bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Stochastic capacity estimation tools applied on remaining useful life prognosis of Lithium ion batteries
Authors: Arrinda Mikel
Published: March 2024

Lithium ion batteries suffer a performance decrease in normal use, which leads to an end to the usability of those batteries under the designed application. Accurate estimation of the useful lifetime of the batteries is important for achieving high energy efficiency and cost reduced designs. However, the aging of a lithium ion battery has a non-linear behavior and the models available nowadays are far from completely describing this aging behavior. Thus, capacity estimation models and tools are applied in order to improve the accuracy of current models. In this sense, this paper evaluates different stochastic tools applied to typical aging models so as to increase the accuracy of the useful life estimations at different aging states. The chosen aging models are semi-empirical models based on power laws, exponentials and polynomials which represent in a simple way the aging behavior of lithium ion cells under concrete conditions. The degradation data used in this paper comes from LiFePO4-graphite and LiNi0.8Co0.1Al0.1O2-graphite aged cells. Then, the estimations are improved applying Particle Filter (PF) and Gaussian Process Regression (GPR) based stochastic tools, which adds uncertainty evaluation and historical evaluation to the model. The benefits and limitations of different configuration of those tools will be discussed in terms of the needed computer resources and future prediction accuracy improvement.
CNC spindle signal investigation for the prediction of cutting tool health
Authors: Hill Jacob L. , Prickett Paul W. , Grosvenor Roger I.
Published: March 2024

The deterioration of cutting tools plays a significant role in the progression of subtractive manufacturing and substantially affects the quality of machined parts. Recognising this most organisations have implemented conventional methods for tool management. These reduce the economic loss associated with time-dependent and stochastic tool wear, and limit the damage arising from tools at end-of life. However, significant costs still remain to be addressed and more development towards tool and process prognostics is desirable. In response, this work investigates process deterioration through the acquisition and processing of selected machine signals. This utilises the internal processor of a CNC Vertical Machining Centre and considers the possible applications of such an approach for the prediction of tool and process health. This paper considers the prediction of tool and process condition with a discussion of the assumptions, benefits, and limitations of such approaches. Furthermore, the efficacy of the approach is tested using the correlation between an offline measurement of part accuracy and an active measure of process variation.
Wireless and autonomous sensor for Integrated Engine Health Management
Authors: HEBRARD Yoann
Published: March 2024

Providing the best availability of aircrafts is a main cause for concern in aeronautics industry. Integrated Engine Health Management (IEHM) is the up to date solution that is used by Aircraft Engine Manufacturers in order to maintain a fleet of engines operative. IEHM detect signs of failure before they happen is the key tool for improving the level of aircraft operability. There is a strong focus on the sensor part and particularly on a wireless autonomous load sensor. Measuring load can have a high value to estimate the remaining useful life of equipment’s like suspension rods or gears after a hard landing. Indeed, after such an event, aircraft companies need to know if suspension rods/gears are safe or could have been damaged. This paper presents the current CORALIE sensor development (autonomous wireless load recorder) that will enable maintenance operators to determine the load level seen by an engine link after a hard landing by simply setting a portable device near the mounting system. Interesting features are Energy Harvesting and RFID communication modules that have been developed for this sensor. Such autonomous SHM systems typically include embedded sensors, and elements for data acquisition, wireless communication, and energy harvesting. Among all of these components, this paper focuses on RFID technologies. Actually, low power sensors and wireless communication components are used in newer SHM systems, and a number of researchers have recently investigated such techniques to extract wirelessly communicate data stored in these stand-alone systems. The first part of the paper is dedicated to benchmark the different wireless communication protocols against RFID available for the project application (engine pods). The second part gives a presentation of the RFID communication system (Antenna, reader and RFID front end circuit) developed for transmitting local data stored in the memory of the system for analysis in a SHM maintenance strategy. The last part presents real test of RFID wireless communication and provide a maturity assessment on the technology.
Ballast Degradation Modeling for Turnouts based on Track Recording Car Data
Authors: Asadzadeh Seyed Mohammad , Galeazzi Roberto , Hovad Emil , Andersen JF , Thyregod Camilla , Rodrigues Andre
Published: March 2024

Turnouts play a central role in the railway infrastructure since they enable increased network capacity and allow for minimal impact of train delays. Their performance is of paramount importance for infrastructure managers, who face large maintenance cost in order to secure proper turnouts operability. Railway turnouts are complex mechanical systems, whose dynamic performance depends on the health state of the different components of the superstructure and substructure. A key component is the ballast as it provides the elastic support to the track and the sleepers and it largely contributes to the safety and reliability of the infrastructure. Ballast degradation can be a root cause of excessive failures in other components. A track recording car is typically used to collect geometry data that is used to assess the quality of the railway tracks; however this type of data has not been widely used for ballast quality evaluation in turnouts. One reason is that maintenance decision for turnouts are dominantly made based on visual inspections and/or manual measurement of track geometry, as turnouts are significantly more complex that traditional railway track.  This study presents the application of fractal dimensioning of track longitudinal level for the monitoring of ballast degradation in railway turnouts. In other words, the irregularities of the track vertical profile related to the ballast degradation are quantified as a ballast quality index. The ballast quality index is the basis for developing ballast degradation models in different sections of the turnout based on a segmentation scheme. Using track geometry data of 88 turnouts in the Danish railway network for the period 2012-2017, this study develops and compares ballast degradation models based on regression analysis and stochastic processes (lognormal and Gamma processes). The models are estimated for different sections of the single turnout, for different turnouts at distinct geographical locations. The proposed method provides an efficient tool for the analysis of the effect of tamping on ballast degradation rate. Moreover, the effects on ballast degradation of track loading rate, train speeds and seasonal changes of weather conditions are quantified.
Data-Driven Fault Detection for Neutron Generator Subsystem in Multifunction Logging-While-Drilling Service
Authors: Mosallam Ahmed , Laval Laurent , Ben Youssef Fares , Fulton James , Viassolo Daniel
Published: March 2024

This paper presents a method for constructing a health indicator to detect neutron-generator faults in a multifunction logging-while-drilling (LWD) service and predict maintenance requirements due to wear. The method is based on extracting fetures from selected channels that hold information about the subsystem degradation with time. These features are used to build a decision-tree model which estimates the tool condition from the recorded data. The model demonstrates excellent value for both maintenance and field engineers due to the fact that in just a few minutes the physical condition of the neutron generator can be determined with high confidence. This work is part of a long-term project with the aim to construct a digital fleet management for drilling tools.
From Data to Physics
Authors: Michau Gabriel
Published: March 2024

Data-driven Prognostic and Health Management is gaining more and more popularity, to the point where it is often seen as a potential solution for any maintenance problem. To the opposite, it is known that, when the physic of a system is known and tractable, it is usually the most efficient way to infer relevant indicators for detecting system behaviour changes.
And sometimes, when the physic of a system is only partially known, data-driven approaches can highlight critical information that would help to make the right physical assumptions, which in turn, can be used for elaborating the right health indicators.
 
This paper presents a real-life example of such process. With focus on the estimation of the degradation of a measurement system head, a data-driven approach, with its limitations, helped to identify the right signal processing tools with which to analyse the data.
With the results obtained from Spectral Analysis with Windowed Fourier Transform, the physics of the system could be hypothesized, and then used for deriving the most relevant indicators for monitoring the degradation. These indicators have been implemented online and so far used successfully on several machines encountering degradation of their measurement head.
 
This successful ``reversed'' case study of degradation monitoring is a strong reminder of the benefits of field symbiosis: data-driven approach made possible the understanding of the physics, which in turn could be used for data monitoring.
Fleet PHM for Critical Systems
Authors: Michau Gabriel , Palmé Thomas , Fink Olga
Published: March 2024

Data-driven approaches are highly relying on the representativeness of the dataset used for training the algorithms. For Prognostics and Health Management (PHM) applications, a lack of representativeness will result in detecting new operating conditions (that have not yet been observed during the period used for training) as faults. This is particularly a challenge for PHM in critical systems, for which long and consistent datasets with all operating conditions are generally not available.
 
Among the many data-driven approaches applied to PHM, Deep Learning has recently brought promising results, enabling an automation of traditional PHM tasks, including signal processing, feature engineering and signal monitoring. Yet, as the parameters to optimize in a Deep Neural Networks are numerous, the training requires also huge datasets: another quality that critical system datasets are often missing.
 
When a fleet of systems is monitored, however, a solution to compose more representative and bigger training datasets is to combine condition monitoring data from systems with similar operating conditions. Identifying similar operating conditions would, traditionally, require comparing the distances and the distributions of multi-dimensional time series, a computationally intensive task worsened by the curse of dimensionality.
 
In this paper, we propose to use a deep neural network, designed for measuring similarities between the training and the testing datasets: a Hierarchical Extreme Learning Machine (HELM). HELM have demonstrated excellent abilities to jointly learn features and monitor deviations from the training data. Training first this network on individual systems, HELM can be used to identify other systems with similar operating conditions. Afterwards, the same network can be trained again with this representative dataset composed of condition monitoring data of several systems, to monitor more efficiently the health of the individual systems. Any deviation in the algorithm output would signify that the system is not anymore in operating conditions seen in the training fleet, and is likely experiencing a fault.
 
The novelty of the proposed approach lies in the usage of the same architecture twice in a bi-level framework: first, for selecting the representative datasets from a fleet of systems and second, using the selected datasets to train the health monitoring algorithm. This approach achieves good performances on both tasks. Learning from the fleet attenuates the impact of changing operating conditions (\eg summer/winter trends), and improves the quality of the fault detection.
 
The approach is tested on a fleet of 112 power plants, some of which experienced a stator vane failure.
Life Prediction of Bearing for the Drive Train of a Wind Turbine
Authors: Seo Yun-Ho
Published: March 2024

A wind turbine is a representative machine with varying rotational speed because unpredictable natural wind is a locomotive force of the rotation. Major mechanical components of the drive train such as bearings encounter varying and irregular loadings in accordance with the variation of the rotational speed. Therefore, the varying and irregular loadings are a critical factor to be considered for the life prediction of the bearings. The degradation processes for constant and varying loadings are measured in order to evaluate the characteristics. An efficient index to stand for the state of bearing is also suggested in the combination of measured vibration, temperature and torque. Then, two methods, which can be used with and without model information respectively, are proposed to predict the life in the case of varying loading. The proposed methods are validated for the several experimental results and expected for the practical application of condition monitoring.
Towards automated detection and diagnosis of suspension system defects in passenger railway vehicles
Authors: Oosterhof Nikolaas N. , Peters Margot F. E.
Published: March 2024

Suspension defects in passenger trains reduce safety and comfort in rail transport systems. We investigated the feasibility of detecting such defects using dynamic wheel load differences (DWLDs), which are measured from trains in operation using sensors in the track.
We found that DWLD data shows considerable variability but in a consistent manner, and developed a method that purifies the data to improve signal-to-noise. Further, we developed an algorithm that uses the purified DWLD data to detect anomalous events in suspension imbalance, which is indicative of a defect or repair event. Our algorithm further provides a diagnosis of an anomalous event as related to either the primary or the secondary suspension system. We validated our algorithm using a limited set of maintenance records and found a high detection and correct classification rate, although more extended validation is needed with more maintenance records and DWLD data.
In sum, our work indicates a promising avenue of high-frequent, automated detection and diagnosis of suspension defects, which would contribute to efficient, economical and save operation of railway vehicles.
Automated Failure Diagnosis in Aviation Maintenance Using Explainable Artificial Intelligence (XAI)
Authors: Zeldam Sophie ten , Jong Arjan de , Loendersloot Richard , Tinga Tiedo
Published: March 2024

An incorrect or incomplete repair card, typically used in aviation maintenance for reporting failures, may result in incorrect maintenance and make it very hard to analyse the maintenance data. There are several reasons for this incomplete reporting. Firstly, (part of) the information is often unknown at the moment the maintenance crew fills in the card. Also, the findings on repair cards are generally filled out as freeform text, making it difficult to automatically interpret the findings. An automatically assessed failure description will lead to more complete and consistent repair cards. This willalso improve the efficiency of troubleshooting since this failure diagnosis can add information which would otherwise not be at the disposal of the maintenance crew at that time. This research will utilise a data driven approach combining maintenance and usage data. The model will be based on Artificial Intelligence (AI) such that it is no longer necessary to completely understand the physics of a (sub)system or component. XAI (eXplainable AI) will be added to the model to provide transparency and interpretability of the assessed diagnosis. The different steps towards this failure diagnosingmodel are applied to a case study with a main wheel of the RNLAF (Royal Netherlands Air Force) F-16. This preliminary feasibility study already showed the value of this automated failure diagnosis model with an improvement in diagnosis accuracy from 60% to 69%
Inter-engine variation analysis for health monitoring of aerospace gas turbine engines
Authors: Jacobs William , Edwards Huw , Kadirkamanathan Visakan , Mills Andrew
Published: March 2024

This study investigates the development of a new inter-enginevariation analysis method for the purpose of Equipment healthmonitoring, in which the similarity - in both system behaviourand external disturbances - across multiple (sister) engines isleveraged. The sister engine provides a baseline descriptionof the engine under observation, such that the challenge becomesthe differentiation between normal inter-engine variationand the anomalous behaviour, bypassing the need todescribe highly complex engine dynamics. The inter-engineresiduals are modelled directly with input data from both engines,using previous healthy data for training. The trainedmodel is used to compensate known differences between realengines. Anomalous data is detected by comparison of thesimulated output with the true residuals. The method is demonstratedon a real data set containing both nominal, healthy enginedata, and engine data containing anomalies.
Enhanced Particle Filter and Cyclic Spectral Coherence based Prognostics of Rolling Element Bearings
Authors: Qi Junyu
Published: March 2024

In the era of Internet of Things and Industry 4.0, the demand for condition monitoring of rotating machinery in industry becomes ever more significant. In addition to the steps of fault detection and diagnosis, the accurate estimation of the Remaining Useful Life (RUL) of machinery components may provide significant economic merits, optimizing the maintenance and avoiding potential human casualties and environmental pollution. During the last decades, a number of methodologies have been developed in the area of Prognostics and Health Management (PHM), categorized mainly in three groups, the physics based, the data-driven and the hybrid approach groups. Physical models are related to the load, the speed, the material, the geometry, etc. of a specific component and are able to make accurate RUL predictions but at an expensive computation cost and high complexity. In order to facilitate the applicability, a number of data-driven methodologies have been proposed including various versions of Kalman and Particle Filters. Among others, the Bayesian inference based Particle Filter provides high prediction accuracy for complex nonlinear systems utilizing little amount of data compared to machine learning techniques. The Monte Carlo step of the method is optimal to deal with the stochastic degradation process of bearings and appears to be able to handle any form of noise distribution. However, the traditional resampling methods frequently present the problem of particle leanness which heavily influences the performance of PF. The existing prediction methodologies are mainly based on classical diagnostic features, e.g. RMS, reaching a limit of efficacy. In order to overcome the abovementioned bottlenecks, an advanced prognostic methodology is proposed based on PF, the “systematic” resampling method and the Cyclic Spectral Coherence (CSCoh). The “systematic” resampling is proposed in order to address the problem of impoverishment. Moreover the CSCoh has been recently proposed as a powerful tool revealing weak modulations masked in the signals. The integration of the CSCoh over the frequency leads to the Enhanced Envelope Spectrum and a diagnostic feature is estimated based on the sum of the amplitudes of three harmonics of the characteristic fault frequencies of rolling element bearings. The methodology is tested and evaluated on experimental vibration signals, while the performance is quantitatively evaluated using prognostic metrics in the consideration of accuracy, precision and convergence.
ROC-based Business Case Analysis for Predictive Maintenance – Applications in Aircraft Engine Monitoring
Authors: Koops Lily Geraldine
Published: March 2024

Predictive maintenance approaches leveraging integrated knowledge, fleet-wide data and machine-learning techniques allow for earlier warnings on impeding failures and for higher accuracy in remaining useful life predictions compared with traditional prognostics. However, in case relative to correctly predicted maintenance needs, missed detections or false alarms occur too often, follow-up costs, e.g. due to cascading effects or unnecessary inspection effort, can outweigh the advantages. Here, we show that a general cost-benefit analysis based on the Receiver Operating Characteristics (ROC) curve of a failure prediction algorithm allows deducing application-specific requirements on the failure prediction quality for achieving a net benefit. Moreover, various prediction algorithms can be compared and optimized regarding cost-efficiency. The value of the approach is demonstrated by an application example in aircraft engine maintenance showing that for reducing unscheduled engine removals by (more) accurate prediction of turbine blade failures, maximal cost-saving potentials of up to 16 Mio $ emerge per mature-run, widebody engine and per Mean-Time Between Removals (MTBRs). Here, realistic, literature-based assumptions on various costs, failure probability and algorithm performance were incorporated and varied within sensible limits. As a further key result, the value of data for predictive maintenance purposes is impressively demonstrated. Compared with the net benefit achievable by failure prediction of a pure physics-based damage accumulation model and by turbine operating data, an up to 6 Mio. $ higher cost-saving potential per MTBRs was shown to emerge from a literature-based hybrid approach fusing physics and further sources of data, e.g. on manufacturing, geography and environment as well as customer and inspection information by means of machine learning techniques. Generalized applications of the presented cost-benefit analysis approach e.g. to optimize costs associated with engine workscope planning or other system maintenance are discussed.
Performance Assessment based on Health Baseline and MML for Hydraulic System
Authors: Song Dengwei , Lu Chen , Ma Jian , Cheng Yujie , Ding Yu
Published: March 2024

In recent years, with the wide application of hydraulic system, the performance assessment for hydraulic system has gained significant attention. However, a few studies focus on health assessment and traditional methods such as distance metric functions have limitation because different metric functions are only suitable for specific requirements. A scheme of performance degradation assessment based on health baseline and metric learning is proposed in this study. First, General regression neural network (GRNN) based observer is employed as health baseline to generate the estimated output of hydraulic system. The residual is obtained by calculating difference between the actual and estimated output. Then, time domain features are extracted from residual error. After that, apply the Mahalanobis metric learning (MML) to find a suitable metric adaptively for the training data set regarding distance. Finally, the distance between current status and normal status is normalized into confidence value (CV) to quantize the performance. A simulation model of hydraulic system is established based on HyPneu and Simulink, then gradual fault are injected to validate the proposed method. The results of experimental analysis demonstrate the effectiveness and adaptability of the performance degradation assessment method.
Multi-Fault Diagnosis for Wind Turbines Based on SCADA Data
Authors: Vidal Yolanda , Pozo Francesc , Tutivén Christian
Published: March 2024

The reliability requirements of wind turbine (WT) components have increased significantly in recent years in the search for a lower impact on the cost of energy. In addition, the trend towards larger WTs installed in offshore locations has significantly increased the cost of repair of the components. In the wind industry, therefore, condition monitoring is crucial for maximum availability.
In this work, without using specific tailored devices for condition monitoring but only using the already available sensors of the SCADA system, a data-driven multi-fault diagnosis strategy is contributed.
Diagnostics of seal and rod degradation in hydraulic cylinders using acoustic emissions
Authors: Shanbhag Vignesh Vishnudas , Meyer Thomas , Caspers Leo , Schlanbusch Rune
Published: March 2024

External leakage from hydraulic cylinders is of a major concern for the offshore oil and gas industry. This occurs mainly as a result of physical damage to the piston rod or due to degradation of the piston rod seals. Numerous studies have been conducted to diagnose leakage from hydraulic cylinders due to seal failure using fluid, pressure, accelerometer or acoustic emission-based condition monitoring techniques. However, very few attempts have been made to diagnose multiple faults in hydraulic cylinders at the same time. Therefore, in this study, acoustic emission-based condition monitoring technique is used to detect and separate acoustic emission features due to different faults that are observed in hydraulic cylinders. An experimental study was performed on a test rig using water glycol as hydraulic fluid. Experiments were performed with different combinations of seals (unworn, semi worn and worn seals), and piston rods (unworn and worn). Acoustic emission features such as root mean square, peak, mean, kurtosis, skewness, mean frequency, median frequency and bandpower were used to identify seal and rod conditions. By using acoustic emission median frequency and mean frequency features it was possible to detect and separate leakage, and seal and rod degradation in the test rig over a large range of hydraulic working pressures. This study indicates that acoustic emission monitoring can be a strong basis for future research to identify and segregate other types of faults that are observed in hydraulic cylinders.
Towards Self-aware and Environment-aware Power Converters
Authors: Bueno Mariani Guilherme , Foube Laurent , Chapalain Nadine , Kawahara Chihiro , Rojko Andreja , Degrenne Nicolas
Published: March 2024

This paper develops the vision of power converter as a sensor for D&P (Diagnostic & Prognostic) of electrical energy systems. Such power converter would rely on available on-board intelligence & sensors. The converters are capable of monitoring components within the converters themselves, i.e. self-awareness and also the systems to which they are connected (e.g. motor, cables...), i.e. environmental awareness.

This vision is supported in this paper by an analysis of the literature. For a selection of components and systems prone to failure, key methodological steps are outlined. They include analysis of failure modes, of their cause and consequences. Then, stressors and precursors to failure are listed in a context of observability at the converter level. Means for processing this measurable data and extracting the useful information such as state of health, remaining lifetime and fault location are exemplified.
In-depth Feature Selection for PHM System's Feasibility Study for Helicopters' Main and Tail Rotor Actuators
Authors: De Martin Andrea , Jacazio Giovanni , Nesci Andrea , Sorli Massimo
Published: March 2024

As deeply complex machines subjected to heavy vibratory environment, helicopters require relative low mean time between overhaul and suffer from high maintenance cost and availability issues. So far, PHM for helicopters has been aimed at detecting the presence of structural defects in the most critical parts of the mechanical transmission conveying power from the engine to the rotor blades, but very little has been presented on other flight-critical components, such as the main and tail rotor actuators. The proposed paper is focused on preliminary diagnostics and prognostics considerations for a traditional configuration of hydraulic solution, where a tandem actuator is aided by a Stability and Command Augmentation System (SCAS) during operations.

At first, the case-study is introduced and the simulation model employed for the analysis is described. Hence, two different failure modes affecting the SCAS are investigated and the physical models used to describe their progression are presented. In-depth data mining is then applied to achieve an accurate feature selection from raw data and an original way to visualize features' performances through an accuracy-sensitivity plane is proposed. Lastly, a particle filtering approach is adopted for failure prognosis and its output evaluated through traditional PHM metrics to assess the algorithm effectiveness. The present research provides encouraging results regarding the opportunity of realising a PHM system for helicopters' flight control actuators without the need of additional sensors, which could make solutions based upon the presented work feasible for both in-service and future platforms.
Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance
Authors: Shukla Bibhudhendu , Fan Ip-Shing , Jennions Ian
Published: March 2024

This paper aims to look at the value and the necessity of XAI (Explainable Artificial Intelligence) when using DNNs (Deep Neural Networks) in PM (Predictive Maintenance). The context will be the field of Aerospace IVHM (Integrated Vehicle Health Management) when using DNNs. An XAI (Explainable Artificial Intelligence) system is necessary so that the result of an AI (Artificial Intelligence) solution is clearly explained and understood by a human expert. This would allow the IVHM system to use XAI based PM to improve effectiveness of predictive model. An IVHM system would be able to utilize the information to assess the health of the subsystems, and their effect on the aircraft. Even if the underlying mathematical principles are understood, they lack an understandable insight, hence have difficulty in generating the underlying explanatory structures (i.e. black box). This calls for a process, or system, that enables decisions to be explainable, transparent, and understandable. It is argued that research in XAI would generally help to accelerate the implementation of AI/ML (Machine Learning) in the aerospace domain, and specifically help to facilitate compliance, transparency, and trust. This paper explains the following areas:

Challenges & benefits of AI based PM in aerospace

Why XAI is required for DNNs in aerospace PM?

Evolution of XAI models and industry adoption

Framework for XAI using XPA (Explainability Parameters)

Discussion about future research in adopting XAI & DNNs in improving IVHM.
Classification of stages of wear in spur gears based on wear debris morphology
Authors: Jangra Dharmender , Darpe Ashish K. , Hirani Harish
Published: March 2024

This work investigates the morphological changes in the wear debris, generated during the different stages of gear wear. Wear debris are generated at the mating load-bearing tooth surfaces having relative motion. The number and size of collected wear debris provide useful information for the gear fault diagnosis.

In the present work, both online and offline analyses of wear debris are carried out for gear fault diagnosis. In the online analysis, the oil from the gear sump is passed through online wear debris counter (to estimate the number of wear particles per minute) and particle size bin. Along with the online process, the oil samples are collected periodically, and wear debris particle images are captured using a scanning electron microscope (SEM). These images are subsequently processed to determine the parameters related to the shape, size and boundary features of the particles. The results of the modified texture in different stages of gear wear are reported. The average wear mass calculated as the actual area of the wear particles is combined with particle per minute is used as the stage’s classification.

The combined online and offline study provides a better prediction of mild wear progression along with the information on the wear mechanisms at different stages of wear. The presence of different type of particles (ferrous and non-ferrous) points to degradation of specific components.
Predictive Maintenance using Incipient Fault Detection
Authors: Ottonello Elia , Gattino Cecilia , Berselli Giovanni , Stecki Jacek
Published: March 2024

One focus of the Industry 4.0 paradigm is to enable Smart Factories with improved productivity and reduced down-times. In this context, Predictive Maintenance (PM) is a proactive approach to industrial services that optimises maintenance actions based on the system’s health. In order to monitor and understand the system’s status, effective PM requires dedicated tools capable of managing a large amount of data and discern the right data set required for analysis. As an aid for engineers, the software called MADe can be used. MADe is a model-based platform that can optimise maintenance actions following the information provided by the software itself, concerning sensor selection and functional models. In particular, among many others, MADe incorporates functionalities for incipient fault detection, which may be extremely useful when monitoring systems comprising fatigue or aging sensitive components. In fact, early fault detection enables scheduling of maintenance that will minimise the impact on production outputs. Owing to these considerations, this paper describes a technique for detection of incipient faults components affected by fatigue using an Equivalent Damage Index (EDI). This technique is tested on data taken from the literature in order to verify its potentials.
Data-driven Approach for RUL Prediction of an Experimental Filtration System
Authors: Beirami Hani , Calzà Davide , Cimatti Alessandro , Islam Manjurul , Roveri Marco , Svaizer Piergiorgio
Published: March 2024

This paper illustrates a data-driven approach adopted to address the PHME2020 Data Challenge competition. The aim of the challenge was to estimate the Remaining Useful Lifetime (RUL) of an experimental filtration device analyzing its clogging status by means of static (e.g. data sheets, fluid type, sensors) and dynamic (e.g. sensing data) information. We address the problem employing different state-of-the-art feature extraction, feature selection, and machine learning techniques. In this paper, we describe the approach followed to assess the problem and to generate robust and adaptable prediction models together with a corresponding performance assessment and robustness evaluation. The performance of the proposed solution is calculated in term of penalty score. The final penalty score 57.24 ranked 2nd in the above-mentioned data challenge competition.
Scouring of Temporary Irrigation Network Canals
Authors:   Georgian Technical University, Kharaishvili Olga , Kechkhoshvili Erekle ,   Georgian Technical University
Published: March 2024

Reduction of soil erosion is one of the main issues of modern engineering ecology. It should be noted that this problem is caused not only by natural factors, but also by peculiarities of some irrigation methods. The article considers the issue of erosion of earthen channels of temporary irrigation network under gravity irrigation. On the temporary irrigation network of three villages in Shida Kartli region of Georgia the amount of erosion was experimentally determined and its dependence on network parameters and soil conditions was established. As a result of this work, to reduce soil erosion (scouring), it is recommended to install canals with compacted crosssection.
Mechanisms for Granting the Authority to Operate Pharmacies
Authors:   Georgian Technical University, Shashiashvili Nana , Tsintsadze Tamar ,   Georgian Technical University, Bakradze Mariam ,   Caucasus University, Mishelashvili Khatuna ,   Georgian Technical University
Published: March 2024

Pharmacy is a crucial healthcare service that ensures access to safe and effective medicines for the population. As such, the regulation of pharmacy and the licensing of pharmacies and pharmacists is essential to ensure the quality and safety of pharmaceutical products and services. The regulatory body is responsible for issuing licenses and permits, conducting inspections, and enforcing laws and regulations related to pharmacy practice. A pharmacy license can have different meanings depending on the context, but generally speaking, a pharmacy license is a legal authorization granted by a government or regulatory agency that allows a pharmacy to operate within a particular jurisdiction. In some cases, the license may be tied to the physical location of the pharmacy, which means that if the pharmacy changes ownership, the license can be transferred automatically to the new owner, as long as the new owner meets all the other requirements for operating a pharmacy. In other cases, the license may be tied to the responsible pharmacist who manages the pharmacy, which means that if the responsible pharmacist changes, the license may need to be revoked and a new one issued to the new responsible pharmacist. To summarize, a pharmacy is a healthcare facility that provides services related to the distribution of medicines. Licensing requirements for pharmacies vary by country, but typically involve both a license for the physical location of the pharmacy and a license for the responsible pharmacist operating the pharmacy. These licenses may be subject to specific conditions and mechanisms for issuance and revocation, depending on the country's legal framework and other factors. Ultimately, the goal of pharmacy licensing is to ensure that patients have access to safe and effective medications, regardless of their geographic location or economic status.
The System of Quantitative Risk Assessment Indicators and Their Economic Efficiency Assessment Methods
Authors:   Georgian Technical University, Abralava Anzor
Published: March 2024

The mathematical models of the company's optimal risk management mechanism ensure the maximum efficiency of the activity at the level of risk acceptable to the enterprise. Classification of risks into operational risk and interest risk is given. One of the possible options for determining the tasks of optimal planning of the enterprise is presented, which includes restrictions on the values of operational effect (OE) (OR). The OE (two) indicator is considered as a limitation, the normative value of which can be considered as a given value during planning. It is a multidimensional linear optimal planning problem with linear constraints. Its size is determined by the number of product types in the enterprise. We have formulated original economic-mathematical models of the distribution type, which are used to create the optimal production plan of the enterprise. In order to minimize the interest risk, we have used the duration indicator analysis method. Duration represents the elasticity of the price of a financial instrument (in this case the present value of a stream of payments) with respect to the interest rate (discount rate) and is therefore a measure of the risk of the price of the instrument changing when the interest rate changes.
Customer Satisfaction as a New Paradigm of Loyalty to Banks
Authors:   Georgian Technical University, Mokhevishvili Tamar
Published: March 2024

At least two parties are involved in any relationship. It is the same in banks – both parties have their own interests. It is in the interest of the bank to get more profit from any transaction, and in the interest of the client – less loss. Until recently, banks functioned only in their own interests. Even now, Georgian banks either do not consider the interests of clients at all, or consider them minimally. According to a special study, the satisfaction of clients in Alpha and Beta banks of Georgia is only at an average level, and some components of satisfaction are at an even lower level. This refers to the appearance of sympathy and consideration for the client on the part of the bank's representative. It is concluded that evaluating the quality of service of Georgian banks, the factor of customer satisfaction will be evaluated as a weak point. Banks should strengthen their efforts in this direction, because customer satisfaction leads to their loyalty to the banks, which is a necessary condition for achieving their long-term goals.
STRUKTUR NARATIF SI PITUNG DALAM MEDIA ANIMASI OLEH KASTARI SENTRA DAN YAPS ANIMATION
Authors: Ruslan Arief
Published: March 2024

Si Pitung merupakan sastra tradisonal tentang peristiwa bersejarah di abad ke-19 dan menjadi bagian yang paling simbolis oleh masyarakat Betawi. Proyeksi penceritaan si Pitung melalui media audio visual pertama kali diproduksi pada tahun 1930 oleh PT. Halimoen Film (Wong Bersaudara), hanya saja data seluloid film tersebut tidak dapat ditemukan. Cerita si Pitung juga dihadirkan di layar lebar pada tahun 1970-an yang diproduksi oleh PT. Dewi Film. Pada tahun 2018, penceritaan si Pitung diangkat oleh Kastari Sentra dan Yaps Animation melalui animasi. Namun struktur narasi yang dikonstruksi pada media animasi memberikan penceritaan dan yang berbeda pada literatur sebelumnya, sehingga peneliti melihat ada narasi besar yang ingin disampaikan oleh kedua rumah produksi tersebut melalui penceritaan si Pitung dimedia animasi. Penelitian ini bertujuan untuk mengungkapkan struktur naratif si Pitung pada media animasi yang dihadirkan oleh Kastari Sentra dan Yaps Animation. Metodologi penelitian ini adalah kualitatif dengan menggunakan analisis stilistika linguistik. Analisis teks media animasi dikaji melalui struktur naratif yang dihadirkan pada tuturan-tuturan serta visualisasinya. Hasil penelitian menunjukkan bahwa, struktur narasi yang diproduksi oleh Kastari Sentra mengkonstruksi penekanan-penekanan pada bagian cerita yang diadaptasinya, sedangkan Yaps Animation mengkostruksi struktur narasi dengan penambahan penceritaan baru. Faktor ideologis serta kognisi produser memainkan peran yang dihadirkan dalam penceritaan si Pitung dalam media animasi. Sisi-sisi tentang kepahlawanan dan ke-Islaman menjadi narasi besar oleh Kastari Sentra, sedangkan penambahan tokoh Cina dalam struktur narasi yang dihadirkan Yaps Animation menunjukan si Pitung yang lebih nasionalis, sehingga nasionalisme sebagai struktur naraif utamanya.
ANALISIS RESEPSI “UNSUR KEKERASAN” DALAM VIDEO GAME AMONG US
Authors: Widyananda Putra Ricky
Published: March 2024

Pemain game merupakan elemen penting dalam video game. Mereka yang mendorong pengembang game untuk meningkatkan kualitas produknya. Salah satu game yang sedang ramai dibicarakan saat ini ialah Game Among Us, game ini merupakan permainan yang terinspirasi oleh permainan Werewolf. Dalam proses perancangan dan pengembangan konsep game sangat penting untuk mengenal user yang nantinya diharapkan akan memainkan game yang dirancang.  Salah satu cara dalam mengenal user dapat dilakukan dengan cara mempelajari dan meneliti game yang sudah ada sebelumnya, sehingga dapat diketahui proses user dalam memaknai sebuah game, hal ini dapat digali dengan beragam pendekatan salah satunya dengan menggunakan teori analisis resepsi. Teori ini memfokuskan pada aktifitas interpretif pemaknaan, produksi, dan pengalaman khalayak dalam interaksi dengan teks media. Kemudian yang akan menjadi responden dalam penelitian ini ialah komunitas Kotak kreatif merupakan suatu wadah komunitas game developer yang berdiri pada tahun 2016 yang terdiri dari para mahasiswa Universitas Budi Luhur, Jakarta, yang bergerak secara langsung dalam memproduksi game dan sebagai pengguna media game, di mana objek material dalam penelitian ini beruba media game. Karena hal itu, peneliti menjadikan kotak kreatif sebagai subjek penelitian dengan menggunakan objek penelitian yakni game Among Us. Maka tujuan yang ingin dicapai dalam penelitian ini ialah Untuk mengetahui pemaknaan terhadap game Among Us yakni berupa Unsur kekerasaan, Visualisasi karakter dan background serta alur cerita dengan menggunakan pendekatan deskriptif kualitatif dan teori analisis resepsi untuk mengurai serta memecahkan permasalahan yang ditemukan.
PERANCANGAN ANIMASI PENDEK SEBAGAI SARAN KAMPANYE STOP BUANG ANJING DI DENPASAR
Authors: Boy Nugraha I Made , Ananta Kusuma Putra Gede Lingga
Published: March 2024

Banyaknya anjing liar di Bali khususnya di Denpasar tidak  terlepas dari peran masyarakat itu sendiri, dengan tindakan membuang  anjing peliharaannya. Permasalahan yang akan terjadi apabila perilaku  membuang anjing ini terus berlanjut akan, munculnya kekerasan  terhadap anjing serta berbagai virus, maka dari itu perlu dilakukan  sebuah upaya salah satunya dengan perancangan animasi pendek  sebagai sarana kampanye Stop Buang Anjing di Denpasar, tujuannya  memberikan edukasi dan informasi untuk merubah pola pikir masyarakat sekaligus menanamkan komitmen di dalam memelihara anjing. dalam  proses perancangan animasi teori yang digunakan dalam merancang  animasi pendek yaitu teori dua belas prinsip animasi dan teori prinsip  desain digunakan dalam perancangan media pendukung. Metode  penelitian yang digunakan adalah metode observasi, metode  wawancara, metode kepustakaan dan metode kajian internet. “Atraktif” sebagai konsep dalam perancangan kampanye Stop Buang Anjing di  Denpasar melalui media animasi pendek, x-banner, poster, t-shirt,  totebag, booth 3D design, standing karakter, konten challenge Instagram  instastory, stiker, konten youtube dengan demikian diharapkan mampu  menekan kasus penelantaran anjing sehingga kasus membuang anjing  peliharaan tidak terjadi lagi di lingkungan masyarakat kota Denpasar.
NARASI BUDAYA DAERAH UNTUK PENCIPTAAN BRAND IDENTITY “BALE BUDAYA PARAHYANGAN” DI KAWASAN KOTABARU PARAHYANGAN BANDUNG
Authors: Setyohadi Sru , Murwonugroho Wegig , Eko Budiwaspada Agung
Published: March 2024

Berkurangnya minat generasi muda berkesenian dan berbudaya lokal adalah dampak dari era globalisasi yang telah mempengaruhi pola kehidupan berbudaya dinegeri ini. Mereka lebih memilih budaya luar yang dianggapnya lebih modern dan berkelas dibanding budaya daerah yang sudah usang dan ketinggalan jaman. Hal ini seseorang yang peduli budaya mendirikan sebuah balai budaya di Kawasan Kota Mandiri Kotabaru Parahyangan di daerah Padalarang Jawa Barat, untuk memfasilitasi kegiatan masyarakat dalam kegiatan pelestarian budaya bangsa. Tidak hanya untuk berkesenian saja tetapi juga sebagai tempat rekreasi, edukasi dan konservasi, namun sayangnya tempat yang diberi nama “Bale Budaya Parahyangan”, belum banyak dikenal oleh masyarakat sekitar karena minimnya informasi serta belum adanya brand identity, hal ini menjadi masalah utamanya. Maka perlu adanya konsep brand identity yang signifikan dengan mengambil narasi budaya daerah sebagai pendekatannya dan berdasarkan pada metode penelitian kualitatif serta teknik perancangan menggunakan proses design thinking, sehingga lebih mudah dikenal dan dipahami baik makna maupun visualnya.