Maintenance Environment Research Articles

Effectiveness Evaluation Method Using System of Systems Architecture Description of Aircraft Health Management in Aircraft Maintenance Program

This study proposes a system modeling method for aircraft maintenance program development that adopts condition-based maintenance using aircraft health management (AHM) based on a systems engineering approach, which considers AHM as a system of systems. The metamodel is tailored on the basis of the Unified Architecture Framework (UAF) and the NASA Systems Modeling Handbook for Systems Engineering. It is described using the modeling tool "Balus 2.0" (Levii, Inc). The applicability and effectiveness of a maintenance program adopting AHM is analyzed on the basis of the Maintenance Steering Group-3 (MSG-3), and its effectiveness is evaluated using the proposed system modeling method. The proposed method considers the uncertainty of the aircraft maintenance environment related to airline operations in addition to the uncertainty of the aircraft system. The effectiveness of the proposed system is investigated through a sample problem that considers a tire system using a pressure monitoring system as AHM based on the MSG-3 approach. Finally, the limitations of the proposed method are discussed.

Solid-liquid triboelectric nanogenerator based self-sensing vibration suppression device

Micro-vibrations adversely affect critical equipment, devices, and systems, resulting in economic losses and safety risks. Hence, the imperative tasks of monitoring and mitigating micro-vibrations present formidable challenges in the field of vibration control. This study introduces a solid-liquid triboelectric nanogenerator (TENG) based self-sensing vibration suppression device (S-L SVD) designed with a vertically structured configuration. This device not only possesses the capability to detect but also effectively reduce vibrations. The S-L SVD consists of an upper self-sensing unit utilizing liquid-silica synergy and a lower damping unit employing magnetic fluid (MF)-permanent magnet synergy. The S-L SVD exhibits a remarkable vibration monitoring sensitivity of 1.9 V/mm, enabling the detection of micro-vibrations with amplitudes as small as 0.368 mm. The self-sensing unit effectively functions as a miniature liquid tuned damper, substantially enhancing the device's damping capability, resulting in a noteworthy 19.6 % damping efficiency improvement. Leveraging solid-liquid TENG, the S-L SVD seamlessly integrates vibration monitoring and suppression, showcasing exceptional durability. This innovation not only provides valuable insights into vibration monitoring but also introduces novel approaches for improving damping performance. Furthermore, the device is well-suited for applications in energy-scarce and challenging maintenance environments.

Human fatigue in the aircraft maintenance environment

The complexity of technologies in modern aircraft makes their maintenance a critical function. The work environment is diverse and dynamic, with uninterrupted working hours and multiple demands, which can result in inadequate sleep and excessive workload for Aircraft Maintenance Technicians. This causes fatigue and increases the likelihood of human errors, directly affecting safety. A study was conducted with 312 participants to investigate how fatigue affects maintenance professionals. Participants responded to questionnaires that included four culturally validated scales to measure levels of fatigue, sleepiness, workload, and quality of life. Statistical analyses were performed to correlate sociodemographic factors and work-related aspects with levels of fatigue. The results revealed a positive and significant correlation between fatigue, sleepiness, and workload, and a significant negative correlation with quality of life. It was found that 52.9% of participants experienced some level of fatigue, highlighting the prevalence of fatigue among the sampled population. Statistically significant differences were also found in participants aged 36 to 50, those working shifts, and those working during nighttime hours. Therefore, regulations and practical measures are needed to assess and manage fatigue-related risks, tailored to the aircraft maintenance environment, given that current regulations on human fatigue in aviation primarily focus on flight crew, paving the way for potential changes in the European Aviation Safety Agency regulations.

Effects of AR-HMD Interactive Interface Layout on Usability and Obstacle Avoidance in Subway Overhauls

The maintenance and overhaul of rail transit vehicle frames are vital for ensuring the safe and reliable operation of equipment. AR-based maintenance has been proven to be cost-reducing and effective. Few studies explored the application of AR-based maintenance on subway overhaul. In this study, 74 participants were selected to conduct the underground maintenance AR recognition and obstacle avoidance experiment. And two-factor ANOVA, independent samples t-test, and non-parametric test were used for data statistics. The results show that a high-opacity AR interface has better recognition but affects obstacle avoidance. Facing different types of obstacles, AR interfaces with different layouts have significant differences in terms of recognizability and obstacle avoidance effects. Compared with the list layout, the matrix layout AR interface will make more effort for workers. The findings provide useful references for designing the visual information layout of AR interfaces and conducting usability testing of AR interfaces in complex scenarios such as underground maintenance. Highlights This study assessed the effects of the AR-HMD interface on workers’ recognition of and ability to safely avoid obstacles in a subway maintenance environment. A high-opacity AR interface leads to better recognition but affects obstacle avoidance. List AR interfaces lead to better recognition but increase the probability of obstacle avoidance failure by 50% under block obstacles. For linear obstacles, layout has no significant effect on recognition or obstacle avoidance. A matrix layout AR interface is more effortful than the list layout AR for the workers.

Quality Evaluation of Equipment Maintenance Process Based on Interpretive Structure Model

Based on the "5M1E" analysis method, the maintenance process quality evaluation system was established, and then the interpretive structure model (ISM) was used to analyze the hierarchical relationship of the influence of each index factor on the maintenance process quality, and the rationality of the ISM model analysis method was verified through the analytic hierarchy process. The results show that maintenance equipment and facilities A4, assembly process A5 and spare parts quality A7 are surface factors of maintenance process quality. Raw material A3 and maintenance personnel A2 are the middle-level factors of maintenance process quality; Maintenance management system A1 and maintenance environment A6 are the deep factors of the quality of maintenance process, and are the influential factors to focus on and improve. In addition, the corresponding maintenance suggestions are given for each factor, which provides theoretical guidance for the formulation of equipment maintenance management procedures.

Bi-objective optimization of flexible flow shop scheduling problem with multi-skilled human resources

In this article, a Multi-skilled Resource-Constrained Flexible Flow Shop Scheduling Problem (MSRC-FFSSP) is introduced. To process each job at every machine within this problem category, it is imperative to possess at least one set of multi-skilled human resources. Each human resource can operate a minimum of 1 and a maximum of N jobs depending on their skills and each job requires a minimum set of different skills. For this problem, the model is formulated as a MILP model with two objectives: i) minimizing the total completion time and ii) minimizing the total idle time for human resources. Moreover, a case study in a preventive maintenance environment as a flow shop is performed to establish the applicability of the model in real-world problems. Given that the problem is NP-Hard, a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm in which the related parameters are tuned by the Taguchi method are presented. Finally, numerical results have been presented to verify the model, in addition to the analysis of the idle time of human resources. Additionally, to solve this problem, it was shown that the GA has better efficiency and performance compared to the SA algorithm. However, the SA algorithm outperforms the GA in terms of running time.

Effect of maintenance environment on the mechanical property and pore structure of cement paste mixed by seawater

This study aims to examine the effects of macroscopic mechanical properties and pore structure of cement paste mixed with seawater and explore the relationship between pore structure and macroscopic properties. The changeable law of mechanical properties and pore microstructure of hardened cement paste under seawater mixing is methodically investigated based on the compressive and flexural strengths using X-ray diffraction (XRD), scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) tests. The relationship between the fractal dimension of the pore structure of cementitious materials and the concentration of chloride ions in simulated seawater, the curing age, and the fly ash dose was obtained based on the thermodynamic fractal model. The obtained results reveal that with the increase in chloride ion concentration of the simulated seawater, the compressive and flexural strengths of the samples substantially grow in the pre-maintenance period, and the strength of the samples tends to stabilize at the age of 28 days. For the case of the chloride ion concentration of 1.3%, the compressive strength of the cement slurry exhibits a growth of 24.08%, 24.83%, and 28.81% compared to the control cement sample after curing at 5 °C, 20 °C, and 50 °C for 3 days, respectively. Additionally, the flexural strengths in order are 41.86%, 46.57%, and 38.89% higher than those of the control group. The interaction of Cl- with C3A, C4AF, or AFm in seawater mixed at high-temperature to generate Friedel's salt stimulated the volcanic ash reaction of fly ash, promoted the generation of C-S-H gels, and improved the internal pore structure of cementitious materials. The results indicate that at 50 °C curing, the most probable pore diameter of cement paste with 1.3% chloride ion concentration is 39.51% less than that of the control cement sample, and its harmless pores are 23% higher than that of the control group. At 5 °C curing, the total specific pore volume of cement paste with 1.3% chloride ion concentration is 30.18% less than that of the control cement sample, and the total specific pore volume is 22.9% less than the control group for the fly ash content of 25%. The obtained results reveal that there exists a fairly low correlation between the fractal dimension of the pore surface area of the cement paste and porosity, compressive strength, and flexural strength. However, the achieved correlation with the mean pore diameter, median pore diameter, and pore surface area is good, which are all above 75%. The fractal dimension fitting curve of high-temperature curing possesses a range of singular points.

Usage of IoT Devices as Progressive Technology in the Era of Digitization: Case Study

This article deals with implementing an IoT approach based on a case study with an emphasis on current data collection concepts and expanding automated workplaces’ functionality. As part of this, the article describes the issues and advantages of IO-Link technology as part of the introduction of the emerging digitization all around us. Part of the presented solution is the direct configuration and diagnostic functions of IO-Link sensors and IO-Link master, which are applied to the FMS 500 modular laboratory system. The proposed case study proves the complexity and compatibility of individual modules, and its goal is to demonstrate the feasibility of practical requirements, research, and teaching in Industry 4.0 standards. Additionally, test experiments have shown the suitability of deploying such concepts in global diagnostics and predictive maintenance environments.

A Methodology of Condition Monitoring System Utilizing Supervised and Semi-Supervised Learning in Railway.

In this paper, research was conducted on anomaly detection of wheel flats. In the railway sector, conducting tests with actual railway vehicles is challenging due to safety concerns for passengers and maintenance issues as it is a public industry. Therefore, dynamics software was utilized. Next, STFT (short-time Fourier transform) was performed to create spectrogram images. In the case of railway vehicles, control, monitoring, and communication are performed through TCMS, but complex analysis and data processing are difficult because there are no devices such as GPUs. Furthermore, there are memory limitations. Therefore, in this paper, the relatively lightweight models LeNet-5, ResNet-20, and MobileNet-V3 were selected for deep learning experiments. At this time, the LeNet-5 and MobileNet-V3 models were modified from the basic architecture. Since railway vehicles are given preventive maintenance, it is difficult to obtain fault data. Therefore, semi-supervised learning was also performed. At this time, the Deep One Class Classification paper was referenced. The evaluation results indicated that the modified LeNet-5 and MobileNet-V3 models achieved approximately 97% and 96% accuracy, respectively. At this point, the LeNet-5 model showed a training time of 12 min faster than the MobileNet-V3 model. In addition, the semi-supervised learning results showed a significant outcome of approximately 94% accuracy when considering the railway maintenance environment. In conclusion, considering the railway vehicle maintenance environment and device specifications, it was inferred that the relatively simple and lightweight LeNet-5 model can be effectively utilized while using small images.

Learning from past in the aircraft maintenance industry: An empirical evaluation in the safety management framework

The growth of commercial air transport arguably translates into more aging passenger aircraft queuing up for major maintenance, modifications, and/or freighter conversion with the aircraft maintenance industry. In the competitive business environment, this increased maintenance demand possesses the potential to stress the industry and make safety vulnerable. In the aircraft maintenance industry, several aircraft accidents and incidents have resulted from organizational failure to learn from the past. To address this chronic problem, this study aims to (a) establish a learning process model for the aircraft maintenance industry, (b) identify the factors that influence learning, and (c) determine the effect of identified factors on learning from the past. A review of scholarly articles and regulatory publications enabled the development of learning from the past process model and a data collection tool, followed by structural equation modeling to quantify the relationship among influencing factors. The study was conducted in the Indian aircraft maintenance environment and is based on the perspective of the front-line maintenance staff. The study found that safety communication is the decisive stage for learning from the past. Contextualization of the safety information and evaluating the lessons learned during safety communication strongly impact learning from the past, for which existing regulatory provisions are vulnerable. The findings of this study are meant to assist State regulators and management of the aircraft maintenance industry; nevertheless, safety managers and practitioners in other ultra-safe, high-risk sectors may also apply the results in compliance with the respective regulatory guidelines.

Effect of Aircraft Health Management on Aircraft Maintenance Program Development by Aircraft Manufacturer

The objective of the research is to propose a system modeling method for aircraft maintenance program development applying the condition-based maintenance using AHM (Aircraft Health Management) from the viewpoint of aircraft manufacturer. The proposed model based on the MSG-3 (Maintenance Steering Group - 3) considers the uncertainty of aircraft maintenance environment related to the airline operation and assumed system degradation levels. Then, the proposed model is formulated by using the concept of a robust optimization method.

Environmental hazard reduction and anti-aging enhancement of steel slag powder-asphalt mastic based on TiO2@UiO-67 composites

Steel slag asphalt pavements (SAPs) will deteriorate during the production and service process, bringing heavy metal ions leaching contamination and irregular release of Volatile Organic Compounds (VOCs), which pose great challenges to maintenance and ecological environment. TiO2@UiO-67 composites were synthesized, their basic physical-chemical properties were investigated, and they were applied to SAPs to prepare an eco-friendly steel slag powder-asphalt mastic (ESAM) with multiple effects of anti-aging, heavy metal ions reduction, and VOCs emission inhibition. The rheological behavior of ESAM was explored, and the anti-aging, heavy metal inhibition, and VOCs reduction mechanism was investigated. The results showed that TiO2@UiO-67 composites with excellent properties such as large surface area, high activity and stability, powerful adsorption ability, controlled structure, and specific functional group/targeted adsorption were prepared. Some terminal hydroxyl groups were reacted with reactive radicals to prevent the growth of asphalt chain segments by reducing the aging of asphalt. The binding energy and adsorption between UiO-67 with heavy metal ions and VOCs were simulated by Materials Studio (MS), and the results showed that the recovery of Cr (VI) was up to 57.7% and the reaction proceeded spontaneously. In addition, the conjugated system, the molecular size and the spatial site resistance synergistically reduced Category 1 carcinogens by up to 85.6%, ultimately reducing the total emission concentration of VOCs by 65.4%. It provides theoretical guidance for the application of TiO2@UiO-67 composites in SAPs, and technical feasibility for anti-aging, and pollutant emission reduction strategies for SAPs.

Optimization for Asphalt Pavement Maintenance Plans at Network Level: Integrating Maintenance Funds, Pavement Performance, Road Users, and Environment

As an infrastructure project, the cost and benefits of road maintenance should be measured through social costs. In order to fully consider user interests and make road maintenance decisions more reasonable, two types of user costs that may affect road maintenance benefits have been quantified through analysis of existing literature. At the same time, environmental issues have gradually become the focus of various industries, and in optimizing road maintenance decisions, the impact of environmental issues on decision making should also be considered. This article first analyzes and quantifies the user costs that affect the effectiveness of road maintenance. Secondly, based on the concept of sustainable development of roads, the optimization of road maintenance decisions is divided into two steps: the first step is to determine the minimum maintenance budget funds, and the second step is to determine the optimal plan. The specific optimization method is to use the 0-1 mathematical programming method to establish a network-level pavement maintenance decision optimization model based on a quantitative model. The most reasonable maintenance optimization plan is determined from four aspects: maintenance funds, maintenance performance improvement value, user benefit improvement, and reducing environmental impact. Finally, a provincial road network for a case study is selected. The applicability of the new model is verified through a case study. This study can help decision makers deal with asphalt pavement maintenance arrangements at the network level with four decision objectives: maintenance funds, pavement performance, road users, and environmental impacts.

The Impact of Covid-19 on Human Factors in the Aircraft Maintenance Environment - Effects within Approved Aircraft Maintenance Organisations Operating in Malta

Since the aviation industry itself creates a risky environment, a high levelof safety must be ensured during the flight of an aircraft. Aircraft maintenance up tohigh standards is a main requirement intended to ensure the safety of flights and hascontributed to drastically reduce the number of aviation accidents. In addition, for everyaviation accident the cause must be identified to prevent re-occurrence and reduce thenumber further down the line. Statistically, most aviation accidents occurring nowadaysare due to human errors, making awareness of human factors in every area of aviation veryimportant, including the maintenance area. With the outbreak of COVID-19 pandemic in2020, peoples everyday life across the whole world was affected. The aviation industrywas heavily impacted by this pandemic with reduction in aviation operations, affectingairports, airlines, as well as approved aircraft maintenance organisations. The effect ofCOVID-19 on human factors has been both physiological and psychological. Since thephysiological and psychological aspects of human factors are known to have considerableeffects on the human error element in aviation, it can be concluded that with COVID-19the risk of committing errors in aviation has increased. This research is aimed to discoverthe element of such increased risk in aviation, particularly within the aircraft maintenanceenvironment. Research participants are people working in the aircraft maintenanceenvironment. Several independent variables identified from the literature review, known toaffect an identified dependent variable, form an initial conceptual framework. A survey hasbeen conducted through which exploratory statements have been set up to address theindependent variables, each in a context of the impact of human factors on the requiredsafety standards in aircraft maintenance. Data collected from a total of 75 respondents hasbeen checked for validity using a test question inserted in the survey. This has given a totalof 68 valid responses which have been analysed. Analysis has been conducted using SPSSSoftware. Analysis for data consistency revealed an acceptable Cronbachs Alpha value ofover 0.8. SPSS which was then used to carry out dimension reduction using ExploratoryFactor Analysis (EFA). Through the EFA technique the number of independent variableshas been reduced for further research from 22 to 6. Final analysis of the data shows thatmost of the respondents agree that in general Covid-19 has had a similar impact on humanfactors in aircraft maintenance, as it has had on the rest of the aviation industry. Based onthis research it is suggested that measures should be taken so that a phenomenon likea pandemic situation, that would affect the physiological and psychological aspects ofpeople, is addressed in the human factors element of aviation, in particular the aircraftmaintenance environment. The pilot-study conducted also sets up a basis for futureresearch in human factors in aircraft maintenance, and how they can be influenced bysituations that can pose higher risks for a person to commit errors while performing workrelated to aviation, particularly while carrying out aircraft maintenance performance andcertification activities before an aircraft can be released for further flights.

Dynamic path planning of intelligent robot in power equipment maintenance environment

Aiming at the problem that the traditional path planning algorithm does not have real-time planning performance in the dynamic and complex power equipment maintenance environment, or it is challenging to avoid dynamic obstacle obstacles due to poor real-time version, this paper proposes an artificial potential field method for real-time planning. The characteristics of dynamic obstacle in a dynamic complex environment, the dynamic obstacles are simplified as moving particles, and the repulsion function of the dynamic obstacles is added to avoid the collision. Within the influence range of dynamic obstacles in a busy complex environment, the robot avoids the dynamic obstacles through real-time local path planning under the influence of the repulsive force of the dynamic obstacle to reach the target point. At the same time, aiming at the problems of unevenness and fluctuation of the path generated by the traditional artificial potential field method, the non-uniform rational B-spline and moving average filtering algorithm are used to improve the local path planning algorithm of the conventional artificial potential field, and the generated path is smoother and smoother than the traditional algorithm.

A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools

Affected by COVID-19, the maintenance process of machine tools is significantly hindered, while unmanned maintenance becomes an emerging trend in such background. So far, three challenges, namely, the dependence on maintenance experts, the dynamic maintenance environments, and unsynchronized interactions between physical and information sides, exist as the main obstacles in its widespread applications. In order to fill this gap, a bio-inspired LIDA cognitive-based Digital Twin architecture is proposed, so as to achieve unmanned maintenance of machine tools through a self-constructed, self-evaluated, and self-optimized manner. A three phases process in the architecture, including the physical phase, virtual phase, and service phase, is further introduced to support the cognitive cycle for unmanned maintenance of machine tools. An illustrative example is depicted in the unmanned fault diagnosis on the rolling bearing of a drilling platform, which validates the feasibility and advantages of the proposed architecture. As an explorative study, it is wished that this work provides useful insights for unmanned maintenance of machine tools in a dynamic production environment.

Green buildings and maintenance planning practices in Malaysia

PurposeGreen building (GB) maintenance is increasingly accepted in the construction industry, so it can now be interpreted as an industry best practice for maintenance planning. However, the performance competency and design knowledge of the practice's building control instrument process can be affected by its evaluation and the information management of building information modelling (BIM)–based model checking (BMC). These maintenance-planning problems have not yet been investigated in instances such as the Grenfell Tower fire (14 June 2017, approximately 80 fatalities) in North Kensington, West London.Design/methodology/approachThis study proposes a theoretical framework for analysing the existing conceptualisation of BIM tools and techniques based on a critical review of GB maintenance environments. These are currently employed on GB maintenance ecosystems embedded in project teams that can affect BMC practices in the automation system process. In order to better understand how BMC is implemented in GB ecosystem projects, a quantitative case study is conducted in the Malaysian public works department (Jabatan Kerja Raya (JKR)).FindingsGB ecosystem projects were not as effective as planned due to safety awareness, design planning, inadequate track insulation, environmental (in) compatibility and inadequate building access management. Descriptive statistics and an ANOVA were applied to analyse the data. The study is reinforced by a process flow, which is transformed into a theoretical framework.Originality/valueIndustry practitioners can use the developed framework to diagnose BMC application issues and leverage the staff competency inherent in an ecosystem to plan GB maintenance environments successfully.

A Feasible Framework for Maintenance Digitalization

The entire industry is changing as a result of new developments in digital technology, and maintenance management is a crucial procedure that may take advantage of the opportunities brought about by industrial digitalization. To support digital innovation in maintenance management, this study intends to meet the cutting-edge necessity of addressing a transformation strategy in industrial contexts. Setting up a customized pathway with adequate methodologies, digitalization tools, and collaboration between the several stakeholders involved in the maintenance environment is the first step in this process. The results of a previous conference contribution, which revealed important digitalization variables in maintenance management, served as the foundation for the research approach herein suggested. We lead a thorough assessment of the literature to categorize the potential benefits and challenges in maintenance digitalization to be assessed in conjunction with the important digitalization aspects previously stated. As a starting point for maintenance management transformation, we offer a feasible framework for maintenance digitalization that businesses operating in a variety of industries can use. As industrial processes and machines have become more sophisticated and complex and as there is a growing desire for more secure, dependable, and safe systems, we see that this transition needs to be tailored to the specific application context.

Influence of Dust Particles on the Insulating Performance of SF6 Gas in a Small Gap at Atmospheric Pressure

SF6 gas is widely used in many fields because of its good insulating property and arc-extinguishing property. However, the emission of SF6 waste gas also accelerates global temperature warming. The insulating performance of the SF6 gas gap is a core property of its efficient utilization and exhaust gas treatment. Therefore, it is very important to study the influencing factors of the insulating performance of the SF6 gas gap. Due to the inevitable dust particles in the production and maintenance environment of SF6 gas equipment, this article studies the influence of dust particles on the insulating performance of SF6 gas in a small gap at atmospheric pressure. The experimental results indicate that in the laboratory environment, the dust particles are numerous and large in size, they adhere to the surface of electrodes, which results in a non-negligible local enhancement of the electric field between small-gap electrodes and reduces the insulating performance of SF6 gas; in a Class 1000 clean room environment, the dust particles are few in number and small in size, which can hardly change the uniform electric field between electrodes and have little influence on the insulating performance of SF6 gas in a small gap. Thus, the dust particles have a non-negligible influence on the insulating performance of SF6 gas in a small gap at atmospheric pressure, and the insulating performance of SF6 gas in a small gap can be changed by controlling the content of dust particles in the gas. This provides valuable guidance for the utilization of SF6 gas and the treatment of SF6 waste gas.

Fast Augmented Reality Authoring: Fast Creation of AR Step-by-Step Procedures for Maintenance Operations

Augmented Reality (AR) has shown great potential for improving human performance in Maintenance, Repair, and Overhaul (MRO) operations. Whilst most studies are currently being carried out at an academic level, the research is still in its infancy due to limitations in three main aspects: limited hardware capabilities, the robustness of object recognition, and content-related issues. This article focuses on the last point, by proposing a new geometry-based method for creating a step-by-step AR procedure for maintenance activities. The Fast Augmented Reality Authoring (FARA) method assumes that AR can recognise and track all the objects in a maintenance environment when CAD models are available, to knowledge transfer to a non-expert maintainer. The novelty here lies in the fact that FARA is a human-centric method for authoring animation-based procedures with minimal programming skills and the manual effort required. FARA has been demonstrated, as a software unit, in an AR system composed of commercially available solutions and tested with over 30 participants. The results show an average time saving of 34.7% (min 24.7%; max 55.3%) and an error reduction of 68.6% when compared to the utilisation of traditional hard-copy manuals. Comparisons are also drawn from performances of similar AR applications to illustrate the benefits of procedures created utilising FARA.