bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengujian Sistem Informasi Magang Industri dengan Metode Blackbox Testing Boundary Value Analysis
Authors: Vikasari Cahya
Published: March 2024

Pengujian merupakan salah satu tahapan dalam pembangunan perangkat lunak. Salah satu metode pengujian perangkat lunak yaitu dengan metode blackbox dengan pendekatan boundary value analysis. Pengujian akan dilakukan pada sistem magang industri Politeknik Negeri Cilacap. Teknik yang digunakan yaitu dengan menguji dengan menggunakan data yang normal, minimum,maksimum dam maksismum+1. Data sampel yang dimasukan pada setiap pengujian fieldnya sejumlah 4 data. Hasil pengujian dengan menggunakan metode blackbox pada pengujian sistem magang industri dapat dihasilkan tingkat kesuksesan sebesar 95%.
Aplikasi Penggunaan SMS Untuk Pengajuan Kerja Praktik Mahasiswa
Authors: Yusuf Dani
Published: March 2024

Salah satu layanan di kampus yang dapat menerapkan teknologi SMS adalah pengajuan Kerja Praktik ke perusahaan. Kerja Praktik adalah salah satu matakuliah wajib beberapa program studi yang harus diikuti oleh setiap mahasiswa. Namun saat ini masih banyak  banyak kampus menggunakan cara manual dalam pengajuan kerja praktik ke staff fakultas maupun ke program studi dengan cara mengisi lembar permohonan kerja praktik kemudian diserahkan ke bagian administrasi untuk diproses yang terkadang memerlukan waktu yang cukup lama. Penelitian ini membahas mengenai perancangan aplikasi pengajuan kerja praktik di lingkungan kampus dengan memanfaatkan teknologi SMS. Sistem layanan SMS Gateway yang dikembangkan disamping melayani pengajuan kerja praktik juga dapat digunakan untuk pengiriman SMS massal untuk keperluan penyebaran informasi kepada para dosen dan mahasiswa. Sistem yang dikembangkan berbasis Intranet untuk  jaringan kampus dengan bahasa pemrograma PHP dan penyimpanan database menggunakan MySQL. Tujuan dari penggunaan aplikasi ini adalah untuk mempercepat proses pengajuan kerja praktik mahasiswa, mengurangi penggunaan kertas dan diharapkan dapat digunakan di lingkungan kampus yang di dalamnya ada matakuliah Kerja Praktik. Manfaat yang diharapkan dari perancangan sistem ini adalah mempercepat proses pengajuan kerja praktik, mengurangi kemungkinan kesalahan tulis nama tempat kerja praktik oleh petugas, memudahkan bagian administrasi dalam menyebaran informasi, mengurangi penggunaan kertas dapat dipakai oleh instansi lainnya yang ingin menerapkan sistem pengajuan kerja praktik menggunakan SMS Gateway.
Rancang Bangun Jaringan Pribadi Menggunakan OpenVPN
Authors: Brinsley Charlie , Fernando Yongky
Published: March 2024

OpenVPN adalah perangkat lunak gratis dengan sumber terbuka yangmengimplementasikan virtual private network (VPN), yang menyediakan fiturotentikasi dan enkripsi dalam komunikasi antar titik ke titik jaringan. Hal yangdiutamakan dalam komunikasi disebuah jaringan adalah menjaga keamanan danprivasi dalam komunikasi tersebut. Tetapi pertukaran maupun pengiriman datadan informasi antara pengguna tersebut rentan terdapat pengintaian dari pihakketiga yang seharusnya tidak memperoleh akses terhadap komunikasi tersebut.Dalam penelitian ini akan dilakukan pembangunan server vpn yangmenghubungkan client ke server, Dimana nantinya client tersebut akanterhubung ke server vpn tersebut dalam jaringan pribadi. Dengan jaringanpribadi tersebut diharapkan komunikasi antara kedua perangkat tersebut amandari peretas yang mencegat komunikasi atau privasi.
IDENTIFIKASI JENIS KEPITING DI PERAIRAN MOLAS, KECAMATAN BUNAKEN, KOTA MANADO (Identification Of Crab Species in Molas Waters, Bunaken District, Manado City)
Authors: Roring Reygina B.S , Kaligis Erly Y. , Bara Robert A. , Kawung Nickson J. , Mingkid Winda M.
Published: March 2024

Crustaceans are an important part of the bentic fauna and one of the groups is infra theorderbrachyura. Brachyura crabs cover about 700 genera and about 5000 to 10000 species. Thepurpose of this study is first to identfy the types of crabs, secondly to calculate abundance, thirdly tofind out the environmetal conditions of the surrounding waters both pH, temperature, salinity occupiedby the types of crabs in Molas waters, Bunaken District, Manado Cit. Identification technique based onthe color and shape of the carapace. The data collection location consists of 2 different places (station)which are 150 m between stations. To compelte the data in the field, supporting water quality data wasalso collected such as: temperature, pH, and salinity at each station. Identification results in Molaswaters, Bunaken District, Manado City found 7 types of crabs with the number recorded namely,Austruca annulipes 155 individuals, followed by Ocypode sp 55 individuals, Ocyopde palidula 53individuals, Leptograpsus variegatus 30 individuals, Gelasimus vocans 20 individuals, Aratus pisonii 15individuals and Portunus pelagicus 10 individuals. Based on the analysis of abudance data with thehighest value, namely Crab Austruca annulipes that is1,55 ind/m2. The quality parameters in Molaswaters are temperature 29-30 oC, Salinity ppt 29-30 ppt, pH 6-8.Keywords: Molas, Crabs, Abudance

ABSTRAKKrustasea adalah bagian penting dari fauna bentik dan salah satu kelompok adalah infra ordobrachyura. Kepiting brachyura mencakup sekitar 700 genera dan sekitar 5000 hingga 10.000 spesies.Tujuan dari penelitian ini adalah yang pertama mengidentifikasi jenis-jenis kepiting, kedua yaitumenghitung kelimpahan, ketiga yaitu mengetahui keadaan lingkungan perairan sekitar baik pH, suhu,salinitas yang di tempati oleh jenis-jenis kepiting di perairan Molas, Kecamatan Bunaken, Kota Manado.Penelitian ini menggunakan metode jelajah. Teknik identifikasi berdasarkan warna serta bentukkarapas. Lokasi pengambilan data terdiri 2 tempat (stasion) berbeda yang berjarak 150 m antar stasion.ntuk melengkapi data di lapangan diambil juga data pendukung kualitas air, seperti: suhu, pH, dansalinitas pada masing-masing stasiun. Hasil identifikasi di Perairan Molas, Kecamatan Bunaken, KotaManado ditemukan 7 jenis kepiting dengan jumlah yang terdata yaitu, Austruca Annulipes yaitu 155individu, kemudian diikuti jenis kepiting Ocypode sp yaitu 55 individu, Ocypode palidula yaitu 53individu, Leptograpsus variegatus yaitu 30 individu, Gelasimus vocans yaitu 20 individu, Aratus pisonii15 individu dan Portunus pelagicus 10 individu. Berdasarkan analisis data kelimpahan dengan nilaitertinggi yakni Kepiting Austruca annulipes yaitu 1,55 ind/m2. Parameter kualitas di perairan Molas yaitusuhu 29-30 oC, salinitas 29-30 ppt, pH 6-8.Kata kunci: Molas, Kepiting, Kelimpahan
UJI TOKSISITAS ANTI KANKER EKSTRAK ALGA COKLAT Padina sp. TERHADAP LARVA UDANG Artemia salina LEACH., DENGAN METODE BRINE SHRIMP LETHALITY TEST
Authors: Kawung Nickson J. , Sumangando Adolfina , Rumampuk Natalie D. , Wagey Billy. Th.
Published: March 2024

Brown Algae Padina sp are marine biota that have secondary metabolites that are useful in the pharmaceutical field, especially raw materials for anti-cancer drugs. Bioactive compounds suspected of having anti-cancer activity were tested for activity by means of a toxicity test. Brine Shrimp Lethality Test Method. The purpose of this study was to test the anticancer activity of the crude extracts of Padina sp against Artemia salina L shrimp larvae using the Brine Shrimp Lethality Test method. Algae samples were taken in the waters of Makupa Village. The activity test was carried out at the Laboratory of Marine Biotechnology and Pharmacy, Faculty of Fisheries and Marine Sciences, Unsrat. The test concentrations used of 10, 50, 100, 250 and 500 ppm, by first making 1000 ppm mother liquor. Data analysis used probit analysis to determine the toxicity value of LC50. The results obtained showed that the increase in concentration was followed by an increase in the number of mortality of the test animals where Padina sp 65%. The results of probit analysis obtained LC50 Padina sp 12.45 mg/l. Based on these data, bioactive compounds from Padina are more toxic, so it can be concluded that the content of bioactive compounds from Padina sp has the potential to be developed as raw materials for anticancer drugs.

Keywords: Anticancer, Padina, Lethtality Test

 

ABSTRAK

 

Alga Coklat Padina sp merupakan biota laut yang memiliki metabolit sekunder yang bermanfaat dalam bidang farmasi terutama bahan baku obat anti kanker. Senyawa bioaktif yang diduga memiliki aktivitas anti kanker terlebih dahulu dilakukan pengujian aktivitas dengan cara uji toksisitas. Metode Brine Shrimp Lethality Test. Tujuan penelitian ini yaitu uji aktivitas antikanker dari ekstrak   Padina sp terhadap larva udang Artemia salina L dengan menggunakan metode Metode Brine Shrimp Lethality Test. Sampel alga di ambil di Perairan Desa Makupa. Uji aktivitas dilakukan di Laboratorium Bioteknologi dan Farmasetika Laut Fakultas Perikanan dan Ilmu Kelautan Unsrat. Konsentrasi uji menggunakan 10, 50, 100, 250 dan 500, ppm, dengan terlebih dahulu membuat larutan induk 1000 ppm. Analisis data menggunakan analisis probit untuk menentukan nilai toksisitas LC50. Hasil yang diperoleh menunjukan bahwa kenaikan konsentrasi diikuti dengan kenaikan jumlah mortalitas hewan uji dimana Padina sp 65%. Hasil analisis probit diperoleh nilai Lethal consentrasi adalah 12.45 mg/l. Berdasarkan data tersebut senyawa bioaktif dari Padina lebih toksik dibandingkan dengan Halimeda sp, sehingga dapat disimpulkan kandungan senyawa bioaktif dari Padina sp berpotensi untuk dikembangkan menjadi bahan baku obat antikanker.

Kata kunci: Antikanker, Padina, Lethality Test
Rancang Bangun Sistem Informasi Warga Berbasis Mobile pada RW 006 Kelurahan Cipinang Jatinegara Jakarta Timur
Authors: Mulyana Dadang Iskandar , Hudzaifah Salim Maula , Al Ammar Muhammad Farros , Siregar Ikhsan Abror
Published: March 2024

Rukun Tetangga/Community Association, hereinafter abbreviated as RT/RW, is an institution formed through RT/RW deliberations which has an important role in maintaining and preserving community values based on cooperation and kinship; expediting the implementation of government duties in the field of rural or urban and community development, as well as empowering all government organizations and the business world to improve community welfare. Therefore, RT/RW must provide good services to their citizens to create a conducive society. People need services at any time, but these services can only be provided to residents in the afternoon or on holidays because RT/RW administrators have personal interests. Currently, the quality of public services provided by government institutions is still not optimal, services to the community are not running optimally. This problem can be overcome by building a mobile-based citizen information system that is capable of being a communication medium between residents and RT/RW administrators. Through the use of mobile technology, citizen information systems enable citizens to quickly access information related to community activities, important announcements, and public services available around them. This application also makes it easier for residents to report problems or community needs directly through an integrated reporting feature. Thus, it is hoped that the adoption of this technology can have a positive impact in improving the quality of life and strengthening the social ties of local communities. This application aims to make it easier to get information about activities/events or other necessary information, as well as make it easier to make reports or complaints in the form of a mobile application.
Servant Leadership Meets Health Equity: Examining the Causal Comparative Impact of Black Health Disparities and the United States in the First States to Mandate Implicit Bias Training
Authors:   Department James T. George School of Business, Hampton University, Hampton, VA, United States of America (USA)., Alexander-Laine Ayanna
Published: March 2024

The racial health disparities gap is widening in the United States. This analysis uses a non-experimental quantitative causal-comparative analysis to support the study's findings. The rates and percentages of Black and National variables were compared to highlight devastating and disproportionate Black health disparities. The data focuses on statistics from some of the first states to require healthcare professionals to take implicit bias training. The health disparities chosen are the most prominent ones plaguing the Black community. Additionally, the article will examine how improving organizational behavior by implementing servant leadership will help improve Black health disparities. The research will highlight Black health disparities, implicit bias, organizational behavior, and servant leadership. The research shows that healthcare professionals become aware of their implicit bias to lead ethically by shifting the organizational behavioral approach toward servant leadership. It also revealed that servant leadership provided a more compassionate, caring, empathetic, selfless, and nurturing form of care that will build trust amongst Black patients and patient-provider relationships, lessening Black health disparities stemming from subconscious implicit decisions. The researcher tested the methodology and found that the data provided a comparative analysis between variables and showed the prevalence of Black health disparities in the United States; however, the analysis did not prove causal inference or cause and effect.
Design and Analysis of Simple Compact MIMO Antenna for Ultra-Wide Band Application
Authors:   Department of Electronics and Telecommunication, Ramrao Adik Institution of Technology, Dr. D. Y. Patil University Nerul, Navi Mumbai (M.H), India., Konade Shivanand , Dongre Dr. Manoj ,   Department of Electronics and Telecommunication, Ramrao Adik Institution of Technology, Dr. D. Y. Patil University Nerul, Navi Mumbai (M.H), India.
Published: March 2024

The proposed research presents a compact two ports Multiple Input Multiple Output (MIMO) antenna for the Ultra-Wide Band Application. The two symmetrical radiators consist for presented antenna and developed on an FR4 substrate for the overall size of the antenna is 20 × 44.1 × 1.6mm3. The microstrip line is fed with a 50Ω for the proposed antenna. The lower than isolation is < −25 dB from 3.1 to 10.6 GHz. The gain is 4.3dB and The Envelope Correlation Coefficients (ECC) < 0.01. The radiators for the better isolations used for the F-shaped stub. The size of the vertical block of stub is 12.3 X 5 mm. Antenna parameters are evaluation for the return loss, ECC, DG, gain, radiation pattern and isolation.
OPBUS: Automating Structural Fault Diagnosis for Graphical Models in the Design of Business Processes
Authors: Varela-Vaca A. J. , Gasca R. Martínez , Parody L.
Published: March 2024

When quality is considered in the design stages of business processes, fault diagnosis must be taken into account. In the business process modeling area there exist several proposed graphical languages. These languages have many different graphical elements that could contain structural fault modes in accordance with the corresponding standard used. It is essential to aid designers to diagnose faults within the graphical models for the business processes before they are put into execution; this is a major factor in risk avoidance. The graphical models must satisfy their associated graphical structural constraints. For organizations compliance is of major importance. Automation of the diagnosis in the design stage is necessary in order to diagnose the non-compliance to the graphical structural constraints of a business process as soon as possible. This article presents a framework with automatic diagnosing capabilities. It provides an early diagnosis of badly designed business processes. This paper describes the proposed general framework and focuses on fault diagnosis where the compliance to graphical structural constraints can be analyzed using various approaches of business processes.
Event-driven Data Mining Techniques for Automotive Fault Diagnosis
Authors: Sankavaram Chaitanya , Kodali Anuradha , Martinez Ayala Diego Fernando , Pattipati Krishna , Singh Satnam , Bandyopadhyay Pulak
Published: March 2024

The increasing sophistication of electronics in vehicular systems is providing the necessary information to perform data-driven diagnostics. Specifically, the advances in automobiles enable periodic acquisition of data from telematics services and the associated dealer diagnostic data from vehicles; this requires a data-driven framework that can detect component degradations and isolate the root causes of failures. The event-driven data consists of diagnostic trouble codes (DTCs) and the concomitant parameter identifiers (PIDs) collected from various sensors, customer complaints (CCs), and labor codes (LCs) associated with the repair. In this paper, we discuss a systematic data-driven diagnostic framework featuring data pre-processing, data visualization, clustering, classification, and fusion techniques and apply it to field failure datasets. The results demonstrated that the support vector machine (SVM) classifier with DTCs and customer complaints as features provides the best accuracy (74.3%) compared to any other classifier and that a tree- structured classifier with SVM as the base classifier at each node achieves approximately 75.2% diagnostic accuracy.
Synthesis of a Distributed and Accurate Diagnoser
Authors: Kan John Priscilla , Grastien Alban , Pencole ́ Yannick
Published: March 2024

The complex behaviour of large discrete event systems makes such systems difficult to diagnose. Using decentralised techniques helps limit combinatorial explosion but is not sufficient. Often, the complexity of the diagnosis is dependent on how components in the system are connected and the number of connections between them. We propose to augment a decentralized junction tree- based approach by ignoring some connections on the system. This helps reduce the complexity, and hence the cost, of the diagnostic reasoning required. However accuracy of the diagnosis is also reduced. We get around this problem by performing an off-line analysis to determine which connections can be safely ignored.
Towards Partial (and Useful) Model Identiﬁcation for Model-Based Diagnosis
Authors: Sadov Vladimir , Khalastchi Eliahu , Kalech Meir , A. Kaminka Gal
Published: March 2024

A fundamental requirement for model-based diagnosis (MBD) is the existence of a model of the diagnosed system. Based on the model, MBD algorithms are able to diagnose the faulty components. Unfortunately, a model is not always available. While it is possible in principle to infer a partial model by repeated trials, performing such trials is time and resource costly for any practical system. Therefore minimizing the number of trials is important. In this paper, we propose three algorithms for learning the model: two algorithms are Depth-ﬁrst search (DFS) based and one algorithm utilizes a binary search algorithm. We evaluate the algorithms theoretically and empirically through thousands of tests and show that one of the DFS-based algorithm scales well and the binary search algorithm is efﬁcient for small systems. Finally, we successfully demonstrate the algorithms on a model of the NAO robot (20 components) to show its capability in real-world domain.
Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators
Authors: Narasimhan Sriram , Roychoudhury Indranil , Balaban Edward , Saxena Abhinav
Published: March 2024

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However, this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electromechanical Actuator (FLEA) test bed.
A Framework for Integrating Requirements-Based Design and Diagnosis
Authors: Provan Gregory
Published: March 2024

Two key impediments for the commercial success of model-based diagnosis (MBD) include(a) a failure to integrate diagnostics modeling within the requirements and design phase, and(b) a high degree of diagnostic ambiguity during run-time. This article addresses both of these impediments by providing a formal framework that integrates requirements-based design with MBD modeling. The proposed framework extends the consistency-based theory of MBD with a requirements-based design theory based on contracts.
Sensor and Actuator Fault Isolation Using Parameter Interval based Method for Nonlinear Dynamic Systems
Authors: Li Zetao , Dahhou Boutaieb , Roux Gilles , Yang Jing , Zhang Cuixia
Published: March 2024

Fault isolation problem for sensor and actuator in nonlinear dynamic systems is studied. Parame- ter interval based fault isolation method has fast isolation speed and fits many kinds of nonlinear dynamic systems. This method is extended to the isolation of sensor fault and actuator fault. The example shows good performance.
Diagnosability Analysis Considering Causal Interpretations for Differential Constrain
Authors: Frisk Erik , Bregon Anibal , Aslund Jan , Krysander Mattias , Pulido Belarmino , Biswas Gautam
Published: March 2024

This work is focused on structural approaches to studying diagnosability properties given a system model taking into account, both simultaneously or separately, integral and differential causal interpretations for differential constraints. We develop a model characterization and corresponding algorithms, for studying system diagnosability using a structural decomposition that avoids generating the full set of system ARRs. Simultaneous application of integral and differential causal interpretations for differential constraints results in a mixed causality interpretation for the system. The added power of mixed causality is demonstrated using a case study. Finally, we summarize our work and provide a discussion of the advantages of mixed causality over just derivative or just integral causality.
Reformulation for the Diagnosis of Discrete-Event Systems
Authors: Grastien Alban , Torta Gianluca
Published: March 2024

Diagnosis is traditionally defined on a space of hypotheses (typically, all the combinations of zero or more possible faults).In the present paper, we argue that a suitable reformulation of this hypothesis space can lead to more efficient diagnostic algorithms and more compact diagnoses, most notably by exploiting opportunities for various forms of model abstraction. We also study several formal properties related to the correctness and precision of the diagnoses obtained through reformulation.
Distributed Consistency-Based Diagnosis without Behavior
Authors: Lamperti Gianfranco , Zanella Marina
Published: March 2024

This paper faces the task of distributed diagnosis without exploiting any component behavioral model. A diagnosis problem is specified by an observation that just states whether each system output is either correct or incorrect. The system is split into parts, and a distinct diagnoser, which is supplied with knowledge that has been compiled off-line and is capable of communicating with its neighbors, is assigned to each of them. A family of methods is proposed to compute local and global minimal diagnoses that are consistent with both the observation and the system description, the latter being a kind of control flow structure.
Proficy Advanced Analytics: a Case Study for Real World PHM Application in Energy
Authors: Nanda Subrat , Hu Xiaohui
Published: March 2024

GE monitors a large number of heavy duty equipment for energy generation, locomotives and aviation. These monitoring and diagnostic centers located world-wide sense, derive, transmit, analyze and view terabytes of sensory and calculated data each year. This is used to arrive at critical decisions pertaining to equipment life management - like useful life estimation, inventory planning and finally assuring a minimum level of performance to GE customers. Although a large number of analytical tools exist in today’s market, however, there is a need to have a tool at disposal which can aid not just in the analytical algorithms and data processing but also a platform for fleet wide deployment, monitoring and online processing of equipment. We describe a Prognostics & Health Management (PHM) application for GE Energy which was implemented using GE Intelligent Platform products and explore some capabilities of both the application and the analytics tool.
Physics Based Prognostic Health Management for Thermal Barrier Coating System
Authors: Kumar Amar , Saxena Bhavaye , Srivastava Alka , Goel Alok
Published: March 2024

Reliable prognostic of thermal barrier coating systems (TBCs) as applied to hot section engine components is a challenging task. Physics based approach is made here involving both experimental physical damage signature analysis and thermal cycle simulations. Thermally grown oxides (TGO) and the developing cracks in TBCs increase with thermal exposures. An exponential relationship is observed between the two parameters. Significant variations in size and characteristics of the damage signatures are observed depending on the four typical cycle profiles considered. In this paper, fourth order Runge-Kutta method is used for the numerical analysis of the differential equation for TGO growth analysis. Damage tolerance approach considering fracture mechanics based stress intensity factor is used to determine the crack tolerance level and remaining useful life. Our earlier fracture mechanical model for composite TBCs is modified assuming the crack to nucleate and grow within the TBC and not inside TGO. An overview of the PHM solution is presented.
Statistical vibration analysis for predictive maintenance of machines working under large variation of speed and load
Authors: F. Villa Luisa , Reñones Aníbal , R. Perán Jose , J. de Miguel Luis
Published: March 2024

Prognosis of defects for machines working under large variation of speed and load conditions is a topic still under development. Wind turbines are recent examples of such kind of machines that need reliable diagnosis methods. Vibration analysis can be of very limited use when the speed variation is too high. An effective angular resampling method can be very valuable as the first step of vibration signal processing but it is important to know what are the appropriate variables to be monitored. The authors present a statistical analysis method consisting of a linear model based on the parameters that characterize the system, in our case the variable speed and load, and the fault condition to which the system is subjected. With this method can be determined if the variable analyzed is significant, that is to say if are sensitive to these parameters and hence can detect the fault faster. The aim of implementing this method is to reduce the number of variables to be monitored, resulting in a savings not only in measuring equipment but also in times of processing and analyzing information. The results of vibration analysis of a test-bed working under large variation of speed and load are shown. Different tests with increasing level of defects are tried and the corresponding vibration is analyzed and modeled so an effective detection and prognosis can be done. Taking in to account such variation of speed and load for the vibration modeling can lead to a very sensitive detection of incipient defects.
A Bayesian Probabilistic Approach to Improved Health Management of Steam Generator Tubes
Authors: Chatterjee Kaushik , Modarres Mohammad
Published: March 2024

Steam generator tube integrity is critical for the safety and operability of pressurized water reactors. Any degradation and rupture of tubes can have catastrophic consequences, e.g., release of radioactivity into the atmosphere. Given the risk significance of steam generator tube ruptures, it is necessary to periodically inspect the tubes using nondestructive evaluation methods to detect and characterize unknown existing defects. To make accurate estimates of defect size and density, it is essential that detection uncertainty and measurement errors associated with nondestructive evaluation methods are characterized properly and accounted for in the evaluation. In this paper we propose a Bayesian approach that updates prior knowledge of defect size and density with nondestructive evaluation data, accounting for detection uncertainty and measurement errors. An example application of the proposed approach is then demonstrated for estimating defect size and density in steam generator tubes using eddy current evaluation data. The proposed Bayesian probabilistic approach helps improve health management of steam generator tubes, thereby enhancing the overall safety and operability of pressurized water reactors.
A Structural Health Monitoring Software Tool for Optimization, Diagnostics and Prognostics
Authors: S.Kessler Seth , B.Flynn Eric , T.Dunn Christopher , D.Todd Michael
Published: March 2024

Development of robust structural health monitoring (SHM) sensors and hardware alone is not sufficient to achieve desired benefits such as improved asset availability and reduced sustainment costs. For SHM systems to be practically deployed as part of an integrated system health management (ISHM), tools must be created for SHM life- cycle management (LCM). To that end, SHM-LCM software has been developed to expedite the adoption of SHM into ISHM. The SHM-LCM software is a flexible application intended to manage the cradle-to-grave life- cycle of an SHM system for generic applications. There are 4 core modules to facilitate critical roles: Optimization, Calibration, Visualization, and Action. The Optimization module seeks to devise optimal sensor placement and excitation parameters in order to achieve probability of detection (POD) coverage requirements. The Calibration module is designed to guide a user through a series of material level tests in order to customize algorithm variables to the system being designed. The Visualization module is dedicated to generating a diagnostic composite picture based on data downloaded from the diagnostic server, which is “stitched” to the original 3D mesh, providing users with a manipulatable GUI to toggle between probability of damage distributions for various calibrated damage modes. Finally, The Action module generates residual performance plots (ultimate load or deflection for example) as a function of probability of damage, so detection confidence can be weighed against impact to the vehicle’s capabilities. SHM- LCM software will enable SHM systems to be incorporated into ISHM by engineers rather than experts, making the technology more accessible, and commercially practical.
A Study on the parameter estimation for crack growth prediction under variable amplitude loading
Authors: Hyuck Leem Sang , An Dawn , Ko Sangho , Choi Joo-Ho
Published: March 2024

Bayesian formulation is presented to address the parameters estimation under uncertainty in the crack growth prediction subjected to variable amplitude loading. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. Model parameters are estimated in probabilistic way and updated conditional on the measured data by Bayesian inference. Markov Chain Monte Carlo (MCMC) method is employed for efficient sampling of the parameter distributions. As the model under variable amplitude loading is more complex, the conventional MCMC often fails to converge to the equilibrium distribution due to the increased number of parameters and correlations. An improved MCMC is introduced to overcome this failure, in which marginal PDF is employed as a proposal density function. A center- cracked panel under a mode I loading is considered for the feasibility study. Parameters are estimated based on the data from specimen tests. Prediction is carried out afterwards under variable amplitude loading for the same specimen, and validated by the ground truth data.
An Adaptive Particle Filtering-based Framework for Real-time Fault Diagnosis and Failure Prognosis of Environmental Control Systems
Authors: A. Raptis Ioannis , Vachtsevanos George
Published: March 2024

Maintenance of critical or/complex systems has recently moved from traditional preventive maintenance to Condition Based Maintenance (CBM) exploiting the advances both in hardware (sensors / DAQ cards, etc.) and in software (sophisticated algorithms blending together the state of the art in signal processing and pattern analysis). Along this path, Environmental Control Systems and other critical systems/processes can be improved based on concepts of anomaly detection, fault diagnosis and failure prognosis. The enabling technologies borrow from the fields of modeling, data processing, Bayesian estimation theory and in particular a technique called particle filtering. The efficiency of the diagnostic approach is demonstrated via simulation results.
Decision and Fusion for Diagnostics of Mechanical Components
Authors: Klein Renata , Rudyk Eduard , Masad Eyal
Published: March 2024

Detection of damaged mechanical components in their early stages is crucial in many applications. The diagnostics of mechanical components is achieved most effectively using vibration and/or acoustical measurements, sometimes accompanied by oil debris indications. The paper describes a concept for fusion and decision for mechanical components, based on vibroacoustic signatures. Typically in diagnostics of complex machinery, there are numerous records from normally operating machines and few recordings with damaged components. Diagnostics of each mechanical component requires consideration of a large number of features. Learning classification algorithms cannot be applied due to insufficient examples of damaged components. The proposed system presents a solution by introducing a hierarchical decision scheme. The proposed architecture is designed in layers imitating expert’s decision reasoning. The architecture and tools used allow incorporation of expert’s knowledge along with the ability to learn from examples. The system was implemented and tested on simulated data and real-world data from seeded tests. The paper describes the proposed architecture, the algorithms used to implement it and some examples.
Design for Fault Analysis Using Multi-partite, Multi-attribute Betweenness Centrality Measures
Authors: Lu Tsai-Ching , Zhang Yilu , L. Allen David , A. Salman Mutasim
Published: March 2024

As electrical and electronic systems (EES) steadfastly increase their functional complexity and connectedness, they pose ever-growing challenges in fault analysis and prevention. Many EES faults are intermittent, emerging (new faults), or cascading, and cannot be addressed by the traditional component-level diagnostic design. Leveraging the latest advancements in Network Science, we take the holistic approach to model and analyze the highly interrelated in-vehicle EES as layered sub-networks of hardware components, software components, and communication links. We develop multi-partite, multi-attribute betweenness centrality measures to quantify the complexity and maintainability of the layered EES network. We then use the betweenness centrality distribution to identify fault analysis monitoring points and fault-mitigation strategies. The promising results obtained by our initial empirical study of an example in-vehicle EES presents a first step toward network-theory based IVHM.
Experimental Polymer Bearing Health Estimation and Test Stand Benchmarking for Wave Energy Converters
Authors: T. Koopmans Michael , Meicke Stephen , Y. Tumer Irem , Paasch Robert
Published: March 2024

Ocean waves can provide a renewable and secure energy supply to coastal residents around the world. Yet, to safely harness and convert the available energy, issues such as bearing reliability and maintainability need to be resolved. This paper presents the application of a Prognostics and Health Management (PHM) based research methodology to derive empirical models for estimating the wear of polymer bearings installed on wave energy converters. Forming the foundation of the approach is an applicable wave model, sample data set, and experimental test stand to impose loading conditions similar to that expected in real seas. The resulting wear rates were found to be linear and stable, enabling coarse health estimations of the bearing surface.
Exploring the Model Design Space for Battery Health Management
Authors: Saha Bhaskar , Quach Patrick , Goebel Kai
Published: March 2024

Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries have been studied in detail in literature, an accurate run-time battery life prediction algorithm has eluded us. Current reliability-based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This paper presents a Particle Filter (PF) based BHM framework with plug-and-play modules for battery models and uncertainty management. The batteries are modeled at three different levels of granularity with associated uncertainty distributions, encoding the basic electrochemical processes of a Lithium-polymer battery. The effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle (UAV) application with emulated flight profiles.
Fault Diagnosis in Automotive Alternator System Utilizing Adaptive Threshold Method
Authors: Hashemi Ali , Pisu Pierluigi
Published: March 2024

In this paper, an observer-based adaptive threshold is developed as part of a fault diagnosis scheme to detect and isolate commonly occurring faults in a vehicle alternator system. Since the mathematical model of the alternator subsystem is quite involved and highly nonlinear; in order to simplify the diagnostic scheme, an equivalent linear time varying model based on the input-output behavior of the system is used for threshold equations derivation. A novel approach using Gaussian distribution to obtain the parameters of the system is investigated. The validity of the proposed diagnosis scheme is tested through simulation and the results are presented.
From measurement collection to remaining useful life estimation: defining a diagnostic-prognostic frame for optimal maintenance scheduling of choke valves undergoing erosion
Authors: Gola Giulio , Helge Nystad Bent
Published: March 2024

Condition Based Maintenance (CBM) aims at regulating maintenance scheduling based on data analyses and system condition monitoring. Clear advantages of optimizing maintenance scheduling include relevant cost savings and improved safety and plant availability. A critical aspect is the integration of CBM strategies with condition monitoring technologies for handling a wide range of information sources and eventually making optimal decisions on when and what to repair. In this work, a practical case study concerning maintenance of choke valves in offshore oil platforms has been investigated. Choke valves used in offshore oil platforms undergo erosion caused by the sand grains transported by the oil-water-gas mixture extracted from the well. Erosion is a critical problem which can affect the correct functioning of the valves, result in revenue losses and cause environmental hazards. In this respect, this work proposes a diagnostic-prognostic scheme for assessing the actual health state of a choke valve and eventually estimating its Remaining Useful Life (RUL). In particular, the focus has been on the identification of those parameters which contribute to the actual erosion of the choke valve, the development of a model-based approach for calculating a reliable indicator of the choke valve health state, the actual estimation of the choke RUL based on that indicator using statistical approaches and, finally, the investigation of methods to reduce the uncertainty of the RUL estimation by adding highly meaningful knowledge on the erosion state of the choke valve
Investigation on the opportunity to introduce prognostic techniques in railways axles maintenance
Authors: Vismara Mattia
Published: March 2024

In this study the opportunity to introduce PHM (prognostic and health monitoring) concepts into a cracked railway axle management is investigated.The performances of two different prognostic algorithm are assessed on the basis of their RUL (remaining useful life) predictions accuracy: a prognostic model based on the Bayesian theory and a physical prognostic model. Both models rely on the measured crack size. The measured crack growth measure has been built from simulated probabilistic crack growth path by adding measurements errors. The effect of monitoring frequency and the measurement HW and SW infrastructure size error to RUL predictions’ accuracy is assessed as well, trying to evaluate the hypothetical measuring infrastructure capabilities’ (sensors + layout) effect on the overall PHM predictions. Furthermore the PHM approach is compared to the classical preventive maintenance approach to railway axle maintenance management based on expensive and regular NDT.
Structural Integrity Assessment Using In-Situ Acoustic Emission Monitoring
Authors: Rabiei Masoud , Modarres Mohammad , Hoffman Paul
Published: March 2024

The work presented in this paper is focused on monitoring fatigue crack growth in metallic structures using acoustic emission (AE) technology. Three different methods are proposed to utilize the information obtained from in-situ monitoring for structural health management.
Fatigue crack growth tests with real-time acoustic emissions monitoring are conducted on CT specimens made of 7075 aluminum. Proper filtration of the resulting AE signals reveals a log-linear relationship between fracture parameters ( da⁄dN and ΔK ) and select AE features; a flexible statistical model is developed to describe the relationship between these parameters.Bayesian inference is used to estimate the model parameters from experimental data. The model is then used to calculate two important quantities that can be used for structural health management: (a) an AE-based instantaneous damage severity index, and (b) an AE-based estimate of the crack size distribution at a given point in time, assuming a known initial crack size distribution.
Finally, recursive Bayesian estimation is used for online integration of the structural health assessment information obtained from AE monitoring with crack size estimates obtained from empirical crack growth model. The evidence used in Bayesian updating includes observed crack sizes and/or crack growth rate observations
Study on MEMS board-level package reliability under high-G impact
Authors: Cui Jiuzheng , Sun Bo , Feng Qiang , Zeng ShengKui
Published: March 2024

Under high-G (104 g or above) impact load conditions, the reliability of micro electro mechanical systems (MEMS) board-level package and interconnection are critical concerned that influence the mission success of total projectile. This paper conducts a research on this problem to analyze package reliability using finite element modelling (FEM) and simulation method. Theoretical analysis and mathematical model for failure mechanism of MEMS package under high-G impact are conducted and established. A FEM dynamic analysis is conducted on a typical MEMS board-level leadless chip carrier (LCC) package. Results show that the solder joints are one of the key weakness points that influence the reliability of MEMS package. The maximum effective stress in the structure occurs at the outer corner in the outermost solder point, and the alloy cover and printed circuit board (PCB) have a greater deformation.
Prognostics Assessment Using Fleet-wide Ontology
Authors: Medina-Oliva Gabriela , Voisin Alexandre , Monnin Maxime , Peysson Flavien , Leger Jean-Baptiste
Published: March 2024

Large complex systems, such as power plants, ships and aircraft, are composed of multiple systems, subsystems and components. When they are considered as embedded in operating systems such as a fleet, mission readiness and maintenance management issues are raised. PHM (Prognostics and Health Management) plays a key role in controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. Moreover considering a fleet implies to provide managers and engineers a relevant synthesis of information and to keep this information updated in terms of the global health of the fleet as well as the current status of their maintenance efforts. In order to achieve PHM at a fleet level, it is thus necessary to manage relevant knowledge arising from both modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme based on ontologies for fleet PHM management applied to marine domain, with emphasis on prognostics modeling.
Statistical Health Grade System against Mechanical failures of Power Transformers
Authors: Hu Chao , Wang Pingfeng , D. Youn Byeng , Lee Wook-Ryun , Taek Yoon Joung
Published: March 2024

A health grade system against mechanical faults of power transformers has been little investigated compared to those for chemical and electrical faults. This paper thus presents a statistical health grade system against mechanical faults in power transformers used in nuclear power plant sites where the mechanical joints and/or parts are the ones used for constraining transformer cores. Two health metrics—root mean square (RMS) and root mean square deviation (RMSD) of spectral responses at harmonic frequencies—are first defined using vibration signals acquired via in-site sensors on fifty-four power transformers in several nuclear power plants in sixteen months. We then investigate a novel multivariate statistical model, namely copula, to statistically model the populated data of the health metrics. The preliminary study shows that the proposed health metrics and statistical health grade system are feasible to monitor and predict the health condition of the mechanical faults in the power transformers.
Anomaly Detection based on Information-Theoretic Measures and Particle Filtering Algorithms
Authors: E. Orchard Marcos , Olivares Benjamín , Cerda Matías , F. Silva Jorge
Published: March 2024

This paper presents an anomaly detection module that uses information-theoretic measures to generate a fault indicator from a particle-filtering-based estimate of the posterior state pdf of a dynamic system. The selected measure allows isolating events where the particle filtering algorithm is unable to track the process measurements using a predetermined state transition model, which translates into either a sudden or a steady increment in the differential entropy of the state pdf estimate (evidence of an anomaly on the system). Anomaly detection is carried out by setting a threshold for the entropy value. Actual data illustrating aging of an energy storage device (specifically battery state- of-health (SOH) measurements [A-h]) are used to test and validate the proposed framework.