bluuebunny/crossref_metadata_2025 huggingface dataset:
DIRECTIONS OF STAFF MOTIVATION IN THE CONDITIONS OF MODERN CHALLENGES
Authors: PROKHOROVSKA Svitlana
Published: November 2023

Introduction. The article researches the components of the personnel motivation system and their role in increasing labor efficiency, outlines directions to improve the functioning of the system. The goals of employee motivation are highlighted.
It has been established that the application of different types of employee motivation in practice can have different effectiveness. Therefore, when choosing them, it is important to take into account how well they correspond to the strategy of the organization and the microclimate in the team.
It was found that motivation of the personnel should be based on the principle of combining the interests of the company and employees. A properly motivated employee makes full use of his skills, abilities, new opportunities, and resources at his workplace, which leads to the desired result and more efficient work of the entire organization.
It was determined that implementation of the personnel motivation system should be a carefully planned and gradual process. The most important thing is to gain the support of employees by informing them about the current and prospective activities of the organization.
Areas of staff motivation are singled out: individualization of wages, involvement of employees in the formation of the strategy and planning of the organization's goals; official employment and social package; creating opportunities for career growth; improving the social and psychological climate in the team and strengthening its cohesion; creation of a comfortable workplace and arrangement of rest areas for the staff; possibilities of long-term work prospects; personal and public praise from the manager, giving personal benefits and using a motivational board; labor activity monitoring; motivation with free time.
It has been established that by attracting and motivating the personnel, management receives the following benefits: high labor productivity; the ability to make innovative decisions; opportunities to quickly enter the market; increase in customer satisfaction; implementation of innovations; low staff turnover; readiness for change; the company's profit growth.
Key words: remuneration, material and non-material incentives, motivation, motivational board, staff motivation, directions of staff motivation, salary, staff, employer, working hours, work, working conditions.
ASSESSMENT OF IMPACT OF MACROECONOMIC INDICATORS ON EMPLOYEE MOTIVATION
Authors: POLHUL Dmytro
Published: November 2023

In today's globalized economy, businesses operate in a constantly changing environment, where external factors such as economic policies, exchange rates, and trade agreements can have a significant impact on their operations. The way in which these macroeconomic indicators affect employee motivation is an important consideration for enterprises, as it can have a direct impact on their bottom line. A motivated workforce is crucial for businesses to achieve their goals and maintain their competitive edge. By understanding the impact of macroeconomic indicators on motivation, businesses can make informed decisions about their human resource strategies and tailor their approach to ensure maximum productivity and job satisfaction among employees.
Purpose. The purpose of this article is to analyze macroeconomic indicators and rank them by their impact on the overall population motivation level, using a mathematical model based on data from the state statistical service for 2010 to 2022.
Research methods. This study uses theoretical methods (deduction, analysis, generalization) and economic and mathematical modeling.
Results. The regression model developed in this study can be used to forecast the initial motivation level of the country's population towards effective and motivated work at the beginning of the next period based on the macroeconomic situation. The results can be used to select the most optimal types of motivation depending on the overall situation in the country, which can help employers attract future employees.
The practical significance of the obtained results lies in the gradation of the influence of the considered macroeconomic indicators on the overall level of motivation of the population. Top management and HR departments can use this information to analyze a set of macroeconomic indicators to estimate the current motivational potential of candidates more accurately, and create job descriptions that can increase the number of applicants.
Conclusions. The developed economic-mathematical model includes only four indicators of influence on the overall motivation level of employees. The research can be expanded by introducing additional macroeconomic indicators to create a stable basis for choosing an optimal set of motivational components in the personnel management system of the enterprise.
Keywords: macroeconomic indicators, staff motivation, unemployment rate, level of shadowing of the economy, average salary
THE NEED AND POSSIBILITIES OF INSTITUTIONAL REGULATION OF THE PROCESSES OF REPRODUCTION OF HUMAN RESOURCES IN THE CONDITIONS OF TERRITORIAL DECENTRALIZATION
Authors: KACHAN Evgen , KOSHIL Anatoly
Published: November 2023

Introduction
Since the beginning of the 90s of the 20th century, a deterioration of the demographic situation has been observed in Ukraine. The permanent natural reduction in the number of people, the consequences of the pandemic, the mass displacement of the population as a result of Russian aggression led to a decrease in the population's employment, an increase in unemployment, a decrease in the standard of living, and an increase in inflationary processes. The mentioned processes actualized the problem of reproduction of human resources, which requires the implementation of measures to stop the processes of decreasing birth rates and increasing mortality rates, especially in rural territorial communities. The reform of the administrative system, carried out since 2015 through territorial decentralization, allows the processes of reproduction of human resources to be analyzed at the micro level, to investigate the trends of natural, mechanical and economic movement of the villagers, to assess the modern capabilities of local self-government bodies to prevent the development of negative processes in the reproduction of an economically active population.
The purpose of the article. To investigate the role of territorial decentralization in overcoming crisis phenomena in the reproduction of human resources of Ukraine and to highlight the possibilities of local self-government bodies in preventing the processes of depopulation of the population on the example of rural territorial communities.
Research methods: statistical, sociological, grapho-analytical, comparative, method of analysis and synthesis.
Research results: Assessment of the demographic situation in the villages of territorial communities. Determining the trends of natural and mechanical movement of the rural population, establishing the main problems of the formation of human resources, employment of the rural population, as well as the peculiarities of the formation of the modern labor market in the conditions of Russian aggression and ways to overcome unemployment in rural areas.
Keywords: territorial community, territorial decentralization, reproduction of human resources, economically active population, balancing supply and demand for labor force.
PENGARUH ESPEKTANSI UPAYA, EKSPEKTANSI KINERJA, DAN PENGARUH SOSIAL TERHADAP INTENSI PERILAKU STUDI IMPLEMENTASI MENU DIGITAL DI WEDANGAN ECHO SURAKARTA
Authors: Goutama Evin William , Leoparjo Fabiola , Soediro Moses
Published: November 2023

Abstract. The objective of this study is to figure out how behavioral intention in the implementation of the digital menu at Wedangan Echo Surakarta is influenced by effort expectations, performance expectations, and social influence. The study's population consists of Wedangan Echo Surakarta customers who own smartphones. Furthermore, the study’s method of data collecting uses questionnaires distribution. In addition, the IBM SPSS 29.0.1.0 for Windows application is used to make significant inferences from the data outputs. This study concludes that effort expectations, performance expectations, and social influence all positively influence behavioral intention in the implementation of the digital menu at Wedangan Echo Surakarta.
 
Abstrak. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana behavioral intention dalam implementasi menu digital di Wedangan Echo Surakarta dipengaruhi oleh ekspektasi usaha, ekspektasi kinerja, dan pengaruh sosial. Populasi penelitian ini adalah pelanggan Wedangan Echo Surakarta yang memiliki smartphone. Selanjutnya, metode pengumpulan data penelitian ini menggunakan penyebaran kuesioner. Selain itu, aplikasi IBM SPSS 29.0.1.0 for Windows digunakan untuk membuat kesimpulan yang signifikan dari keluaran data. Penelitian ini menyimpulkan bahwa ekspektasi usaha, ekspektasi kinerja, dan pengaruh sosial semuanya berpengaruh positif terhadap niat perilaku dalam penerapan menu digital di Wedangan Gema Surakarta.
ANALISIS KONTRIBUSI ANGGARAN DAERAH TERHADAP PELAKSANAAN KEBIJAKAN HIJAU DI KABUPATEN SIAKANALISIS KONTRIBUSI ANGGARAN DAERAH TERHADAP PELAKSANAAN KEBIJAKAN HIJAU DI KABUPATEN SIAK
Authors: Prayitno Hadi , Digdowiseiso Kumba
Published: November 2023

Abstract.    Green Siak well known as policy breakthrough which was popularized by Siak Regent in 2016. That district has a wealth of natural resources, and part of the Riau Province which is known for its intensive activities of forest and land- based extractive industry, that policy has considered as an anti-mainstream and has a strong stand for environmental protection and preservation. When the Siak District Mid-Term Development Plan for the period 2016-2021 was enacted, Green Siak had not yet been established as a policy and had no formulated goals, targets and road map. As a result, Green Siak has not been integrated into the five-year local development plan. Finally, in 2018 Green Siak was officially stipulated as a policy in the form of a Regent Regulation, which was followed by a roadmap plan through a Regent Decree in 2019. Since it was formulated as an idea until it was stipulated as a policy, Green Siak has been implemented with the support of programs, activities, and budget allocations every year an average of 2 percent to 3.1 percent of the regional expenditure. Upon further analysis, the financed activities were not fully relevant and contributed significantly to Green Siak’s performance targets.

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Abstrak.     Siak Hijau merupakan suatu terobosan kebijakan yang dipopulerkan oleh Bupati Siak sejak tahun 2016 silam. Sebagai daerah yang memiliki kekayaan sumber daya alam, dan merupakan bagian dari Provinsi Riau yang dikenal dengan intesitas kegiatan industri ekstraktif berbasis hutan dan lahan, kebijakan tersebut dinilai berani melawan arus dan memiliki keberpihakan yang kuat terhadap perlindungan dan pelestarian lingkungan hidup. Ketika Rencana Pembangunan Jangka Menengah Daerah Kabupaten Siak periode 2016-2021 ditetapkan, Siak Hijau belum ditetapkan sebagai kebijakan dan belum memiliki rumusan tujuan, sasaran, target dan peta jalan. Akibatnya Siak Hijau belum terintegrasi dalam rencana pembangunan lima tahunan tersebut. Akhirnya pada tahun 2018 Siak Hijau resmi ditetapkan sebagai kebijakan dalam bentuk Peraturan Bupati, yang disusul dengan penetapan peta jalan melalui Keputusan Bupati tahun 2019. Sejak hadir dalam bentuk gagasan sampai ditetapkan dalam kebijakan, Siak Hijau telah dilaksanakan secara bertahap dengan dukungan program, kegiatan dan alokasi anggaran setiap tahun rata-rata 2 persen sampai 3,1 persen dari total belanja daerah. Setelah ditelaah lebih jauh, kegiatan-kegiatan yang dibiayai tersebut tidak sepenuhnya relevan dan berkontribusi signifikan terhadap target kinerja Siak Hijau.
ANALISA FAKTOR-FAKTOR YANG MEMPENGARUHI PROFITABILITAS BANK DI INDONESIA YANG DIMODERASI OLEH UKURAN
Authors: Fahru Rachman  , Muslim Ahmad , Widiastuti Maria C.
Published: November 2023

ABSTRAK

 

Penelitian ini bertujuan untuk menganalisis pengaruh rasio keuangan yang terdiri dari Debt to Equity Ratio (DER), Biaya Operasional Pendapatan Operasional (BOPO), Non-Performing Loan (NPL), dan Capital Adequacy Ratio (CAR) terhadap profitabilitas (ROA) pada industri perbankan di Indonesia, dengan ukuran bank sebagai variabel pemoderasi. Penelitian ini menggunakan metode kuantitatif dengan analisis regresi data panel. Data yang digunakan berasal dari laporan keuangan 36 bank umum yang terdaftar di Bursa Efek Indonesia periode 2018-2021. Berdasarkan hasil pengujian, ditemukan bahwa BOPO berpengaruh negatif, NPL dan CAR berpengaruh positif terhadap profitabilitas bank. Sementara itu, DER tidak berpengaruh signifikan terhadap profitabilitas. Selain itu, ukuran bank terbukti memoderasi hubungan antara BOPO, NPL, CAR dengan profitabilitas bank. Hasil penelitian ini berimplikasi bahwa manajemen bank perlu mengelola rasio keuangan seperti BOPO, NPL, dan CAR secara optimal untuk meningkatkan profitabilitas, dengan mempertimbangkan ukuran bank.
PENGARUH PROFITABILITAS, RISIKO KEUANGAN, DAN PERTUMBUHAN PERUSAHAAN TERHADAP MANAJEMEN LABA PADA PERUSAHAAN MANUFAKTUR YANG TERINDEKS LQ45 DI BURSA EFEK INDONESIA PERIODE 2016-2021
Authors: Zulfia Ghina , Setyowati Tupi , Komara
Published: November 2023

Penelitian ini bertujuan untuk mengetahui dan menganalisis pengaruh Profitabilitas (ROA), Risiko Keuangan (DER), dan Pertumbuhan Perusahaan terhadap Manajemen Laba pada perusahaan manufaktur yang terindeks LQ45 di Bursa Efek Indonesia. Populasi yang digunakan berjumlah 45 perusahaan kategori indeks saham LQ45 yang terdaftar di Bursa Efek Indonesia periode 2016-2021. Data penelitian terdiri dari data sekunder yang diambil dari laporan keuangan, yang kemudian dianalisis dengan regresi data panel menggunakan program SPSS 25. Terdapat 10 sampel perusahaan yang dapat memenuhi kriteria dalam penelitian ini. Teknik pengolahan data dan analisis meliputi analisis manajemen keuangan, analisis statistik deskriptif, analisis regresi linier berganda, uji asumsi klasik, uji hipotesis, analisis koefisien korelasi, dan uji koefisien determinasi. Hasil penelitian ini menyatakan bahwa secara parsial, variabel profitabilitas berpengaruh positif dan signifikan terhadap manajemen laba. sedangkan variabel risiko keuangan berpengaruh negatif dan signifikan terhadap manajemen laba. Dan variabel pertumbuhan perusahaan tidak berpengaruh terhadap manajemen laba. Dan secara simultan, variabel profitabilitas, risiko keuangan, dan pertumbuhan perusahaan berpengaruh signifikan terhadap manajemen laba. Hasil koefisien determinasi pada penelitian ini menyatakan bahwa profitabilitas, risiko keuangan, dan pertumbuhan perusahaan memiliki kontribusi sebanyak 15,2% sedangkan 84,8% dijelaskan variabel lain diluar model.
TRANSFORMASI PERILAKU KONSUMEN DI ERA DIGITAL: STUDI DAN IMPLIKASI UMKM SAMBAL DEDE SATOE
Authors: Ratna Dewi Margaretta , Setyaningrum Idfi , Ariani Mintarti , Bujangga Bagus  Adi Pramana  , Livia Theterissa
Published: November 2023

Abstract.     Productive endeavors owned by individuals or businesses within the micro, small, and medium-sized scope are referred to as micro, small, and medium enterprises (MSMEs). In the era of digitalization, MSMEs are compelled to adapt to digital technology in their business operations, as well as changes in business practices and consumer behavior. Sambal Dede Satoe is one of the MSMEs that was initiated in 2011, producing various types of chili sauce. It is essential to recognize changes in consumer behavior in the digital era. The purpose of this research is to understand how consumer behavior has changed for Sambal Dede Satoe and how online media is utilized to sustain its business. The research method employed is qualitative, using data triangulation for validity, with primary data obtained through questionnaires and secondary data from the Sambal Dede Satoe website. The research results indicate that there are four factors influencing the transformation of consumer behavior in the digital era: product information sources, product purchasing mechanisms, satisfaction levels, and sales levels. The implications of the transformation in consumer behavior necessitated Sambal Dede Satoe to make changes in various aspects of their business, particularly in digital marketing strategies, to enhance product sales and business sustainability.
 

Abstrak.   Usaha produktif yang dimiliki perorangan maupun badan usaha dengan lingkup mikro, kecil dan menengah disebut Usaha Mikro, Kecil, dan Menengah (UMKM). Pada era digitalisasi, UMKM  dituntut beradaptasi terhadap teknologi digital dalam operasional usahanya, demikian pula cara berbisnis dan perilaku konsumen mengalami perubahan. Sambal Dede Satoe merupakan salah satu UMKM yang dirintis sejak tahun 2011 dengan produksi aneka sambal, perlu mengenali perubahan perilaku konsumen-nya di era digitalisasi. Tujuan penelitian ini adalah mengetahui bagaimana perubahan perilaku konsumen Sambal Dede Satoe serta pemanfaatan media online yang  diterapkan  guna  mempertahankan  bisnisnya. Metode penelitian yang digunakan adalah metode kualitatif dengan teknik keabsahan triangulasi data, sedangkan data yang digunakan adalah data primer melalui kuesioner serta data sekunder melalui website Sambal Dede Satoe. Hasil penelitian menunjukkan bahwa ada empat faktor yang mempengaruhi transformasi perilaku konsumen di era digital yaitu sumber informasi produk, mekanisme pembelian produk, tingkat kepuasan, dan tingkat penjualan. Implikasi dari transformasi perilaku konsumen yang terjadi menuntut Sambal Dede Satoe melakukan perubahan di berbagai aspek bisnis khususnya pada strategi pemasaran digital guna peningkatan penjualan produk dan keberlanjutan usaha.
ANALISIS URGENSI PELATIHAN DALAM PENGEMBANGAN KOMPETENSI APARATUR SIPIL NEGARA (ASN) DI PENGADILAN NEGERI GUNUNGSITOLI
Authors: Lase Yusnidar , Ndraha Ayler Beniah
Published: November 2023

Abstract.     This research investigates the imperative need for training in enhancing the competencies of Civil Servants (ASN) within the Gunungsitoli District Court. Competency development among ASN is a critical aspect of ensuring effective and efficient public service delivery. Through a comprehensive analysis, this study assesses the current competencies of ASN, identifies skill gaps, and evaluates the role of training programs in addressing these gaps. The research also explores the impact of competency development on organizational performance and service quality. Findings from this study are expected to inform policy recommendations for optimizing training initiatives and enhancing the overall capacity of ASN at the Gunungsitoli District Court.
Kinerja Keuangan Perbankan Syariah: Peran Lingkungan Eksternal, Sumber Daya dan Strategi
Authors: Suganda Uce Karna , Rachmawati Mariana , Kurniawan Ryan
Published: November 2023

This research aims to determine the influence of external environmental forces, company resources, and competitive strategy on the performance of the BJB Syariah Bandung banking business. The research method used is the explanatory method. The sample used was 99 BJB Syariah Bandung offices in West Java. The data analysis technique used is multiple linear regression analysis. The research results show that the strength of the external environment, company resources, and competitive strategies as well as banking financial performance are in "fairly good" condition. External environmental forces, company resources and competitive strategies have a positive and significant impact on banking financial performance at BJB Syariah Bandung. External environmental forces, company resources, and competitive strategies influence banking financial performance. Suggestions for improvement were submitted to the management of Bank BJB Syariah Bandung, including several analyzes and improvements related to external environmental forces, company resources and competitive strategies.
Automating daily inspection for Expressways using anomaly detection model
Authors: Shirakawa Yuta , Ito Satoshi , Noda Reiko , Yoshitani Naoto , Wake Masahide , Takano Honoka
Published: November 2023

Because of the high speed at which vehicles travel on highways, even small irregularities on the road surface can lead to serious accidents. It is important to conduct daily visual inspections to detect these abnormalities at an early stage and to repair them quickly. We are considering replacing a part of visual inspection with automatic classification using image recognition. Automating inspections will make it possible to increase inspection frequency and expected to reduce the variation in quality due to the skill of inspectors. In this paper, we report on an AI-based inspection system and evaluation results using actual highway driving video captured by an in-vehicle camera.
improved OAKR approach to condition monitoring of rotating machinery
Authors: Zhang Kexin , Jiang Xiaomo
Published: November 2023

Faults in main subsystems or components of a rotating machine often causes unscheduled shutdown, which may lead to not only huge economic losses, but also safety accidents. As an important part of intelligent maintenance, condition monitoring becomes a powerful tool in reducing maintenance costs through automatic fault alarming, thereby reducing potential downtime while improving system safety and reliability. An optimized auto-associative kernelregression (OAKR) model has been proposed recently and demonstrated as a promising tool for condition monitoring of various turbomachines, which is independent of fault mode and machine type. However, the fault identification accuracy of this approach largely relies on data quality in practical applications. Data incompleteness, parameter variation and system complexity often result in the inaccuracy of fault alarming for complicated rotating machinery. This paper proposes an improved OAKR method to address these issues, including utilizing wavelet packet Bayesian thresholding method (WPB) to reduce noise in the raw multivariate data, developing the Manhattan distance to calculate the sample similarity, and constructing a multivariate health index based on Multivariate Permutation Entropy to identify potential faults in equipment condition monitoring. Parametric analysis and a comparison study with original AAKR and OAKR methods by using the actual data of a gas turbine are conducted to illustrate the effectiveness and feasibility of the proposed methodology.
Signal Pre-processing Method for Condition Monitoring based on Vibration Signals from On-Site Manipulators
Authors: Seo Hea-Ryeon , Lee Geonhwi , Sik Gun , Min Jae , Kim Deog Hyeon , Park Jin Woo , Choi Hae-Jin
Published: November 2023

Handling irregular and noisy field data is challenging in condition monitoring. In contrast to refined lab data, where external influences are kept to a minimum, acquired signal from accelerometer attached to mechanical devices involves a great deal of uncontrollable variables. Especially, irregular operation cycles of the process make difficult to specify significant vibration signals for monitoring without mechanical expertise and information of the manipulators' motion. In this study, we distinguish motion signals from noisy raw signal using Shannon Energy Envelope (SEE). The extracted individual motion signals are algorithmically clustered through the signal graph characteristics for each robot motion. Clusters are evaluated for the effectiveness of monitoring, and it enables users to obtain a reference whose signal can perform the same accuracy for condition monitoring with expert knowledge.
Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses
Authors: Hoang Van Truong , Nguyen Quang , Nguyen Quoc Cuong , Nguyen Xuan Phong , Nguyen Hoang
Published: November 2023

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experimentsshow that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.
Automatic Generation of Seven-Segment Display Image for Machine-Learning-Based Digital Meter Reading
Authors: Gushima Kota , Kashima Takahiro
Published: November 2023

This paper presents a novel approach for automating the reading of seven-segment displays using machine learning, specifically addressing the concern of acquiring training data. By developing an algorithm that can automatically generate training images, the need for seven-segment display digit image acquisition was significantly reduced, making the process more efficient and cost-effective. In addition, by automatically generating images, a large amount of training data can be acquired. The training images include noise, such as sunlight reflections, shadows, and blurring due to camera shaking. The proposed method employs a machine-learning model trained on a diverse dataset of synthetic images generated by an algorithm. This dataset includes various fonts and styles, enabling the model to predict the meter values displayed on various fonts of the seven-segment liquid crystal display. By leveraging this auto-generated image set, the model effectively eliminates the labor-intensive process of manually capturing and annotating real-world meter images. The experimental results demonstrated the effectiveness of the proposed approach, with a reading accuracy of 96.8%.
Health Monitoring of Power Semiconductor Module Using Temperature Sensitive Electrical Parameter
Authors: Lee Guesuk , Choi Sungsoon , Ma Byongjin , Jemini Kim
Published: November 2023

Power semiconductor modules (PSMs) are critical components in power electronics applications such as motor drives, renewable energy systems, and electric vehicles. The reliable operation of these modules is crucial for the safe and efficient operation of these systems. One of the most common failure mechanisms in PSMs is due to overheating or repeated heating and cooling, which can result in thermal stress and component degradation. Therefore, monitoring the temperature of PSMs is essential for ensuring their health and preventing catastrophic failures.
By monitoring temperature-sensitive electrical parameters (TSEPs), such as the on-state voltage drop, it is possible to detect changes in the temperature of the PSM in real-time. The change in voltage drop can be used as an early warning sign of a potential failure or degradation of the PSM. The advantage of this method is that it provides a non-invasive and real-time monitoring solution that can detect changes in the PSM's temperature distribution.
Simplified Framework for Fault Prediction in Radar Transmitter based on Vector Autoregression Model
Authors: Murtala Sheriff , Hur Soojung , Park Yongwan
Published: November 2023

The prediction of faults in radar subsystems remains a challenge. It is common practice to use multiple sensors to monitor the performance of electronic components in radar. The complexity of processing the measurements increases with the number of monitored quantities. In this paper, we presented a simple method to predict the fault degradation of radar transmitter. Using historical data of monitored quantities leading to two different faults, the vector autoregression model is applied to predict future values of monitored quantities resulting in fault degradation in marine radar. The results showed that the proposed method can be useful for cases where failure in subsystem needs to be promptly detected and corrected to avoid overall system failure. We also demonstrated the performance of the proposed method on interpolated data generated from radar transmitter fault data.
Easy-to-Use and Customizable Data Science Tool for Predictive Maintenance in Manufacturing
Authors: Sugawara Naoki
Published: November 2023

Even if many manufacturing companies have attempted to utilize data analytics for predictive maintenance but have not been able to do so. This is because predictive with data requires both knowledge of equipment in production site and knowledge of data science, and such workers are scarce. Another problem is that it takes time and effort to apply the created machine learning models on site and to develop applications for monitoring the diagnosis results.
Therefore, we developed “MELSOFT MaiLab”. The application has an intuitive interface and functions to automatically create machine learning models, making it possible for workers with no knowledge of data science to easily create machine learning models for predictive maintenance. The application also has functions for deploying the created machine learning models to the production site and monitoring the diagnostic results.
Demonstration of model-based real-time anomaly detection in a JAXA 6.5m×5.5m low-speed wind tunnel.
Authors: Hamato Shotaro , Tsutsumi Seiji , Yamashita Hirotaka , Shiohara Tatsuro , Hirotani Tomonari , Kato Hiroyuki
Published: November 2023

In this study, real-time anomaly detection in a wind tunnel was conducted using a threshold based on uncertainty quantification of a numerical model. A model-based numerical model of a wind tunnel was developed, and the uncertainty consisting of input uncertainty, model form uncertainty, and numerical approximation was quantitatively evaluated. The threshold of anomaly obtained here was demonstrated in a 6.5m×5.5m wind tunnel of Japan Aerospace Exploration Agency (JAXA). Synthetic anomaly injected into the measurement system was successfully detected.
Anomaly Detection in Air Handling Units using Motor Current Signal Imaging for Belt Looseness Detection
Authors: Yun Sung Hyun , Jung Wonho , Lim Daeguen , Park Yong-Hwa
Published: November 2023

An air handling unit (AHU) is a critical component of heating, ventilation, and air conditioning (HVAC) systems. Slip of AHU is an intuitive key feature for monitoring a belt looseness fault of an AHU. However, fluctuating rotation speed of the motor and fan makes slip hard to monitor. Since the role of the belt is to deliver torque between the motor and fan, this leads to change of the motor current signal. This paper suggests a normal data-based anomaly detection that utilizes motor current signal imaging to identify belt looseness in AHUs. The overall process proceeds as followings: (1) converting 1-dimensional motor current signal into 2-dimentional image in the amplitude domain, (2) extracting features of normal data by applying convolutional neural networks, (3) calculating health index to detect the belt looseness fault. The technique to transform time-series current data to an image is based on its histogram. The image is obtained by the inner product of the histogram obtained from a current signal and its transpose. The effect of torque load on a motor induces an amplitude modulation of the current signal. Current signal imaging based on histogram provides the fault features in a robust way. To validate the proposed method, a case study using an AHU testbed is conducted. The results demonstrate that the proposed method can detect belt looseness faults in AHU using only normal data, providing an approach for early fault detection in HVAC systems.
Improvement in Identification Accuracy of a Failure Diagnostic System for a Reusable Rocket Engine
Authors: Nagashima Fumihisa , Mori Hatsuo , Ishikawa Yasuhiro , Masaki Sato , Hashimoto Tomoyuki
Published: November 2023

As a technology for safe and efficient operation of reusable rockets, we are developing failure diagnosis technology for reusable rocket engines. In order to follow the changes in rocket engine operating conditions, a failure diagnostic method which monitors an error vector: the difference between the predicted and measured values of the sensors was developed. The method contains anomaly detection by Mahalanobis distance and failure identification by support vector machines (SVMs). In this report, combinations of monitoring sensors of SVMs for each failure mode were optimized by using design of experiments. By using optimal sensor combinations, the F-score of SVMs were improved in all failure modes. From the results of the orthogonal table experiments, it was supposed that sensors which show the difference in failure modes are important to distinguish failure modes. In addition, a failure classifier combined with the optimized SVMs for each failure mode was developed and demonstrated. The performance of the combined failure classifier with the optimal sensor combination was mostly greater than that with all sensors. However, degradation of the classification performance was also obtained. It is necessary to consider how integrate the results of SVMs which are optimized individually.
Experimental Study of the Effect of Patrol Inspection Strategy for Improving Detection Rate of Abnormality of Industrial Plants
Authors: Gofuku Akio , Kishimoto Riku
Published: November 2023

Safe and stable operation is very important in large-scale plants. Patrol inspections are comprehensive and frequent on-site inspections and are indispensable works because early detection of a malfunction or failure of a component is desirable to achieve safe and stable operations. This study supposed that the detection rate of anomalies by patrol inspections is related with not only the amount of knowledge about plants but also the modalities such as vision and listening that are conscious during inspections. This study experimentally examined the difference in the detection rate of anomalies depending on patrol inspection strategies. In the experiment, a simulated small plant using the devices that are similar to plant components was constructed as a site for patrol inspections, and two types of patrol inspection strategies were examined: visual strategy that focuses on watching component conditions and auditory strategy that focuses on listening abnormal sounds. Fifteen volunteers participated in the experiment. The relationship between patrol inspection strategies and the approach and performance of patrol inspections were investigated by observing patrol inspection behaviors of participants and measuring their anomaly detection rates. As the results, although there was a small difference in the detection rate for visually detectable anomalies, the effect of the auditory strategy was suggested to improve the detection rate for not only auditory detectable anomalies but also visually detectable anomalies.
Unsupervised Retrieval Based Multivariate Time Series Anomaly Detection and Diagnosis with Deep Binary Coding Models
Authors: Mizoguchi Takehiko , Kobayashi Yuji , Ajiro Yasuhiro
Published: November 2023

Retrieval based multivariate time series anomaly detection and diagnosis refer to identifying abnormal status in certain time steps and pinpointing the root cause input variables, i.e., sensors, by comparing a current time series segment and its relevant ones that are retrieved from huge amount of historical data. Binary coding with a deep neural network can be applied to reduce the computational cost of the retrieval tasks. However, it is hard to pinpoint the root cause sensors that are responsible for the anomaly, once multivariate time series segments are transformed into binary codes. In this paper, we present an unsupervised retrieval based multivariate time series anomaly detection and diagnosis method with deep binary coding model, to secure both efficiency and explainability. Specifically, we first transform input multivariate time series segments into low dimensional features with a temporal encoder. Subsequently, two hash functions predict two binary codes with different lengths from each feature. The binary codes with two different lengths can contribute to accelerate both anomaly detection and anomaly diagnosis. Experiments performed on datasets from various domains including real optical network, demonstrate the effectiveness and efficiency of the proposed method.
Health Index for Satellite System Based on Characteristics of Telemetry Data
Authors: Katsube Shun , Sahara Hironori
Published: November 2023

Since satellites are non-repairable systems, it is important to detect anomalies early to prevent failures. Data-driven approaches to anomaly detection, which have been activelystudied in recent years, have problems such as low explainability and insufficient training data in initial operation. Thus, we propose a health index that is commonly available for all satellites. It is possible to share training data and examples of anomalies by monitoringhealth status with the same index across different satellites. In this study, we extended our health index defined for the satellite power system to the entire satellite system. Then,we applied the health index to the operational data of the Suzaku satellite and confirmed that the index is useful for anomaly detection.
Integration of Health Monitoring of Cutting Tools and Production Scheduling in Smart Factory
Authors: Komoto Hitoshi , Herrera German , Herwan Jonny , Furukawa Yoshiyuki
Published: November 2023

Smart factory evolves by adding new functions or upgrading existing functions to meet the needs of manufacturers in the use stage of the life cycle. To reduce complexity of smart factory, these functions must be carefully designed considering interactions with other functions. This study analyzes integration of functions situated in the different layers in the functional hierarchy of smart factory. In this study, a health monitoring system for cutting tools in a shop floor, which offers a function to manage the lifetime of cutting tools, is presented. The system is integrated with a production scheduling system, which offers a function to schedule machining processes considering efficient usage of machines as well as cutting tools, while maintaining the quality machining processes. Primary evaluation of the functional integration shows that activities in the shop floor regarding selection and replacement of cutting tools are considered in defining production schedules. It also shows that such functional integration results in increase in complexity regarding the behavioral model of humans in smart factory.
Demonstration of Sensor Monitoring of Lubricants
Authors: Kojima Kyoko
Published: November 2023

Due to the spread of carbon neutral, the effective use of lubricant has been drawing attention. Since the main component of lubricant is petroleum-derived hydrocarbon oil, reducing the amount used by 1 kg will reduce CO2 by approximately 3 kg. The value of CO2 reduction is very important. In order to reduce the amount of lubricant used, there is a movement to reduce the frequency of lubricant exchange or continue to use lubricant without exchanging it. However, it is known that lubricant-induced mechanical failures occur. For this reason, equipment condition monitoring using oil sensors has been spread. The color of the lubricant, also called machine blood, indicates the condition of the machine. The oil sensor measures contamination, which has a fatal effect on machine failure, and oxidation degradation, which is related to the performance of lubricant and the machine failure. Contamination includes water and wear debris, and oxidative degradation includes consumption of additives and oxidation of base oil. By digitizing the hue of wind turbine gear oil through color diagnosis using an oil sensor, the oil contamination and degradation is identified. Additives in the gear oil were quantified by liquid chromatography-mass spectrometry, and it was found that the color change of gear oil was highly correlated with the depletion of the extreme pressure additive. It is known that the depletion of the extreme pressure additive is correlated with the useful life of the gear oil. Using the technique, the remaining life diagnosis of the gear oil was shown. Demonstration in the gearbox with the oil sensor was succeeded by avoidance of the effects of air bubbles in the gear oil.
Digital Twin for condition based maintenance within a railway infrastructure testing lab
Authors: Guillén López Antonio J. , Gómez Fernández Juan Fco. , Urda Pedro , Escalona Jose Luis , Crespo Márquez Adolfo , Olivencia Fernando
Published: November 2023

This article presents a digital twin development in a railway case, in order to improve operations and maintenance decisions, aligned with an asset management strategy. A digital framework for the sustainable management of these assets is defined with the purpose of facilitating the implementation on a cloud platform, searching the generation and sharing of the produced models and the evaluation from different perspectives. The developed digital twin allows for the digital representation of railway lines and vehicles, the connection between different entities based on an ontology, the management of data ingestion and storage, and the administration of models for the detection, diagnosis, and prognosis, as well as the representation and control of the level of risk of the assets. When emulation of railway line degradation is searched, different types of data are combined, from on-board sensors in railway vehicles, and physical behaviours, up to machine learning algorithms for estimation. In this way, the degradation behaviour model for the railway line is shown and validated through intelligent models easily replicated in several areas of the railway network, showing risk levels for each one.
Research on the method of digital twin operation and maintenance platform for intelligent early warning of wind turbine tower
Authors: Jia Yu , Jiang Xiaomo
Published: November 2023

Wind power generators have a complex structure and operate in harsh environments, where working conditions are highly variable. As a result, the operation and maintenance of wind turbines face numerous challenges. In response to the need for the development of wind power operation and maintenance informatization, it is necessary to satisfy the requirements for multi-party collaborative monitoring to ensure the long-term safe and reliable operation of wind turbines. In this paper,we proposed a method for building an intelligent early-warning digital twin platform focused on the simulation of wind turbines and tower components. The platform construction method proposed in this article is based on the Web and from the perspective of intelligent operation and maintenance of wind turbines. It establishes a warning model for tower agent simulation and vibration signal time series prediction. The tower mechanism model is established based on the operating data set of a 4MW wind turbine at Shanghai Electric. Different physical responses of the tower under different wind speeds are simulated, and an agent model using LSTM and decision tree models is established for predictive analysis. To account for uncertainty, a Bayesian-LSTM model is established to warn against predictive errors. Finally, a data-driven digital twin wind turbine platform is achieved on the Web.
Model Based and Big Data Enabled Predictive Maintenance Capability Development Experience
Authors: Mazarek Mark , Macer Darren , Wang Changzhou
Published: November 2023

Airplane health management and predictive maintenance have been in place for many years and has been implemented across the industry on various platforms with some success and some challenges. Predictive maintenance leaders have a unique challenge of developing talent pipelines, technology focus areas, balanced with delivery of prognostic insights to customers. This paper will discuss strategies, lessons learned for how to build, grow and sustain a team of engineers, data scientists, software developers and others focused on delivering aerospace prognostic insights. It will also consider how to implement metrics for measuring performance such that the team is persistent, and motivated in their endeavors.
Industrial automation findings: Smart Manufacturing Kaizen Level (SMKL) based maturity evaluation access to digital transformation and decarbonization
Authors: Otoki Nicole S. , Onodera Yasuo , Fujishima Mitsushiro
Published: November 2023

Digital transformation is fostering the evolution within the manufacturing industry, entering a new era of smart manufacturing. Manufacturers are facing the challenge in organizing various of disruptive events, while supporting their business sustainability in a coordinated manner across manufacturing. Especially, in response to Paris Agreement, decarbonization has arisen as one of the essential keys to smart manufacturing for manufacturers to achieve their business sustainability. In this paper, we present a maturity model called Smart Manufacturing Kaizen Level (SMKL), which evaluate smart manufacturing implementation maturity by applying manufacturing data and supporting on implementation with reasonable investment. With SMKL, manufacturers can have a better understanding of their efforts in the smart manufacturing journey and how to commit to their decarbonization targets in scaling up implementation continuously. We also provide real life case studies utilizing SMKL to lower the barriers in understanding and assure the effectiveness of smart manufacturing assessment in practice.
Anomaly data synthesis and detection via domain randomization
Authors: Jun Joonha , Lee Jongsoo
Published: November 2023

The demand for a large amount of data necessary for learning is increasing with the great development of artificial intelligence. The synthesis of engineering data is challenging in that it is not only to combine data, but also to proceed with data synthesis while keeping the engineering characteristics intact. To address this problem, this work proposes a synthesis and detection model of anomalous data utilizing domain randomization. This model learns data from existing systems to identify patterns and synthesizes new data by itself with domain randomization. The learned model can accurately detect anomaly data in the system in various environments.
Automatic Detection of Concrete Surface Defects Using Deep Learning and Laser Ultrasonic Visualization Testing
Authors: Saitoh Takahiro , Ohyama Syumpei , Kato Tsuyoshi , Hirose Sohichi
Published: November 2023

In recent years, nondestructive testing of civil engineering structures has become increasingly important. Non-destructive inspection methods for civil engineering structures generally use ultrasonic waves. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors. Therefore, this study proposes a laser ultrasonic visualization testing using AI. The proposed method will be applied to a concrete structure with surface defects to confirm the effectiveness of the proposed method.