtual datasets because gathering bigdata for training a neural network is extremely difficult.
Online fault detection for industrial processes through Kalman filter
Authors: Liu Wenyi , Yairi Takehisa
Published: November 2023

Industrial processes suffer from a wide range of damages including normal wear, environmental changes, physical structural defects and so on. This paper describes the possibility of system health management based on a prediction model, i.e., state space model realized by Kalman filter. The categorical target was mapped to numerical values in advance for this purpose. To deal with the time-varying and streaming characteristics of the industrial process, the model is applied in an online fashion. Comparing with conventional fault detection techniques, this model has the advantages of monitoring not only the production process of interests through observation equation, but also the structural anomalies described via unseen states estimation. In addition, the process and measurement noises provide valuable information about the unstructured uncertainties caused by other reasons. Experiments have been conducted to valid the effectiveness of the proposed method.
Prognostics in Highly Accelerated Limit Testing Using Deep Learning Data Analysis
Authors: Shibutani Tadahiro
Published: November 2023

In this study, an anomaly detection analysis of electronic components was conducted using deep learning algorithms on time-series data of voltage monitored during highly accelerated limit testing (HALT) on inverters used in automobiles and other vehicles. We demonstrated that the anomaly detection technology of time-series data using deep learning could detect equipment anomalies/failures to achieve effective data representation, improving the reliability assurance technology with HALT.
Physical Reservoir-Based Health Monitoring of a Structure with Nonlinear Attachments
Authors: Masuda Arata , Takashima Konosuke
Published: November 2023

The purpose of this work is to discuss the possibility of the concept of physical reservoir computing (PRC) in the field of structural health monitoring (SHM) by regarding the target structure of SHM as the physical reservoir. To this end, the dynamics of the structure, which is assumed extrinsically linear, is tailored to be strongly nonlinear by installing nonlinear attachments. Our purpose is then to detect the change occurred in this augmented physical reservoir. As one possible methodology to achieve this, we propose in this study to train the output layer to learn a specific nonlinear mapping of the input so that the increase of the error may indicate the change of the reservoir. Numerical experiments are presented to examine the validity of the proposed concept.
Data-driven satellite monitoring method applicable to various telemetry
Authors: Omata Noriyasu , Tsutsumi Seiji , Masaharu Abe , Shinohara Iku
Published: November 2023

It is difficult to detect signs of faults for the rule-based health monitoring systems currently installed on artificial satellites in principle, and manual monitoring of satellite telemetry is conducted. However, due to lack of human resources, much of the data is left not yet well reviewed. In this study, a systematic telemetry monitoring method that screens anomalous ones applicable to various time-series telemetry is proposed. The proposed method estimates the normal range of future telemetry values by focusing on quantile statistics of each telemetry. The demonstrative application results to the real telemetry data are also reported.
Applications of Active Learning in Predictive Maintenance
Authors: Zaman Navid , Jun You Jung , Li Yan , Chan Daniel
Published: November 2023

Nowadays, the common choice in maintenance strategies is predictive maintenance (PdM), deprecating the corrective and preventive kinds. Even with various machine learning techniques to get advanced predictive models to achieve PdM, difficulties remain in the data acquisition process. While there is a plethora of unlabeled data from sensors, most of those available techniques can only process labeled data, i.e, supervised learning. To combat the fact that the availability of the labeleddata is limited, this paper proposes the use of Active Learning to label and annotate the informative instances while minimizing overall processing time. This approach maintains high performance and decreases the number of labeled instances, with support from experimental results and a discussion of the applicability of this method.
Enhanced Model-Based Algorithm for Early Internal Short Circuit Detection in Lithium-Ion Batteries
Authors: Jia Yiqi , Brancato Lorenzo , Giglio Marco , Cadini Francesco
Published: November 2023

Electric vehicles (EVs) are becoming more popular due to concerns about fuel shortages and environmental pollution. Lithium-ion batteries are the preferred power source for EVs because they have high energy and power densities. Ensuring the efficient, safe, and reliable operation of these batteries has been a significant focus of research in recent decades. One major concern that can affect Li-ion battery performance is thermal runaway, which can cause dangerous battery fires. Internal short circuits (ISCs) are believed to be the root cause of thermal runaway incidents in batteries, making early detection of spontaneous ISCs a critical diagnostic task. This study presents a new and simple early ISC detection method for a Li-ion cell based on the augmentation of the state space of an Extended Kalman Filter (EKF) that includes voltage and surface temperature observations. The framework allows for an estimation of the cell's internal ISC state while remaining computationally efficient. The proposed approach is demonstrated in a simulated environment using dynamic stress tests that reflect a practical battery working cycle. The results demonstrate that the method can promptly detect ISC occurrences.
Failure Prediction of Hard Disk Drives in Redundant Arrays Using Disk-Level Performance Metrics
Authors: Natsumeda Masanao
Published: November 2023

Although many hard drive failure prediction methods utilize Self-Monitoring Analysis and Reporting Technology (SMART) features, they are not collected in IT systems with demanding performance requirements to meet their specification. We present a novel data-driven method for the prediction utilizing disk-level performance metrics collected by Redundant Array of Independent Disk (RAID) controllers instead of SMART features. The proposed method computes relational anomaly scores leveraging logical relationships of Hard Disk Drives (HDDs) based on RAID configuration for better failure prediction. In addition, it further utilizes error codes from HDDs to filter out false positives. We evaluate the proposed method on a real-world dataset collected for this study from 881 disks used in disk arrays of RAID-6 and 1660 disks used in disk arrays of RAID-10 in a data center. The results show consistent performance improvement by the logical relationships and error-code-based filtering. In addition, seven out of nine failures are predicted one day before the failure at the latest. This result suggests that the proposed method provides plenty of time for HDD replacement before a failure occurs.
Development of a space exploration rover digital twin for damage detection
Authors: Pinello Lucio , Giglio Marco , Cadini Claudio , De Luca Giuseppe Francesco
Published: November 2023

This study focuses on the creation of a digital twin of a space exploration rover to perform damage detection. The digital twin incorporates various subsystems of real rovers to accurately simulate the rover’s behaviour. Damage detection is performed by introducing damages into the digital twin and comparing signals obtained in healthy and damaged conditions. By using the multiphysics model created by integrating different subsystems, the effect of damages can be observed in other subsystems of the rover. The study aims to demonstrate the potentiality of a digital twin for damage detection, reducing the risk of mission failure and data loss.
Modeling of Journal Bearings for Wear Diagnosis and Its Verification Using SVM
Authors: Goto Masaki , Inoue Tsuyoshi , Heya Akira , Katayama Keiichi , Kimura Shogo , Tomimatsu Shigeyuki , Yabui Shota
Published: November 2023

Recently, deterioration of infrastructure facilities has become problem. To reduce inspection costs, wear diagnosis of journal bearings in operating conditions using machine learning has been studied. However, constructing a highly accurate machine learning model requires training data, and it may be difficult to conduct numerous tests depending on the application. In this study, a mathematical model of a rotor system for wear diagnosis of journal bearings was developed, and the vibration data was obtained when the clearance changed due to wear. Then, changes in the features used for condition monitoring were examined. Furthermore, the important features for wear diagnosis were selected and SVM models were constructed to verify the mathematical model.
Development of demonstration system for fault diagnosis of rotating equipments using RK4 test rig
Authors: Lee Hyun Joon , Jang Jae Won , Park Hyung Jun , Noh Kyoung Rae , Lee Sangchul , Choi Joo-Ho
Published: November 2023

Rotating machinery is an essential equipment in the manufacturing industry, of which the failure can lead to the interruption of whole production line. To prevent such failure, there have been a large number of studies for Prognostics and Health Management (PHM) technology. However, most studies have been focused on a specific algorithm or component, making it difficult to apply them in the field. In this study, a demonstration system for integrated fault diagnosis is developed for critical failure modes in the rotating equipment such as the mass imbalance, shaft misalignment, and the bearing fault using the vibration signals. To this end, Bently Nevada's RK4 rotor kit is revised to impose the failure modes easily. The solution is developed to detect anomalies, identify failure modes, and diagnose them in real time. Coupling ISO-based anomaly detection, rotational failure diagnosis and bearing failure diagnosis, the system is configured to enable comprehensive condition monitoring. The results are demonstrated by real-time simulation of each fault on the test-rig.
Analysis of Diagnostic Capabilities for Degradation of Brushless Direct Current Motors Depending on Varying Simulation Data
Authors: Weigert Max
Published: November 2023

As the use of unmanned aerial vehicles (UAVs) becomes more widespread and their missions more complex, the need for safety measures for their technical components is also increasing. Among the components that are critical for the operation of UAVs, Brushless Direct Current (BLDC) motors are particularly important. This is due to their compact design and low number of wear parts, which make them well-suited for use in UAVs. In this work, test rig and simulation data of BLDC motors degradation are utilized to investigate the capabilities and limitations of different machine learning algorithms. For this purpose, suitable features representing the motor behavior are discussed. Classification and regression tasks are applied to analyze both the fault type and the degradation progress. The simulated data allows for an assessment of the influence of noise and degradation progress on the diagnosis performance. Furthermore, characteristics of various fault types and the representation of their degradation process in the simulation are discussed. The database and the derived features are shared publicly.
Application of Model-based Deep Reinforcement Learning Framework to Thermal Power Plant Operation Considering Performance Change
Authors: Watanabe Yutaka , Yairi Takehisa
Published: November 2023

In recent years, there have been increasing expectations for the development of advanced plant operational support systems that can automate complex tasks and autonomouslyoptimize operational procedures in thermal power plants. The performance of the equipment changes during operation and maintenance; hence, it is necessary to adjust the operating process to satisfy the operational constraints. In this study, we investigated a framework based on model-based deep reinforcement learning for acquiring control methods that are robust to changes in equipment performance using a digital twin model. A case study of the operational planning of a thermal power plant was presented and it was demonstrated that a stable control system can be constructed even when plant characteristics are changing.
Prediction of Impact Information of Composites Laminates Considering the Practicality
Authors: Hasebe Saki , Higuchi Ryo , Yokozeki Tomohiro , Takeda Shin-ichi
Published: November 2023

Recently, carbon fiber reinforced plastics (CFRP) have been used in various applications, including aircraft. Because they are vulnerable to out-of-plane loads, internal and externaldamage occurs when foreign objects impact them. Internal damage that can affect residual properties is difficult to find and judge from the outside without special devices, which arehighly costed and are sometimes difficult to conduct in some locations. In this study, surface contour information was obtained from impact tests on CFRP laminates, and thepredictability of compression after impact (CAI)strength was investigated using a conventional single-task random forest model, and a decision tree-based multi-task learning model with other objective variables related to impact tests. The models estimated CAI strength with around 75% R2, and the conventional single-task learning model showed the highest value. The importance of each model indicated that factors that contribute to impact-related objective variables (impactor shape, delamination area, and delamination length) and those to CAI strength do not have a strong relationship.
Observation and Prediction of Instability due to RD Fluid Force in Rotating Machinery by Operational Modal Analysis
Authors: Goto Daiki , Inoue Tsuyoshi , Heya Akira , Kimura Shogo , Nakamura Shinsaku , Watanabe Yusuke
Published: November 2023

In rotating machinery, rotordynamic (RD) fluid force is generated in fluid elements such as journal bearings, impellers, and seals. This RD fluid force is generated by the interaction of shaft vibration and fluid force and is difficult to predict because of nonlinearity. The RD fluid force has a significant effect on the stability of rotating machinery and is known to cause instability. In this study, we predicted the instability caused by RD fluid force by using operational modal analysis, which is a method to identify vibration characteristics based only on operating data.
Anomaly Sign Detection for Automatic Ticket Gates by the Histogram Limitation Method
Authors: Ueno Ken , Maya Shigeru , Endo Kiyoku
Published: November 2023

It is crucial for automatic ticket gates (ATGs) on railways, also known as fare collection systems, to detect anomalies at an early stage, especially in the automatic separation module for multiple tickets. It is also required for efficient and low-cost monitoring without any additional sensors especially for old-type ATGs that need to be maintained frequently. However, the failure rate is basically very low, and monitoring data contain various kinds of normal status indicators depending on complicated mechatronics controls. In addition, it is hard to collect high quality learning data because ATGs are affected by various ticket conditions or timing when releasing tickets by users, which makes detecting anomaly signs difficult. For these reasons, conventional machine learning or deep learning methods are not suitable for anomaly detection for ATGs. In this paper, we propose a simple anomaly detection method with new anomaly sign index, called the histogram limitation method (HLM), for effective monitoring to realize preventive maintenance of ATGs based only on system log data. Despite being a quite simple and compact method, HLM provides anomaly sign scores that agree adequately with assessments by maintenance service engineers in our evaluation with real field ATGs in operation.
Size Estimation of Flaking in Rolling Bearings Using Deep Learning with Explainability
Authors: Yoshimatsu Osamu , Taguchi Keiichiro , Yoshihiro Sato , Yairi Takehisa
Published: November 2023

To improve the availability of rotating machines such as wind turbines, where rolling bearing replacement is costly and time-consuming, it is effective to estimate the damage progression of the rolling bearings. As one of the damage progressions, the size of flaking in rolling bearings is estimated by vibration analysis using rule-based methods. However, these rule-based methods require expert knowledge of rolling bearings. Therefore, an estimation model using deep learning was proposed and its performance was evaluated. Furthermore, it was verified that the proposed model had extracted the features of physical phenomena using Grad-CAM.
Anomaly Detection in Spacecraft Propulsion System using Time Series Classification based on K-NN
Authors: Kato Yoshiki , Kato Taku , Tanaka Tsubasa
Published: November 2023

In this paper, we propose an anomaly detection method developed by the team called “Team Tsubasa” in the PHMAP2023 Data Challenge. This is an anomaly detection competition for spacecraft propulsion systems (PHM Society, 2023). We joined the Data Challenge with the aim of deepening our knowledge of anomaly detection technology through the competition. In spacecraft propulsion systems, solenoid valve faults and bubble anomalies can occur, and it is considered important to detect them. Also, when other unknown anomalies occur, it is necessary to detect them without confusing them with known anomalies. In this paper, we propose time series classification by k-NN algorithm (Cover & Hart, 1967) as one of the methods to detect these anomalies. In this data challenge, we tried to classify anomalies by k-NN and to identify the location of the anomalies. For those classified as solenoid valve faults, we estimated the opening ratio of the solenoid valve from the similarity of the time series waveforms. As a result, the proposed method achieved a score of 99.05% based on the scoring rules given by the PHMAP 2023 Secretariat and our team won third place.
Deep Neural Network Anomaly Detection and Statistical Estimation of High Pressure Liquefied Natural Gas Pipe
Authors: Yang Dabin , Lee Jongsoo
Published: November 2023

Anomaly detection method using neural network is performed for diagnosis. Liquefied natural gas pipeline is designed using finite element method. To consider abnormal condition, a damage was applied to the model. Then failure mode and effect analysis are performed to determine if the location of damage is acceptable. The designed system was validated through literatures and showed that the model is suitable to replace the actual model. Data collection was done by changing each design variables in certain range from the designed model. Designable generative adversarial network was used for data augmentation and anomaly detection with adversarial network was used for anomaly detection. The performance of anomaly detection of the proposed model showed 95% of accuracy before data augmentation and 99% of accuracy after data augmentation. The result provides statistical estimation of diagnosis range for each design variables, which clearly showed the difference of performing data augmentation. By diagnosis result, the variables are used back to the designed model for validation of the result and showed accuracy of 85%.
Development of an Operational Digital Twin of a Locomotive Braking System Solenoid Valve for Fault Classification
Authors: Davidyan Gabriel , Bortman Jacob , Kenett Ron.S
Published: November 2023

In recent years, a growing role in digital technologies has been filled by model-based digital twinning. A digital twin produces a mapping of a physical structure, operating in the digital domain. Combined with sensor technology and analytics, a digital twin can provide enhanced monitoring, diagnostic, and optimization capabilities. This research harnesses the significant capabilities of digital twining for the unmitigated challenge of fault type classification of a locomotive braking system solenoid valve. We develop a digital twin of the solenoid valve and suggest a method for fault type classification based on the digital twin. The diagnostic ability of the approach is demonstrated on a large experimental dataset.
Outlier Analysis of Bridge Deflections Using Satellite SAR and Structural Simulation: A Case Study on a Collapse Accident in a Water Pipe Bridge in Japan
Authors: Kinoshita Kosuke , Yajima Yoshiyuki , Kumura Takahiro , Nishio Mayuko
Published: November 2023

This paper proposes a method for outlier analysis of bridge deflections using satellite Synthetic Aperture Radar (SAR) and structural simulation based on the finite element (FE) analysis. The proposed method is unique in combining the following two points: (1) detecting anomalous displacement of bridges before a severe accident occurs using satellite SAR displacement analysis, and (2) attempting to estimate the factors anomalous responses on the bridge, such as damages on structural members, using FE analysis. This paper describes the results of the practical application of the proposed method to MUSOTA water pipe bridge in Japan, which collapsed on October 3, 2021.
MLOps for PHM Systems
Authors: Yemane Mikael
Published: November 2023

Advances in machine learning (ML) techniques allow practitioners to generate substantial predictive value from historical data. Modern sensors generate vast amounts of data which inform prognostic health management (PHM) systems. As ML techniques continue to grow in importance for PHM, the system that manages and deploys ML models becomes critical for successful production software. Machine Learning Operations (MLOps) is centered around implementing continuous integration and deployment (CI/CD) practices in the context of ML applications. We will present MLOps designs for deploying machine learning based PHM software and discuss ML pipelines that automate data ingestion, model training, testing, deployment, and monitoring. The principles we will examine ensure model quality, performance, and software stability. We will call attention to important design considerations and demonstrate solutions for the full model lifecycle when building MLOps pipelines for PHM systems.
Study on the Estimation of Concrete Defects Volume on Dam Body Surface
Authors: Ishii Akira , Sugawara Hiroaki , Amakata Masazumi
Published: November 2023

To maintain the safety and functionality of dams over the long term, it is necessary to make inspections more labor-saving and efficient using the latest technology and to improve the sophistication of inspections based on data. Although dam inspections cover a wide range of items, this study focuses on the continuous monitoring of popouts, a phenomenon of concrete deterioration occurring on the surface of a dam body. It is difficult to predict whether a popout will occur from information on the body surface of the dam, owing to the generation mechanism of the popout. The number of popouts was monitored over time; however, no examples of shape changes were monitored over time. Advancements in various digital technologies are required to accurately evaluate changes in the dam body's surface over time; therefore, in this study, three-dimensional (3D) point-cloud data is created by the Structure from Motion (SfM) from images captured by a Unmanned Aerial Vehicle (UAV) of the concrete defect area due to the popout in an arch dam in the Tohoku region of Japan. The volume of concrete defects of a popout in each of two different periods was calculated by estimating the plane shape of the surface of the dam body. In addition, the shapes of two popouts were compared to confirm the possibility of predictive signs of change.
Quantum Optimization for Location Assignment Problem in ASSR
Authors: Satori Kuniaki , Yoshikawa Nobuyuki
Published: November 2023

In an Automated Storage and Retrieval System (AS/RS), a location assignment of products is important to improve the picking efficiency. In this paper, the optimization of shelf location assignment with a quantum annealing is investigated. Product pairs are considered in order of picking frequency and are assigned to empty shelves in order of distance from an outlet. Then swapping the position of product in the pair is considered as the decision variable. This reduces the number of required qubits and guarantees the feasibility of solution. The efficiency of quantum algorithm is evaluated by comparing with mixed integer programming (MIP).
Non-destructive Prognostics for Rolling Bearings by Eddy Current Testing
Authors: Kobayashi Daisuke , Ono Koichiro , Natori Masahide , Komata Hiroki
Published: November 2023

Rolling bearings, which are assembled into various industrial machinery, are regularly replaced after being used for a period of time, even if they have not failed. It is important to predict failure of rolling bearings not only for safe operation of machineries but also for resource conservation. The X-ray diffraction (XRD) is known as an effective method for estimating the remaining useful life (RUL) of rolling bearings. However, it is a destructive approach sometimes requiring cutting bearing rings. Therefore, non-destructive and simple diagnostic method for estimating RUL of rolling bearings using Eddy Current Testing (ECT) has been developed by focusing on the experimental evidence that changes in microstructure of the steel cause changes in the magnetic property. Rolling contact fatigue tests were conducted using several types of rolling bearings, and it was found that the ECT measurement results on raceway surface show a determined behavior with fatigue progress. The tendency of changes did not depend on bearing type, material or heat treatment. Additionally, measurement results by ECT were related to those by XRD. Above experimental results suggest that ECT can be applied to estimate RUL of rolling bearings as a non-destructive and simple method.
V-belt Tension Reduction Diagnostic Method By Using Multi-Frequency Current Power Spectrum Density
Authors: Inoue Hiroshi , Hirakida Ken , Kanemaru Makoto , Chen Peng
Published: November 2023

Motors are used in many factories and need stable operation. A V-belt is a device that transmits motor power to a load, and its tension decreases with prolonged use, which degrades the motor efficiency and causes such abnormalities as wear and cracks. The timing of belt replacement when the belt tension decreases is presently determined by regular inspections. An automatic diagnosis of belt-tension decrease is required to lower labor costs. One automatic diagnosis method applies FFT analysis to the phase current that is applied to the motor and focuses on the signal intensity at a specific frequency. Since this method can only detect belt tension after it has progressed, early detection is necessary. Our new method using motor phase current enabled early detection of belt-a tension decrease.
New Challenge in Predictive Maintenance Analysis for Aircraft
Authors: Takemura Takashi
Published: November 2023

JAL Engineering Co., Ltd. (JALEC) has been challenging to develop the predictive maintenance by big data analysis using the flight sensor data and the maintenance data as one of our initiatives to improve our aircraft reliability. Our analysis method is to verify hypotheses based on mechanics and engineer expertise with the data. Furthermore, in order to enhance our predictive maintenance, we introduced a new analysis method to search for hypotheses based on the information obtained from the data with AI technology. This article describes how JALEC has succeeded in developing a new analysis method with AI technology in the predictive maintenance and what we want to realize in the near future as a total engineering company.
Quality Management for Machine Learning Systems
Authors: Oiwa Yutaka
Published: November 2023

We have been developing a methodology for process-based quality management of machine learning-based AI systems. Our fruit is compiled as a guideline document named “Machine Learning Quality Management Guideline”, published as our technical report. We will describe our background motivation, surrounding situation and our proposal for quality management.
Systemic symptom detection in telemetry of ISS with explainability using FRAM and SpecTRM
Authors: Iino Shota , Nomoto Hideki , Fukui Takashi , Ishizawa Sayaka , Sasaki Miki , Yagisawa Yohei , Hirose Takayuki , Michiura Yasutaka , Shibayama Hiroharu
Published: November 2023

Explainability is important for machine learning-based anomaly detection of safety critical systems. In this respect, we propose a new systemic symptom detection method by combining two methodologies: the Functional Resonance Analysis Method (FRAM) and the Specification Tools and Requirement Methodology-Requirement Language (SpecTRM-RL) with machine learning-based normal behavior prediction model. The method was verified with data of thermal control system of Japanese Experimental Module of the International Space Station, and the result found that the proposed method enables flight controllers and specialists to obtain additional information for identifying causes of anomaly with the method.