bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Google Earth Engine, Machine Learning, and GIS for Land Use Land Cover Change Detection in the Federal Capital Territory, Abuja, between 2014 and 2023
Authors: Okoduwa Akus Kingsley , Amaechi Chika Floyd
Published: June 2024

This study aims to visualize various land use land cover (LULC) classes, estimate the net change in LULC types between the years 2014 and 2023, and use transition mapping to track LULC transitions within waterbody, vegetation, bareland, and buildup to better understand how land use types change from one another. Google Earth Engine (GEE), Random Forest (RF), and Geographic Information System (GIS) were used for LULC classification and change detection. The dataset employed for this study is Landsat-8 with 30 m resolution, and four land cover classes, which include waterbody, vegetation, bareland, and buildup, were classified for this study using RF. The training samples for each class were divided into two groups: 60% were used for LULC classification, and 40% were used for accuracy assessment. The overall accuracy (OA) for 2014 and 2023 was 91% and 92%, while the Koppa coefficient (KC) for 2014 and 2023 was 0.88% and 0.89% giving validation to the quality and reliability of this research. The result showed that between 2014 and 2023, waterbody increased from 27.56 km2 to 36.67 km2, vegetation cover decreased from 3,153.73 km2 to 1,283.62 km2, bareland increased from 3,660.74 km2 to 4,845.30 km2, and buildup area increased from 541.21 km2 to 1,217.69 km2. The transition results show that vegetation cover is being rapidly replaced by bareland and buildup. The results of this study are crucial for directing land use planning in Abuja and other developing regions of the world to enhance sustainable urban expansion. To help the government and policymakers support sustainable development, further research should be conducted to deepen the understanding of urban growth and its consequences.
Kultura pedagogiczna rodziców jako czynnik sprzyjający kształtowaniu i wzmacnianiu rezyliencji u dzieci
Authors: Błasiak Anna
Published: June 2024

Przemiany społeczno-kulturowe z jednej strony stwarzają szanse rozwojowe, a z drugiej stanowią zagrożenie i wymuszają konieczność zmiany. Współczesny człowiek, aby nadążyć za tempem życia, musi reagować na zmiany, adaptować się do nich oraz aktywnie w nich uczestniczyć. Ważne staje się nabycie nowych umiejętności. Dlatego pojawiają się nowe oczekiwania i wymagania związane z wychowaniem rodzinnym najmłodszego pokolenia. W tej perspektywie ujawnia się zjawisko resilience/rezyliencji, które może być sposobem na dostosowanie się do zmiennych okoliczności oraz sprzyjać otwartości na modyfikację w warunkach pojawiających się barier i nieprzewidzianych wydarzeń życiowych. Kultura pedagogiczna rodziców jest zjawiskiem, które zasługuje na szczególną uwagę, ponieważ w zależności od jej poziomu, może przyczyniać się do dobrostanu oraz sukcesu życiowego i edukacyjnego dziecka lub do jego niepowodzeń. Celem artykułu jest naukowa refleksja nad kulturą pedagogiczną rodziców w kontekście kształtowania rezyliencji u dzieci, która może być źródłem budowania i utrzymania ich psychicznej odporności i dobrostanu. Postawiono problem badawczy: w jaki sposób elementy strukturalne kultury pedagogicznej rodziców mogą przyczyniać się do kształtowania i wzmacniania rezyliencji dziecka? Dla realizacji postawionego celu zastosowano syntetyczno-analityczny przegląd literatury istotnej dla podejmowanej problematyki. Dokonane analizy odwołują się przede wszystkim do ujęcia kultury pedagogicznej rodziców przez Józefa Wilka oraz psychologicznych i pedagogicznych
Penyuluhan Ekonomi Syariah bagi Generasi Milenial
Authors: Wahyudi Heru , Usman Moneyzar , Palupi Widia Anggi
Published: June 2024

Purpose: The purpose of this activity is to increase the knowledge of the millennial generation about the urgency of the Islamic economy so that the millennial generation has enthusiasm to learn about the Islamic economy. This is appreciated by studying Islamic economics to increase the understanding of the millennial generation and can be implemented in everyday life.
Research methodology: The method used in this service is classroom learning or training in the form of lectures, discussions, questions, and answers given to all participants involved in this activity. The participants in this activity were students of the Faculty of Economics and Business, University of Lampung, with as many as 80 people. Participants were selected because their age was very potent in increasing their understanding of the Islamic economy.
Results: The material presented included an introduction to the basics of the Islamic economic economy, the urgency of the Islamic economy, and the Islamic economic community, successfully absorbed by 90% of participants with a value of A and the rest with a value of B.
Limitations: The limitations of community service need to improve the use of technology, such as when conducting post-tests.
Contribution: This activity causes the knowledge of the Islamic economy that has been obtained to be rounded and invites blessings and rejects reinforcements.
Pengusaha Kecil Naik Kelas, Pelatihan Wirausaha Desa Tingkat Provinsi Jawa Barat
Authors: Sukandi Pipin
Published: June 2024

Purpose: Sociologist David McClelland said that, if you want a developed country, at least 2% of the population is an entrepreneur. This was realized by the West Java provincial government to continue to intensify entrepreneurship training for the community, especially in Bandung Regency.
Method: This method was carried out offline in the form of entrepreneurial training for the community in Bandung Regency, starting with developing an entrepreneur’s mindset to create jobs for themselves and the surrounding community.
Results: The results will be continuous training,  and in the future, it is hoped that there will be mentoring for those who open entrepreneurs.
Limitation: The limitation of this activity is in terms of time and cost, so it can only be implemented for the people of Bandung Regency, West Java Province.
Preservasi Artefak Islam Situs Bongal dan Penegasan Penting Awal Islam di Nusantara
Authors: Siregar Muklis , Prayogo Agung
Published: June 2024

Penelitian ini menyoroti peran penting Situs Bongal yang terletak di Desa Jago-jago, Kecamatan Badiri, Kabupaten Tapanuli Tengah, Sumatera Utara, dalam meningkatkan pemahaman penyebaran Islam dan peradaban maju di nusantara. Metode perpustakaan digunakan dengan mengumpulkan hasil temuan artefak peneliti BRIN dan arkeolog Sumut kemudian disajikan dengan teknik triangulasi data. Hasil penelitian menjelaskan bahwa temuan arkeologi seperti sisa-sisa perahu kayu kuno yang erat kaitannya dengan tradisi tambuku-tikan menggambarkan adanya peradaban yang mapan pada abad ke 7 Masehi, memperkuat teori masuknya Islam. melalui jalur perdagangan laut. Kehadiran koin Umayyah pada temuan Situs Bongal juga memperkuat teori masuknya Islam ke nusantara, khususnya teori Mekkah yang menyatakan bahwa Islam masuk pada abad ke-7 Masehi oleh bangsa Arab. Penelitian ini menyoroti upaya yang lebih besar dalam pelestarian artefak yang ditemukan di Situs Bongal. Diperlukan upaya regulasi yang efektif dan partisipasi aktif masyarakat lokal, pemerintah daerah, dan lembaga terkait untuk menjamin kelestarian dan keberlanjutan situs ini sebagai bagian warisan budaya Indonesia yang tak tergantikan. Dengan menekankan peran Situs Bongal sebagai titik awal penyebaran agama Islam di nusantara serta mendesak untuk memperkenalkan dan mensosialisasikan temuan tersebut kepada masyarakat.
Riyadhah sebagai Model Alternatif Pembinaan Akhlak
Authors: Basri Junaidin , Sulaiman Husnan , Mutaqin Ade Zaenul
Published: June 2024

Penelitian berujuan untuk menemukan sistem pendidikan akhlak melalui kegiatan riyadhah, kajian In-put (syarat santri dan tujuan riyadhah, Proses Riyadhah (materi/bahan, tahapan kegiatan, evaluasi kegiatan, dan Out-put-nya. Metode yang digunakan dalam penelitian adalah kualitatif dengan pendekatan fenomenologis, analsis data penelitian menggunakan teori sistem. Hasil penelitian menunjukkan bahwa antara Riyadhah dan akhlak berkorelasi dalam mewujudkan alternatif pembinaan akhlak para santri khususnya dan manusia pada umumnya. Hal tersebut sejalan dengan unsur-unsur dalam Ilmu Pendidikan Islam, yakni : (1) Input menjadi peserta riyadha; tidak terbatas pada usia, pendidikan dan profesi. (2) Tujuan Riyadhah; mendidik santri lahir batin menuju kedekatan kepada Allah SWT (sang pencipta), melalui pemantapan iman dan takwa, membentuk kepribadian yang berbudi pekerti luhur dan berakhlak mulia. (3) Materi riyadhah; membaca Al-Qur’an, bacaan wirid, berdzikir, shalat-shalat sunat, berpuasa dan uzlah. (4) Proses kegiatan riyadhah terbagi kepada tiga bagian, yakni rutinitas wajib, rutinitas sunah, dan kegiatan insidentil. Sedangkan ke-(5) Evaluasi kegiatan riyadhah terdiri dari evaluasi dari guru (Kyai) dan evaluasi dari pelaku riyadhah (santri). evaluasi dari guru seperti teguran/arahan langsung tentang berbagai kekurangan dan kelemahan di dalam menjalankan riyadhah. Evaluasi dari santri sebagai upaya memperbaiki diri dari berbagai pelanggaran menuju kesempurnaan akhak. Sedangkan outputnya menjadi manusia yang berakhlakul karimah. Oleh karena itu peneliti merekomendasikan kepada sekolah untuk menggunakan model Riyadhah sebagai salah satu alternatif dalam melakukan pembinaan akhlak para siswa.
Presentación n. 23 n. 2 (2023)
Authors: Arancibia María Cristina , Gomes Maria Carmen
Published: June 2024

En esta oportunidad tenemos el agrado de presentar el número 23(2) de la Revista Latinoamericana de Estudios del Discurso. Este segundo número cierra 2023, un año que ha sido intenso mundialmente por los conflictos bélicos que han afectado a Europa y al Medio Oriente. En nuestra región, los procesos políticos y sociales dan cuenta del dinamismo vital que experimentan nuestras sociedades. El segundo número de nuestra revista ha querido hacerse eco de este dinamismo social, a través de ocho artículos y una reseña de libro.
As fake news como base do populismo de direita brasileiro: entre o político e o digital, o algoritmo
Authors: Carreon Renata
Published: June 2024

O populismo, entendido como uma forma autoritária de democracia eleitoral, tem prosperado de maneira global. A figura do populista carismático, messiânico e “porta-voz” do povo contra “os inimigos” desponta em várias nações em resposta ao apelo de uma parcela da população à retomada do conservadorismo. Em razão disso, objetivamos compreender (i) de que forma as fake news divulgadas por Bolsonaro são o sustentáculo do populismo autoritário no Brasil e (ii) como o digital é a própria condição de possibilidade desses dizeres. Como corpus, utilizaremos enunciados que circularam ao vivo pelas redes do ex-presidente e que foram considerados mentirosos pela plataforma de checagem Aos Fatos. Em seguida, dividiremos o material em: (a) ideologia de gênero; (b) defesa do aborto; (c) defesa do uso de drogas; (d) incentivo à criminalidade. Para tal, mobilizaremos o arcabouço teórico e metodológico da Análise de discurso produzida no Brasil por Orlandi (2020a, 2020b) e Dias (2018, 2020).
Una guerra contra criminales: metáfora e ideología en el contenido lingüístico de grafitis sobre la protesta en Chile
Authors: Vásquez-Bustos Víctor Patricio , Alarcón Hernández Paola Lorena
Published: June 2024

Este estudio aborda el lenguaje metafórico expresado por grafitis en el contexto de las protestas de 2019-2020 en Chile. Con base en el Análisis Crítico de la Metáfora, se adoptan como objetivos de investigación describir las conceptualizaciones metafóricas construidas por grafitis presentes en la ciudad de Concepción y explicar su alcance en la construcción y transmisión de ideologías. Para esto, se aplican las etapas de identificación, interpretación y explicación de la metáfora, des- critas por Charteris-Black (2004) e Imani (2022). Como resultado, se evidencia la construcción de un macroescenario cognitivo, que articula las metáforas la protesta es una guerra y las instituciones son criminales, junto con escenarios de fuego y espacialidad, para representar las manifestaciones sociales como una guerra en contra de entes criminales. Dicha conceptualización tiende a polarizar el entendimiento de los participantes del conflicto chileno, promover la práctica de la protesta social y viabilizar la transmisión de creencias ideológicas en virtud de conocimiento aceptado y compartido socialmente.
Análisis de los marcadores discursivos de una historia de vida en kari'ña
Authors: Valenzuela José Beria
Published: June 2024

El mantenimiento de la cohesión y coherencia en el discurso ocurre a través de funciones realizadas por elementos léxicos. Este estudio analiza algunos marcadores discursivos del kari’ña, una lengua caribe hablada en varios países, en sus aspectos formales, semánticos y funcionales. Se optó por una investigación cualitativa de naturaleza descriptiva porque respondió a la necesidad de estudiar las intenciones del hablante cuando expresa los marcadores discursivos en el discurso. La metodología fue el Análisis Fenomenológico Interpretativo que permitió estudiar las intenciones del hablante, cómo el hablante expresa sus intenciones, y cómo dichas intenciones son entendidas por el oyente. Se analizó una historia de vida contada por un aborigen kari'ña en su lengua. Luego de clasificar los marcadores considerados, se procedió con un análisis funcional para contextualizarlos. Esto permitió precisar algunas funciones comunicativas concernientes a las operaciones estructurales para enlazar las secuencias verbales con las secuencias de eventos.
Generating Realistic Failure Data for Predictive Maintenance: A Simulation and cGAN-based Methodology
Authors: Waldhauser Felix , Boukabache Hamza , Perrin Daniel , Dazer Martin
Published: June 2024

Absence of failure data is a common challenge for data-driven predictive maintenance, particularly in the context of new or highly reliable systems. This is especially problematic for system level failure prediction of analog electronics since failure characteristics depend on the actual system layout and thus might change with system upgrades. To address this challenge, this work pursues a novel sim\-u\-la\-tion-as\-sist\-ed failure analysis methodology enabling automated and comprehensive evaluation of system level failure effects and failure detectability. While results obtained from simulations are suitable for comparative studies, they are confined to the simulation environment. To overcome this limitation, failure simulations are combined with generative models to generate realistic representations of missing failure data. Preliminary results demonstrate the capability of conditional generative adversarial networks (cGANs) to generate operational data of healthy systems, which accurately reflects correlations present in the source dataset. The proposed approach, using simulations as an additional source for generative models, not only targets the scarcity of failure data for highly reliable electronic systems but also ensures the adaptability of predictive maintenance algorithms to accommodate future system modifications and upgrades.
Timeseries Feature Extraction for Dataset Creation in Prognostic Health Management
Authors: Kontogiannis Thanos , Melfo Wanda , Eleftheroglou Nick , Zarouchas Dimitrios
Published: June 2024

This study focuses on a critical aspect of implementing prognostics and health management (PHM) for assets: the creation of a descriptive dataset. In real-world applications, dealing with sparse and unlabelled big data is common, particularly in industries like production lines where complex subprocesses are monitored by multiple sensors. Moreover, selective application of quality control means that much of the data lacks information about end properties, making datasets provided by manufacturers unsuitable for PHM frameworks. This work aims to bridge the gap between raw production data and PHM frameworks, focusing on steel manufacturing management. In the context of steel manufacturing, compromised surface quality, characterized by thicker oxide layers chipping during milling, has been observed. We propose inferring compromised coils by analyzing temperature profiles directly before the coiling station to address this. Deviations from the goal temperature profile can indicate compromised surface quality, eliminating the need for tedious oxide layer thickness measurements, which are not feasible for continuous hot strip milling processes. The available dataset comprised multiple years of production, with no direct indication of the surface quality. Exploratory clustering analysis was the first step in the lack of labels. Even though indicative of the underlying pattern of the healthy/damaged coils distinction, three shortcomings were identified. Clustering was solely based on the similarity between the temperature profiles of the coils, so no domain knowledge was included regarding the goal temperature profile. Additionally, since different steel grades have different goal profiles, the model needs to be specifically trained for each grade. Also, a soft classification between healthy and damaged can provide more detailed information about the surface quality. Coils with low-confidence classifications can be identified and treated accordingly, thereby improving PHM framework performance by providing a dataset with only high-confidence samples. To tackle these issues, an expert-knowledge-based normalization technique and feature engineering, paired with synthetic labelling, contributed to the creation of a soft neural network classifier. This study presents the reality of handling real-world data for PHM applications and highlights the need for careful and informed feature extraction. This ensures the seamless integration of PHM frameworks into real-world systems, ultimately enhancing production yield by improving end-product quality.
Design Of Digital Twins for In-Service Support and Maintenance
Authors: Barimah Atuahene
Published: June 2024

This research aims to examine the challenges in developing Prognostics and Health Management (PHM) analytics for Digital Twin (DT) use cases in industrial applications, with a particular focus on Multi-Component Degradation (MCD) scenarios. A hybrid methodology, integrating physics-informed and data-driven models, is employed, using limited asset degradation data for model development. Preliminary work includes an analysis of the impact of data quality on Fault Detection and Isolation (FDI) algorithm performance, as well as the proposal of a weighted ensemble hybrid approach for assets experiencing MCD scenarios Preliminary results indicate enhanced diagnostics in asset health management through the use of Physics-Informed models for FDI in MCD scenarios with limited prior degradation data. Expected contributions for this research are the development of physics-informed PHM analytics for DT applications in MCD scenarios, adaptive PHM analytics for evolving asset lifecycles in DT applications, and interpretable DT model analytics for PHM in systems facing Multi-Component Degradation.
LSTM and Transformers based methods for Remaining Useful Life Prediction considering Censored Data
Authors: NOOT Jean-Pierre , BIRMELE Etienne , REY François
Published: June 2024

Predictive maintenance deals with the timely replacement of industrial components relatively to their failure. It allows to prevent shutdowns as in reactive maintenance and reduces the costs compared to preventive maintenance. As a consequence, Remaining Useful Life (RUL) prediction of industrial components has become a key challenge for condition based monitoring. In many applications, in particular those for which preventive maintenance is the general rule, the prediction problem ismade harder by the rarity of failing instances. Indeed, the interruption of data acquisition before the occurrence of the event of interest leads to right censored data.
There are few articles in the literature that take that phenomenon into account for RUL prediction, even though it is common in the industrial environment to have a high rate of censored data. The present article proposes a deep-learning approach based on multi-sensor time series which allows to consider censored data during the training of the neural networks. Two methods are proposed, respectively based on the Dual Aspect Self Attention based on Transformer proposed by (Z. Zhang, Song, & Li, 2022) for non-censored data and on a recurrent neural network. Their evaluation on the C-MAPSS benchmark dataset shows, compared to the state-of-the-art RUL prediction methods, no loss in the absence of censoring, and outperformance on censored data.
Enhanced Diagnostics Empowered by Improved Mechanical Vibration Component Extraction in Nonstationary Regimes
Authors: Karkafi Fadi , Antoni Jérôme , Leclère Quentin , Yazdanianasr Mahsa , Gryllias Konstantinos , El Badaoui Mohammed
Published: June 2024

When analyzing vibration and sound signals from rotating machinery, accurately tracking individual orders is crucial for diagnostic and prognostic objectives. These orders correspond to sinusoidal components, also known as deterministic signals, whose amplitude and phase are modulated in response to the angular speed of the machine. The extraction of these components leads to a more comprehensive approach to differential diagnostics. When the machine operates under varying conditions, consistently tracking the orders becomes challenging, particularly in nonstationary regimes with very fast variations. Typically, this issue is addressed using common techniques such as Vold-Kalman filter (VKF), where the bandwidth of the selective filter is adjusted to handle the speed variations. However, in the presence of high-speed fluctuations, manual adjustment of these weights becomes difficult to balance the compromise between achieving accurate tracking by effectively filtering around the speed variations, and maintaining a low estimation bias by reducing noisy errors. To overcome this constraint, the proposed methodology is driven by the need to integrate speed fluctuations into an optimal solution using VKF. This adaptation involves the consideration of angular acceleration profiles within the innovation process. In this context, the bandwidths are automatically adjusted to their optimal values according to the machine’s regime. Optimality is achieved by crafting a model dependent on the order signal-to-noise ratio (SNR) and the auto-regression coefficient. This optimization allows for a practical adjustment tailored to the distinctive characteristics of each order. A comprehensive analysis of the resulting model transfer function reveals crucial insights into the impact of the given order SNR and the speed fluctuations. Subsequently, the methodology undergoes performance assessment through simulations and synthetic cases, showcasing its viability and effectiveness across various regimes. Notably, its practical application is highlighted in envelope-based bearing diagnosis, during operations characterized by variable-speed conditions, thus underlining its promise in real-world applications.
Feasibility of Condition Monitoring of Belt Splices in Belt Conveyor Systems Using IoT Devices*
Authors: Lindström Henrik , Öhman Johan , Meulenberg Vanessa , Gnauert Reiner , Weimann Claus , Birk Wolfgang
Published: June 2024

This paper investigates fully automated condition monitoring of belt splices within operational belt conveyor systems, using IoT devices to predict and inform on potential belt breakage or tearing. Such events cause production stops and potentially harm workers. Belt splices are laminated belt connections subject to deterioration during operation and are usually weak spots. The proposed scheme circumvents manual inspection efforts and uses the HOSCHiris DISCOVER IoT device for sensing and data acquisition. Each belt conveyor is equipped with one individual IoT device acquiring the motion signal of the scraper which is used to learn signal patterns of the pulley and the belt to identify both location and deterioration of the individual splices. Deterioration is characterized from an initial healthy condition to a severe condition of the splice to inform on the potential need for action. To assess the feasibility of the scheme, several tests are designed and performed in an industrial belt conveyor system. The results indicate that the scheme can provide valuable insights into the splice condition and its degradation.
A Flexible Methodology for Uncertainty-Quantified Monitoring of Abrasive Wear in Heavy Machinery Using Neural Networks and Phenomenology-Based Feature Engineering
Authors: Thbate  , Orchard Marcos E. , Tagle Nicolás
Published: June 2024

This paper introduces a cutting-edge methodology for the monitoring of abrasive wear, particularly focusing on SAG (Semi-Autogenous Grinding) mills liners. The lack of a regular inspection regime has historically led to opportunistic and thus, irregular wear measurements that are challenging to integrate into machine learning algorithms for condition-based maintenance. The study unveils a virtual sensor designed to estimate the mill liner's remaining thickness, aiming to offer daily updates and assist the maintenance team in determining the optimal timing for liner replacements without the need for halting operations. This approach is positioned as a strategic response to the critical need for efficient maintenance strategies, addressing the inherent challenges in real-world industrial settings where data quality may be poor and operational realities dominate. A significant aspect of this methodology is its emphasis on uncertainty quantification, vital for informed maintenance decision-making. This novel approach has been successfully applied to SAG mills at Minera Los Pelambres, showcasing its potential for broader applications across scenarios characterized by sporadic data collection. The results showcase an error of ±7.4254 mm of remaining thickness on the validation set, demonstrating the effectiveness of the methodology. The key contributions of this work lie in its ability to utilize low-quality data effectively and its low complexity, reducing barriers to implementing predictive health monitoring (PHM) algorithms. The successful implementation highlights the methodology's adaptability and flexibility, marking a significant advancement in the domain of maintenance strategy for the mining industry.
Integrated design of negative stiffness honeycomb structures considering performance and operational degradation
Authors: Kim Hyeong-Do , Noh Taemin , Kang Young-Jin , Kim Nam-Ho , Noh Yoojeong
Published: June 2024

This study introduces an integrated framework for conceptualizing the design of negative stiffness honeycomb (NSH) structures, specifically considering the durability and performance of their unit cells. Unlike conventional energy-absorbing structures that rely on plastic deformation, NSH offers a promising alternative for reusable energy absorption (EA) and high initial stiffness, making it suitable for a wide range of engineering applications. The research considers the variability in characteristics of NSH based on the shape of the configured negative stiffness beam (NSB), selecting a single curved-beam unit cell as the focal point. Extensive testing, including quasi-static and cyclic compression tests, is conducted on NSH unit cell fabricated using polylactic acid/polyhydroxy alkenoate (PLA/PHA) filament, to analyze performance under stress and to assess degradation over time. Central to the study is the use of multi-objective optimization (MOO) to explore the trade-off between performance and operational durability, thereby emphasizing the significance of degradation in the design process. The results demonstrate the potential for NSH structures, particularly in terms of their reusability and efficiency, highlighting the viability of incorporating durability considerations in the early stages of design, especially for structures intended for additive manufacturing processes.
Fault Diagnosis of Multiple Components in Complex Mechanical System Using Remote Sensor
Authors: Oh Jeongmin , Oh Hyunseok , Ryu Yong Hyun , Lee Kyung-Woo , Sung Dae-Un
Published: June 2024

This study proposes an approach to monitor multiple components in complex mechanical systems using a single, externally placed remote sensor. In automobiles and petrochemical plants, where numerous components (e.g., powertrain, bearing, and gear), sensor placement is often compromised by cost and installation environment constraints, resulting in sensing the components far from the regions of interest. To address this challenge, this paper proposes an Operational Transfer Path Analysis (OTPA)-based approach that derives the transfer functions between the vibration excitation source and the measurement point (i.e., receiver). The model for OTPA enables the reverse estimation of the excitation source’s signal from the receiver. Subsequently, the estimated (i.e., synthesized) source signal is fed into a diagnostic model to identify system faults. The OTPA and diagnostic models are constructed using neural network architectures, enabling better adaptation to operational conditions and system-induced nonlinearities. The proposed approach is validated from case studies using hydraulic piston pumps in construction vehicles and next-generation electric vehicles.
Development of a PHM system for electrically actuated brakes of a smallpassenger aircraft
Authors: Andrea De Martin  , Achille Riccardo , Bertolino Antonio Carlo , Jacazio Giovanni , Sorli Massimo
Published: June 2024

The evolution towards “more electric” aircraft has seen a decisive push in the last decade, due to the growing environmental concerns and the development of new market segments (Urban Air Mobility). Such push interested both the propulsion components and the aircraft systems, with the latter seeing a progressive trend in replacing the traditional solutions based on hydraulic power with electrical or electromechanical devices. Electro-mechanical brakes, or E-Brakes hereby onwards, would present several advantages over their hydraulic counterparts, mainly related to the avoidance of leakage issues and the simplification of the system architecture. Moreover, although it is expected a weight increase of the brake, the elimination of the hydraulic lanes would still come with an overall weight reduction. Despite these advantages, it remains a new, relatively unproven technology within the civil aviation field. Within this context, the development of PHM solutions would align with the need for an on-line monitoring of a relatively unproven component. This paper deals with the preliminary stages of the development of such PHM system for the E-Brake of a future executive class aircraft, iterating on previously published material and presenting a particle filtering approach based on a new degradation model and data provided through a revised high-fidelity model. The paper opens with the introduction to the research project and the technological demonstrator, positioning the performed work within the available literature. PHM activities, performed on simulated data-set are then presented and the preliminary results discussed.
Uncertainty in Aircraft Turbofan Engine Prognostics on the C-MAPSS Dataset
Authors: Salinas-Camus Mariana , Eleftheroglou Nick
Published: June 2024

Prognostics and Health Management (PHM) plays a crucial role in maximizing operational efficiency, minimizing maintenance costs, and enhancing system  reliability. Predicting Remaining Useful Life (RUL) is a key aspect of PHM, inherently incorporating uncertainty. This paper focuses on uncertainty quantification (UQ) within Data-Driven Models (DDMs), particularly Machine Learning (ML), such as Long Short-Term Memory (LSTMs), and stochastic models namely Hidden Markov Models (HMMs). While ML models emphasize accuracy, stochastic models offer a different paradigm for prognostics, directly addressing uncertainty. Traditional categorizations of uncertainty as aleatory and epistemic face challenges in practical implementation. This paper explores how, in prognostics,  HMMs primarily tackle aleatory uncertainty, whereas LSTMs predominantly address epistemic uncertainty. It also discusses the complexities of uncertaintymanagement in prognostics and analyzes further an already proposed alternative approach to categorize uncertainties. Despite theoretical advancements, practical implementation remains challenging, especially for DL models due to their limited interpretability. This study sheds light on UQ challenges and offers insights for future research directions in prognostics.
Probabilistic Uncertainty-Aware Decision Fusion of Neural Network for Bearing Fault Diagnosis
Authors: Mostafavi Atabak , Siami Mohammad , Friedmann Andreas , Barszcz Tomasz , Zimroz Radoslaw
Published: June 2024

Reliability is a central aspect of machine learning applications, especially in fault diagnosis systems, where only an accurate and reliable diagnosis system is economically justifiable, considering that any false diagnosis would lead to an increase in maintenance costs or a reduction in system efficiency. Recent advances in machine learning (ML) techniques have encouraged condition monitoring researchers to focus their efforts on finding suitable ML-based solutions for system condition assessment. However, to address the reliability issue, it is crucial to consider a larger amount of data measured by heterogeneous sensors on the system together with non-sensor information. The trend of data fusion has already started in other areas of ML application, and many of today's state-of-the-art models benefit from various types of fusion techniques to improve their accuracy. However, traditional classifiers do not provide any information about the prediction uncertainty, and they tend to show falsely high confidence when encountering low-quality data or previously unseen classes. Fusion of different data sources without considering the epistemic or aleatory uncertainty can lead to a deterioration of the result. Bayesian frameworks have traditionally been used to quantify uncertainty of systems; however, only recent advances made it possible to successfully implement Bayesian ML models.
The research methodology was investigated using the MAFAULDA dataset generated by SpectraQuest's Machinery Fault Simulator. This simulator experimentally simulated various bearing conditions, including normal operation and inner and outer ring bearing failures, at variable speeds. The dataset consists of 1951 instances measured using two triaxial accelerometers, a microphone, and a tachometer.
Diagnosis has been done via two multi label 1D Convolutional Neural Networks - each for a selected sensor - and their prediction along with their associated uncertainty quantity has been fused utilizing Bayesian model averaging. The methodology is capable of fusion of various decisions made based on different data sources and generate a unified decision with associated confidence level. Fusion process is uncertainty aware and application of 1D networks reduce the amount of data needed.
Enhancing Lithium-Ion Battery State-of-Charge Estimation Across Battery Types via Unsupervised Domain Adaptation
Authors: Mbadfar  , Chinnam Ratna Babu , Yildirim Murat
Published: June 2024

Accurate estimation of the state-of-charge (SOC) in lithium-ion batteries (LIBs) is paramount for the safe operation of battery management systems. Despite the effectiveness of existing SOC estimation methods, their generalization across different battery chemistries and operating conditions remains challenging. Current data-driven approaches necessitate extensive data collection for each battery chemistry and operating condition, leading to a costly and time-consuming process. Hence, there is a critical need to enhance the generalization and adaptability of SOC estimators. In this paper, we propose a novel SOC estimation method based on Regression-based Unsupervised Domain Adaptation. We evaluate the performance of this method in cross-battery and cross-temperature SOC estimation scenarios. Additionally, we conduct a comparative analysis with a widely-used classification-based unsupervised domain adaptation approach. Our findings demonstrate the superiority of the regression-based unsupervised domain adaptation method in achieving accurate SOC estimation for batteries.
Towards a Hybrid Framework for Prognostics with Limited Run-to-Failure Data
Authors: Keizers Luc , Loendersloot Richard , Tinga Tiedo
Published: June 2024

The introduction of cyber-physical systems with increased availability of sensor data creates a lot of research interest in prognostic algorithms for predictive maintenance. Although a lot of algorithms are successfully applied to benchmark case studies based on simulated data and experimental set-ups, deploymentin industry lags behind. From a comparison between three benchmark case studies with two real-world case studies based on prognostic metrics (monotonicity, prognosability and trendability), two main issues are observed: 1) the lack of run-to-failures and 2) low prognostic metrics due to a low signal-to-noise ratio of degradation trends, as a result of unexplained physical phenomena. To make prognostics feasible, a hybrid framework is proposed that focuses on improving system knowledge. The framework consists of a quantitative diagnostic assessments, guided by (modular) system models in which damage is induced. This quantitative damage assessment provides input for prognostics based on Bayesian filtering, enabling prognostics for assets in varying operational conditions. Implementation and validation of the framework requires investments, but modularity within the framework can accelerate development for new systems.
Detection of Abnormal Conditions in Electro-Mechanical Actuators by Physics-Informed Long Short-term Memory Networks
Authors: Lai Chenyang , Baraldi Piero , Quattrocchi Gaetano , Dalla Vedova Matteo Davide Lorenzo , Baldo Leonardo , Bertone Matteo , Zio Enrico
Published: June 2024

Electro-Mechanical Actuators (EMAs) are projected to revolutionize the flight control actuator paradigm, potentially replacing hydraulic-powered systems in the future. Consequently, the functioning of EMAs is destined to become critical for the safe and reliable operation of aircraft. Abnormal conditions of the mechanical components of EMAs can lead to their failure. The objective of this work is to develop a method for the early detection of abnormal conditions of the components of EMAs. The proposed method is based on a signal reconstruction model that estimates the motor position of EMA as expected in normal conditions of its components. Then, the presence of an abnormal condition is identified when the difference between the motor position and its reconstructed position in normal conditions exceeds a preset threshold. The signal reconstruction model employs a Physics-Informed Long Short-Term Memory network (PILSTM), whose architecture combines a physics-informed cell for the solution of the differential equations governing the EMA operation, and a data-driven Long Short-Term Memory (LSTM) cell which receives in input the output of the physics-informed cell and reconstructs the position expected in normal conditions. The proposed method is applied to data simulated by a high-fidelity model of EMAs. The results show that PILSTM is able to provide accurate, physics-consistent estimates of the motor position of EMA in normal conditions and the missed and false detection alarms are lower than those of other state-of-the-art methods.
Bayesian Networks for Remaining Useful Life Prediction
Authors: Hostens Erik , Eryilmaz Kerem , Vangilbergen Merijn , Ooijevaar Ted
Published: June 2024

Remaining useful life (RUL) prediction is a critical task in the field of condition-based maintenance. It is important to perform RUL prediction in a statistical sound way. However, it is not straightforward to properly combine multiple information sources about an asset, such as available statistics, measurements, derived features, and prior knowledge in the form of mathematical models and relations, including their uncertainties. Bayesian networks (BNs) are a means of graphically representing all statistical information in a comprehensible way and allow for correctly combining all information. BNs allow for inference in all directions, thereby not merely providing a RUL prediction with explicit uncertainty, but select the most informative features, diagnose which degradation mechanism is manifest if multiple mechanisms exist, provide decision support in the form of optimal condition-based maintenance points when combined with a cost model. BNs also explicitly quantify the model uncertainty arising from the scarcity of the training data. We illustrate these benefits on two realworld industrial examples: solenoids and bearings. We also provide a method to correctly include the effect of changing operating conditions.
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions
Authors: SUN HAN , Ammann Kevin
Published: June 2024

Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of condition monitoring data from complex industrial systems increases. Despite these advances, early fault detection remains a challenge under real-world scenarios. The high variability of operating conditions and environments makes it difficult to collect comprehensive training datasets that can represent all possible operating conditions, especially in the early stages of system operation. Furthermore, these variations often evolve over time, potentially leading to entirely new data distributions in the future that were previously unseen. These challenges prevent direct knowledge transfer across different units and over time, leading to the distribution gap between training and testing data and inducing performance degradation of those methods in real-world scenarios. To overcome this, our work introduces a novel approach for continuous test-time domain adaptation. This enables early-stage robust anomaly detection by addressing domain shifts and limited data representativeness issues. We propose a Test-time domain Adaptation Anomaly Detection (TAAD) framework that separates input variables into system parameters and measurements, employing two domain adaptation modules to independently adapt to each input category. This method allows for effective adaptation to evolving operating conditions and is particularly beneficial in systems with scarce data. Our approach, tested on a real-world pump monitoring dataset, shows significant improvements over existing domain adaptation methods in fault detection, demonstrating enhanced accuracy and reliability.
Case Study of Product Development through Generative Design according to Anemometer Replacement Cycles
Authors: CHOI Joongyu , Shin Soyoung , Lee Sangboo
Published: June 2024

Product Lifecycle Management (PLM) systems are commonly used to manage various product data generated throughout the product lifecycle. This paper explains the results obtained by multiple participants using commercial software within the PLM environment to perform structural and vibration analyses of an Anemometer. Generative design techniques were employed for 3D CAD modeling of the Anemometer, and the commercial analysis software NASTRAN was used for simulation analyses. The open-source PLM system ARAS Innovator's project and workflow management modules were utilized to manage the generated design data, allocate tasks among participants, and control schedules. Through this approach, we propose a method to predict and manage the replacement cycle of Anemometer.
Fully Automated Diagnostics of Induction Motor Drives in Offshore Wind Turbine Pitch Systems using Extended Park Vector Transform and Convolutional Neural Network
Authors: Mathew Manuel Sathyajith , Kandukuri Surya Teja , Omlin Christian W
Published: June 2024

Due to their location and related complexities, the offshore wind farms (OWF) have higher downtimes and operation and maintenance (O&M) costs compared to their onshore counterparts. Condition monitoring could help in bringing down the O&M costs of OWFs. The pitch system is one of the components most prone to failure. This paper details an approach for enhanced diagnosis of the electric pitch systems especially focusing on the induction motor drives (IMD) in wind turbines. The proposed method uses an extended Park vector approach (EPVA) in conjunction with a convolutional neural network (CNN) to accurately classify the condition of an IMD and localize the faults. The method is validated on data collected from a laboratory setup. The advantage of the proposed approach is that the condition of the IMD can accurately be classified, and faults localized in operating conditions with varying load and frequency without any additional information on the instantaneous operating speed, frequency, or load on the motor drives. This results in a non-invasive diagnostic approach incurring least additional expenses to implement.
Feature Selection Method for Gear Health Indicator Using MIC Ranking
Authors: Song Hongliang , Gao Hongli , Zhou Ruiyang , He Jianing , Chen Mengfan
Published: June 2024

In the construction of health indicator for electromechanical equipment, selecting features that exhibit monotonicity, trend characteristics, and a strong correlation with equipment health is paramount to accurately reflect these indices. With the advent of numerous libraries and models for time-series data feature extraction, the range of potential features has expanded significantly. Despite this proliferation, there is a lack of extensive research on effective feature selection. This paper investigates the efficacy of the Maximum Information Coefficient (MIC) method in extracting features that align with the monotonicity and trend-related requirements of electromechanical equipment health indicator. Our experiments indicate that the MIC method adeptly identifies pertinent features for the construction of these indices, underlining its utility in the field of health monitoring for electromechanical systems.
State-of-Charge and State-of-Health Estimation for Li-Ion Batteries of Hybrid Electric Vehicles under Deep Degradation
Authors: Lee Hyunjoon , Yoo Min Young , Choi Joo-Ho , Sung Woosuk , Heo Jae Sung
Published: June 2024

In recent industry, hybrid vehicles are gaining more recognition as a practical means for future transportation due to the longer distance, reduced charging time, and less charging stations dependency. The batteries in the hybrid vehicles, however, undergo more complex operation of charge depleting and sustaining modes alternately, which may need more accurate battery state estimation. In this study, a model based method is explored for the Li-ion batteries in the hybrid electric vehicles to estimate State-of Charge (SOC) and State-of-Health (SOH) accurately. While there have been widespread studies for this topic in the batteries research, not many are found that have investigated hybrid operation modes. Also the estimations are mostly limited to normal batteries or shallow degradation with the SOH higher than 90%. In this study, an algorithm based on the dual extended Kalman filter (DEKF) and enhanced self-correcting (ESC) model is developed for the simultaneous estimation of the SOC and SOH. Degradation data for plug-in hybrid vehicle (PHEV) are taken for the study, which undergo the deep degradation of 30%. In order to maintain the accuracy such that the root mean square error (RMSE) of the SOC is within 5% over the entire degradation cycles, two practical methods are proposed: First, the SOH is estimated separately during the battery charging, and is used as a constant in the SOC estimation in the discharging cycles. Second, battery modeling is conducted and the parameters are reset in every intermittent cycles at which the SOH is reduced by 10% initially and by 5% thereafter.