bluuebunny/crossref_metadata_2025 huggingface dataset:
Physics Based Electrolytic Capacitor Degradation Models for Prognostic Studies under Thermal Overstress
Authors: Kulkarni Chetan S. , Celaya José R. , Goebel Kai , Biswas Gautam
Published: March 2024

Electrolytic capacitors are used in several applications ranging from power supplies on safety critical avionics equipment to power drivers for electro-mechanical actuators. This makes them good candidates for prognostics and health management research. Prognostics provides a way to assess remaining useful life of components or systems based on their current state of health and their anticipated future use and operational conditions. Past experiences show that capacitors tend to degrade and fail faster under high electrical and thermal stress conditions that they are often subjected to during operations. In this work, we study the effects of accelerated aging due to thermal stress on different sets of capacitors under different conditions. Our focus is on deriving first principles degradation models for thermal stress conditions. Data collected from simultaneous experiments are used to validate the desired models. Our overall goal is to derive accurate models of capacitor degradation, and use them to predict performance changes in DC-DC converters.
Theoretical and Experimental Evaluation of a Real-Time Corrosion Monitoring System for Measuring Pitting in Aircraft Structures
Authors: Brown Douglas , Darr Duane , Morse Jefferey , Laskowski Bernard
Published: March 2024

This paper presents the theory and experimental validation of Analatom’s Structural Health Management (SHM) system for monitoring corrosion. Corrosion measurements are acquired using a micro-sized Linear Polarization Resistance (uLPR) sensor. The uLPR sensor is based on conventional macrosized Linear Polarization Resistance (LPR) sensors with the additional benefit of a reduced form factor making it a viable and economical candidate for remote corrosion monitoring of high value structures, such as buildings, bridges, or aircraft.
A series of experiments were conducted to evaluate the uLPR sensor for AA 7075-T6. Test coupons were placed alongside Analatom’s uLPR sensors in a series of accelerated tests. LPR measurements were sampled at a rate of once per minute and converted to a corrosion rate using the algorithms presented in this paper. At the end of the experiment, pitdepth due to corrosion was computed for each sensor from the recorded LPR measurements and compared to the average pit-depth measured on the control coupons. The results demonstrate the effectiveness of the sensor as an efficient and practical approach to measuring pit-depth for AA 7075-T6.
Using structural decomposition methods to design gray-box models for fault diagnosis of complex industrial systems: a beet sugar factory case study
Authors: Pulido Belarmino , Zamarreño Jesus Maria , Merino Alejandro , Bregon Anibal
Published: March 2024

Reliable and timely fault detection and isolation are necessary tasks to guarantee continuous performance in complex industrial systems, avoiding failure propagation in the system and helping to minimize downtime. Model-based diagnosis fulfils those requirements, and has the additional advantage of using reusable models. However, reusing existing complex non-linear models for diagnosis in large industrial systems is not straightforward. Most of the times the models have been created for other purposes different from diagnosis, and many times the required analytical redundancy is small. In this work we propose to use Possible Conflicts, which is a model decomposition technique, to provide the structure (equations, inputs, outputs, and state variables) of minimal models able to perform fault detection and isolation. Such structural information can be used to design a gray box model by means of state space neural networks. We demonstrate the feasibility of the approach in an evaporator for a beet sugar factory using real data.
Damage identification and external effects removal for roller bearing diagnostics
Authors: M. Pirra , A. Fasana , L. Garibaldi , S. Marchesiello
Published: March 2024

In this paper we introduce a method to identify if a bearing is damaged by removing the effects of speed and load. In fact, such conditions influence vibration data during acquisitions in rotating machinery and may lead to biased results when diagnostic techniques are applied. This method combines Empirical Mode Decomposition (EMD) and Support Vector Machine classification method. The vibration signal acquired is decomposed into a finite number of Intrinsic Mode Functions (IMFs) and their energy is evaluated. These features are then used to train a particular type of SVM, namely One-Class Support Vector Machine (OCSVM), where only one class of data is known. Data acquisition is done both for a healthy bearing and for one whose rolling element presents a 450 micron damage. We consider three speeds and three different radial loads for both bearings, so nine conditions are acquired for each type of bearing overall. Feature evaluation is done using EMD and then healthy data belonging to the various conditions are taken into account to train the OCSVM. The remaining data are analysed by the classifier as test object. The real class each element belongs to is known, so the efficiency of the method can be measured by counting the errors made by the labelling procedure. These evaluations are performed by applying different kinds of SVM kernel.
Expert Guided Adaptive Maintenance
Authors: Lindgren Tony , Biteus Jonas
Published: March 2024

The heavy truck industry is a highly competitive business field; traditionally maintenance plans for heavy trucks are static and not subject to change. The advent of affordable telematics solutions has created a new venue for services that use information from the truck in operation. Such services could for example aim at improving the maintenance offer by taking into account information of how a truck has been utilized to dynamically adjust maintenance to align with the truck’s actual need. These types of services for maintenance are often referred to as condition based maintenance (CBM) and more recently Integrated Vehicle Health Management (IVHM).In this paper we explain how we at Scania developed an expert system for adapting the maintenance intervals dependent on operational data from trucks. The expert system is aimed at handling components which maintenance experts have knowledge about but do not find it worth the effort to create a correct physical wear-model for.We developed a systematic way for maintenance experts to express how operational data should influence the maintenance intervals. The rules in the expert system therefore are limited in what they can express, and as such our presented system differs from other expert systems in general.In a comparison between our expert system and another general expert system framework, the expert system we constructed outperforms the general expert framework using our limited type of rules.
Modeling the Semantics of Failure Context as a means to offer Context-Adaptive Maintenance Support
Authors: Pistofidis Petros , Emmanouilidis Christos , Papadopoulos Aggelos , N. Botsaris Pantelis
Published: March 2024

Acting upon the data involved in typical diagnostics and prognostics tasks is often confounded by the complexity of the corresponding situation and needs to take into account domain-specific or even installation-specific knowledge considerations. While domain knowledge is often captured in various forms, such as is typically done in Fault Modes, Effects and Criticality Analysis (FMECA), the contextualisation of the captured data and related knowledge to a corresponding situation, in other words a situated-aware modeling of data and knowledge, is often missing. Our research leverages the efficiency of maintenance support for mobile actors. Investing in modern service provision technologies, this work targets the effectiveness of capturing and sharing field expertise. An analysis of both the modeling specification and the functional requirements for such an approach. is provided. The semantics of “Failure Context”, a context that guides user’s navigation  towards relevant diagnostics and maintenance-related knowledge, are mapped into an appropriate data schema. Based on this, a system capable of managing the core information of the Failure Context, while offering adequate tools that support experts to build on, browse through, and reach contextuallyrelevant decisions is implemented. The development follows a reference-annotation design pattern to deliver on spot capture and enrichment of maintenance-related knowledge. Thus, the developed system provides the means for the effective management and exploitation of 'micro-knowledge fragments', associated with FMECA-related entities and knowledge. This is a significant enabler for the effective elicitation and management of field-captured expertise, enabling the enrichment and validation of maintenancerelated knowledge.
A Joint Predictive Maintenance and Spare Parts Provisioning Policy for Multi-component Systems Using RUL Prediction and Importance Measure
Authors: Nguyen Kim-Anh , Do Phuc , Grall Antoine
Published: March 2024

The paper presents a joint predictive maintenance and spare parts provisioning policy for gradually deteriorating multicomponent systems with complex structure. The decisionmaking process related to maintenance, spare parts ordering, as well as inspections scheduling is based on both RUL prediction and structural importance measure. Moreover, economic dependency between components is studied and integrated in decision rules. In addition, the impacts of the system structure on components deterioration process are also investigated. This dependency may have a significant influence on the RUL estimation of components. In order to evaluate the performance of the proposed joint predictive policy, a cost model is used. Finally, a numerical example of a 6-component system is introduced to illustrate the use and the advantages of the proposed joint maintenance and spare parts provisioning policy.
A Certifiable Approach towards Integrated Solution for Aircraft Readiness Management
Authors: Adhikari Partha Pratim , Makhecha Dhaval , Buderath Matthias
Published: March 2024

Aircraft readiness management plays pivotal role for aviation authorities to enhance mission availability, reliability and reduce maintenance cost. This has been the focus area of the industry for many years now. This paper focuses on developing an approach for maximizing the aircraft readiness based on the Aircraft Health Assessment and a novel approach for Maintenance Planning. Anintegrated solution using results from Prognostic Health Management (PHM) functions has been proposed. The concept is based on the condition based mission planning, operational risk assessment, maintenance planning and supply chain management. Also an insight is provided into the systematic approach to derive maintenance strategy leading towards certification. Although, the solution can be used for both commercial and military aviation, the focus in this paper is on implementation for military platforms. Details on implementation are discussed in brief and theresults of this implementation on some hypothetical scenarios are presented. The results outline the effectiveness of the approaches in improving the aircraft readiness.
Structural Health Monitoring of Composite Structures using embedded PZT Sensors in Space Application
Authors: Cenek Sandera , Mudit Rastogi , Radek Hedl
Published: March 2024

The use of composite structures in the space domain has increased significantly over the past years owing to its high strength to weight ratio. Because of the criticality and huge amount of money associated with these missions, there is an urgent requirement to monitor the structural integrity and its degradation by novel SHM techniques.In this paper we use ultrasonic guided wave technology and study the different possibilities of embedding piezoelectric sensors (PZT) into the carbon composites made by filament winding. We demonstrate the sensing capabilities of our developed sensor system to damages which can arise due to any accidental low-energy impact. A series of lab test was conducted on composite coupons to inspect the ability of PZT sensors to detect individual damages with high probability based on their distance from the impact location. The results show that PZT sensors are very promising in detecting all the damages caused by impacts with varying energies and can be a possible answer to needs of the structural health monitoring and non-destructive evaluation of advanced space structures.
Development of Diagnostics & Prognostics for Condition-Based Decision Support
Authors: Mikat Heiko , Siddiolo Antonino Marco , Buderath Matthias
Published: March 2024

The market for civil and military aerospace applications shows an increasing demand for service-based contracting ("Performance Based Contracting" - PBC). These contractual-concepts are based on guaranteed performance indicators over a fixed period, enabling a share of the financial risk between the system provider and the operator. The realization of efficient condition monitoring capabilities and reliable prognostics for prediction of spares and personnel demands has been identified as one key enabling factor for a successful implementation of PBC-concepts. To ensure an optimal incorporation of the diagnostic & prognostic functions needed for this purpose, the integration has to be considered as a standard design task during the development and certification phase, rising the need to adapt existing development processes. This adaption includes the extension of certification guidelines, definition of dedicated requirements and realization of innovative verification strategies. During the last years Airbus Defence & Space was working on the definition of a development process for integration of an innovative health management strategy into new aircraft systems to support condition-based operations. Following a summary of condition monitoring and prognostic techniques, selected requirements and guidelines for development of diagnostic & prognostic functions will be presented and discussed.
A Prognostic Framework For Electromagnetic Relay Contacts
Authors: Wileman Andrew J , Perinpanayagam Suresh
Published: March 2024

Electromagnetic relays provide a well proven solution to switching loads in a variety of applications. However, relays are known for their limited reliability due to mechanical wear of internal switching elements, essentially the life of the relay may be determined by the life of the contacts. Failure to trip, spurious tripping and contact welding, can in critical applications such as control systems in avionics and signaling for rail networks, cause significant costs due to downtime as well as safety implications. Prognostics provides a way to assess the remaining useful life of an electromagnetic relay based on its current state of health.In this paper, the cause of failure and degradation for electromagnetic relays used in avionic power controllers are examined. A first principle model of an electromagnetic relay, including contact wear is proposed. The degradation observations and measurements form the basis for developing a model based remaining useful life prediction algorithm. Our overall aim is to derive a simple but accurate model of the relays contact degradation, and provide prediction of performance changes within the component.
Advanced Data Mining Approach for Wind Turbines Fault Prediction
Authors: Toubakh Houari , Sayed-Mouchaweh Moamar
Published: March 2024

Wind turbine operation and maintenance costs depend on the reliability of its components. Thus, a critical task is to detect and isolate faults, as fast as possible, and restore optimal operating conditions in the shortest time. In this paper, a data mining approach is proposed for fault prediction by detecting the faults inception in the wind turbines, in particular pitch actuators. The role of the latter is to adjust the blade pitch by rotating it according to the current wind speed in order to optimize the wind turbine power production. The fault prediction of pitch actuators is a challenging task because of the high variability of the wind speed, the confusion between faults and noise as well as outliers, the occurrence of pitch actuator faults in power optimization region in which the fault consequences are hidden and the actions of the control feedback which compensate the fault effects. To answer these challenges, the proposed approach monitors a drift from normal operating conditions towards failure condition. To achieve drift detection, two drift indicators are used. The first indicator detects the drift and the second indicator confirms it. Both indicators are based on the observation of changes in the characteristics of normal operating mode over time. A wind turbine simulator is used to validate the performance of the proposed approach.
Comparison of binary classifiers for data-driven prognosis of jet engines health
Authors: Loyer Jean-Loup , Henriques Elsa , Wiseall Steve
Published: March 2024

A reliable prognosis is crucial to manage asset health and predict maintenance needs of large civil jet engines, which in turn contribute to enhanced aircraft airworthiness, longer time on wing and optimized lifecycle costs. With the accumulation of large amount of data over the last decade, one can relate the number of components serviced during a maintenance visit to the history of parameters inside and outside the engine (temperatures, pressure, shaft rotation speeds, vibration levels, etc.). While established statistical models had been developed for small samples, more recent computer-intensive statistical techniques from the field of Machine Learning (ML) can handle more complex datasets. In particular, binary classifiers constitute an attractive option to predict the probability of servicing the components of a given jet engine at the next maintenance visit. This paper demonstrates the validity of such data-driven methods on an industrial case study involving failures of thousands of compressor blades in aeronautical turbomachines. The prediction accuracy obtained with the ML techniques presents a significant improvement over the state-of-the-art. Moreover, the performance of six binary classifiers with different characteristics - logistic regression, support vector machines, classification trees, random forests, gradient boosted trees and neural networks - was compared according to four qualitative and quantitative criteria. Results show that there is no clear winner, although ensemble models based on trees (random forests and boosted trees) offer a good overall compromise while neural networks offer the best absolute performance. In the industrial world, the business objectives, the environment in which the models are deployed and the users’ skills should dictate the choice of the most adequate statistical technique.
A Review on Condition Monitoring Technologies for Railway Rolling Stock
Authors: Kundu Pradeep , Darpe Ashish K , Singh S P , Gupta K
Published: March 2024

In recent years, considerable research has been carried out to improve the safety of the railways. Much of the research has been in the area of development of sensors to capture health of the railway equipment. So far, no comprehensive review of literature has been carried out for condition monitoring of the railways. Most of the accidents in the railways are due to wheel and bearing failures, which causing derailment of the train. The present paper gives a comprehensive review of the sensors available for assessing the health of these components. Wayside sensing technology is found to be more popular compared to the on-board sensing technology because of economic modeling of damage. Comparative analyses of various sensing technologies have been performed to understand their usefulness for estimation of a particular fault. The paper also summarizes different diagnostic tools used for fault identification of the component such as wheel and bearing. Case studies are included to show the usefulness of condition monitoring technologies for fault identification in railways.
Remaining Lifetime Prediction for Reliability-based Dry Clutch Control
Authors: Strommenger Daniel
Published: March 2024

Increasing requirements for reliability of modern powertrainscan be achieved by predictive maintenance and reliabilitybasedcontrol based on lifetime prediction. This contributionpresents lifetime prediction for a dry clutch, being an essentialcomponent of automated manual transmissions. Modelbaseddevelopment of lifetime prediction requires knowledgeof dry clutch wear, which was identified in previous experiments.The derived wear model allows estimation of characteristicwear-dependent values, like friction lining materiallosses and friction coefficient changes. Based on these estimatedvalues the presented lifetime prediction was developedby fusing these estimated values into a health index (HI) describingthe systems healthiness. Furthermore, the remaininguseful lifetime (RUL) becomes predictable from observationsof health index trend using an exponential weightedmoving average. Eventually, the presented lifetime predictionwas implemented and tested on real-time operating hardwaresimilar to common transmission control units. In order tocontrol the system lifetime in normal operation, target trendsfor health index and predicted remaining useful lifetime weredefined. Based on trend deviations, a fuzzy-logic based controlstrategy was realized, which sets the optimization targetfor a reliability-based control. Thus, the optimization targetcan be varied between comfort-optimized or wear-optimizedclutch engagements. Finally, an outline of reliability-basedcontrol concepts is given.
Lamb Wave-based Damage Indicator for Plate-Like Structures
Authors: Cantero-Chinchilla Sergio , Chiachío-Ruano Juan , Chiachío-Ruano Manuel , Etxaniz Josu , Aranguren Gerardo , Jones Arthur , Essa Yasser , Martin De La Escalera Federico
Published: March 2024

Structural health monitoring based on ultrasonics typically involves complex data analysis. Ultrasound monitoring based on Lamb waves techniques are extensively used nowadays due to their efficiency in exploring large areas with relatively small attenuation. In recent years, baseline based methods have been developed to identify structural damage based on the mismatch between the measured signal and the baseline one. To this end, complex time-frequency transformations are required to obtain signal features such as the time of arrival or the energy content, as indicators of damage onset and growth. Notwithstanding this, on-board applications require highly efficient processing techniques due to information storage and exchange limitations. This paper proposes a very high efficiency signal processing methodology to obtain a novel cumulative damage factor using Lamb wave raw data. The new methodology has been tested using ultrasonic and damage data from a fatigue test in carbon-epoxy composite laminates. The data is taken from NASA Prognostics data repository. In view of the results, the method is able to efficiently detect the onset and extent of damage from early stages of degradation. Moreover, the results demonstrate a remarkable agreement between the growth of delamination area and the predicted cumulative damage factor.
Simulation of wind turbine faulty production profiles and performance assessment of fault monitoring methods
Authors: Aziz Usama , Charbonnier Sylvie , Bérenguer Christophe , Lebranchu Alexis , Prevost Frederic
Published: March 2024

Wind turbines being one of the fastest growing sources of renewable energy have garnered significant scientific interest for the monitoring and fault analysis using SCADA data. Various monitoring approaches using power curves, i.e. industry wide characteristic curves expressing produced power as a function of wind speed, have been proposed in the literature. However, an objective comparison of the performance of these methods is difficult. The difficulty comes from (i) the variability in operational and environmental conditions taken into account; (ii) the nature, size and type of data-sets used and (iii) the type and signatures of faults considered for validation.  To solve this problem, an approach with a twofold contribution is proposed in this work: 1) an original procedure to generate realistic and controlled simulations of 10 minutes SCADA data, simulating situations when the wind turbine is operating in normal or faulty conditions, is presented; 2) a framework for objective performance assessment of the fault detection methods, based on the proposed controlled and standardized simulation scheme is presented. Objective performance evaluation metrics, such as detection probability and false alarm rates are computed and represented as characteristic Receiver Operating Curves (ROC). The proposed simulation approach is shown to provide a useful global framework for objective performance analysis. A number of realistically simulated and controlled data streams are used to compare and discuss the performances of two fault detection methods referenced in the literature.
Intelligent Diagnostics for Aircraft Hydraulic Equipment
Authors: Jacazio Giovanni , Ritter Oliver , Wende Gerko , Gentile Rocco , Bertolino Antonio Carlo , Marino Francesco , Raviola Andrea
Published: March 2024

In aviation industry, unscheduled maintenance costs may vary in a large range depending on several factors, such as specific aircraft system, operational environment and aircraft usage and maintenance policy. These costs will become more noteworthy in the next decade, due to the positive growing of worldwide fleet and the introduction of more technologically advanced aircraft. The new implemented technologies will bring new challenges in the Maintenance, Repair and Overhaul (MRO) companies, both because of the rising number of new technologies and high volume of well-established devices, such as Electro-Hydraulic Servo Actuators for primary flight control. Failures in aircraft hydraulic systems deeply influence the overall failure rate and so the relative maintenance costs. For this reason, overhaul procedures for these components still represents a profitable market share for all MRO stakeholders. Innovative solutions able to facilitate maintenance operations can lead to large cost savings.
This paper proposes new methodologies and features of the Intelligent Diagnostic system which is being developed in partnership with Lufthansa Technik (LHT). The implementation of this innovative procedure is built on a set of failure detection algorithms, based on Machine Learning techniques. This development requires first to bring together the results from different parallel research activities:

Identification of critical components from historical data;
Designing and testing automatic and adaptable procedure for first faults detection;
High-fidelity mathematical modeling of considered test units, for deeper physics analysis of possible failures;
Implementation of Machine Learning reasoner, able to process experimental and simulated data.
System reliability upper bound assessment for health-aware control of complex systems
Authors: Salazar Jean Carlo , Sarrate Ramon , Nejjari Fatiha
Published: March 2024

When a system is composed of a large number of components, the computation of its exact reliability turns computationally costly and depending on the number of components it has such computation could be infeasible. This paper investigates the possibility of using a system reliability approximation. Specifically, an upper bound of the system reliability will be computed in order to overcome the computational problem of determining the exact system reliability and its integration into a health-aware control strategy. A comparison between the exact and the approximated approaches is presented on a drinking water network system.
Hybrid Fault Prognosis for Excitation Capacitors of Self-Excited Induction Generator for Wind Energy Applications
Authors: Derbal Massinissa , Toubakh Houari , Sayed-Mouchaweh Moamar , Bouchachia Abdelhamid
Published: March 2024

This paper presents a new fault prognosis approach applied to wind turbine system based on self-excited induction generator (SEIG) for offshore and isolated areas. This generator is very sensitive to wind speed variation and excitation source. The SEIG is excited by a capacitor bank with an appropriate value to ensure the good operating of the production system. Capacitor bank faults are usually related to chemical aging, electrical and thermal stress conditions. These abnormalities can affect one or more properties of the system, which can lead to failures or even complete breakdown of the production system. Specifically, in this paper, we propose a saturated flux model for the SEIG and develop a hybrid monitoring method that detects faults occurrence gradually and estimates the remaining useful life (RUL). Such monitoring method applies data mining techniques in order to identify and track the faults using only useful data that captures the dynamics of the degradation. Moreover, to deploy efficient maintenance schedules, RUL is estimated by exploiting wind speed (variable and max speed) information. The proposed hybrid fault prognosis method is tested under variable excitation capacitors degradation scenarios. The obtained results confirm the robustness and accuracy of the proposed method.
Prognostics in Aeronautics with Deep Recurrent Neural Networks
Authors: Baptista Marcia , Prendinger Helmut , Henriques Elsa
Published: March 2024

Recurrent neural networks (RNNs) such as LSTM and GRU are not new to the field of prognostics. However, the performance of neural networks strongly depends on their architectural structure. In this work, we investigate a hybrid network architecture that is a combination of recurrent and feed-forward (conditional) layers. Two networks, one recurrent and another feed-forward, are chained together, with inference and weight gradients being learned using the standard back-propagation learning procedure. To better tune the network, instead of using raw sensor data, we do some preprocessing on the data, using mostly simple but effective statistics (researched in previous work). This helps the feature extraction phase and eases the problem of finding a suitable network configuration among the immense set of possible ones. This is not the first proposal of a hybrid network in prognostics but our work is novel in the sense that it performs a more comprehensive comparison of this type of architecture for different RNN layers and number of layers. Also, we compare our work with other classical machine learning methods. Evaluation is performed on two real-world case studies from the aero-engine industry: one involving a critical valve subsystem of the jet engine and another the whole reliability of the jet engine. Our goal here is to compare two cases contrasting micro (valve) and macro (whole engine) prognostics. Our results indicate that the performance of the LSTM and GRU deep networks are significantly better than that of other models.
overview on diagnosability and prognosability for system monitoring
Authors: Vignolles Amaury , Chanthery Elodie , Ribot Pauline
Published: March 2024

The complexification of systems has brought the emergence of a new field of study: system health monitoring. This field is deemed necessary because it improves system availability and it avoids unnecessary maintenance costs. System health monitoring is performed through diagnosis and prognosis methods. Diagnosis consists in detecting and identifying faults that may lead to system failures. Prognosis is related to the prediction of the system Remaining Useful Life (RUL) that corresponds to the remaining time until the system failure. This paper aims at giving an overview on the properties related to diagnosis and prognosis on different types of systems. We will focus on the diagnosability and prognosability properties. This paper will first briefly present the different types of systems of interest for the system health monitoring community. We will consider Discrete Event Systems (DES), Continuous Systems (CS), Hybrid Systems (HS) or Heterogeneous Systems (HtS). The rest of this paper will present the definitions given in the literature for the concepts of diagnosability and prognosability. The similarities and differences in these definitions for the different types of systems will be highlighted. Some metrics associated with the prognosability property will also be discussed.
Predicting RUL of Bearings in the Pumps of a Critical Industrial Air Treatment System
Authors: Beirami Hani , Calzà Davide , Cimatti Alessandro , Islam Manjurul M , Roveri Marco , Svaizer Piergiorgio
Published: March 2024

Predictive maintenance is emerging as a promising technique to overcome the limitations of periodic maintenance. It integrates automatic condition monitoring to evaluate the health status of a system or device, with the estimation of Remaining Useful Life (RUL) of its components, in order to schedule maintenance only when really needed, minimizing the downtime of a plant.

In this paper, we report our results in evaluating the health conditions of the rotating ball bearings of a critical air pump which is part of the Clean Room industrial facility operating in the Micro-Technologies Laboratory of Fondazione Bruno Kessler. We instrumented such component with vibration and acoustic sensors with the aim of identifying a model of the evolving degradation and estimating the RUL of the bearings. The current dataset covers a period of one month before bearing replacement and about six months after the replacement. The first models, based on regression and particle filter processing of critical spectral components extracted from the sensors, indicate an estimated RUL of about 12 months that is in agreement with the average lifetime based on scheduled maintenance. Subsequent model evolutions have been observed in conjunction with scheduled greasing and periods of lower stress, which resulted in remarkable deviations from the initial degradation trends and in a consequent notable increase of the estimated RUL.

Results achieved so far are promising and could be used to extend the temporal distance among periodic maintenance interventions according to the estimated RUL.
On Condition Monitoring of Wind Turbines without Speed Sensor
Authors: Marhadi Kun , Saputra Dhany
Published: March 2024

We discuss implementation of wind turbine condition monitoring system (CMS) without speed sensor. The main method used is based on implementing Hilbert transform to extract the instantaneous frequency, where derivative of the analytic signal is done in the frequency domain. We analyze how to determine which vibration source, such as generator, gearbox high speed stage, or other turbine components should be used for speed extraction. The best choice of component is evaluated based on how good speed is estimated from various components in comparison to information from real speed sensor. Data from wind turbines collected over the years are used for statistical comparisons and selections of proper implementation. Information from estimated speed is then used along with an automatic diagnosis algorithm to detect different wind turbine faults.
Application of Machine Learning Methods to Predict the Quality of Electric Circuit Boards of a Production Line
Authors: Schmidt Immo , Dingeldein Lorenz , Hünemohr David , Simon Henrik , Weigert Max
Published: March 2024

For the data challenge of the 2022 European PHM conference, data from a production line of electric circuit boards is provided to assess the quality of the produced components. The solution presented in this paper was elaborated to fulfill the data challenge objectives of predicting defects found in an automatic inspection at the end of the production line, predicting the result of a following human inspection and predicting the result of the repair of the defect components. Machine learning methods are used to accomplish the different prediction tasks. In order to build a reliable machine learning model, the steps of data preparation, feature engineering and model selection are performed. Finally, different models are chosen and implemented for the different sub-tasks. The prediction of defects in the automatic inspection is modeled with a multi-layer perceptron neural network, the prediction of human inspection is modeled using a random forest algorithm. For the prediction of human repair, a decision tree is implemented.
Energy Efficiency of Homemade Wood Stove
Authors:   Georgian Technical University, Mikiashvili Temur , Kighuradze Omar ,   Georgian Technical University, Chkhaidze Baadur ,   Georgian Technical University
Published: March 2024

The results of the study of the energy effectiveness of wood stoves, which were obtained by the authors based on dozens of experiments and mathematical modeling are presented. It shows the advantages and disadvantages of wood-burning stoves with fireboxes internally covered by fire bricks versus typical stoves (without fire bricks) produced and widely distributed in Georgia. The necessity of using energy-efficient stoves in agrarian areas of the country is emphasized. According to the authors, the easiest way to increase the efficiency of stoves manufactured in Georgia and widely available to the population is to cover their combustion chambers (fireboxes) internally with thermally inertial material – fire bricks.
Oscillating Dryer
Authors:   Georgian Technical University, Isakadze Tamaz , Goletiani Givi ,   Georgian Technical University, Gugulashvili Givi ,   Georgian Technical University
Published: March 2024

Based on the analysis of the drying process, it was shown that it is better to dry food raw materials in an oscillating mode, which will take into account the changes in the physical and mechanical properties of the product at different stages of drying. A schematic diagram of a new device for drying bulk food products is presented, in which the friction of the product with the working elements of the device is reduced to a minimum and drying is carried out under conditions of a suspended state of particles in the flow of the working agent. In accordance with the different stages of the drying process, each stage is carried out with the optimal parameters for this stage (temperature, humidity, speed) of the working agent. This ensures maximum preservation of the quality indicators of raw materials during drying, and also reduces the energy costs for the preparation of the working agent.
Controlling the Possible Risks of Working at Height with the Rail and Fall Arrest Systems
Authors:   Georgian Technical University, Korkotadze Alexandre , Razmadze Nana ,   Georgian Technical University, Ratiani Nino ,   Georgian Technical University
Published: March 2024

Thousands of people are employed in the construction field in the countries with both, advanced and developing economies. It is characterized with: constantly changing operations at construction sites; extended chain of contracts, where the relationship between the contractors, sub-contractors and self-employed individuals is hard to ascertain; various jobs concurrently performed by various companies; majority of operations accompanied by high level of risk. Therefore, this is a priority sector for the labour inspectors across the world. Proper and relevant measures are required to prevent any possible risk to the maximum degree when work is performed at height and damage can be caused (e.g.: the use of protective rails). Personal protective equipment for working at height must meet the following demands: a) Linking equipment should be manufactured of pressed or formed steel or similar material; b) The linking equipment should have anti-corrosion ends and a smooth surface to prevent damaging the system components. Personal protective equipment for working at height must provide for arresting the falling employee at 1.8 m height above the ground surface. Body harness, other equipment and components should be used for the employee safety protection only. Their use for lifting/handling other materials is prohibited.
Prospects for the IMEC Against the Backdrop of Security Threats in the Middle East and the Existence of the Competing BRI Initiative
Authors:   Georgian Technical University, Tkabladze Zviad
Published: March 2024

The appearance of “India - Middle East - Europe Economic Corridor” (IMEC) is not accidental. On the one hand, this is the desire of the United States of America in the new context of the existence and development of a multipolar world, to create a basis for maintaining its influence in the Middle East, on the other hand – to create a system under its auspices that can withstand the growing power of China and its “One Belt One Road” (BRI). The purpose of this transport trade and economic corridor is to stimulate economic integration between Asia, the Persian Gulf and Europe. The planned corridor will connect India, the Kingdom of Saudi Arabia, the United Arab Emirates, the Hashemite Kingdom of Jordan, the State of Israel and the European Union through seaports and rail routes to make trade faster and cheaper, and develop economic cooperation and digital connectivity in the region. Despite the potential for significant economic and strategic benefits, the project faces a number of challenges and risks. Among them are interstate tensions, geopolitical rivalries and security threats, among others, especially the beginning of a new conflict in the Middle East.
Media as Art and Art as Media (Magazines: “Theatre”, “Aril”, “Art in America”, “ARTFORUM”)
Authors:   Caucasus International University, Tsereteli Asmat
Published: March 2024

Media and culture, two influential and important spheres of public life, have a long history of coexistence. There is no media art and no art without media. It is especially important to research the forms of impact on the formation of public consciousness in these areas, as well as the results. British media theorist Dennis McQuail in his work Journalism and Society notes that all theories that study the relationship between media, culture, and society, despite conceptual differences, agree that the media can serve to liberate and unify society, as well as to fragment and divide it – both development, advancement and retreat. Despite a kind of indeterminacy, the media, which shapes and reflects society, is the main message about society. (McQuail, 1992) Culture and art are a fundamental part of public life, which creates values, develops and strengthens public relations, together with the media, forms public consciousness. “Political confrontations and wars do not cause a cultural crisis, but, on the contrary, incompatibility, aggression and wars are the result of a cultural crisis” - this conclusion of European researchers is a kind of postulate, which says everything about the role of culture in the process of improving society.
Factors Influencing the Transformation of Banking Regulation and its Necessity in Modern Conditions
Authors:   Georgian Technical University, Gelitashvili Goga
Published: March 2024

The question of what factors influence the processes of transformation of banking regulation, as well as the need to transform banking regulation in modern conditions, has been little studied to date. There are two opposing views about the nature of the relationship between regulators and the banking sector: one is that regulators are dependent on the banking sector and that banks can influence the decisions made by the regulator. In the second case, regulators use the banking system to achieve their own goals. The factors influencing the transformation of banking regulation are analyzed, the need for transformation of banking regulation is substantiated and the need to strengthen the differentiation of regulatory regulation of banking institutions based on various indicators of their activities and the degree of risk is proposed. Summarizing the main provisions, it should be noted that the analysis of theories confirming the need for the development of banking regulation made it possible to reveal the content and systematize the main factors that have the most significant impact on transformation processes, and highlight the significance of the transformation of banking regulation.
Fundamentals of Ergonomics and Management
Authors:   Georgian Technical University, Kiknadze Nato , Kiknadze Nana ,   Georgian Technical University
Published: March 2024

The scientific basis of the origin and development of ergonomics and the important role that workplace arrangement plays in the effective work of employees is discussed. The necessity and importance of using knowledge in the field of science and ergonomics for labor’s safety and health, as well as for achieving high productivity in enterprises is reviewed.
Thermodynamic Prediction of Expected Processes in the Dispersed System Flue Gas - Clinoptilolite in the Process of CO2 Capture
Authors:   Georgian Technical University, Skhvitaridze Rajden , Giorgadze Irakli ,   Georgian Technical University, Mukhadgverdeli Nino ,   Georgian Technical University, Gordeladze Vladimer ,   Georgian Technical University, Kutsiava Nazibrola ,   Georgian Technical University
Published: March 2024

CO2 generated in production processes and emitted into the atmosphere with flue gases is the cause of the greenhouse effect and negative processes of global climate change towards warming. Therefore, prevention of CO2 emissions into the atmosphere is the research topic of the article. CCUS type climate change mitigation projects are being developed in Georgia, where natural zeolite mineral containing clinoptilolite, Khekordzula tuff is a sorbent for CO2 capture. Its formation enthalpy (ΔfH0 298), Gibbs free energy (ΔfG0 298), and entropy (ΔfS 0 298) have not been studied, also temperatures of its thermal dehydration, CO2 adsorption, expected chemisorption and other thermodynamic quantities. It was determined: Enthalpy of clinoptilolite tuff formation (ΔfH0 298=-39826 kJ/mol), Gibbs free energy (ΔfG0 298=-36735 kJ/mol), entropy (ΔfS 0 298=-10.37 kJ/K-mol); Temperatures of dehydration (94.800C), CO2 capture using it (physical-96.5700C), start of chemo-sorption (95.2400C) and pressure corresponding to these temperatures (745, 636, 534) mm Hg. Tuff can be successfully used for CCUS from flue gases.
KAJIAN BENTUK GUNUNGAN WAYANG SEBAGAI REPRESENTASI BUDAYA INDONESIA PADA VISUAL LOGO HALAL INDONESIA
Authors: Pugersari Dewi
Published: March 2024

Perpindahan kewenangan sertifikasi halal di Indonesia diikuti dengan perubahan logo halal secara nasional. Logo halal baru menimbulkan pro-kontra, salah satu penyebabnya yaitu bentuk desain logo yang dinilai tidak mewakili keberagaman budaya yang ada di Indonesia, hanya mewakili budaya Jawa. Penelitian ini dilakukan untuk mengkaji keberadaan dan bentuk gunungan wayang di berbagai budaya daerah di Indonesia. Tujuan penelitian ini yaitu mengkaji kesesuaian visual bentuk gunungan wayang pada logo halal Indonesia sebagai representasi budaya Indonesia. Penelitian kualitatif menggunakan metode deskriptif berdasarkan teori Ikonografi Erwin Panofsky dilakukan Peneliti dengan didukung kajian pustaka dan pengamatan. Berdasarkan penelitian, gunungan wayang ditemukan di 4 dari 6 pulau besar di Indonesia, yaitu Pulau Jawa, Pulau Bali Nusa, Pulau Sumatera, dan Pulau Kalimantan. Setidaknya terdapat lima bentuk visual gunungan wayang di Indonesia, di mana gunungan wayang di berbagai daerah tersebut dipengaruhi oleh wayang dari budaya Jawa. Oleh karena itu dapat disimpulkan bahwa bentuk visual gunungan wayang pada logo halal Indonesia dapat merepresentasikan budaya Indonesia. Namun secara khusus memiliki pengaruh kuat budaya Jawa, terlebih bentuk visual gunungan wayang yang digunakan pada logo halal Indonesia adalah gunungan wayang Jawa.