Modelling of the Military Helicopter Operation Process in Terms of Readiness

The processes of exploitation of military objects are usually characterised by the specificity of the operation and the complexity of both the process itself and the object. This specificity may relate both to the type of tasks that these objects carry out and to the environment in which these processes take place. Complexity is usually reflected in the very structure of an object (for example, a ship, an aircraft or a helicopter) and, consequently, in its operation/maintenance system. The above mentioned features, as well as the limited access to data, naturally limits the set of publications available on this subject. In this article, the authors have presented a method of assessing the readiness of military helicopters operated by the Armed Forces of the Republic of Poland. The readiness of technical objects used in military exploitation systems is a basic indicator of equipment preparation for executing tasks. In exploitation process research, the mathematical models are usually discrete in states and continuous in time stochastic processes, in the set of which Markov models are included. The paper presents an example of using Markov processes with discrete time and with continuous time to assess the readiness of a technical object performing tasks appearing in random moments of time. At the same time, the aim of the examined system to achieve a state of balance is presented

State-of-the-Art Review and Synthesis: A Requirement-based Roadmap for Standardized Predictive Maintenance Automation Using Digital Twin Technologies

Recent digital advances have popularized predictive maintenance (PMx), offering enhanced efficiency, automation, accuracy, cost savings, and independence in maintenance. Yet, it continues to face numerous limitations such as poor explainability, sample inefficiency of data-driven methods, complexity of physics-based methods, and limited generalizability and scalability of knowledge-based methods. This paper proposes leveraging Digital Twins (DTs) to address these challenges and enable automated PMx adoption at larger scales. While we argue that DTs have this transformative potential, they have not yet reached the level of maturity needed to bridge these gaps in a standardized way. Without a standard definition for such evolution, this transformation lacks a solid foundation upon which to base its development. This paper provides a requirement-based roadmap supporting standardized PMx automation using DT technologies. A systematic approach comprising two primary stages is presented. First, we methodically identify the Informational Requirements (IRs) and Functional Requirements (FRs) for PMx, which serve as a foundation from which any unified framework must emerge. Our approach to defining and using IRs and FRs to form the backbone of any PMx DT is supported by the track record of IRs and FRs being successfully used as blueprints in other areas, such as for product development within the software industry. Second, we conduct a thorough literature review spanning fields to determine the ways in which these IRs and FRs are currently being used within DTs, enabling us to point to the specific areas where further research is warranted to support the progress and maturation of requirement-based PMx DTs.Comment: (1)This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Research Risk Factors in Monitoring Well Drilling—A Case Study Using Machine Learning Methods

This article takes an approach to creating a machine learning model for the oil and gas industry. This task is dedicated to the most up-to-date issues of machine learning and artificial intelligence. One of the goals of this research was to build a model to predict the possible risks arising in the process of drilling wells. Drilling of wells for oil and gas production is a highly complex and expensive part of reservoir development. Thus, together with injury prevention, there is a goal to save cost expenditures on downtime and repair of drilling equipment. Nowadays, companies have begun to look for ways to improve the efficiency of drilling and minimize non-production time with the help of new technologies. To support decisions in a narrow time frame, it is valuable to have an early warning system. Such a decision support system will help an engineer to intervene in the drilling process and prevent high expenses of unproductive time and equipment repair due to a problem. This work describes a comparison of machine learning algorithms for anomaly detection during well drilling. In particular, machine learning algorithms will make it possible to make decisions when determining the geometry of the grid of wells—the nature of the relative position of production and injection wells at the production facility. Development systems are most often subdivided into the following: placement of wells along a symmetric grid, and placement of wells along a non-symmetric grid (mainly in rows). The tested models classify drilling problems based on historical data from previously drilled wells. To validate anomaly detection algorithms, we used historical logs of drilling problems for 67 wells at a large brownfield in Siberia, Russia. Wells with problems were selected and analyzed. It should be noted that out of the 67 wells, 20 wells were drilled without expenses for unproductive time. The experiential results illustrate that a model based on gradient boosting can classify the complications in the drilling process better than other models.publishedVersio

Bayesian Network Analysis for Diagnostics and Prognostics of Engineering Systems

Bayesian networks have been applied to many different domains to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military field and human performance data sets in an industrial environment. Methods frequently rely on a clear understanding of causal connections leading to an undesirable event and detailed understanding of the system behavior. Methods may also require large amount of analyst teams and domain experts, coupled with manual data cleansing and classification. The research performed utilized machine learning algorithms (such as Bayesian networks) and two existing data sets. The primary objective of the research was to develop a diagnostic and prognostic tool utilizing Bayesian networks that does not require the need for detailed causal understanding of the underlying system. The research yielded a predictive method with substantial benefits over reactive methods. The research indicated Bayesian networks can be trained and utilized to predict failure of several important components to include potential malfunction codes and downtime on a real-world Navy data set. The research also considered potential error within the training data set. The results provided credence to utilization of Bayesian networks in real field data – which will always contain error that is not easily quantified. Research should be replicated with additional field data sets from other aircraft. Future research should be conducted to solicit and incorporate domain expertise into subsequent models. Research should also consider incorporation of text based analytics for text fields, which was considered out of scope for this research project

Uncertainty analysis in product service system: Bayesian network modelling for availability contract

There is an emerging trend of manufacturing companies offering combined products and services to customers as integrated solutions. Availability contracts are an apt instance of such offerings, where product use is guaranteed to customer and is enforced by incentive-penalty schemes. Uncertainties in such an industry setting, where all stakeholders are striving to achieve their respective performance goals and at the same time collaborating intensively, is increased. Understanding through-life uncertainties and their impact on cost is critical to ensure sustainability and profitability of the industries offering such solutions. In an effort to address this challenge, the aim of this research study is to provide an approach for the analysis of uncertainties in Product Service System (PSS) delivered in business-to-business application by specifying a procedure to identify, characterise and model uncertainties with an emphasis to provide decision support and prioritisation of key uncertainties affecting the performance outcomes. The thesis presents a literature review in research areas which are at the interface of topics such as uncertainty, PSS and availability contracts. From this seven requirements that are vital to enhance the understanding and quantification of uncertainties in Product Service System are drawn. These requirements are synthesised into a conceptual uncertainty framework. The framework prescribes four elements, which include identifying a set of uncertainties, discerning the relationships between uncertainties, tools and techniques to treat uncertainties and finally, results that could ease uncertainty management and analysis efforts. The conceptual uncertainty framework was applied to an industry case study in availability contracts, where each of the four elements was realised. This application phase of the research included the identification of uncertainties in PSS, development of a multi-layer uncertainty classification, deriving the structure of Bayesian Network and finally, evaluation and validation of the Bayesian Network. The findings suggest that understanding uncertainties from a system perspective is essential to capture the network aspect of PSS. This network comprises of several stakeholders, where there is increased flux of information and material flows and this could be effectively represented using Bayesian Networks

Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Automated Detection of Dental Caries from Oral Images using Deep Convolutional Neural Networks

The urgent demand for accurate and efficient diagnostic methods to combat oral diseases, particularly dental caries, has led to the exploration of advanced techniques. Dental caries, caused by bacterial activities that weaken tooth enamel, can result in severe cavities and infections if not promptly treated. Despite existing imaging techniques, consistent and early diagnoses remain challenging. Traditional approaches, such as visual and tactile examinations, are prone to variations in expertise, necessitating more objective diagnostic tools. This study leverages deep learning to propose an explainable methodology for automated dental caries detection in images. Utilizing pre-trained convolutional neural networks (CNNs) including VGG-16, VGG-19, DenseNet-121, and Inception V3, we investigate different models and preprocessing techniques, such as histogram equalization and Sobel edge detection, to enhance the detection process. Our comprehensive experiments on a dataset of 884 oral images demonstrate the efficacy of the proposed approach in achieving accurate caries detection. Notably, the VGG-16 model achieves the best accuracy of 98.3% using the stochastic gradient descent (SGD) optimizer with Nesterov’s momentum. This research contributes to the field by introducing an interpretable deep learning-based solution for automated dental caries detection, enhancing diagnostic accuracy, and offering potential insights for dental health assessment

Optimization of aeroengine utilization through improved estimation of remaining useful life (RUL) of on condition (OC) parts

Gas Turbine Operators and Maintainers face the challenge of increasing the operationallife, availability, and reliability of Aero Engine during the operations amid the advancements in the technology for the speed, Power, SFC, and Comfort which led to closing in the safety margins and increasing in the failure rate of Aero Engines, and low serviceability of aero engines due to the rapid increase in demand and expansion of the aircraft fleet by the various airlines for various reasons which led to increase in downtime and lead time of serviceability of Aero Engines. This study focuses on the inherent Aero Engine deterioration caused due to various Gas Turbine Faults or Physical Problems and how Performance, Trend, and Systems Monitoring contain this deterioration and contribute to the optimization of aero engine utilization. The purpose of thispaper is to present the importance of accurate estimation of Remaining Useful Life (RUL) of On Condition (OC) Parts and how it optimizes aero engine life. The benefits associated with the introduction of a novel estimation of RUL of OC Parts are also presented. A Standardized Replacement Model (SRM), which improves the estimation of RUL of OC Parts, is proposed for optimization of Aero Engine Utilization