bluuebunny/crossref_metadata_2025 huggingface dataset:
Depremde Kadın Olmak: Osmaniye Düziçi Çadır Kent Örneği
Authors: AKBAL Alev , TAŞBAŞ Sultan
Published: March 2024

Teknolojinin gelişmesi, kurumsallaşma düzeyinin yüksek olması, iş sahalarının fazlalılığı ve çeşitliliği nedeniyle insanlar yaşamak için kentsel yaşam alanlarını tercih etmektedirler. Kırsal alandan kente yoğun göçün yaşanması kentlerde yeni yerleşim yeri arayışının artması, alt yapı sorunları, ulaşım problemleri, eğitim ve sağlık sorunlarının armasına neden olmaktadır. Bununla birlikte coğrafi koşulların uygun olmamasına rağmen yani doğal afetlerin görülme riskinin yüksek olmasına rağmen insanların afet bölgelerine yerleşim yeri kurmasına neden olmaktadır. Bu durum ise herhangi bir afetle karşılaşılması durumunda toplumsal direncin düşmesine ve kırılganlıkların artmasına neden olmaktadır. Dirençlilik kişilik özellikleri, cinsiyet, eğitim durumu, toplumsal statü, kentsel alt yapı, coğrafi konuma göre değişkenlik göstermektedir. Dezavantajlı gruplar ise bu afetlerden çok daha fazla etkilenmektedir. Dezavantajlı gruplar arasında tanımlanan kadınların direnci sahip oldukları toplumsal cinsiyet rollerinden dolayı daha düşük konumdadır. Bu kapsamda depremde kadının toplumsal cinsiyet eşitsizliğinin beraberinde getirdiği dezavantajlılığın kadınların kırılganlıklarını artırması çalışmanın temel problemini oluşturmaktadır. Depremde kadınların toplumsal cinsiyet rollerinden kaynaklanan kırılganlıklarını ve bu kırılganlıkların nedenlerini ortaya koymak çalışmanın temek amacını oluşturmaktadır. Çalışma üç bölümden oluşmaktadır. Birinci bölümde kadının toplumsal cinsiyet rolleri , doğal afetler ve doğal afetlerin ortaya çıkardığı kırılganlıklar ve çalışmanın yöntemine ilişkin bilgiler yer almaktadır. İkinci bölümde çalışmanın araştırma bulgularına yer verilmekte ve afetzede kadınların depremde yaşadıkları kırılganlıklar sosyolojik açıdan tartışılmakta; sonuç bölümde ise elde edilen bulgulara ilişkin kavramsal ve kuramsal tartışmalar yer almaktadır.
Exploring the Caves as Shelters, Sacred Spaces, and Photography Subjects
Authors: SAĞLAMTİMUR Baybars
Published: March 2024

Caves are underground cavities with unique structures that form over time due to various forces, including geochemical and tectonic processes, water erosion, and hydrothermal activity. From past to the present humans have used caves for many different purposes such as shelter, dungeon, place of worship, cold storage, therapy, treatment of respiratory diseases, use of underground water, tourism, etc. Since caves are protected places, they are also the places where the oldest known works of art of mankind are located. The wide range of uses of caves, their dark and closed structures, and the different roles they have played throughout history, have always made them places of curiosity, discovery, and artistic creation. In this study, caves, which serve as shelters, were examined in terms of their role in human life, the emotions they evoke, their status as sacred places, and their representation through photography.
PENGARUH SERVICE QUALITY, BRAND IMAGE, DAN BRAND TRUST TERHADAP KEPUTUSAN PENGGUNAAN MASKAPAI CITILINK DI INDONESIA
Authors: Deza Saffana Khalisha , Agus Agus Maolana Hidayat
Published: March 2024

Perusahaan penerbangan merupakan industri jasa yang mendorong perekonomian nasional melalui jasa transportasi udara. Industri ini menjadi yang paling terdapak Covid-19 ke tiga di sektor transportasi. Terjadinya penurunan permintaan, pembatasan perjalanan, dan regulasi negara telah mengubah persepsi atau perspektif dari penumpang terkait transportasi udara melalui kualitas layanan, citra merek, kepercayaan merek, dan keputusan penggunaan dari penumpang maskapai. Penelitian ini bertujuan untuk menetukan respon dan pengaruh perubahan persepsi atau perspektif penumpang terkait transportasi udara melalui kualitas layanan, citra merek, dan kepercayaan merek terhadap keputusan penggunaan maskapai Citilink di Indonesia setelah pandemi Covid-19. Jenis peneltian ini adalah kuantitatif dengan pendekatan deskriptif yang melibatkan 420 penumpang pesawat yang menggunakan Citilink di Indoensia setelah pandemi. Teknik pengambilan sampel pada penelitian ini menggunakan non-probability sampling, yang diolah menggunakan SmartPLS 3.2.9 Penelitian ini membuktikan bahwa kualitas layanan, citra merek, dan kepercayaan merek membawa pengaruh positif dan signifikan dengan berbagai tingkat terhadap keputusan penggunaan maskapai Citilink di Indonesia dengan persepsi konsumen yang dapat dikategorikan baik.
NAVIGATING CORPORATE BRAND CRISIS MANAGEMENT IN THE NETWORK ENVIRONMENT: AN IN-DEPTH ANALYSIS
Authors: Xiayu Song
Published: March 2024

Abstract.  This research immerses itself in Indonesia's dynamic network landscape during the transformative Internet era. Geographically centered, it provides nuanced insights into brand crisis management within the local cultural and regulatory context, utilizing big data analytics to explore public sentiment during brand crises through online channels. The temporal focus on 2023 allows a detailed analysis of crisis impacts and evolving management strategies in Indonesia. Real-time data collection ensures immediate scrutiny of post-crisis public responses on social media, illuminating sentiment dynamics within the local online ecosystem. While rooted in Indonesia, the research aims to contribute globally, offering insights applicable across regions and industries. Focusing on case studies from Chinese and Indonesian firms over the last five years ensures relevance, comparability, and a robust analytical foundation for effective brand crisis management.

 

Abstrak Penelitian ini mengeksplorasi ekosistem daring yang dinamis di Indonesia dalam era Internet transformatif. Penelitian berfokus secara geografis, memberikan perspektif berbeda mengenai manajemen krisis pada sebuah brand dalam konteks budaya dan peraturan lokal, memanfaatkan analisis big data untuk mengeksplorasi sentimen publik selama krisis brand terjadi melalui saluran daring. Fokus temporal pada tahun 2023 memungkinkan analisis rinci mengenai dampak krisis dan strategi pengelolaan yang berkembang di Indonesia. Pengumpulan data secara real-time memastikan pengawasan langsung terhadap tanggapan masyarakat pasca krisis di media sosial, sehingga memperjelas dinamika sentimen dalam ekosistem daring lokal. Meskipun penelitian ini berbasis di Indonesia, penelitian ini bertujuan untuk berkontribusi secara global dan memberikan wawasan yang dapat diterapkan di berbagai wilayah dan industri. Berfokus pada studi kasus perusahaan Tiongkok dan Indonesia selama lima tahun terakhir memastikan relevansi, komparabilitas dan landasan analitis yang kuat untuk manajemen krisis pada sebuah brand yang efektif.
PENGARUH PERFORMANCE EXPECTANCY, EFFORT EXPECTANCY, SOCIAL INFLUENCE, FACILITATING CONDITIONS, HEDONIC MOTIVATION, PRICE VALUE DAN HABIT TERHADAP CUSTOMER ACCEPTANCE (STUDI PADA PENGGUNA LAYANAN MAXIM DI KOTA BANDUNG)
Authors: Lianto Fazrian , Dewi Citra
Published: March 2024

Abstract. Transportation is one of the important and commonly used things in the modern era like today. In this era of Globalization, as the population increases, transportation is very popular to meet human needs to carry out their activities. Globalization itself has a big impact in this case technology that makes it easier for people to get to a place without the hassle of going to a public transportation terminal. One of the transportation services in Indonesia is ride hailing. Various kinds of ride hailing in Indonesia, making competition quite high among online transportation. Maxim comes with something different to offer. The purpose of this study was to determine how the influence of Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value and Habit on Customer Acceptance on Maxim service users in Bandung City both partially and simultaneously. This research data collection through distributing questionnaires and using quantitative data analysis. Sampling was carried out using nonprobability sampling method with purposive sampling technique.
Abstrak. Transportasi merupakan salah satu sesuatu yang penting dan biasa digunakan di era Modern seperti saat ini. Di era Globalisasi ini, seiring bertambahnya jumlah penduduk, transportasi begitu sangat di gemari guna memenuhi kebutuhkan manusia untuk menjalankan kegiatannya. Globalisasi sendiri membawa dampak yang besar dalam hal ini teknologi yang memudahkan masyarakat dalam menuju suatu tempat tanpa perlu repot menuju ke terminal transportasi umum. Salah satu jasa transportasi di Indonesia adalah ride hailing. Berbagai macam ride hailing di Indonesia, menjadikan persaingan yang cukup tinggi di kalangan transportasi online. Maxim hadir dengan menawarkan sesuatu yang berbeda. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana pengaruh dari Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value dan Habit Terhadap Customer Acceptance pada pengguna layanan Maxim di Kota Bandung baik secara parsial maupun secara simultan. Pengumpulan data penelitian ini melalui penyebaran kuesioner serta menggunakan analisis data kuantitatif. Pengambilan sampel dilakukan dengan menggunakan metode nonprobability sampling dengan teknik purposive sampling.
KNOWLEDGE WORKER PRODUCTIVITY DI PT TRIKAYARASA JAYA FOOD: PERAN KNOWLEDGE MANAGEMENT MELALUI KEMAMPUAN ADAPTASI DAN KEPUASAN KERJA KARYAWAN
Authors: Fernanda Sherly Amalia , Larassaty Ayu Lucy
Published: March 2024

Penelitian ini bertujuan untuk memberikan gambaran terkait relevansi dari knowledge manajemen terhadap knowledge worker productivity yang juga dimediasi dengan kemampuan adaptasi dan kepuasan kerja karyawan. Penelitian ini menggunakan pendekatan kuantitatif dengan PT Trikayarasa Jaya Food Sidoarjo sebagai objek. Teknik pengambilan data penelitian menggunakan purposive sampling. Serta menggunakan 70 karyawan PT Trikayarasa Jaya Food sebagai sample. Data yang didapat berasal dari pengisian kuesioner yang diolah dengan SmartPLS 4.0. Dari hasil penelitian ini dapat disimpulkan bahwa knowledge management berpengaruh terhadap knowledge worker productivity, knowledge management berpengaruh terhadap kemampuan adaptasi, kemampuan adaptasi berpengaruh terhadap knowledge worker productivity, knowledge management berpengaruh terhadap kepuasan kerja, kepuasan kerja tidak berpengaruh terhadap knowledge worker productivity, kemampuan adaptasi dan kepuasan kerja memiliki peran mediasi penuh terhadap pengaruh knowledge management terhadap knowledge worker productivity.
STUDI EKSPLORASI PENGALAMAN PEKERJA GENERASI Z TERKAIT PHK MASSAL
Authors: Zumhas Churin Sukmadina
Published: March 2024

Penelitian ini dilatarbelakangi oleh isu PHK massal yang umum terjadi dalam lingkungan perusahaan start-up, di mana sifat volatil yang melekat pada start-up seringkali menghasilkan kondisi kerja yang tidak pasti. Start-up, yang didorong oleh upaya inovasi dan pertumbuhan cepat, seringkali menggunakan strategi pemutusan hubungan kerja massal sebagai cara untuk mengurangi biaya, yang pada akhirnya membuat karyawan berada dalam posisi yang tidak pasti. Penelitian ini bertujuan untuk menyelidiki beragam tantangan yang dihadapi oleh pekerja Generasi Z dalam ekosistem perusahaan start-up di Indonesia setelah mengalami pemutusan hubungan kerja massal. Penelitian ini berupaya memahami konsekuensi emosional, psikologis, dan sosial dari pemutusan hubungan kerja ini, serta isu lebih luas mengenai ketidakpastian pekerjaan yang memengaruhi kelompok demografi ini. Penelitian ini menggunakan metodologi penelitian kualitatif, terutama melalui wawancara mendalam dengan 13 karyawan survivor Generasi Z. Pendekatan ini memberikan pemahaman mendalam dan interpretatif terhadap perspektif yang kompleks tentang konsekuensi emosional yang dialami oleh karyawan Generasi Z setelah mengalami pemutusan hubungan kerja massal dalam ekosistem industri start-up di Indonesia.
Hindu Orthodoxy versus Indian Pluralism
Authors: Amin Zukhruf
Published: March 2024

The rise of Hindutva or Neo-Hindu nationalism has undermined the inclusive, all embracing and coexisting nature of Indian secular democracy. The political utilization of the doctrine of Hindutva has led to multi-dimensional challenges in the Hindu majoritarian state of India. The exercise of extant communalism has driven the Indian minorities to a state of constant insecurity. It reflects religio-nationalist identity politics particularly under the Modi regime, which is posing security challenges to multiple communities in India. The divided Communalism with Hindu majoritarianism has characterized the state of India with deeply entrenched Hindu racial supremacy that has caused increasing human insecurity in India. The study analyses how the rise of Hindutva is creating problems for the Indian pluralism. The study argues that the rise of ultra-Hindu-nationalism in practice of populist identity intellectual leadership in India has threatened the human security at the domestic level.
The Application of Multifactorial Diagnostic Criteria for Early Vibration Diagnostics of Aircraft Gas Turbine Engine Bearings
Authors: Adamenko V. , Drokin I.
Published: March 2024

The paper proposes basic approaches and results of working out and application of early vibration diagnostics of gas turbine engine bearings using multifactorial diagnostic criteria.Fatigue spalling, not detected at an early stage on working surfaces of ring raceways and rollers of a turbine bearing subsequently leads to its failure and considerable expenses for regenerative engine repairing. Thus the regular alarm system detecting debris presence in oil due to insufficient sensitivity and selectivity gives an alarm signal too late, at a high stage of damage that excludes the possibility of malfunction elimination without the engine withdrawing from the operation. The vibrations generated by a bearing at its work in the aircraft GTE structure, mostly, have a very low intensity in relation to the noise level and consequently, for working out of an early and reliable diagnostics, it is necessary on the one hand, to take measures in decreasing the levels of external and internal noise, on the other hand, to develop reliable diagnostic criteria on the basis of multifactorial models, allowing to minimize a zone of uncertainty of diagnostic parameters, to lift their diagnostic sensitivity by taking into account various influencing additional parameters such as shaft speed, ambient temperature, etc.
Health assessment of traction-motor blowers regarding their deformation degradation
Authors: Trilla Alexandre , Gratac`os Pau , Guinart David , Alessi Allegra , Lamoureux Benjamin
Published: March 2024

Traction motor blowers are essential components of electric trains. Their failure entails a complete disruption of the operational service, in addition to a safety hazard. Thus, maintaining them effectively is a must to guarantee the availability and reliability of the rolling-stock units. To this end, the predictive maintenance approach can add a lot of value because blowers display a complex behaviour, they seldom fail, but when they do the costs associated to their replacement and the subsequent time out of service of the train (not generating revenue) are prohibitively high and may challenge the viability of a business case. However, getting to deploy an adequate data-driven predictive approach is difficult because it entails collecting streams of useful information in order to generate bespoke diagnostics and prognostics in a timely manner. In this article, we have developed and deployed a network of intelligent wireless sensors that enable to capture vibration data easily on board, and to seamlessly integrate it into our data processing pipeline for a remote inspection of the blowers. In order to adapt the data analysis modules to the blower characteristics and test conditions, we have conducted a feature mapping with the complete fleet of blowers (288 component units) and a statistical analysis to detect anomalies. Then we have fitted a performing diagnostic function taking into account the criticality criteria from the ISO 10816 norm that is currently used as the only indicative reference for general rotational machine maintenance. Additionally, we have checked the validity of these analysis outputs with the dismantlement and visual inspection of some blowers. Our purpose is to develop a new schedule for the maintenance actions Alexandre Trilla et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. as we can now better determine the condition and predict the failure of a blower ahead of time, thus increasing the detection effectiveness of degraded blowers. We believe that an adequate maintenance of traction motor blowers with a remote predictive approach based on intelligent wireless sensors may increase the availability and reliability of the trains, and thus make the rail transport service more appealing.
Probabilistic Method to predict Remaining Usage Life of Aircraft Structures
Authors: Rastogi Mudit
Published: March 2024

Current trends in the design of new aircraft components and high level innovations in the older type of aircraft are clearly pointing to automation and integration of all aircraft systems to give increased cost efficiency in the aircraft operation. The same trend can be observed in the design, operation and maintenance of aircraft structures where advanced Structure Health Monitoring (SHM) systems are about to enter into service. These advanced systems are designed to provide both diagnostic and prognostic information enabling application of Condition Based Maintenance (CBM) or even Prognostics Maintenance (PM) concepts into the maintenance of the aircraft structures. To make this CBM/PM concept a reality, an accurate, reliable and robust method to predict the Remaining Usage Life (RUL) of the structure is the foremost step.This paper presents a probabilistic method for RUL prediction. A hybrid approach is used, comprising of two different algorithms. The first algorithm adopts fracture- mechanics based fatigue crack growth model. This approach uses physics of failure to predict the crack growth curve and underlying degradation process. It calculates the accurate value of Stress Intensity Factor (SIF) to calculate the crack growth curve. The second algorithm is a mathematical model which quantify various sources of uncertainty such as future loading, crack length, model parameters etc. The process described in this paper results in enhanced remaining usage life estimation by compensating for the aforementioned modeling uncertainties. The model results were verified and validated on a typical aero structure with experimental, FEA simulation and fractography data.
Prognostic and Health Management System for Hydraulic Servoactuators for Helicopters Main and Tail Rotor
Authors: Macaluso Andrea
Published: March 2024

The research herein presented continues an initial work on this area performed at Politecnico di Torino in the past years and moves forward in the definition of an effective PHM system for Electro-Hydraulic Servo Actuators (EHSA). PHM of EHSAs is an area only little addressed, but of great interest for the aerospace industry and the air fleet operators. The PHM algorithm consists of three subroutines: the first subroutine extracts a set of relevant features from the data acquired by the sensors during a pre or post flight set of commands and mitigate their dependencies with environmental condition. Fault identification is then performed using mathematical data-driven techniques, three methods are presented: Nominal bands, Percentual error and Euclidean distance. Classification of a single and multiple degradations is performed by the second subroutine with the aid of two dual layer Neural Networks. Finally, the remaining useful life (RUL) is estimated by the third subroutine using a particle filter framework, where the feature evolution model is estimated online. Electro-hydraulic Servo-valves (EHSV) faults are widely addressed with the aid of a nonlinear model and the performance of the PHM algorithm is assessed using relevant metrics.
Extended Kalman filter development in Lebesgue sampling framework with an application to Li-ion battery diagnosis and prognosis
Authors: Yan Wuzhao , Zhang Bin
Published: March 2024

Extended Kalman filter in Riemann sampling framework (RS-EKF) has been widely used in diagnosis and prognosis, navigation systems, and GPS for its advantage of simplicity and reasonable solution for nonlinear systems. New particle filter based fault diagnosis and prognosis algorithms in Lebesgue sampling framework have been developed to enable the implementation on systems with limited computational sources, such as embedded systems. In this Lebesgue sampling-based approach, Lebesgue states are defined on the fault dimension axis and algorithm is executed only when the measurement causes a transition from one Lebesgue state to another, or an event happens. This is a need-based fault diagnosis and prognosis (FDP) philosophy in which the algorithm is executed only when necessary, thus less computational resources are required. In order to make algorithms more efficient, EKF algorithm is developed in Lebesgue samplingframework (LS-EKF). With the philosophy of “execution only when necessary”, the proposed approach is able to eliminate unnecessary computations, especially in the scenario that the fault grows slowly. The prediction horizon defined by Lebesgue states on the fault dimension axis is usually small and, therefore, LS-EKF naturally benefits the uncertainty management by reducing the uncertainty accumulation. One feature of diagnosis and prognosis in Lebesgue sampling is that it requires two models, one for diagnosis and one for prognosis. The diagnostic model describes the dynamics of fault and is used to estimate the fault state. Prognostic model for LS-EKF describes the time for fault state reaching each defined Lebesgue state. The new algorithms is verified with an application to the diagnosis and prognosis of the state of health of Li-ion battery. The results show that LSEKF and RS-EKF have comparable performance in diagnosis Wuzhao Yan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. but LS-EKF has much less computation. Moreover, LS-EKF is more accurate and time-efficient on long term prognosis than RS-EKF algorithms, which makes it a promising solution for FDP in distributed applications.
Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification
Authors: Lessmeier Christian , Kimotho James Kuria , Zimmer Detmar , Sextro Walter
Published: March 2024

This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of data-driven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research.
Fault Detection on Large Slow Bearings
Authors: Bechhoefer Eric , Schlanbusch Rune , Waag Tor Inge
Published: March 2024

Large, slow turning bearings remain difficult to analyze for diagnostics and prognostics. For critical equipment, such as drilling equipment, top drives, mining equipment, wind turbine main rotors, helicopter swash plates, etc. this poses safety and logistics support problems. An undetected bearing fault can disrupt service, and causes delays, lost productivity, or accidents. This paper examines a strategy for analysis of large slow bearings to improve the fault detection of condition monitoring systems, thus reducing operations and maintenance cost associated with these bearing faults. This analysis was based on vibration, temperature and grease analysis from three wind turbines, where one turbine was suspected of having a faulted main bearing.
Modeling of Complex Redundancy in Technical Systems with Bayesian Networks
Authors: Kaul Thorben , Meyer Tobias , Sextro Walter
Published: March 2024

Redundancy is a common approach to improve system reliability, availability and safety in technical systems. It is achieved by adding functionally equivalent elements that enable the system to remain operational even though one or more of those elements fail. This paper begins with an overview on the various terminologies and methods for redundancy concepts that can be modeled sufficiently using established reliability analysis methods. However, these approaches yield very complex system models, which limits their applicability. In current research, Bayesian Networks(BNs), especially Dynamic Bayesian Networks (DBNs) have been successfully used for reliability analysis because of their benefits in modeling complex systems and in representing multi-state variables. However, these approaches lack appropriate methods to model all commonly used redundancy concepts. To overcome this limitation, three different modeling approaches based on BNs and DBNs are described in this paper. Addressing those approaches, the benefits and limitations of BNs and DBNs for modeling reliability of redundant technical systems are discussed and evaluated.
Beyond Condition-Monitoring: Comparing Diagnostic Events with Word Sequence Kernel for Train Delay Prediction
Authors: Lee Wan-Jui , Tax David M.J. , Duin Robert P.W.
Published: March 2024

In the modern trains operated by the Dutch Railways (Nederlandse Spoorwegen) in the Netherlands, there are on-board train management systems continuously monitoring the conditions of various train modules such as traction, climate, brake electronics and so forth. When an abnormal or particular situation occurs, the system will generate and store an event log on the local disk or on a remote disk using wireless data communications. These diagnostic events might give an indication of the train condition, and currently critical events are selected by business rules to give alarms on failure or malfunction to the control room. To give a better prediction on the trains status based on the condition monitoring data, sequences of diagnostic events instead of individual critical events are analyzed in this work. Moreover, train delays instead of train failures are used as targets for providing more insight on the degeneration behavior of trains. We have adopted the word sequence kernel for learning the similarity between all sequence pairs, where each diagnostic event is considered as a word. To include multi-length word interpretations, we propose to combine the word sequence kernels of various lengths, where length=1 means one word is matched, length=2 means two words are matched, and so on. A kernel machine or similarity-based model can be learned directly on this combined word sequence kernel. The experimental results demonstrate that combining word sequence kernels of different lengths can bring a richer description to similarity measurements and gives better prediction performance.
Optimal tuning of particle filtering random noise for monotonic degradation processes
Authors: Corbetta Matteo , Sbarufatti Claudio , Giglio Marco
Published: March 2024

Particle filtering is a model-based, Bayesian filtering algorithm widely employed in many scientific and engineering fields. It has been recently applied many times for the diagnosis and prognosis of engineering systems. Within the prognostics and health management scenario, particle filtering stood out as an effective and robust algorithm to predict the system’s remaining useful life because of its capability in tracking nonlinear/non-Gaussian systems. One of the fundamental equations underneath particle filtering is the evolution equation describing the system’s dynamics. This equation composes of a deterministic model and an artificiallyadded random process or random noise, thus making the evolution equation a stochastic equation. The selection of randomnoise is up to the algorithm’s designer discretion and may vary on a case-by-case basis. Concentrating on the field of structural degradation processes, many studies on particle filtering-based prognostics have shown encouraging results. Though, they did not provide detailed discussions in supporting the appropriateness of the selected random noises altering the degradation model. An improper choice may cause the algorithm to be inefficient, moving the projected trajectories outside the state-space domain of the system, sometimes also introducing a bias in the stochastic evolution model. Therefore, this work examines the evolution equation with the aim of creating an optimal prognostic framework for monotonic degradation phenomena. The paper gives special emphasis to structural degradation caused by fatigue, which is a widespread monotonic damage progression process. Some of the existing works are reviewed, discussing the effectiveness of therandom noises embedded in the algorithm. Eventually, the paper presents an unbiased, optimal random noise for monotonic damage progression, pointing out the strengths of the proposed solution against formulations suggested in literature. The presented particle filtering-based prognostic algorithm is applied to experimental crack growth observations on an aeronautical stiffened structure and the prediction performance is validated using dedicated prognostic metrics.
Prognosticating fault development rate in wind turbine generator bearings using local trend models
Authors: Skrimpas Georgios Alexandros , Palou Jonel , Sweeney Christian Walsted , Mijatovic Nenad , Holboell Joachim
Published: March 2024

Generator bearing defects, e.g. ball, inner and outer race defects, are ranked among the most frequent mechanical failures encountered in wind turbines. Diagnosis and prognosis of bearing faults can be successfully implemented using vibration based condition monitoring systems, where tracking and trending of specific condition indicators can be used to evaluate the former, current and potentially future condition of these components. The latter, i.e. evaluation of the fault progression rate and remaining useful lifetime (RUL), is of essential importance to owners and operators in regards to maintenance planning and component replacement. The above approach offers numerous benefits from financial and operational perspective, such as increased availability, uptower repairs and minimization of secondary and catastrophic damages. In this work, a non-speed related condition indicator, measuring the signal energy between 10Hz to 1000Hz  is utilized as feature to characterize the severity of developing bearing faults. Furthermore, local trend models are employed to predict the progression of bearing defects from a vibration standpoint in accordance with the limits suggested in ISO 10816. Predictions of vibration trends from multi-megawatt wind turbine generators are presented, showing the effectiveness of the suggested approach on the calculation of the RUL and fault progression rate.
Model Based Approach to Zonal Safety Analysis
Authors: Henderson Rachael , Hussain Ghulam , Stecki Jacek
Published: March 2024

Zonal Safety Analysis (ZSA) is an analysis technique for assessing the safety of complex systems however current tools limit its repeatability, thoroughness and time efficiency. The model based method proposed in this paper remedies these limitations.Zonal Safety Analysis is widely used in the aerospace industry with similar analysis techniques also seen in the design of offshore oil & gas systems, mining equipment, defence platforms and other high-risk systems. The importance of ZSA comes from its ability to allow the designer to analyse the spread of hazards through the system from a physical standpoint. This is done by dividing the system into zones and understanding how hazardous forms of energy or material (e.g. fire or oil) could spread between these zones. This paper suggests a method for tracking the spread of hazards using a model of the system that can automatically generate the potential propagation of the hazards. To give the designer a better understanding of the source of the hazard, and greater flexibility in preventing it from occurring, the causes of these hazards are also defined in the model. Using a model based approach allows the analysis process to be efficiently repeated for a design variant at any stage in the product lifecycle by updating the structure (i.e. different components/configuration) or parameters (i.e. hazard causes, criticality) of the system model.
Proposal of a model-based fault identification genetic technique for more-electric aircraft flight control EM actuators
Authors: Vedova Matteo D. L. Dalla , Germanà Alfio , Maggiore Paolo
Published: March 2024

In the last years, Electro-Mechanical Actuators (EMAs) are gradually replacing the older type of actuators based on the hydraulic power. In order to detect incipient failures due to a progressive wear of a primary flight command EMA, prognostics could employ several approaches; the choice of the best ones is driven by the efficacy shown in failure detection, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a prognosticalgorithm able to identify the precursors of the above mentioned EMAs faults and their degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew such to properly schedule the servomechanism replacement. The goal of this paper is to propose an innovative modelbased fault detection and identification (FDI) method, based on Genetic Algorithms (GA), able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior; in particular four kinds of EMA progressive fault are considered: friction, backlash, coil short circuit and electronics fault of controller. To assess the effectiveness of the proposed technique, an appropriate simulation test environment wasdeveloped: in particular, two MATLAB Simulink models representing the real EMA and the corresponding monitor have been used to simulate failures and evaluate the accuracy of the FDI algorithm. The results showed an adequate robustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures. This paper aims to be a starting point to future works based on this method for PHM applications.
Case Study: Vibration trip and post-event Analysis with Auto-Associative Neural Networks on a Large Steam Turbine
Authors: Fromaigeat Luc , Nicchiotti Gianluca
Published: March 2024

This 300 MW steam turbine at a coal-based thermal power plant is equipped with a protection system, a condition monitoring analysis software and an automatic diagnostic tool. The Machine Protection System (MPS) and Condition Monitoring System (CMS) configuration combines sensors, electronic hardware, firmware and software specific to this application. The protection system initiated a trip having identified high vibration. The trip prevented further damage. Subsequent analysis of the data using the condition monitoring software established the bearings most affected and pin pointed the source of high vibration. The data is post processed using an Auto-Associative Neural Networks (AANN) that has been trained with healthy data recorded several hours prior to the trip. AANN are methodologies widely used for novelty and anomaly detection. The AANN results indicates that such approach would be capable of detecting the failure event in advance compared to the automatic diagnostic system based on rules, demonstrating the validity of the approach in this context. Various aspects related to vibration: protection, condition monitoring, analysis, automatic diagnostics using rules and Neural Networks are presented and their results discussed.
On discrete-time observer structures for fault estimation
Authors: Krokavec Dusan , Filasov´ Anna , Liščinsky Pavol
Published: March 2024

In the paper there is proposed a modified technique for system faults estimation in linear dynamic systems, which gives the possibility simultaneously estimate the system state and faults. Using a discrete-time observer form, the considered faults are assumed to be additive, thereby the principle can be applied for a broader class of time-varying fault signals. An enhanced algorithm is provided to verify stability of the observer with improved performance of fault estimation. Exploiting the given procedure the proposed technique allows to obtain signals that can be further used for thresholds setting in the fault residual scheme. The approach utilizes the measurable output vector variables and the design conditions are based on linear matrix inequality technique.
Implementation Of A Bayesian Linear Regression Framework For Nuclear Prognostics
Authors: Panni Omer , West Graeme , Catterson Victoria , McArthur Stephen , Shi Dongfeng , Mogridge Ieuan
Published: March 2024

Steam turbines are an important asset of nuclear power plants (NPPs), and are required to operate reliably and efficiently. Unplanned outages have a significant impact on the ability of the plant to generate electricity. Therefore, predictive and proactive maintenance which can avoid unplanned outages has the potential to reduce operating costs while increasing the reliability and availability of the plant. A case study from the data of an operational steam turbine of a NPP in the UK was used for the implementation of a Bayesian Linear Regression (BLR) framework. Anappropriate model for the deterioration under study is selected. The BLR framework was applied as a prognostic technique in order to calculate the remaining useful life (RUL). Results show that the accuracy of the technique varies due to the nature of the data that is utilised to estimate the model parameters.
Spall size estimation in bearing races based on vibration analysis
Authors: Kogan G. , Madar E. , Klein R. , Bortman J.
Published: March 2024

Rolling element bearings are essential components of rotating machinery. Therefore, preventing bearing failure is a subject of great interest. Effective failure prevention requires maintenance decisions that are based on both detection of bearing faults and estimation of the fault severity. Since spalling of raceways is a common bearing failure mechanism, the objective of this study is to estimate the spall width by using vibration analysis.The ball-spall interaction of faults in both the inner and outer races was studied using a dynamic model of bearings. A new algorithm for determination of the spall width is proposed based on physical insights from the dynamic model. Experimental results are used to validate this algorithm in the case of faults in the outer race.
Fault diagnosis of rolling element bearings from current and vibration measurements
Authors: Bravo Iñaki , Leturiondo Urko , Arnaiz Aitor , Salgado Oscar
Published: March 2024

As rolling element bearings are used in many rotary machines, it is crucial to monitor their response in order to apply condition-based maintenance. One of the main tasks consists in carrying out the diagnosis process, i.e., to do fault detection, localization and identification. Thus, the replacement of rolling element bearings can be done once the diagnosis process gives relevant information that is not only valid for maintenance purposes, but also to design purposes when the causes of the faults appearing in the system are analysed. There are many ways to study the state of these components, which vary from vibration analysis until acoustic emissions, through current or thermal analysis, among others.In this work the vibrational response of rolling element bearings as well as the current of the motor that drives the motion of these components are analysed. A test rig consisting of a drive motor, a couple of gearboxes and a load motor is used. Damaged bearings have been used in the first gearbox, in such a way that their response is studied and compared to that of a healthy bearing. Some indicators are extracted from both the vibration and current signals that can be used for diagnosis purposes. Moreover, the effect of other factors such as the size of the damage or the position of the damaged zone on the vibrational and electrical response is also studied.
Bearing health monitoring using optical fiber sensors
Authors: Konforty Shlomi , Khmelnitsky Michael , Kressel Iddo , Klein Renata , Tur Moshe , Bortman Jacob
Published: March 2024

Bearings are vital elements in rotating machinery. Failures in bearings can result in irreversible damage. Therefore, early detection of bearing damage and monitoring of fault severity are necessary for optimization of maintenance decisions.The classical methods for bearing monitoring are based on analysis of vibration signals captured by accelerometers, usually located on the machine case. Two difficulties arise when diagnosing bearings in that manner. The first difficulty is the distortion of the signals due to the transmission path to the sensor. The second difficulty is the characteristic low signal to noise ratio, resulting from the weak bearing signals in the presence of the strong surrounding noise, originating from the vibrations of other rotating components in the machine.The goal of the present study was to research the possibility of using an optical fiber sensor of the Fiber Bragg Grating (FBG) type, which senses strain and temperature changes, for bearing diagnostics. Due to its small dimensions, this sensor can be embedded close to, or even inside the bearing, suggesting a possible solution to the two difficulties which were discussed above.The results of this study open new options to monitor and detect early failure signs in critical bearings. FBG-based diagnostics was found applicable and useful for detecting damage in bearings.The analysis of signals measured on bearings with various widths of spall, demonstrates the power of the FBG-based local sensing methodology. In addition, the study also aimed to understand the capabilities and limitations of FBG for wideband sensing, and the effect of different sensor attaching techniques on the signal.
Maximal information-based nonparametric exploration of condition monitoring data
Authors: Hu Yang , Palmé Thomas , Fink Olga
Published: March 2024

The system condition of valuable assets such as power plants is often monitored with thousands of sensors. A full evaluation of all sensors is normally not done. Most of the important failures are captured by established algorithms that use a selection of parameters and compare this to defined limits or references.Due to the availability of massive amounts of data and many different feature extraction techniques, the application of feature learning within fault detection and subsequent prognostics have been increasing. They provide powerful results. However, in many cases, they are not able to isolate the signal or set of signals that caused a change in the system condition.Therefore, approaches are required to isolate the signals with a change in their behavior after a fault is detected and to provide this information to diagnostics and maintenance engineers to further evaluate the system state.In this paper, we propose the application of Maximal Information-based Nonparametric Exploration (MINE) statistics for fault isolation and detection in condition monitoring data.The MINE statistics provide normalized scores for the strength of the relationship, the departure from monotonicity, the closeness to being a function and the complexity. These characteristics make the MINE statistics a good tool for monitoring the pair-wise relationships in the condition monitoring signals and detect changes in the relationship over time.The application of MINE statistics in the context of condition monitoring is demonstrated on an artificial case study. The focus of the case study is particularly on two of the MINE indicators: the Maximal information coefficient (MIC) and the Maximum Asymmetry Score (MAS).MINE statistics prove to be particularly useful when the change of system condition is reflected in the relationship between two signals, which is usually difficult to be captured by other metrics.
A Semi-Supervised Feature Selection Approach for Fault Diagnostics in Evolving Environments
Authors: Hu Yang , Baraldi Piero , Maio Francesco Di , Zio Enrico
Published: March 2024

This paper introduces a Semi-Supervised Feature Selection (SSFS) approach for selecting the most suitable features for fault diagnostics in evolving environments. The effectiveness of the proposed SSFS approach is verified with respect to an application concerning the classification of the defect type of bearings in Fully Electric Vehicles operating at different loads. The results show that SSFS allows adapting the diagnostic model to the varying load by updating the set of features used for the classification and achieves more satisfactory diagnostic accuracy than the traditional diagnostic models. The proposed diagnostic approach can contribute significantly to the maintenance practice of components such as gearboxes, alternators, shafts and pumps, whose working conditions are usually characterized by evolving environment.