bluuebunny/crossref_metadata_2025 huggingface dataset:
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

ABSTRAKAlga 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
ESTIMASI KARBON TERSIMPAN PADA VEGETASI MANGROVE DI SEKITAR MARINE FIELD STATION UNIVERSITAS SAM RATULANGI KECAMATAN LIKUPANG KABUPATEN MINAHASA UTARA (Estimation of Stored Carbon in Mangrove Vegetation Around Marine Field Station Sam Ratulangi University
Authors: Soares Olivio E. De Jesus , Schaduw Joshian N. W. , Rumengan Antonius P. , Gerung Grevo S. , Boneka Farnis B. , Sondak Calvyn F. A.
Published: March 2024

Mangroves are plants that are able to survive in coastal areas under environmental conditionsthat are influenced by tides and muddy seawater. Mangrove forests have an ecological function, whichis to play a role in efforts to mitigate global warming because mangrove forests absorb and store carbondioxide (CO2) which is carried out through the process of photosynthesis and storing it in biomass stock.There are 2 objectives of this study, namely: (1) to estimate the carbon stored in mangrove vegetationin Likupang District, North Minahasa Regency (2) to find out how much potential carbon stock is storedin mangrove vegetation and absorption of carbon dioxide (CO2) on mangrove stands. The method usedin this study was a sample plot transect line (Dharmawan and Pramudji, 2001). Based on the resultsobtained from the calculation of the biomass and carbon stock stored in mangrove vegetation inLikupang District, North Minahasa Regency, it can be seen that the average value is 452.42 tons/hawith a carbon content or the amount of carbon stored is 212.64 tons C/ ha or equivalent to 780.38 tonsof CO2/ha.Keywords: Mangrove Vegetation, Carbon, North Minahasa

ABSTRAKMangrove merupakan tumbuhan yang mampu bertahan hidup di daerah pesisir pantai dengandi bawah kondisi lingkungan yang dipengaruhi oleh pasang surut air laut dan berlumpur. Hutanmangrove memiliki salah satu fungsi ekologis yaitu berperan dalam upaya mitigasi pemanasan globalkarena hutan mangrove sebagai penyerap dan penyimpan karbon dioksida (CO2) yang dilakukanmelalui proses fotosintesis dan menyimpannya dalam stok biomassa. Tujuan dari penelitian ini ada 2yaitu: (1) mengestimasi karbon tersimpan pada vegetasi mangrove di Kecamatan Likupang, KabupatenMinahasa Utara, dan (2) mengetahui berapa besar potensi stok karbon tersimpan pada vegetasimangrove dan serapan karbondioksida (CO2) atas tegakan mangrove. Metode yang digunakan dalampenelitian ini adalah garis transek petak contoh (Dharmawan dan Pramudji, 2001). Berdasarkan hasilyang diperoleh dari perhitungan biomassa dan stok karbon yang tersimpan pada vegetasi mangrove diKecamatan Likupang, Kabupaten Minahasa Utara, dapat nilai rata-rata sebesar 452,42 ton/ha dengankandungan karbon atau jumlah karbon tersimpan sebesar 212,64 ton C/ha atau setara 780,38 tonCO2/ha.Kata Kunci: Vegetasi Mangrove, Karbon, Minahasa Utara
STRUKTUR KOMUNITAS ALGA DI SEKITAR PERAIRAN KELURAHAN MERAS KECAMATAN BUNAKEN KOTA MANADO
Authors: Chairudin Daniel Y. , Kumampung Deislie R.H. , Kaligis Erly Y. , Wagey Billy Th. , Rimper Joice R.T.S.L. , Kemer Kurniati
Published: March 2024

Algae in North Sulawesi are still relatively abundant and have begun to be cultivated to be usedas food or medicinal products. However, clear information about the presence of algae species,especially information about algae community structure, has not been widely reported, so it is necessaryto do research on the algae community structure in North Sulawesi waters, more specifically aroundMeras Village waters. This study aims to determine the species of algae and the structure of the algaecommunity around the waters of the Meras Village, namely density, species diversity and dominance.The method used in this study is the line transect method. The results of the study found 9 species ofalgae, namely Dictyota mayae, Halimeda macroloba, Gracilaria edulis, Padina australis, Sargassumpolyceratium, Sargassum polycystum, Eucheuma spinosum, Bornetella oligospora and Amphiroafragilissima. The highest algae species density was G. edulis at 2.31 (ind/m2) and the lowest was E.spinosum at 0.27 (ind/m2). The species dominance index in this study was T1 0.17; T2 0.19 and T30.22. And the algae diversity index on each transect obtained T1 which was 1.76, T2 was 1.85, and T3was 1.91.Keywords: Algae, Community Structure, Meras

ABSTRAKAlga di Sulawesi Utara terbilang masih melimpah dan sudah mulai dibudidayakan untukdijadikan bahan makanan ataupun produk obat-obatan. Namun informasi yang jelas tentangkeberadaan jenis-jenis alga terutama informasi tentang struktur komunitas alga belum banyakdilaporkan, sehingga perlu dilakukan penelitian mengenai struktur komunitas alga di Perairan SulawesiUtara lebih khusus lagi di sekitar Perairan Kelurahan Meras. Penelitian ini bertujuan untuk mengetahuijenis-jenis alga dan struktur komunitas alga di sekitar Perairan Kelurahan Meras yaitu kepadatan,keanekaragaman jenis dan dominansi. Metode yang digunakan dalam penelitian ini adalah metode linetransek. Hasil penelitian ditemukan 9 spesies alga pada lokasi penelitian, yaitu Dictyota mayae,Halimeda macroloba, Gracilaria edulis, Padina australis, Sargassum polyceratium, Sargassumpolycystum, Eucheuma spinosum, Bornetella oligospora dan Amphiroa fragilissima. Nilai kepadatanjenis alga tertinggi yakni G. edulis sebesar 2,31 (ind/m2) dan terendah adalah E. spinosum sebesar0,27 (ind/m2). Indeks dominansi jenis dalam penelitian ini T1 0,17; T2 0,19 dan T3 0,22. Dan Indekskeanekaragaman alga pada masing-masing transek diperoleh T1 yakni 1,76, T2 sebesar 1,85, dan T3sebesar 1,91.

Kata kunci: Alga, Struktur Komunitas, Meras
Rancang Bangun Sistem Aplikasi Kasir Pintar Enterprise Resource Planning (ERP) Berbasis Odoo pada UMKM Rumah Makan Tradisional Uni Mita Lintau
Authors: Nendi  , Saputra Hendra Ekky , Suprianto Ahmad , Aprilia Putri Dwi , Lestari Syntha Agung
Published: March 2024

The application of information technology on a Micro, Small, and Medium Enterprises (MSME) scale is crucial in increasing competitiveness and operational efficiency in various industrial sectors. In the food and beverage industry, the implementation of a smart cashier application system based on Enterprise Resource Planning (ERP) using the Odoo platform has become a significant concern. This research aims to design and build an ERP-based smart cashier application system using Odoo at the MSME Uni Mita Lintau Traditional Restaurant. By combining Odoo technology with MSME business practices, this research provides a solution to improve operational efficiency and customer experience in traditional restaurants. Through implementation analysis and evaluation, this research offers practical guidance and insight for other MSMEs interested in adopting similar technology to improve their business performance. Thus, this research not only contributes to the practical understanding of the application of technology to MSMEs but also contributes to further developments in the literature on the application of information technology in the food and beverage industry.
Trust di azioni di società dell'Arabia Saudita dove il trust non esiste (Inghilterra, Byers and others v Saudi National Bank, 2023)
Authors: Hepaj Ermelinda
Published: March 2024

Massima
In presenza di giurisdizioni che non conoscono il trust, ovvero la distinzione tra legal ed equitable ownership, la domanda per knowing receipt, esperita dai beneficiari nei confronti di un terzo, che non sia un trustee, per aver ricevuto un bene in trust pur sapendo che questo fosse frutto di una breach of trust, può essere accolta solo qualora sussista in capo ai beneficiari un diritto proprietario ai sensi della lex situs che regola i trasferimenti transfrontalieri dei beni riconducibili al trust fund.
Il riconoscimento tacito della scrittura privata di conferimento in trust di un immobile e la sua immediata trascrivibilità (Trib. Prato, 18 settembre 2023)
Authors: Bazzoni Giulia
Published: March 2024

Massima
L’autenticità della sottoscrizione di una scrittura privata avente ad oggetto la istituzione di un trust e il conferimento a quest’ultimo di un immobile può essere riconosciuta anche tacitamente in giudizio. A seguito dell’accertamento giudiziale di tale scrittura privata costituisce ex art. 2657 cod. civ. titolo idoneo per la trascrizione del conferimento in trust dell’immobile.
Il reddito di capitale generato da un trust statunitense è imponibile rispetto al trustee residente in Italia (CGT Umbria, II grado, 2 novembre 2023)
Authors: Tarini Federico
Published: March 2024

Massima
Il reddito di capitale generato da un trust statunitense è imponibile in capo al trustee residente in Italia, unico beneficiario del reddito del trust.
Imponibilità delle distribuzioni derivanti da trust opachi nell’Unione Europea
Authors: Nicolosi Francesco
Published: March 2024

Tesi
I redditi distribuiti da trust localizzati in territori a fiscalità agevolata vengono tassati come reddito di capitale per i beneficiari residenti in Italia in ragione del criterio di cassa. In teoria tale imponibilità non dovrebbe operare con riferimento ai trust residenti all’interno dell’UE/SEE, pena una violazione della libertà di stabilimento. Il risparmio fiscale ottenuto dal trust non è di per sè una ragione per legittimare la violazione della libertà di stabilimento. Tale imponibilità, in base al diritto unionale, sarebbe ammissibile solo laddove il trust fosse qualificato come “costruzione artificiosa”, secondo la definizione fornita dalla Corte di Giustizia dell’Unione Europea.
 
The author’s view
The law provides for the taxation in the hands of Italian beneficiaries that all sums paid to Italian beneficiaries by non resident opaque trusts located in low tax jurisdictions. In theory such sums should not be taxable as long as the trust il located within EU, in order to comply with the freedom of establishment principle, notwithstanding any tax saving obtained by the taxpayer. Such distributions could be taxable only if the distributing trust was a “wholly artificial arrangement”, according to the jurisprudence of the European Court of Justice.
Sequestro di quote societarie e fallimento del loro conferente (Cass. pen., 6 ottobre 2023, n. 40797)
Authors: Arbotti Mario
Published: March 2024

Tesi
I rapporti tra sequestro preventivo e procedure concorsuali meriterebbero una diversa articolazione rispetto a quella delineata dalla costante giurisprudenza di legittimità che, emancipata dalla sistematica primazia della pretesa punitiva sugli interessi dei terzi creditori, sia in grado di raggiungere un equilibrio maggiormente armonico fra istanze oppositive, valorizzando la possibilità per il giudice di far prevalere l’uno o l’altro vincolo in ragione delle peculiarità della situazione contingente.
The author’s view
The interrelation between preventive measures and insolvency proceedings deserves a different articulation compared to the one set out by the constant jurisprudence of legitimacy that, emancipated from the systematic primacy of the punitive claim over the interests of third-party creditors, is able to achieve a more harmonious balance between opposing instances, enhancing the possibility for the judge to prevail one or the other constraint according to the peculiarities of the contingent situation.
Self-adapting Strategies guided by Diagnosis and Situation Assessment in Collaborative Communicating Systems
Authors: Subias A. , Exposito E. , Chassot C. , Travé-Massuyes L. , Drira K.
Published: March 2024

Coping with context changes in networked systems requires considering self-adaptive communication protocols in the design of the future generation of communication systems. The communication system conﬁguration then dynamically changes according to the user’s requirements and to the load of the communication resources. Dealing with this problem requires the capacity of detecting the possible degradations of the Quality of Service (QoS) and of dynamically modifying the behavior of the communication protocols for each new context situation. This requires in turn both monitoring the QoS values, detecting the degradations, identifying their origins through appropriate diagnosis and executing reconﬁguration actions. We propose to implement such functions by considering an event-based model-driven diagnosis approach leading the dynamic composition of communication protocols for the execution of the reconﬁguration. We consider a chronicle-based diagnosis approach and we apply our ideas to the Transport layer of communication systems. In this position paper, we show the relevance of our ideas and present preliminary ideas towards an integrated automated approach.
Diagnosability for Patterns in Distributed Discrete Event Systems
Authors: Yan Yuhong , Ye Lina , Dague Philippe
Published: March 2024

A pattern is a Finite State Machine that can describe rich faulty scenarios, such as the occurrence of single faults, multiple faults, multiple occurrences of a fault, or the repair of a system. In distributed systems, the events in the pattern, as well as in the system trajectories, are emitted from different components. Our approach is based on distributed simulation and communication to check the recognition of the pattern from the conclusion of local recognition of local patterns. The components communicate observable events and shared communication events, as well as their local recognition results during the checking process without sharing their local models in any way.
On non-invertibilities for Structural Analysis
Authors: De Flaugergues Vincent de Flaugergues , Cocquempot Vincent , Bayart Mireille , Pengov Marco
Published: March 2024

This article deals with structural analysis, which is a simple but efficient method in the field of Fault Detection and Isolation (FDI), to determine systems properties, such as observability, fault detectability or diagnosability. Moreover, it allows to determine subsets of the model equations which may or may not yield fault indicators, namely residuals. Because some residuals are obtained by inverting parts of the model, the notion of constraint invertibility is used to assess the possibility of building a residual. Invertibilities are often considered a posteriori, after that the structural analysis has been performed, in order to keep the computable residuals. Taking into account these invertibility constraints in all steps of the structural method would allow, firstly, to provide directly computable residuals, and secondly, to reduce the complexity of structural analysis algorithms. Two types of non-invertibilities may be distinguished: those which are defined according to the nature of the functions, and those which are due to the structure of the model. Two algorithms are proposed for determining the latter ones. Integration of the two kinds of invertibilities from the first step of the structural analysis is the objective of this paper.
Fleet-wide health management architecture
Authors: Monnin Maxime , Voisin Alexandre , Leger Jean-Baptiste , Iung Benoit
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 system operating as a fleet, it raises mission readiness and maintenance management issues. PHM (Prognostics and Health Management) plays a key role for controlling the performance level of such systems, at least on the basis of adapted PHM strategies and system developments. However, considering a fleet implies to provide managers and engineers with a relevant synthesis of information and keep it updated regarding both the global health of the fleet and the current status of their maintenance efforts. For achieving PHM at a fleet level, it is thus necessary to manage relevant corresponding knowledge arising both from modeling and monitoring of the fleet. In that way, this paper presents a knowledge structuring scheme for fleet PHM management applied to marine domain.
Condition Based Maintenance Optimization for Multi-component Systems
Authors: Tian Zhigang , Zhang Youmin , Cheng Jialin
Published: March 2024

Most existing condition based maintenance (CBM) work reported in the literature only focuses on determining the optimal CBM policy for single units. Replacement and other maintenance decisions are made independently for each component, based on the component’s age, condition monitoring data and the CBM policy. In this paper, a CBM optimization method is proposed for multi-component systems, where economic dependency exists among the components subject to condition monitoring. In a multi-component system, due to the existence of economic dependency, it might be more cost-effective to replace multiple component at the same time rather than making maintenance decisions on components separately. Deterioration of a multi-component system is represented by a conditional failure probability value, which is calculated based on the predicted failure time distributions of components. The proposed CBM policy is defined by a two- level failure probability threshold. A method is developed to obtain the optimal threshold values in order to minimize the long-term maintenance cost. An example is used to demonstrate the proposed multi-component CBM method.
A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging
Authors: R. Celaya Jose ́ , Kulkarni Chetan , Biswas Gautam , Saha Sankalita , Goebel Kai
Published: March 2024

A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.
A Combined Anomaly Detection and Failure Prognosis Approach for Estimation of Remaining Useful Life in Energy Storage Devices
Authors: E. Orchard Marcos , Tang Liang , Vachtsevanos George
Published: March 2024

Failure prognosis and uncertainty representation in long- term predictions are topics of paramount importance when trying to ensure safety of the operation of any system. In this sense, the use of particle filter (PF) algorithms -in combination with outer feedback correction loops- has contributed significantly to the development of a robust framework for online estimation of the remaining useful equipment life. This paper explores the advantages of using a combination of PF-based anomaly detection and prognosis approaches to isolate rare events that may affect the understanding about how the fault condition evolves in time. The performance of this framework is thoroughly compared using a set of ad hoc metrics. Actual data illustrating aging of an energy storage device (specifically battery state-of-health (SOH) measurements [A-hr]) are used to test the proposed framework.
Fault-Tolerant Trajectory Tracking Control of a Quadrotor Helicopter Using Gain-Scheduled PID and Model Reference Adaptive Control
Authors: Sadeghzadeh Iman , Mehta Ankit , Zhang Youmin , Rabbath Camille-Alain
Published: March 2024

Based on two successfully and widely used control techniques in many industrial applications under normal (fault-free) operation conditions, the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and Model Reference Adaptive Control (MRAC) strategies have been extended, implemented, and experimentally tested on a quadrotor helicopter Unmanned Aerial Vehicle (UAV) test- bed available at Concordia University, for the purpose of investigation of these two typical and different control techniques as two useful Fault-Tolerant Control (FTC) approaches. Controllers are designed and implemented in order to track the desired trajectory of the helicopter in both normal and faulty scenarios of the flight. A Linear Quadratic Regulator (LQR) with integral action controller is also used to control the pitch and roll motion of the quadrotor helicopter. Square trajectory, together with specified autonomous and safe taking-off and landing path, is considered as the testing trajectory and the experimental flight testing results with both GS-PID and MRAC are presented and compared with tracking performance under partial loss of control power due to fault/damage in the propeller of the quadrotor UAV. The performance of both controllers showed to be good. Although GS-PID is easier for development and implementation, MRAC showed to be more robust to faults and noises, and is friendly to be applied to the quadrotor UAV.
Gear Health Threshold Setting Based On a Probability of False Alarm
Authors: Bechhoefer Eric , He David , Dempsey Paula
Published: March 2024

There is no established threshold or limit for gear vibration based condition indicators (CI) that indicates when a gear is in need of maintenance. The best we can do is set CI thresholds statistically, based on some small probability of false alarm. Further, to the best of our knowledge, there is no single CI that is sensitive to every failure mode of a gear. This suggests that any condition based maintenance system for gears will have some form of sensor fusion.Three statistical models were developed to define a gear health indicator (HI) as a function of CI: order statistics (max of n CIs), sum of CIs and normalized energy. Since CIs tend to be correlated, a whitening process was developed to ensure the HI threshold is consistent with a defined probability of false alarm. These models were developed for CIs with Gaussian or Rayleigh (skewed) distributions. Finally, these functions, used to generate HIs, were tested on gear test stand data and their performance evaluated as compared to the end state of the gear (e.g. photos of damage). Results show the HIs performed well detecting pitting damage to gears.
Real-Time Corrosion Monitoring of Aircraft Structures with Prognostic Applications
Authors: Brown Douglas , Darr Duane , Morse Jefferey , Laskowski Bernard
Published: March 2024

This paper presents the theory and experimental validation of a Structural Health Management (SHM) system for monitoring corrosion. Corrosion measurements are acquired using a micro-sized Linear Polarization Resistance (μLPR) sensor. The μLPR sensor is based on conventional macro-sized Linear Polarization Resistance (LPR) sensors with the additional benefit of a reduced form factor making it a viable and economical candidate for remote corrosion monitoring of high value structures, such as buildings, bridges, or aircraft.A series of experiments were conducted to evaluate the μLPR sensor for AA 7075-T6, a common alloy used in aircraft structures. Twelve corrosion coupons were placed alongside twenty-four μLPR sensors in a series of accelerated tests. LPR measurements were sampled once per minute and converted to a corrosion rate using the algorithms presented in this paper. At the end of the experiment, pit-depth due to corrosion was computed from each μLPR sensor and compared with the control coupons.The paper concludes with a feasibility study for the μLPR sensor in prognostic applications. Simultaneous evaluation of twenty-four μLPR sensors provided a stochastic data set appropriate for prognostics. RUL estimates were computed a-posteriori for three separate failure thresholds. The results demonstrate the effectiveness of the sensor as an efficient and practical approach to measuring pit-depth for aircraft structures, such as AA 7075-T6, and provide feasibility for its use in prognostic applications.
System PHM Algorithm Maturation
Authors: Massé Jean-Remi , Hmad Ouadie , Boulet Xavier
Published: March 2024

The maturation of PHM functions is focused on two Key Performance Indicators (KPI): The NFF, No Fault Found ratio, P(No degradation|Detection), and the Probability Of Detection POD, P(Detection|Degradation). The estimation of the second KPI can be done by counting the global abnormality threshold trespassing when each different kind of degradation is simulated. The  estimation of the first KPI can be done through the following formula using the Bayes rule:( )( | )* ( )( | )P DetectionP Detection NoDegradation P NoDegradationP NoDegradation Detection P(Degradation) may be known through FMEA or field experience. Typically, for a probability of 10-7, a specified NFF ratio of 1%, and an expected POD of 90%, the order of magnitude of P(Detection| No degradation) should be 10-9. The estimation of such extreme level of probability needs some parametric adjustment of the distribution of the global abnormality score with no degradation. Two PHM functions are considered as case studies: Turbofan engine start capability (ESC) and turbofan engine lubrication oil consumption (EOC). In ESC the global abnormality score is a norm of a vector of specific abnormality scores. The specific scores are centered and reduced residues between expected values and observed values. Some specific scores are devoted to starter air supply. Examples are duration of phase 1 from starter air valve open command to ignition speed. Other scores are devoted to fuel metering. Examples are duration of phase 2 from ignition to cut off speed. The expected values are estimations through regression relations using as inputs the other specific scores and context parameters such as lubrication oil temperature at start. The regression relations are learnt on start records with no degradations. Impact simulations of degradations on specific scores are learnt on a phase 1 simulator based on torques balance and on start test records including fuel metering biases. In EOC, the global abnormality score is the daily weekly or monthly consumption estimations on a daily basis. Consumption estimations use linear regressions of oil level measurements versus time at an invariable ground idle speed corrected according to oil fill detections and oil temperature. The over consumptions are simulated by drifts in mean of the consumption estimations.To reach acceptable POD at the specified NFF ratio three improvements are needed for ESC:  Adjust the abnormality decision threshold according to each candidate degradation using extreme value quantiles on the global abnormality score distribution Average the global abnormality score on five consecutive starts Learn the regression relations specifically on each engine.The first improvement is a novelty. It is successfully applied to both ESC and EOC functions. It is generic to all airborne system PHM functions based on abnormality scores.
A distributed Architecture to implement a Prognostic Function for Complex Systems
Authors: Desforges Xavier , Diévart Mickaël , Charbonnaud Philippe , Archimède Bernard
Published: March 2024

The proactivity in maintenance management is improved by the implementation of CBM (Condition-Based Maintenance) and of PHM (Prognostic and Health Management). These implementations use data about the health status of the systems. Among them, prognostic data make it possible to evaluate the future health of the systems. The Remaining Useful Lifetimes (RULs) of the components is frequently required to prognose systems. However, the availability of complex systems for productive tasks is often expressed in terms of RULs of functions and/or subsystems; those RULs provide information about the components. Indeed, the maintenance operators must know what components need maintenance actions in order to increase the RULs of the functions or subsystems, and consequently the availability of the complex systems. This paper aims at defining a generic prognostic function of complex systems aiming at prognosing its subsystems, functions and at enabling the isolation of components that needs maintenance actions. The proposed function requires knowledge about the system to be prognosed. The corresponding models are detailed. The proposed prognostic function contains graph traversal so its distribution is proposed to increase the calculation speed. It is carried out by generic agents.
A multivariate statistical approach to the implementation of a health monitoring system of mechanical power drives
Authors: Bellazzi Alberto , Jacazio Giovanni , Maino Bruno , Mihaylov Gueorgui , Pellerey Franco
Published: March 2024

The implementation in service of accelerometric health monitoring systems of mechanical power drives has shown that a considerable number of false failure alarms is generated. The paper presents a combined application of several multivariate statistical techniques and shows how a monitoring method which integrates these tools can be successfully exploited in order to improve the reliability of the diagnostic systems.
A Data-Driven Approach for on-line Gas Turbine Combustion Monitoring using Classification Models
Authors: Allegorico Carmine , Mantini Valerio
Published: March 2024

Given the critical nature of Gas Turbines in most industrial plants, it is a high priority to find ways of reducing maintenance costs and increasing the availability. Quickly detecting and identifying combustion anomalies enables the choice of an appropriate recovery strategy, potentially mitigating the consequences of unscheduled down time and increased maintenance costs. Monitoring the Exhaust Gas Temperature (EGT) profiles is a good means of detecting combustion problems: plugged nozzles and/or combustor and transition piece failures will always result in distorted exhaust gas temperature patterns. However the conventional monitoring systems do not allow robust discrimination between instrumental failures and real gas turbine issues; furthermore weak diagnostic methods can be source of numerous false alarms.In this paper, we investigate the problem of monitoring the combustion chambers of a gas turbine and we attempt to address this issue by introducing a strategy for automatic and efficient patterns recognition by using Machine Learning Classification algorithms. Some historical events have been firstly retrieved and analyzed to discover which features are useful for classification. Based on the observations, two multiclass classification algorithms, one based on logistic regression, the other on Artificial Neural Networks (ANN), have been developed. Finally, real-world datasets have been used to benchmark the performance of the proposed algorithms against a traditional physics-based approach.
An approach for feature extraction and selection from non-trending data for machinery prognosis
Authors: Kimotho James Kuria , Sextro Walter
Published: March 2024

With the paradigm shift towards prognostic and health management (PHM) of machinery, there is need for reliable PHM methodologies with narrow error bounds to allow maintenance engineers take decisive maintenance actions based on the prognostic results. Prognostics is mainly concerned with the estimation of the remaining useful life (RUL) or time to failure (TTF). The accuracy of PHM methods is usually a function of the features extracted from the raw data obtained from sensors. In cases where the extracted features do not display clear degradation trends, for instance highly loaded bearings, the accuracy of the state of the art PHM methods is significantly affected. The data which lacks clear degradation trend is referred to as non-trending data. This studypresents a method for extracting degradation trends from nontrending condition monitoring data for RUL estimation. The raw signals are first filtered using a discrete wavelet transform (DWT) denoising filter to remove noise from the acquired signals. Time domain, frequency domain and timefrequency domain features are then extracted from the filtered signals. An autoregressive (AR) model is then applied to the extracted features to identify the degradation trends. Features representing the maximum health information are then selected based on a performance evaluation criteria using extreme learning machine (ELM) algorithm. The selected features can then be used as inputs in a prognostic algorithm. The feasibility of the method is demonstrated using experimental bearing vibration data. The performance of the method is evaluated on the accuracy of RUL estimation and the results show that the method can be used to accurately estimate RULwith a maximum error of 10%.
Physics-Based Degradation Modelling for Filter Clogging
Authors: Eker O. F. , Camci F. , Jennions I. K.
Published: March 2024

Separation of solids from fluid is a vital process to achieve the desired level of purification in industry. Contaminant filtration is a common process in a variety of applications in industry. Clogging of filter phenomena is the primary failure mode leading to replacement or cleansing of filter. Reduced performance and efficiency or cascading failures are the unfortunate outcomes of a clogged filter. For instance, solid contaminants in fuel may lead to performance reduction in the engine and rapid wear in the fuel pump. This paper presents the development of an experimental rig to collect accelerated filter clogging data and a physics-based degradation model to represent the filter clogging. In the experimental rig, pressure drop across the filter, flow rate, and filter mesh images are acquired during the accelerated clogging experiments. The pressure drop across the filter due to deposition of suspended solids in the liquid is modelled and employed in the degradation modelling. Then, the physics based degradation model simulated using MatLab is compared with the real clogging data and the effectiveness of the degradation model is evaluated.
Accelerated life tests for prognostic and health management of MEMS devices
Authors: Skima Haithem , Medjaher Kamal , Zerhouni Noureddine
Published: March 2024

Microelectromechanical systems (MEMS) offer numerous applications thanks to their miniaturization, low power consumption and tight integration with control and sense electronics. They are used in automotive, biomedical, aerospace and communication technologies to achieve different functions in sensing, actuating and controlling. However, these microsystems are subject to degradations and failure mechanisms which occur during their operation and impact their performances and consequently the performances of the systems in which they are used. These failures are due to different influence factors such as temperature, humidity, etc. The reliability of MEMS is then considered as a major obstacle for their development. In this context, it is necessary to continuously monitor them to assess their health status, detect abrupt faults, diagnose the causes of the faults, anticipate incipient degradations which may lead to complete failures and take appropriate decisions to avoid abnormal situations or negative outcomes. These tasks can be performed within Prognostics and Health Management (PHM) framework. This paper presents a hybrid PHM method based on physical and data-driven models and applied to a microgripper. The MEMS is first modeled in a form of differential equations. In parallel, accelerated life tests are performed to derive its degradation model from the acquired data. The nominal behavior and the degradation models are then combined and used to monitor the microgripper, assess its health state and estimate its Remaining Useful Life (RUL).
Enhanced Particle Filter framework for improved prognosis of electro-mechanical flight controls actuators
Authors: De Martin Andrea , Jacazio Giovanni , Sorli Massimo
Published: March 2024

The technology of flight control actuators is at the forefront of the “more electric aircraft” research trend since its beginning, due to the several advantages that the introduction of Electro Mechanical Actuators (EMAs) would provide over the traditional hydraulic/mechanical solution for both primary and secondary flight control surfaces. However when compared to hydraulic actuators, technological barriers still persist for a wide adoption of EMAs especially when considering their sensitivity to certain single point of failures that can lead to mechanical jams, resulting in a reluctance to adopt EMAs for flight safety critical applications as solutions are heavy and costly (redundancy, fail safe behavior, etc.), thus creating difficulties for adoption and certification and impacts on costs. The development of an effective and reliable PHM system for EMAs could help mitigating the risk of a sudden critical failure by properly recognizing and tracking the ongoingfault and anticipating its evolution, and will offer a possible way contributing to the acceptance of EMAs as primary flight control actuators in commercial aircraft. The paper firstly presents an enhanced Particle Filter framework for improved prognosis, discussing its benefits and its implementation inside a general PHM framework developed for EMAs. Then, three significant examples of its application are provided relevant to an electrical and a mechanical fault of the EMA: degradation of the electric motor and backlash increase in the mechanical transmission due to the growing wear of its components. The positive results obtained from the application of the enhanced Particle Filtering framework to these faults provide good confidence on the possibility of extending it to other EMAs faults and to successfully implement this technique to EMAs for flight control actuators.
Deep Neural Networks Analysis of Borescope Images
Authors: Svensen Markus , Powrie Honor E G , Hardwick David S
Published: March 2024

This paper presents results on applying deep neural networks to analysis of images from borescope inspections of large turbofan engines, carried out in the field. Such inspections are done as a part of routine monitoring and maintenance as well as an initial, investigative response to alerts from automatic monitoring systems, pilots or engineers. Across GE’s commercial engines fleet, a substantial number of images have been gathered this way. The deep learning techniques that have come out of computer vision and machine learning research in the last decade offer new possibilities for analyzing and mining such data collections. This paper presents initial results on separating borescope images from images created with regular digital cameras, as well as classifying images containing various engine parts, with average accuracy of 95% and 77%, respectively, on unseen validation data.
Analysis and comparison of multiple features for fault detection and prognostic in ball bearings
Authors: Nguyen Khanh T.P. , Amor Khaleif , Medjaher Kamal , Picot Antoine , Maussion Pascal , Tobon Diego , Chauchat Bertrand , Cheron Regis
Published: March 2024

Feature extraction is one of the most important elements in Prognostics and Health Management (PHM) systems. Numerous techniques have been proposed for fault detection, diagnostics and prognostics in ball bearings which are key components of rotating machineries, widely used in the industry. Considering the strengths and weaknesses of these techniques, this paper aims at evaluating and analyzing different features in all three signal processing domains: time, frequency and time-frequency. The crucial indicators related to normal and abnormal cases are extracted from both vibration signals and stator current signals. Then, a new metric is proposed to measure the evolution of these indicators with respect to degradation levels of bearings. The performance of every indicator is analyzed to study which feature(s) is(are) better than other(s) and which feature(s) is(are) the best appropriate for vibration and current signals. These results could be effectively used in future for fault detection, diagnostics and prognostics applications.
Insight into Wind Turbine Planet Bearing Fault Prediction Using SCADA Data
Authors: Koukoura Sofia
Published: March 2024

Condition based maintenance is being adopted into the decision making process of wind farms, in order to reduce operation costs. SCADA systems are integrated in wind turbines, providing  low frequency operational data and are increasingly being used in condition monitoring. The aim of this paper is to explore how can wind turbine gearbox components be monitored using SCADA data. The proposed methodology utilises 10-minute averaged data. Data preprocessing is applied using a clustering filter in order to improve prediction confidence. Normal behaviour models are used to predict potential faults. The efficacy of the proposed methodology is demonstrated with a case study using SCADA data from three operating wind turbines that have a double planetary stage gearbox. Historic data is collected for more than a year before the occurrence of a bearing failure on a planet of the first planetary stage. The case study results indicate the potential importance of generator speed estimation for planet bearing faults. A successful prediction of the bearing health state can be performed through this model and some insight is given into into the optimal SCADA sensors utilization for this type of failure mode.
Early Fault Detection Based on Wind Turbine SCADA Data Using Convolutional Neural Networks
Authors: Ulmer Markus , Jarlskog Eskil , Pizza Gianmarco , Manninen Jaakko , Goren Huber Lilach
Published: March 2024

Early fault detection in wind turbines using the widely available SCADA data has been receiving growing interest due to its cost-effectiveness. As opposed to the large variety of fault detection methods based on high resolusion vibration data, the use of 10-minute SCADA data alone does not require any additional hardware or data storage solutions and would be immediately implementable in most wind farms. However, the strong variability of these data is challenging and requires significant improvements of existing methods to ensure early and reliable fault detection and isolation. Here we suggest to use Convolutional Neural Networks (CNNs) to enhance the detection accuracy and robustness. We demonstrate the superiority of the CNN model over standard fully connected neural networks (FCNN) using examples for faults with very different time dependent characteristics: an abruptly evolving and a slowly degrading fault. We show that the CNN is able to detect the faults earlier and with a higher accuracy and robustness of prediction than the FCNN model. We then extend the CNN model to a multi-output CNN (CNNm) which provides early fault detection based on a multitude of output variables simultaneously. We show that with the same training time and a similar detection quality as the single output CNN, the CNNm model is an ideal candidate for a practical and scalable fault detection algorithm based on already available 10-minute SCADA data for wind turbines.
Retargeting PHM tools: from industrial to medical field
Authors: Belaala Abir , Al Masry Zeina , Terrissa Labib Sadek , Zerhouni Noureddine
Published: March 2024

Prognostics and Health Management (PHM) approach, and theoretical models have had great success for industrial systems. Therefore, this accomplishment motivates us to think about potential extension of the PHM approach in such area as the medicine. The aim of this paper is to apply an adaptation of a PHM model from fault diagnosis of aircraft engine to diagnosis human heart disease. For that adaptation, an algorithm for retargeting extreme learning machine (ID-RELM) is applied. The complete process from data pre-processing to classification is developed. Numerical results using heart disease benchmark dataset showed that the combination of random forest and ID-RELM provides the highest classification accuracy and outperforms other algorithms in classifying this chronic disease status.
Data-driven Performance Fault Detection in Commercial Wind Turbines
Authors: Meyer Angela , Brodbeck Bernhard
Published: March 2024

The operation and maintenance costs of wind parks make up a major fraction of a park's overall lifetime costs. They also include opportunity costs of lost revenue from avoidable power generation underperformance. We present a machine-learning based decision support method that minimizes these opportunity costs. By analyzing the stream of telemetry sensor data from the turbine operation, estimating highly accurate power reference relations and benchmarking, we can detect performance-related operational faults in a turbine- and site-specific manner. The most accurate power reference model is selected based on combinations of machine learning algorithms and regressor sets. Operating personnel can be alerted if a normal operating state boundary is exceeded. We demonstrate the performance fault detection method in a case study for a commercial grid-connected onshore wind turbine. Diagnosing a detected underperformance event, we find that the observed power generation deficiencies coincide with rotor blade misalignment related to low hydraulic pressure of the turbine's blade actuators.