bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrated Diagnosis and Prognosis of Uncertain Systems: A Bond Graph Approach
Authors: M.S Jha. , Tanguy.G Dauphin- , .B Ould-Bouamama
Published: March 2024

Bond Graph (BG) methodology is used to model the dynamic uncertain systems. Uncertainty is considered on the system parameters in form of intervals. The uncertain parameters are allowed to deviate within their prescribed interval limits. Single fault hypothesis is considered in this work such that the parameter undergoing degradation is known a priori. A new method for generation of interval valued thresholds is briefly described in the framework of BG models in Linear Fractional Transformation form. The diagnostic module is formed using such thresholds which detect the beginning of degradation of a parameter in the real system. The new concept of Interval Extension of Analytical Redundancy Relations (IE-ARRs) is introduced which consider the parametric uncertainties and the evolution of degrading parameter in real time. Then, the Centre and Range method for fitting linear regression models to interval symbolic data is adapted to fit piece wise linear models to the interval valued times series data of IE-ARRs. Further, the new concept of generation of failure thresholds from a nominal system model is introduced and developed. Finally, the fitted linear model is used to estimate the remaining useful life of the parameter under degradation. Simulations are carried out on an example DC motor model. Linear and non-linear parametric degradations are considered. Results are presented in form of simulations.
Remaining Useful Life Estimation of Stochastically Deteriorating Feedback Control Systems with a Random Environment and Impact of Prognostic Result on the Maintenance Process
Authors: Nguyen Danh Ngoc , Dieulle Laurence , Grall Antoine
Published: March 2024

The objective and originality of this work are twofold. On one hand, it considers the degradation modeling and Remaining Useful Life (RUL) estimation for the closed-loop dynamic systems, which have not been addressed extensively in the literature. On the other hand, the paper examines how the prognosis result impacts the maintenance process. Indeed, due to their natural ageing and/or non desired effects of the operating condition, actuators deal with the loss of effectiveness which is a source of performance degradation of closedloop system. In this paper, we consider a control system with classical Proportional-Integral-Derivative controller and stochastically deteriorating actuator. It is assumed that the actuators are subject to shocks that occur randomly in time.An integrated model is proposed which jointly describes the states of the controlled process and the actuators degradation. The RUL can be estimated by a probabilistic approach which consists of two steps. First, the system state regarding the available information is estimated online by Particle Filtering method. Then, the RUL of the system is estimated by Monte Carlo simulation. To illustrate the approach and highlight the impact of the prognosis result on the maintenance process, a well-known simulated tank level control system is used. The maintenance decision rule is based on the quantiles of RUL histogram. In order to evaluate the performance of the maintenance policy, a cost model is developed.
Degradation prognosis based on a model of Gamma process mixture
Authors: Grall-Maes Edith , Beauseroy Pierre , Grall Antoine
Published: March 2024

A novel method is proposed to exploit jointly degradation measurements originating from a set of identical systems for making a degradation prognosis. The systems experience differentdegradation processes depending on operational conditions. The degradation processes are assumed to be Gamma processes. The aim is to cluster the degradation paths in classes corresponding to the different operational conditions in order to group properly the data for the estimation of degradation process parameters. A model of Gamma process mixture is considered and an expectation-minimization approach is proposed to estimate the unknown parameters. The feasibility of the method is shown on simulated cases. Prognosis results obtained with the proposed method are compared with results obtained with basic strategies (considering each system alone or all system together).
Refining Envelope Analysis Methods usingWavelet De-Noising to Identify Bearing Faults
Authors: Bertot Edward Max , Beaujean Pierre-Philippe , Vendittis David
Published: March 2024

In the field of machine health monitoring, vibration analysis is a proven method for detecting and diagnosing bearing faults in rotating machines. One popular method for interpreting vibration signals is envelope-demodulation, which allows the maintainer to clearly identify an impulsive fault source and its severity. In some cases, in-band noise can make impulses associated with incipient faults difficult to detect and interpret. In this paper, we use Wavelet De-Noising (WDN) after envelope-demodulation to improve the accuracy of bearing fault diagnostics. This contrasts the typical approach of de-noising raw vibration signals prior to demodulation. We find that WDN removes low amplitude harmonics and spurious reflections which may interfere with FFT techniques to identify low-frequency peaks in the signal spectrum. When measuring impact frequencies in the time-domain using a peakthresholding method, the proposed algorithm exhibits increasingly confident periodicity at bearing fault frequencies.
Wireless Modular System for Vessel Engines Monitoring, Condition Based Maintenance and Vessel’s Performance Analysis
Authors: Katsikas Serafeim , Dimas Dimitrios , Defigos Angelos , Routzomanis Apostolos , Mermikli Konstantina
Published: March 2024

Circumstances in shipping have rapidly changed within the past few years. The large increase of fuel cost, the decrease in the price of fares, the rapid progress in telecommunications, the crew reduction per vessel, the new environmental restrictions and the reinforcement of Green Shipping significance are facts that make remote monitoring and the evaluation of the vessel engines’ performance an imperative need. The challenges occurring from changing the typical vessel engine monitoring and maintenance model are many, such as: equipment installation on moving vessels, lack of long-term vessel availability, experienced and trained crew being on land, many different types or ages of vessel and vessel manufacturers.An extremely advantageous solution with proven positive results for this specific matter is the use of monitoring systems consisting of wireless smart sensors. These systems provide flexibility, adaptability, scalability and easy installation. The only system of this kind available in the global market, adjusted for Shipping and specifically for monitoring vessel engines, is the LAROS platform by NOMIA S.A. (member of Prisma Electronics SA).In this paper we will present the current status of maintenance in maritime vessels and the abovementioned new innovative remote monitoring of a vessel’s operational status electronic platform, which can greatly reduce the operational costs, enhance the operational vessel status and ensure the high quality of service a maritime company provides, as well as improve its environmental policy.Moreover, a case study of performance analysis regarding a vessel with the LAROS platform on board will be presented, showing the possibilities and the dynamics of vessel performance monitoring.
Prognostics and Energy Efficiency: Survey and Investigations
Authors: HOANG Anh , DO Phuc , IUNG Benoît , LEVRAT Eric , VOISIN Alexandre
Published: March 2024

The paper presents firstly an overview of various definitions/concepts of energy efficiency and their related applications in different contexts, especially in industrial sectors. Each definition/concept is analyzed and recommended for different decision-making levels. Then a multi-level approach is described in detail for evaluating energy efficiency index of an industrial process. In addition, the paper discusses potential prognostic approaches in order to forecast energy efficiency index by underlining difficulties and opportunities to implement such approaches. Finally, a specific example based on an air-fan system is introduced to illustrate energy efficiency concepts and the added value of the prognostics to predict energy efficiency evolution.
Data-Driven Prediction of Unscheduled Maintenance Replacements in a Fleet of Commercial Aircrafts
Authors: Nicchiotti Gianluca
Published: March 2024

Aerospace industries have become increasingly concerned about system availability and reliability.  Data driven based prognosis is an emerging application aimed at building predictive models from readily available maintenance and operational databases. After validation, these models can be integrated into PHM systems to monitor equipment health and predict component failures before such events disrupt operations.
For this project legacy data collected from two databases associated to a fleet of civil aircrafts during a period spanning 5 years have been used. The first database contains Central Management System (CMS) data (BIT messages and Flight Deck Effects), the second logs of maintenance activities.  Part of the data collected from 2012 to mid 2015 have been used for the learning phase the rest spanning 2012-2016 period have been used for validation
The goal is to predict failure events within an interval ranging from two to ten flights in advance to avoid unscheduled maintenance activities and operational disruptions. Hence two flights represent the minimal notice period/prognostics horizon whilst 10 flights is the maximal acceptable wasted life. Data-driven based prognostic uses pattern recognition and machine learning techniques to train historic data. In the proposed approach both techniques have been used. Through Support Vector Machine a prognostics anomaly detection step is initially performed to select the flight legs candidate for a prognostics alert. In a further step a subspace technique, borrowed from image processing domain and named Eigenface, allows to produce the signatures of the different types of maintenance actions and a template matching algorithm determines among the prognostics alert candidates the component to be replaced.
Several tests have been conducted for different types of replacements and results will be presented using Receiver Operating Characteristic (ROC) curves and precision/recall metrics. Information contained in ROC allows the airliner to identify, according to its economic criteria, the optimal prognostics operating points.
Optimizing fixed-interval maintenance periods for mobile asset sub-systems operating in remote locations
Authors: Hodkiewicz Melinda , Bonser Deion , Chin Alastair , While Lyndon
Published: March 2024

Fixed-interval planned maintenance is the predominant strategy for looking after mobile asset fleets such as cars, trains, and trucks. To understand the impact of maintenance interval selection on the fleet's performance it is necessary to mathematically link these planned maintenance tasks to the asset's subsequent reliability performance and the ability to complete planned journeys without incident. Failures, particularly those happening in remote locations, are costly to manage, cause significant operational disruption, and increase safety exposure for maintainers. A typical mobile asset has some number of sub-systems, each with its own maintenance interval and reliability. Our challenge is to set appropriate maintenance intervals for each asset sub-system to minimize the probability of an unplanned failure, without limiting unnecessarily the availability of the asset. To do this we generate sets of maintenance intervals using a genetic algorithm and test them using a discrete event simulation (DES) model of the operations and maintenance functions. We are motivated by two industry examples of fleets in remote locations: long-distance freight trucks, and heavy-haulage rail locomotives. In the truck case, the model found optimal intervals similar to those used by the operator. The locomotive case is more complex, but the model suggests improvements are possible in interval selection, maintenance practices, and data collection. Each model is conceptualized for its specific context; this process identifies assumptions that need to be considered when linking maintenance and operations models.
A Data-Driven Particle Filter For Lithium-Ion Batteries State-Of-Life Prognosis
Authors: Cadini Francesco , Sbarufatti Claudio , Cancelliere Francesco , Giglio Marco
Published: March 2024

In recent years, Lithium-Ion rechargeable batteries have quickly become the most popular portable power sources, with applications ranging from consumer electronics (smartphones, laptops, etc.) to electric vehicles and unmanned aerial/space vehicles. Indeed, Li-ion batteries are subject to degradation over time, in particular due to the irreversibility of the electro-chemical processes driving their functioning. The deterioration of the performances is further worsened by the operational and environmental boundary conditions in which they operate (e.g. discharge rates, usage and storage temperatures, etc.). The consequences of unexpected failures due to degradation may range from mild, for example in consumer electronics, to very severe, if not catastrophic, in particular in aerospace applications, both from the economical and the safety points of view. In this context, the prediction of future degradation performances of the batteries plays a fundamental role. In this work, we exploit a recently introduced prognostic algorithmic scheme, which combines the real-time prediction capabilities of particle filters with the flexibility and simplicity of feed-forward neural networks, for adaptively predicting the State-of-Life (SOL), i.e. the capacity, of Li-Ion batteries, on the basis of past and current capacity observations. The major advantage of the proposed method lies in the fact that the algorithm automatically adapts to different degradation dynamics, without the need to derive and/or calibrate any physics-based model. The method is demonstrated with reference to actual Li-Ion battery discharge data taken both from the prognostics data repository of the NASA Ames Research Center database and from literature.
Rare Event Simulation to Optimise Maintenance Intervals of Safety Critical Redundant Subsystems
Authors: Pfaff Raphael , Melcher Karin , Franzen Julian
Published: March 2024

In the railway sector, many redundant subsystem structures are applied to increase the safety and availability of the overall railway system. Failures to single paths of these structures occur and are found during routine inspection. Routine inspections are, depending on their type and the equipment location, quite costly and limit the vehicle availablity.
The present paper analyses occurrences based on simulated data resembling field data of a fleet of rail vehicles. The system is analysed statistically to identify the wear mechanisms leading to the failures. Failure data is then used to identify wear models which are consequently used in a Markov Chain (MC) to simulate the probability of multiple path failure.
The failure rate of the overall system is typically expected to be in the range $\left(10^{-9}\cdots10^{-6}\right)\, \mathrm{h}^{-1}$ due to the safety critical nature of the railway system. For this reason, it is required to apply rare event simulation techniques to the MC simulation in order to limit the number of simulations.
The simulation results are then applied to an optimisation of the inspection routine, which yields an appropriate failure rate for the associated hazards.
Predictive Maintenance Approach for Complex Equipment Based on Petri Net Failure Mechanism Propagation Model
Authors: Blancke Olivier , Combette Amélie , Amyot Normand , Komljenovic Dragan , Lévesque Mélanie , Hudon Claude , Tahan Antoine , Zerhouni Noureddine
Published: March 2024

The aim of this paper is to propose a comprehensive approach for predictive maintenance of complex equipment. The approach relies on a physics of failure model based on expert knowledge. The model can be represented as a multi-state Petri Net where different failure mechanisms have been discretized using physical degradation states. Each state can be detected by a unique combination of symptoms that can be measured from diagnostic tools. Based on actual existing diagnostic information, a diagnostic algorithm enables the identification of active failure mechanisms and estimates their progression in the Petri Net. Specific maintenance actions and their potential effect on the system can be associated with targeted states. Thereafter, a prognostic algorithm using a coloured Petri Net propagation method spreads active failure mechanisms though their related remaining states towards the targeted states. This allows specific maintenance actions to be proposed in a timeframe and thus enables predictive maintenance. Case study is presented for a real hydro generator. Finally, model limits are discussed and potential areas of further research are identified.
A study on the use of discrete event data for prognostics and health management: discovery of association rules
Authors: Liu Bin
Published: March 2024

This study addresses prognostics and health management (PHM) for manufacturing machines. Different from previous researches where continuous monitoring is assumed for PHM, we investigate the issue with discrete event data. Various event data are recorded during system operation, which can provide useful information for fault diagnosis and failure prediction. We focus on discovery of association rules based on the industrial discrete data. Apriori algorithm is employed to discover the frequent event groups and identify strong association rules. To accommodate the algorithm, the initial event data is transformed into the form of transactional data as a first step. The obtained association rule estimates the occurrence probability of certain significant events within specified time interval. It is concluded through a case study that the number of frequent event groups and strong association rules increases with the time interval that the events are grouped as one transaction.
Characterising Conveyor Belt System Usage from Drive Motor Power Consumption and Rotational Speed: A Feasibility Study
Authors: Gebler Owen , Hicks Ben , Yon Jason , Barker Matt
Published: March 2024

Conveyor belt systems comprise a fundamental means of transferring bulk materials throughout industry. Typically, they are subjected to very high levels of utilisation and, because of their criticality to process flow, high reliability is demanded. Currently preventative approaches to the maintenance of conveyor systems are implemented in the majority of applications, in which time-based intervention intervals are applied. The effectiveness of time-based intervals can be limited, as their premise is based upon an assumption that asset condition is solely a function of operating time. In practice degradation of systems is primarily a function of usage, affected by intensity as well as duration of operation. Consequentially, this paper aims to investigate the feasibility of inferring the usage of a conveyor belt system from observation of changes in drive motor electrical power consumption and speed, to support improvements in maintenance effectiveness.First, an industrial conveyor belt system is instrumented, and a range of test scenarios are designed such that the responses of parameters to various system usage patterns can be characterised. Through inspection of these responses it is found that the monitored parameters do show sensitivity to changes in operational conditions such as utilisation, inclination and loading events, and it is therefore feasible to infer conveyor belt system usage from motor power consumption and speed only. Next, a series of proxies for describing system usage are then proposed, as a means of transforming raw parameters into usage profiles. Finally, the technical challenges facing an industrial implementation of the proposed approach are discussed, and consideration given to how usage information may provide value to operators outside of maintenance.
Model based diagnosis in complex industrial systems: a methodology
Authors: Barbini Leonardo , Bratosin Carmen , Van Gerwen Emile
Published: March 2024

To fulfil market demand, the complexity of high-tech industrial systems is increasing every year. As a consequence, diagnosing failures leads to long down times, often because the issue at hand has to be escalated all the way to the development department to access the required knowledge. In this paper we propose a model based diagnostic methodology to support the field service engineer in finding the root cause of an unscheduled downtime in a timely way by bringing actionable design knowledge at the service engineer’s fingertips. We start by modeling the knowledge on the machine hardware decomposition, deployment and functional behavior by using a domain specific language. The language allows for an object oriented description of the system, with the functional behavior captured as probabilistic relationships between hardware components. This system description is then automatically transformed into a Bayesian belief network (BN): the diagnostic model. When a diagnosis is required, we infer the health state of the system’s components by instantiating the BN with discretized sensor and control data collected prior to the occurrence of a downtime. Finally, we compute the root cause hypotheses as the minimum sets of faulty components that are consistent with the evidence. We explain in a visual way the essential part of the diagnostic reasoning by pruning the causal graph underlying the BN, augmented with the data items supporting the hypothesis. We illustrate the methodology on a thermal conditioning subsystem. Our approach is domain independent and applicable to a wider range of cyber physical systems (CPS).
Fleet-based System Health Assessment: Reasoning about Change and Differences
Authors: Borth Michael
Published: March 2024

Monitoring and managing the health of technical systems with advanced diagnosis and prognosis benefits from fleet analytics: insights on the degradation of other but similar systems help, e.g., to forecast actual issues for predictive maintenance as does detecting and correcting anomalies in usage profiles helps to prevent undue wear and tear. Successes in this field usually depend on the similarity of the fleet’s systems: although not necessarily equal, they need to have alike key characteristics, e.g., in the way they age, such that observations on one or many systems constitute an expectation for others.

We introduce fleet-based system health assessments that complement such approaches by reasoning on differences, e.g., those introduced by interventions like upgrades. Given that such change is the only constant for many of today's complex systems, we believe that our addition to health assessment is necessary to cope with the variation in systems that fleet operators face over the lifecycles of all systems.

Technically, our approach is based on probabilistic reasoning. The related modeling and inference techniques allow us to incorporate insights from past observations in individual systems, but also from comparable systems and, at the same time, they permit comparisons between parts of the fleet and estimate possible effects of existing differences. Our work shows that especially such comparisons are valuable for fleet health management over long lifecycles that is typically linked to continuous improvement processes.
Estimation of Health Indicators using Advanced Analytics for Prediction of Aircraft Systems Remaining Useful Lifetime
Authors: Azevedo Daniel , Cardoso Alberto , Ribeiro Bernardete
Published: March 2024

A valuable asset for the improvement of aviation maintenance is the correct assessment of the aircraft systems health condition, for a more accurate planning and execution of maintenance routines. As such, the creation of a Prognostic and Health Management (PHM) system, supported by Condition Based Maintenance (CBM) can bring important benefits to the aeronautical field. The ultimate goal of a PHM system is the correct prediction of the Remaining Useful Lifetime (RUL) of a certain aircraft system, using the sensors data extracted during flights. Nevertheless, a relevant stage in the PHM pipeline concerns the estimation of the system condition, expressed by the Health Indicator (HI). The HI value reflects the health condition of a specific aircraft system, which can possibly be affected by degradation, failures or external conditions occurred during flight time. Henceforth, due to the relevancy of the HI assessment for the accuracy of the PHM model, this paper aims to propose a new formulation for the HI computation, derived from raw anonymized data retrieved from different sensors within the aircraft system. The proposed formulation combines information from the different variables (like sensors) that have an impact on the overall system condition, by assigning a positive or negative weight to each variable depending on the influence on the system behaviour. The weights are determined based on the typical and standard data patterns. Thus, the estimated HI aims to reflect the number of hours of flight expected to be flown, based only on raw data extracted from the system. Furthermore, considering that the available sensors data is anonymized, a study of the relevancy of the different sensors features for the degradation assessment is performed, using specific metrics. Considering a scenario where the HI ground truth is unknown, the failure data of each aircraft system is used to evaluate and discuss the formulation suitability. The HI formulation is applied in real datasets, on the environmental systems of two wide body aircraft types.
Novel Fault Indicator for Local Demagnetization in Fractional-Slot Permanent Magnet Synchronous Motor using Winding Function Theory
Authors: Golzar Milad , Kandukuri Surya Teja , Huynh Van Khang , Choux Martin Marie Hubert , Midtbø Versland Alf Magne
Published: March 2024

Local demagnetization is an irreparable failure mode in permanent magnet synchronous motors (PMSMs), resulting in reduced motor efficiency and high cogging torque. These effects are particularly disadvantageous to fractional-slot wound PMSMs used in industrial applications, such as robotics and automations, that require high power density and precise operation. In this article, a diagnostic indicator is developed for detecting local demagnetization fault in fractional-slot wound PMSMs based on winding function theory, utilizing back-electromotive force (back-EMF) as a medium for fault detection. Further, the developed indicator is validated in a commercial fractional-slot wound PMSM of an in-house experimental setup.
Machine Learning Interpretability Techniques for Outage Prediction: A Comparative Study
Authors: Jalali Anahid , Schindler Alexander , Haslhofer Bernhard , Rauber Andreas
Published: March 2024

Interpretable machine learning has recently attracted a lot of interest in the community. Currently, it mainly focuses on models trained on non-time series data. LIME and SHAP are well-known examples and provide visual-explanations of feature contributions to model decisions on an instance basis. Other post-hoc approaches, such as attribute-wise interpretations, also focus on tabular data only. Little research has been done so far on the interpretability of predictive models trained on time series data. Therefore, this work focuses on explaining decisions made by black-box models such as Deep Neural Networks trained on sensor data. In this paper, we present the results of a qualitative study, in which we systematically compare the types of explanations and the properties (e.g., method, computational complexity) of existing interpretability approaches for models trained on the PHM08-CMAPSS dataset. We compare shallow models such as regression trees (with limited depth) and black-box models such as Long-Short Term Memories (LSTMs) and Support Vector Regression (SVR). We train models on processed sensor data and explain their output using LIME, SHAP, and attribute-wise methods. Throughout our experiments, we point out the advantages and disadvantages of using these approaches for interpreting models trained on time series data. Our investigation results can serve as a guideline for selecting a suitable explainability method for black-box predictive models trained on time-series data.
Integrated Risk-Based Strategy for Real-Time Decision Making and Mission Reconfiguration in Unmanned Aerial Vehicles
Authors: Fabry Jorge , Carvajal Ignacio , Rozas Heraldo , Orchard Marcos , Tamssaouet Ferhat , Nguyen Khanh T. P. , Medjaher Kamal
Published: March 2024

Small unmanned aerial vehicles (UAVs) have been increasingly popular in the last years, being employed in a wide range of applications in diverse areas, including, for instance, military, medicine, and package delivery. These aerial vehicles are commonly energized by rechargeable batteries, and, as a result, their autonomy can be significantly affected by several uncertainty sources, including variable ambient conditions or mechanical mishaps. For this reason, the ability to predict their electric consumption for a determined path is critical to design UAV missions and complete them successfully. Additionally, due to the potential occurrence of unexpected events (e.g., mechanical failures or changes in the weather conditions), airborne implementation of real-time decision-making schemes for mission replanning is of utmost importance. Hence, this paper presents an integrated Risk-based strategy utilizing a State-of-Charge (SOC) prognostics algorithm to quantify the risk associated with a given path in terms of future consumption, intending to make real-time decisions on the UAV operation. More specifically, we consider a UAV mission framework with a discrete set of possible targets, where each one has costs, rewards, and failure risks, which is characterized by calculating the total probability using sample-resampling methods. Then, we seek the optimal choice by employing a Bayesian-Risk inspired approach whose outcome results in a compromise between risks and rewards.
Condition Monitoring System for Internal Blowout Prevention (IBOP) in Top Drive Assembly System using Discrete Event Systems and Deep Learning Approaches
Authors: Noori Nadia Saad , Bianchi Filippo , Waag Tor Inge
Published: March 2024

Offshore oil drilling is a complex process that requires careful coordination of hardware and control systems. Fault monitoring systems play an important role in such systems for safe and profitable operations. Thus, predictive maintenance and monitoring operating conditions of drilling systems are critical to the overall production cycle. In this paper, we are addressing the topic of condition monitoring of a critical part in the process of oil drilling, the Internal Blowout Preventer (IBOP) system in the top drive assembly in offshore oil drilling. In our work, we aim to design an intelligent system for monitoring the health of IBOP system using discrete event systems (DES) based control method in combination with multivariate time series classification deep learning method. The proposed system comprises two stages: 1) produce IBOP system logical behaviour analysis using Hierarchical Colored Petri Nets (HCPN) approach; 2) develop an activity detection or a classifier module using reservoir computing framework for classification of multivariate time series for activity monitoring and fault detection for the top drive assembly.

The combination of these methods would enable automation of monitoring and early detection of incidents during drilling operations. We present the preliminary results of a model in Petri Nets used to simulate a monitoring system for IBOP valve in top drive assembly and activity classification of activities relevant to IBOP condition monitoring. The effects of failure rate and repair time of each component on system performance are to be researched at a later stage.
A Hierarchical XGBoost Early Detection Method for Quality and Productivity Improvement of Electronics Manufacturing Systems
Authors: Gaffet Alexandre , Barbosa Roa Nathalie , Ribot Pauline , Chanthery Elodie , Merle Christophe
Published: March 2024

This paper presents XGBoost classifier-based methods to solve three tasks proposed by the European Prognostics and Health Management Society (PHME) 2022 conference. These tasks are based on real data from a Surface Mount Technologies line. Each of these tasks aims to improve the efficiency of the Printed Circuit Board (PCB) manufacturing process, facilitate the operator’s work and minimize the cases of manual intervention. Due to the structured nature of the problems proposed for each task, an XGBoost method based on encoding and feature engineering is proposed. The proposed methods utilise the fusion of test values and system characteristics extracted from two different testing equipment of the Surface Mount Technologies lines. This work also explores the problems of generalising prediction at the system level using information from the subsystem data. For this particular industrial case: the challenges with the changes in the number of subsystems. For Industry 4.0, the need for interpretability is very important. This is why the results of the models are analysed using Shapley values. With the proposed method, our team took the first place, capable of successfully detecting at an early stage the defective components for tasks 2 and 3.
ZINE SEBAGAI REPRESENTASI BUDAYA PERKOTAAN DI JAKARTA
Authors: Purnama Putra Dhika
Published: March 2024

Kajian mengenai zine sebagai representasi budaya perkotaan dilihat dari penerapan desain tata letaknya. Zine dalam konteks budaya perkotaan mempunyai hubungan yang kompleks dengan dinamika sosial terhadap manusia serta kelompoknya. Sebagai produk subkultur masa kini, Zine beradaptasi dengan segmentasi tertentu berdasarkan nilai-nilai yang dianut oleh pembuat zine dan komunitasnya. Zine akan selalu berkembang mengikuti budaya induknya yang lahir dari komunitas subkultur tertentu. Dengan tidak adanya aturan baku mengenai format serta praktiknya, Zine akan selalu berkembang mengikuti kedinamisan subkultur masyarakat perkotaan. Penelitian ini merupakan penelitian deskriptif kualitatif dengan pendekatan analisis desain tata letak yang selanjutnya dilakukan pembacaan konteks dan pembahasan mengenai konten visual zine dan melihat makna yang terkandung dengan menggunakan teori semiotika visual Roland Barthes. Dengan analisis tersebut, penelitian ini mencoba mengungkap budaya urban melalui Zine dalam kajian budaya. Masalah yang hendak dipecahkan dalam tulisan ini adalah bagaimana budaya perkotaan mempengaruhi desain tata letak pada Zine sebagai representasi budaya perkotaan. Dalam penelitian ini, terungkap bahwa hubungan kota dan Zine saling berkaitan dalam proses membentuk dan dibentuk. Kota bisa membentuk Zine dan begitu pula sebaliknya. Penelitian ini melihat Zine sebagai representasi budaya perkotaan.
PERANCANGAN ILUSTRASI NOVEL “AKU ANAK BROKEN HOME” KARYA DENNIS CHRISTOPHER
Authors: Rinaldi Rana Syakirah , Ruslan Arief
Published: March 2024

Membaca buku novel tidak terlalu sulit, karena bacaan yang ringan dan alur membaca yang rapi membuatnya mudah dimengerti. Makalah ini menjelaskan bagaimana sebuah ilustrasi bisa menjadi media pendukung untuk sebuah buku novel. Menurut wawancara dengan novelis, Dennis Christopher sendiri bahwa ia membutuhkan ilustrasi sebagai penguat cerita untuk novel yang ditulisnya berjudul "Aku Anak Broken Home". Buku novel ini bercerita tentang bagaimana rasanya menjadi anak broken home, dalam buku novel ini Dennis menginginkan sesuatu yang berbeda dari buku-buku sebelumnya, ia ingin menambahkan ilustrasi untuk setiap cerita dalam buku ini. Menurut Dennis sendiri visualisasi sangat penting, menambahkan ilustrasi di setiap halaman dapat menyentuh audiens
PERANCANGAN KAMPANYE SOCIAL TENTANG PENGAWASAN GADGET PADA ANAK UNTUK ORANG TUA
Authors: Lestari Harahap Widiya , Darmawan Dicky
Published: March 2024

Berkembangnya ilmu pengetahuan dan teknologi membawa perubahan hampir pada seluruh tatanan kehidupan manusia. Penggunaan gadget atau smartphone yang hampir semua orang miliki saat ini. Dampak negatif penggunaan gadget anak tanpa adanya pengawasan orang tua diantaranya anak-anak akan mudah marah, bahkan suka membangkang, bahkan mencontoh perilaku dari apa yang mereka tonton dari gadget tersebut selain itu dengan penggunaan gadget yang berlebihan tanpa pengawasan orangtua dapat memberikan dampak negative kepada perkembangan anak-anak. Oleh karena itu dibutuhkan sebuah media yang mampu menyampaikan pesan kepada orangtua bahwa anak-anak yang diberikan gadget tanpa pengawasan akan berdampak negative terhadap perkembangannya. Kampanye “Batasi Gadget Anak” dengan konsep edukasi bahaya penggunaan gadget berlebihan tanpa pengawasan orang tua untuk memberikan kesadaran akan bahaya gadget pada pertumbuhan anak-anak.
Histological Evaluation of Fracture Healing after Thyroidectomy
Authors: Simawy M. S. H. , Nahi H. H. , Fadhil Mustafa , Al-Kafagy S. M.
Published: March 2024

This study aimed to examine the impact of thyroidectomy on the process of fracture healing. A total of sixteen adult male rabbits of the local breed were utilized. The animals were segregated into two equivalent groups, namely the control group and the treatment group. The animals were administered atropine sulfate (1mg /Kg B.W) intramuscularly as a premedication. Ten minutes later, a combination of xylazine hydrochloride (20mg /Kg B.W) and Ketamine hydrochloride (40 mg /Kg B.W) was administered intramuscularly. The control group had the induction of a mid-shaft femoral fracture, followed by fixation with intramedullary pinning. In the treated group, the identical treatment mentioned above, which involved thyroidectomy, was performed. The clinical examination showed that the inflammatory symptoms, such as swelling, discomfort, and increased temperature in the surgical site, were more severe in the control group compared to the treated group. The indications vanished during a span of four to five days in the treatment group and five to six days in the control group. The histological analysis showed that the control group had a greater presence of granulation tissues, trabecular bone, and compact bone compared to the treated group. Ultimately, it was seen that the fracture healing process was more advanced in the control group compared to the treated group.
ΙΝΔΙΚΤΙΩΝ in editions of papyri and inscriptions: A philologist’s comment on the spelling and declension of the term
Authors: Danielewicz Jerzy
Published: March 2024

The article points out misspellings of the word ΙΝΔΙΚΤΙΩΝ occurring in papyrological and epigraphic publications. Determining the correct spelling of the term entails conclusions regarding, more generally, the issue of editorial practices.
DESAFIOS DA INCLUSÃO ESCOLAR DE ESTUDANTES AUTISTAS NO ENSINO FUNDAMENTAL
Authors: Bezerra de Holanda Monyque Mary , Feitosa e Paiva Geórgia Maria
Published: March 2024

Este estudo parte da compreensão de que as adaptações das metodologias de ensino utilizadas com estudantes diagnosticados com Transtorno do Espectro Autista podem contribuir, de forma significativa, para o processo de aprendizagem e inclusão escolar desses alunos. O objetivo deste trabalho é discutir o processo de inclusão escolar de estudantes autistas do Ensino Fundamental de uma escola particular do interior do Ceará. Para este estudo, baseamo-nos nos trabalhos desenvolvidos por Sassaki (1998), Redmerski (2018), Rocha e Lemos (2014), Costa et al. (2019), Oliveira et al. (2020), Camargo e Camargo (2020), além de documentos oficiais, como a Declaração de Salamanca, a CID-10 e a CID-11. Neste artigo, apresentamos os resultados da primeira etapa de uma pesquisa-ação que estamos realizando com essa escola. Para esta etapa, utilizamos o método qualitativo exploratório com uso de entrevistas semiestruturadas com uma equipe de seis docentes, uma coordenadora pedagógica e uma gestora escolar. Os resultados demonstraram que a escola não passou por nenhum tipo de formação ou treinamento no que se refere à inclusão de alunos autistas e que, atualmente, ela apresenta sete estudantes com essa deficiência, sendo que dois deles não possuem laudo. Embora seja desafiador, o processo inclusivo dos alunos autistas tem sido realizado com acolhimento e intuição da equipe pedagógica.
LEI DE COTAS NA UNIVERSIDADE FEDERAL DO CEARÁ:
Authors: Castro Araújo Adriana , Pinheiro Moreira Tainah
Published: March 2024

O objetivo deste artigo é analisar a Taxa de Sucesso da Graduação (TSG) nos cursos da Universidade Federal do Ceará (UFC) com as maiores notas de corte no Sistema de Seleção Unificado (SISU), comparando, entre si, as médias dos alunos cotistas e não cotistas. Para tanto, realizou-se uma pesquisa do tipo quantitativa, cujos dados foram processados no software SPSS (v. 21.0) e apresentados por meio de estatística descritiva. Os resultados encontrados provam que a implementação da lei de cotas não gerou aumento do insucesso nos cursos caracteristicamente mais elitizados da UFC e revelam ainda que os cotistas se formam na mesma proporção e no mesmo tempo que os não cotistas. Por outro lado, a democratização da educação superior não pode se limitar a expandir quantitativamente o acesso sem, ao mesmo tempo, atingir a plena cobertura e a igualdade de oportunidades. Dessa forma, aponta-se para a necessidade de que se realizem análises mais específicas, assim como pesquisas de natureza qualitativa, no sentido de investigar as idiossincrasias que permeiam a realidade dos jovens que ingressam por meio das cotas, ampliando o debate sobre os novos papéis a serem desempenhados pelas instituições de ensino superior frente à complexidade que esse diversificado grupo de estudantes forja no cotidiano universitário.
POLÍTICA NACIONAL DO IDOSO:
Authors: Ribeiro Mamede Roseli
Published: March 2024

Em um país onde a expectativa de vida encontra-se em crescimento, torna-se essencial pesquisar acerca dos fatores que contribuem para uma melhor qualidade de vida da população idosa. Esta pesquisa busca analisar as contribuições das ações extensionistas do projeto “Cidadania Ativa: Interação do Idoso junto à Universidade”, da Universidade do Estado de Mato Grosso (UNEMAT) – Campus Sinop, para a promoção da inserção social desse público. Trata-se uma pesquisa descritiva com abordagem qualitativa e caracteriza-se como estudo de caso e pesquisa de campo. Os dados indicam que as ações desenvolvidas pelo projeto “Cidadania Ativa” integram a universidade com a comunidade externa e, além de contribuir com a qualidade de vida do idoso, proporciona sua emancipação digital, na medida em que disponibiliza um espaço de aprendizagem contínua e convivência social, oportunizando que este se mantenha ativo.
RETENÇÃO DISCENTE NO ENSINO SUPERIOR:
Authors: Lima Almeida Evangelista Leilane , Bandeira Andriola Wagner
Published: March 2024

O presente artigo analisa os fatores que contribuem para a retenção discente nos cursos de graduação do Instituto Federal de Ciência e Tecnologia do Ceará (IFCE). A temática tornou-se pertinente, pois tal condição, quando não analisada institucionalmente, pode acarretar prejuízos sociais, econômicos e acadêmicos tanto para o aluno quanto para a IES. Partindo de Pereira (2013) e Moraes (2015), categorizaram-se os fatores de retenção em três grupos de variáveis: Características Individuais, Ambiente Institucional e Aspectos Socioculturais e Econômicos Externos. Dessa forma, investigou-se em que medida tais fatores influenciaram a retenção acadêmica a partir da percepção dos próprios estudantes. A presente pesquisa, classificada como descritiva, quanti-qualitativa e de campo, teve como participantes os estudantes matriculados nos cursos de graduação que ultrapassaram o prazo regular para conclusão dos seus respectivos cursos. Adotou-se como instrumento de pesquisa um questionário eletrônico no qual se mensurou a percepção discente quanto à influência de cada fator para a retenção. Para análise, optou-se pelo uso de métodos estatísticos clássicos, tais como a análise exploratória e a inferência estatística. A amostra contou com a participação de 69,13% do universo da população. Os resultados demonstram que o fator “Necessidade de trabalhar enquanto frequenta o curso”, inserido na variável Aspectos Socioculturais Externos, alcançou maior média de influência entre os fatores analisados na constituição da retenção discente, ainda que os demais fatores influenciem na composição do cenário propício para consolidação da retenção, tais como dificuldades para conciliar horário das disciplinas e do trabalho, curso ofertado em horário incompatível, dentre outros.
DEFICIÊNCIA VISUAL CORTICAL E A NECESSIDADE DE INTERVENÇÃO PRECOCE
Authors: Silva Torres Matsushita Luciana , Branco Gongora Mariana , Pinheiro Diógenes
Published: March 2024

Este trabalho tem como objetivo geral a identificação do contexto biopsicossocial em que se desenvolve a deficiência visual cortical, suas características e as origens da necessidade de intervenção precoce. Para tal, apresenta um breve histórico da deficiência e da educação de cegos no Brasil. Seus objetivos específicos consistem na realização de pesquisa bibliográfica sobre os seguintes temas: sistema visual; deficiência visual ocular e cortical; diagnóstico e intervenção precoce; características da pessoa com deficiência visual cortical; possíveis causas; dificuldades no diagnóstico e necessidade de intervenção precoce para estímulo ao uso da visão e ao desenvolvimento da criança, ampliando sua autonomia e qualidade de vida. Como conclusão, aceita-se a hipótese de que a intervenção precoce é essencial na vida das crianças com deficiência visual cortical em função da variedade e complexidade de casos bem como da crença na oportunidade de melhoria da qualidade de vida da criança.
DEPENDÊNCIA DE REDES SOCIAIS
Authors: Nobre Lopes Samuel
Published: March 2024

Este artigo tem o objetivo de apresentar uma discussão acerca do fenômeno da internet voltado para o uso das redes sociais como possível causador de alienação, destacando a sua incidência na vida dos jovens e demonstrando que tal fenômeno pode gerar, principalmente, dependência e isolamento. Propõe ainda identificar as fronteiras entre o mundo real e o virtual, proporcionando uma análise das relações humanas, bem como dos comportamentos sociais provenientes dessa relação. Para isso tomamos a teoria do filósofo Herbert Marcuse, teórico da Escola de Frankfurt, principalmente a partir das suas obras Ideologia da sociedade industrial e Eros e civilização, porém sem excluir outras obras suas. A pesquisa é de caráter teórico, e sua metodologia será de aspecto qualitativo por realizar uma análise imanente das obras em pauta. Em nossas conclusões, destacamos que, embora as redes sociais tenham os seus pontos positivos, elas se apresentam de modo bem mais negativo quando geram dependência e alienação. Portanto, é preciso uma educação para o seu uso de modo comedido e orientado, principalmente no que toca aos jovens da nova geração. Essa poderá ser uma das formas para superar o paradoxo das redes sociais: elas conectam e, ao mesmo tempo, desconectam os jovens do mundo real, provocando uma alienação generalizada entre eles.