On-board Measurements Research Articles

On-board cable attitude measurement and controller for outdoor aerial transportation

AbstractDeploying quadcopters for aerial transportation can be cost effective in impromptu material handling applications. However, such applications are limited mainly due to the requirement of onboard localization sensors and associated computation. The current work presents a human-controlled modality to successfully execute spontaneous outdoor flight of a quadcopter with a cable-suspended payload. Stable and smooth flights are achieved through an onboard integration of a custom-built sensor system and a controller to minimize payload oscillations. The feasibility of the proposed modality is demonstrated by conducting outdoor experiments and a case study in an unstructured environment.

An application of metadata-based image retrieval system for facility management

For facility management, photography is an efficient and accurate method of recording the physical state of infrastructure. However, without an effective organizational scheme, the difficulty of retrieving relevant photos from historical databases can become overly burdensome for highly complex or long-lived assets. To make strategic decisions, it is crucial to retrieve the right information from a plurality of sources in a timely manner. The main objective of this paper is to present a method for organizing and retrieving photos from massive facility management photo databases using photo-metadata: photographed location, camera perspective, and image semantic content information. Indoor localization experiments were performed using Bluetooth technology to infer the location information. Perspective is inferred from the device’s on-board inertial measurement unit (IMU). Image semantic content is inferred using a Convolutional Neural Network (CNN)-based deep learning algorithm. Fusing these three features, seven query options were provided for the user when retrieving images. Leveraging Building Information Modeling (BIM) as a process and Geographic Information Systems (GIS) as a framework, this paper also envisions a federated information management by connecting 2D and 3D facility assets with our real-world map which can be smoothly bridged with our image retrieval system. The realization of the integrated application with BIM and GIS is significantly beneficial for the facility management domain by advancing the understanding of projects in a broader view with a federated data platform. In this research, the framework is illustrated with 21 institutional buildings within the University of Texas at Austin’s main campus, and the authors conclude that the proposed metadata-based image retrieval system can ultimately enhance the better-informed decision-making process through rapid information retrieval.

A GC-SSIM-CRDS system: Coupling a gas chromatograph with a Cavity Ring-Down Spectrometer for onboard Twofold analysis of molecular and isotopic compositions of natural gases during ocean-going research expeditions

Carbon dioxide (CO2) and methane (CH4) are two climate-sensitive components of gases migrating within sediments and emitted into the water column on continental margins. They are involved in several key biogeochemical processes entering into the global carbon cycle. In order to perform onboard measurements of both the molecular and stable carbon isotope ratios (δ13C) of CH4 and CO2 of natural gases during oceanic cruises, we have developed a novel approach coupling gas chromatography (GC) with cavity ring-down spectroscopy (CRDS). The coupled devices are connected to a small sample isotope module (SSIM) to form a system called GC-SSIM-CRDS. Small volumes of natural gas samples (<1 mL) are injected into the GC using a headspace autosampler or a gas-tight syringe to separate the chemical components using a Shincarbon ST packed column and for molecular quantification by thermal conductivity detection (TCD). Subsequently, CO2 from the sample is trapped in a 7 mL loop at 32 °C before being transferred to the CRDS analyzer for sequential determination of the stable carbon isotope ratios of CH4 and CO2 in 24 min. The loop is an open column (without stationary phase). This approach does not require the use of adsorbents or cooling for the trapping step. Optimization of the separation step prior to analysis was focused on the influence of two key separation factors 1) the flow of the carrier gas and 2) the temperature of the oven. Our analytical system and the measurement protocol were validated on samples collected from gas seeps in the Sea of Marmara (Turkey). Our results show that the GC-SSIM-CRDS system provides a reliable determination of the molecular identification of CH4 and CO2 in complex natural gases, followed by the stable carbon isotope ratios of methane and carbon dioxide.

Experimental Study of Large-Temperature-Range and Long-Period Monitoring for LNG Marine Auxiliary Based on Fiber Bragg Grating Temperature Measurement

Temperature is a key variable to evaluate the energy consumption and thermodynamic performance of traditional marine auxiliary machinery, chillers and piping systems. In particular, for the cryogenic storage tanks and fuel gas supply systems of LNG ships, explosion-proof and low-temperature-resistance properties bring new challenges to the onboard temperature measurement and monitoring. In order to promote the development of high-performance and safer monitoring systems for LNG ships, this paper adopted fiber Bragg grating (FBG) technology to ensure the measurement safety and accuracy of temperature sensors, and performs a series of experiments in a large temperature range on the chiller, pipeline, and cryogenic storage tank of an LNG ship and their long-term reliabilities. Firstly, the principle and composition of the designed FBG temperature sensors are introduced in detail, and the measurement accuracy and range of different metal-coated optical fibers were tested in a large temperature range and compared against the traditional thermistors. Then, the effects of different operating conditions of the LNG marine chiller system and cryogenic storage tank on the temperature measurements were investigated. In addition, the drift degrees of the optical fibers and industrial thermistors were analyzed to figure out their reliabilities for long-term temperature measurements. The results showed that for the long-period (16 months) monitoring of LNG ships in a large temperature range (105–315 K) under different shipping conditions, the optical temperature measurement based on FBG technology has sufficient accuracy and dynamic sensitivity with a higher safety than the traditional thermoelectric measurement. Besides, the ship vibration, ambient humidity, and great temperature changes have little impact on its measurement reliability and drifts. This research can provide references and technical supports to the performance testing systems of LNG ships and other relevant vessels with stricter safety standards.

Analysis of Ship Noise Characteristics Generated According to Sailing Conditions of the G/T 1000-ton Stern Fishing Trawler

In this study, we conducted onboard noise measurement experiments under the conditions of anchoring, sailing, casting, and hauling to determine whether noise generated in the G/T 1000-ton fishing trawler triggers zone-specific effects upon vessel operation. It was shown that most accommodation areas of the trawler comfortably met the IMO acceptance criteria regardless of the sailing condition, but most of the stern area, where the fishing actually occurs, exceeded the permitted limit of 75 dB (A). Furthermore, the statistical analysis revealed a significant difference (p &lt; 0.05) only in the bow and the stern, which are both open areas. In the case of the former, improvements were deemed possible due to the influence of the fluid emission noise from the seawater piping in the bow, and the acceptance criteria were also appropriate. However, in the case of the latter, a significant difference was seen in hauling conditions, and on-site analysis confirmed frictional noise from hydraulic oil in the trawl winch and between the chains and the metal hull, leading to the conclusion that various improvements are required, such as the mandatory wearing of safety equipment by workers and stricter legal standards for permitted noise levels.

The emission factor adjustments of the passenger cars in multi-story car parks under drive modes.

The drive mode is an important factor that affects the adjustment requirements for emission factors of the conventional simulation methods in multi-story car parks. In order to propose comprehensive emission factor adjustments for passenger cars based on the drive modes, the present study is aimed to investigate the effects of different drive modes on emission factors for multi-story car parks. Thus, to achieve this aim, the tailpipe emissions based on the on-board measurement and international vehicle emission (IVE) model are obtained. The results indicate that the drive modes significantly affect the emissions. Accordingly, the change in drive mode from minimum to maximum leads to an increase in the vehicle-specific power (VSP) by 106%. Furthermore, the results of emission factors show the discrepancy between on-board measurement and IVE model with the maximum and minimum adjustment factors by 3.28 and 1.28 for carbon monoxide (CO) and carbon dioxide (CO2), respectively.

Validation of vessel seakeeping model tuning algorithm based on measurements at model scale

Wave-induced vessel motion prediction plays a critical role in ensuring safe marine operations. The operational limiting criteria can usually be calculated by applying presumed linearized vessel motion transfer functions based on the specified vessel loading condition, which may deviate from the real vessel condition when the operation is executed. Reducing the uncertainties of the onboard vessel loading condition can therefore improve the accuracy of vessel motion prediction and hence improve the safety and cost-efficiency for marine operations. However, parameters related to the onboard vessel loading condition can be difficult to measure directly, such as the center of gravity and moments of inertia. In addition, the hydrodynamic viscous damping terms are always subject to significant uncertainties and sometimes become critical for accurate vessel motion predictions. A very promising algorithm for the tuning of these important uncertain vessel parameters based on the unscented Kalman filter (UKF) that uses onboard vessel motion measurements and synchronous wave information was proposed and demonstrated previously by application to synthetic data. The present paper validates the UKF-based vessel seakeeping model tuning algorithm by considering measurements from model-scale seakeeping tests. Validation analyses demonstrate rational tuning results. The observed random errors and bias in relation to the measurement functions due to the applied simplification and linearization in the seakeeping simulations can lead to biased tuning. The importance of designing the state space and the measurement space is demonstrated by case studies. Due to the nonlinear relationship between the uncertain vessel parameters and the vessel motions, the tuning is shown to be sensitive to the mean state vector and selection of the surrounding sigma points.

Research on Actual Road Emission Prediction Model of Heavy-Duty Diesel Vehicles Based on OBD Remote Method and Artificial Neural Network

In order to establish a prediction model of PM and NOx emission factors for heavy-duty diesel vehicles under actual road conditions based on OBD remote monitoring and big data, this paper carried out actual road tests on two China V heavy-duty diesel vehicles to obtain transient OBD and emission data by a Portable Emission Measurement System (PEMS) and self-developed On-board Remote Emission Measurement System (OREMS). According to the degree of influence of different parameters in the engine OBD on PM and NOx emissions, the principal component analysis method is used to extract the principal component parameters used to predict the model input, and the construction of a "Heavy-duty Diesel Vehicle Predictive Model based on Remote Monitoring Data and Neural Network Technology". Finally, the predictive model is trained and verified by PEMS test data. The prediction model provides new means and methods for the future development of large-scale heavy-duty diesel vehicle NOx and PM emission predictions under actual road operation.

A probabilistic approach to online classification of bicycle crashes

When a bicycle crash takes place, it is paramount for an emergency center to recognize the physical state of the cyclist as early as possible. However, an injured bicyclist may be incapable of making a phone call to the emergency center. In this study, we propose an online approach to classify bicycle crashes based on signals from an onboard inertial measurement unit (IMU), which can be used as a trigger function for an automatic emergency system. For this purpose, we define several bicycle crash features according to the kinematic properties of bicycle accidents. The input signals (variables) influencing the individual crash features are determined by the ANOVA method (analysis of variance). With the determined input signals, probabilistic models for each crash feature are trained on the base of logit models and 20,000 km naturalistic driving data including 20 real crashes. In addition, further crash and corner case data has been collected for the model training. A decision tree describing all probabilistic crash features is used to classify different kinematic events and crash scenarios. A series of driving tests with a crash-dummy and a crash-car are performed to verify the proposed crash classification approach, showing a sensitivity of 96.8%, a specificity of 99.6% and an accuracy of 99.5% of the trained model.

Influencing factors of carbonyl compounds and other VOCs in commercial airliner cabins: On-board investigation of 56 flights.

Volatile organic compounds (VOCs) as a non-negligible aircraft cabin air quality (CAQ) factor influence the health and comfort of passengers and crew members. On-board measurements of carbonyls (short-chain (C1 -C6 )) and other volatile organic compounds (VOCs, long-chain (C6 -C16 )) with a total of 350samples were conducted in 56 commercial airliner cabins covering 8 aircraft models in this study. The mean concentration for each individual carbonyl compound was between 0.3 and 8.3μg/m3 (except for acrolein & acetone, average=20.7μg/m3 ) similar to the mean concentrations of other highly detected VOCs (long-chain (C6 -C16 ), 97% of which ranged in 0-10μg/m3 ) in aircraft cabins. Formaldehyde concentrations in flights were significantly lower than in residential buildings, where construction materials are known formaldehyde sources. Acetone is a VOC emitted by humans, and its concentration in flights was similar to that in other high-occupant density transportation vehicles. The variation of VOC concentrations in different flight phases of long-haul flights was the same as that of CO2 concentration except for the meal phase, which indicates the importance of cabin ventilation in diluting the gaseous contaminants, while the sustained and slow growth of the VOC concentrations during the cruising phase in short-haul flights indicated that the ventilation could not adequately dilute the emission of VOCs. For the different categories of VOCs, the mean concentration during the cruising phase of benzene series, aldehydes, alkanes, other VOCs (detection rate>50%), and carbonyls in long-haul flights was 44.2µg/m3 , 17.9µg/m3 , 18.6µg/m3 , 31.5µg/m3 , and 20.4µg/m3 lower than those in short-haul flights, respectively. Carbonyls and d-limonene showed a significant correlation with meal service (p<0.05). Unlike the newly decorated rooms or new vehicles, the inner materials were not the major emission sources in aircraft cabins. Practical Implications. The on-board measurements of 56flights enrich the VOC database of cabin environment, especially for carbonyls. The literature review of carbonyls in the past 20years contributes to the understanding the current status of cabin air quality (CAQ). The analysis of VOC concentration variation for different flight phases, flight duration, and aircraft age lays a foundation for exploring effective control methods, including ventilation and purification for cabin VOC pollution. The enriched VOC data is helpful to explore the key VOCs of aircraft cabin environment and to evaluate the acute/chronic health exposure risk of pollutants for passengers and crew members.

General Purpose Low-Level Reinforcement Learning Control for Multi-Axis Rotor Aerial Vehicles.

This paper proposes a multipurpose reinforcement learning based low-level multirotor unmanned aerial vehicles control structure constructed using neural networks with model-free training. Other low-level reinforcement learning controllers developed in studies have only been applicable to a model-specific and physical-parameter-specific multirotor, and time-consuming training is required when switching to a different vehicle. We use a 6-degree-of-freedom dynamic model combining acceleration-based control from the policy neural network to overcome these problems. The UAV automatically learns the maneuver by an end-to-end neural network from fusion states to acceleration command. The state estimation is performed using the data from on-board sensors and motion capture. The motion capture system provides spatial position information and a multisensory fusion framework fuses the measurement from the onboard inertia measurement units for compensating the time delay and low update frequency of the capture system. Without requiring expert demonstration, the trained control policy implemented using an improved algorithm can be applied to various multirotors with the output directly mapped to actuators. The algorithm’s ability to control multirotors in the hovering and the tracking task is evaluated. Through simulation and actual experiments, we demonstrate the flight control with a quadrotor and hexrotor by using the trained policy. With the same policy, we verify that we can stabilize the quadrotor and hexrotor in the air under random initial states.

Lost in Space? Relativistic Interstellar Navigation using an Astrometric Star Catalog

The exploration of interstellar space will require autonomous navigation systems that do not rely on tracking from the Earth. Here I develop a method to determine the 3D position and 3D velocity of a spacecraft in deep space using a star catalog. As a spacecraft moves away from the Sun, the observed positions and velocities of the stars will change relative to those in a Earth-based catalog due to parallax and aberration. By measuring just the angular distances between pairs of stars, and comparing these to the catalog, we can infer the coordinates of the spacecraft via an iterative forward-modeling process. I perform simulations with existing star catalogs to demonstrate the method and to compute its performance. Using the 20 nearest stars and a modest angular distance measurement accuracy of 1″, the position and velocity of a spacecraft light years from the Sun moving at relativistic speeds can be determined to within 3 and 2 respectively. These accuracies improve linearly with the measurement accuracy, e.g., with angles measured to 1 the navigation accuracy is 1000 times better. Performance can also be improved using more stars, or by including onboard measurements of the stars’ radial velocities, as these too are affected by the spacecraft’s position and motion.

Assessment of air quality in car cabin in and around Paris from on-board measurements and comparison with 2007 data

In this paper, we report results on commuter's exposure to particulate matter (PM). Particle number concentrations (PNC) were recorded simultaneously inside and outside the cabin of a vehicle during 45 field trips in and around Paris (France) between 2016 and 2017. For a fixed ventilation setting, the impacts of external parameters were investigated to assess commuter's exposure for a wide range of traffic conditions, including the presence of tunnels and ring road, various times during the day, the specific driving routes, different road types and vehicle speeds. Overall, our results indicate that roadway tunnels represent the worse situation for commuters resulting in the highest concentrations outside and inside the car cabin, followed by the ring road around Paris. Comparisons with a previous study undertaken in Paris and its inner and outer suburbs in 2007 reveal drastic reduction of Particle Number Concentrations (PNC) in the car cabin over this period of 10 years. More stringent emission regulations, changes in the vehicle fleet and development of low emission zones explain these promising results. Similar trends found in the recent literature for different countries corroborate the present findings.

Characteristics of real-world gaseous exhaust emissions from cars in heterogeneous traffic conditions

In this study, researchers have explored real-world driving conditions and developed emission factors for 58 passenger cars using on-board emission measurement technique while driving on five different routes in Delhi. The measured average emission factors of CO, HC, and NO were 3.99, 0.34, and 0.54 g/km for diesel vehicles, 7.26, 0.17, and 0.62 for petrol vehicles respectively. Road, traffic, vehicle type, and driving characteristics affect the quantity of emissions released. However, speed and acceleration significantly impact emission rates increasing with the increase in speed and acceleration. Also, emissions were minimal at 40–60 kmph and −0.5–0.5 m/s2. The estimated city-wide CO, HC, and NO emissions were 60.8, 4.8, and 9.72tonnes/day. These results demonstrate the importance of monitoring the real-world exhaust emissions given the substantial difference between test cycle measurements used for compliance testing of new vehicles.

MyBas driving cycle for Kuala Terengganu city

Driving cycles are series of data points that represent vehicle speed versus time sequenced profile developed for specific road, route, city or certain location. It is widely utilized in the application of vehicle manufacturers, environmentalists and traffic engineers. Since the vehicles are one of the higher air pollution sources, driving cycle is needed to evaluate the fuel consumption and exhaust emissions. The main objectives in this study are to develop and characterize the driving cycle for myBAS in Kuala Terengganu city using established k-means clustering method and to analyse the fuel consumption and emissions using advanced vehicle simulator (ADVISOR). Operation of myBAS offers 7 trunk routes and one feeder route. The research covered on two operation routes of myBAS which is Kuala Terengganu city-feeder and from Kuala Terengganu to Jeti Merang where the speed-time data is collected using on-board measurement method. In general, driving cycle is made up of a few micro-trips, defined as the trip made between two idling periods. These micro-trips cluster by using the k-means clustering method and matrix laboratory software (MATLAB) is used in developing myBAS driving cycle. Typically, developing the driving cycle based on the real-world in resulting improved the fuel economy and emissions of myBAS.

Tuning of vessel parameters including sea state dependent roll damping

Online tuning of vessel models based on onboard measurement data can reduce the uncertainties of vessel motion prediction, and therefore potentially increase the safety and cost efficiency for marine operations. Among the uncertain vessel parameters, the roll damping coefficient is very important and highly nonlinear. In reality, roll damping depends on the sea state and vessel condition. This paper proposes two different procedures for tuning the sea state dependent roll damping coefficient together with other uncertain vessel parameters, i.e., 1-step tuning and 2-step tuning procedures. In addition, a roll damping prediction model based on Gaussian process regression is also proposed to predict the roll damping for future sea states based on historical data. The tuning procedure together with the proposed prediction model form an iterative closed loop of continuously improving the knowledge about the roll damping online, also estimating the model uncertainty based on prior knowledge, sampling uncertainties, and the applied kernel. Case studies are presented to demonstrate the procedures.

Track Irregularity Monitoring on High-Speed Railway Viaducts: A Novel Algorithm with Unknown Input Condensation

The inverse dynamic analysis of vehicle-bridge (VB) systems provides a superior scheme for track irregularity identification using the onboard measurement data of vehicle dynamic responses. However, available algorithms face a great challenge when applied to high-speed railway bridges because of the increase in the number of unknown inputs, resulting in a degradation of identification accuracy. Here, we propose a condensation procedure to reduce the unknown inputs of the VB system, and then propose a novel algorithm for track irregularity identification using the augmented Kalman filter. A numerical simulation of a real continuous three-span simply supported railway bridge was conducted to validate the estimation accuracy of the proposed algorithm. Numerical results illustrate that the proposed algorithm can accurately estimate the track irregularities by using the vertical acceleration responses of the vehicle body only, thanks to the unknown input condensation. A comparative study demonstrates that the proposed algorithm outperforms the conventional track irregularity identification methods in terms of estimation accuracy. To evaluate the actual performance of the proposed algorithm, a set of onboard measurement data recorded by a typical high-speed train when running on the Hangzhou–Changsha high-speed railway in China was employed to identify the track irregularities of the railway line, and the results suggest that the proposed algorithm is highly efficient and low in cost. This study may help develop highly efficient real-time track irregularity monitoring in the years ahead.

An analysis of the ballast voyage of an LNG Carrier. The significance of the loading and discharging cycle

• Introduction of the Loading and Discharging Cycle in the analysis of laden and ballast voyages of LNG carriers. • Conjugate heat transfer analysis of the insulation- cargo system. • Projection of the Loading and Discharging Cycle on to the boiling curve. • Explanation of the pressure fluctuations observed during the laden voyage. • Ballast voyage being controlled by thermodynamic equilibrium. • Laden voyage being controlled by the boiling curve. A conjugate heat transfer analysis combined with a non-equilibrium thermodynamic model for LNG ageing during ship transportation has been developed based on experimental data. The laden and the ballast voyages are being considered as a continuous and inseparable process interconnected through the loading and discharging cycle with the thermal dynamics of the insulation of the cargo containment system. The loading and discharging cycle is instrumental not only in the understanding of the ballast voyage behavior but it is also the key for the explanation of the pressure fluctuations during the laden voyage as soon as the insulation temperatures during the cycle are projected on to the boiling curve. Thus, the pressure fluctuations may be attributed to the complex and turbulent vapor release mechanism associated with the various boiling modes imposed by the temperature non-uniformity over the insulation surface of the tanks. The secondary barrier temperature measurements of the insulation show that significant temperature variations exist across the tank walls, suggesting, that different heat fluxes cross the primary insulation barrier and consequently different boiling modes (regimes) simultaneously exist on the tank wall surface which is in contact with the liquid cargo. Moreover, the on-board pressure and temperature measurements in the tank reveal that the gas and the liquid phases during the ballast voyage are almost in local thermodynamic equilibrium, since the tank walls are interacting primarily with the gas phase and the multi-boiling regimes envisaged by the boiling curve are no longer the defining characteristic of the physical process. This is an important finding, resulting in a drastic reduction of the required computational effort, compared with the laden voyage, enabling the use of simpler equations of state and even Raoult’s ideal mixture law.

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach.

The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

CONSIDERATIONS ON ALGORITHMS OF SELECTION AND COMPONENTS FORMING OF ON-BOARD INFORMATION AND MEASURING COMPLEXES AND REGISTRATION SYSTEMS. PECULIARITIES OF THEIR APPLICATION

On-board information and measuring complexes and registration systems are intended to control the parameters of both systems and components of the prototype and the sample of automotive and armored vehicles in general to obtain qualitative and quantitative characteristics to assess their compliance with specifications of technical development requirements.&#x0D; At present stage, there is often a need to improve methods for optimizing the development of on-board systems for collecting and processing information to ensure conducting of testing works, which take into account particulars of the electronic equipment development and are based on a unified approach to the optimal use of resources of collection and information processing systems.&#x0D; Analysis of the problem of rational use of resources of standard means of measurement, registration and indication in the system of onboard measurements and further processing of the results in automated systems for collecting and processing information for their use in testing mass produced (modernized) aircraft was previously considered by Soviet, Ukrainian and foreign scientists. But they do not give a complete picture of the algorithm, completeness and quality of tests using standard measuring instruments installed on board aircraft and do not answer a number of questions related to the optimization of the computational process.&#x0D; In particular, the potentials of standard means of measurement, registration and indication as part of the on- board measurement system for testing mass produced (modernized) aircraft and installed components, units and systems under test are not fully explored. The issue of building optimal data collection programs that implement the inquiry of measuring channels with restrictions on the frequency of channel inquiry and information processing speed is insufficiently studied.&#x0D; The novelty of the research consists in the developing of an algorithm for capabilities analysis and procedure for incorporating standard measuring, registration and indication instruments in the on-board measuring system within a single measuring and information field for testing mass produced (modernized) samples of weapons.&#x0D; Approbation and implementation of the abovementioned algorithms will allow test teams to minimize time spent on the development (selection) of the onboard measurement system to ensure research and testing of both modernized aircraft, vehicles and armored vehicles and newly created with advanced information systems.