Debris Monitoring Research Articles

Sapienza Space debris Observatory Network (SSON): A high coverage infrastructure for space debris monitoring

Optical observations represent a passive method for space debris tracking and monitoring. Although being constrained to limited time intervals, e.g. when the target is in sunlight and the observatory is in darkness, a debris observatory network distributed over multiple locations can improve the observational interval and favor the data integration for more consistent and significant results. The Sapienza Space Systems and Space Surveillance Laboratory (S5Lab) At Sapienza University of Rome has established Sapienza Space debris Observatory Network (SSON), an international network of optical observatories addressed at debris surveillance. The network is composed of six observatories owned and controlled by Sapienza, plus five observational sites controlled by collaborators (mainly University of Michigan and the University of Bern). The network operations have been carried out during several observations campaign, including the Tiangong-1 space station re-entry campaign performed in support of the Inter-Agency space Debris Committee (IADC). The heterogeneous capabilities of the different observatories represent an advantage for acquiring a wider set of debris monitoring data with different techniques. A strong synergy between our institution and the Italian specialized industry has also revealed to be very productive in the context of the network establishment.The present paper will describe the SSON infrastructure and the opportunities, improvements and future perspectives for research institutions or space industry of this wide observatories network will be discussed.

Major sources and monthly variations in the release of land-derived marine debris from the Greater Jakarta area, Indonesia

As marine debris becomes increasingly prevalent and induces cascading impacts on marine ecosystems, monitoring of land-derived debris is key for identifying effective mitigation strategies. Indonesia plays a pivotal role in reducing land-derived debris into the oceans considering its extensive coastline, large population and high waste production. We present the first marine debris monitoring data from Indonesia’s capital, the Greater Jakarta area, by characterizing major sources and monthly variations of debris release at nine river outlets into Jakarta Bay between June 2015-June 2016. Our data show plastics as the most common debris entering Jakarta Bay representing 59% (abundance) or 37% (weight) of the total collected debris. Styrofoam was dominating among plastic debris, highlighting the urgency of reducing plastic and styrofoam uses. Higher debris releases during the rainy season (December-February) highlight the need to intensify river clean-up activities. We estimated an average daily debris release of 97,098 ± 28,932 items or 23 ± 7.10 tons into Jakarta Bay with considerably lower inputs from the capital compared to its neighboring municipalities. Within the plastics category, field monitoring data yield a daily plastic debris release of 8.32 ± 2.44 tons from the Greater Jakarta area, which is 8–16 times less than global-scale model estimates.

Monitoring of meso and microplastic debris in Playa Grande beach (Tenerife, Canary Islands, Spain) during a moon cycle

The occurrence and composition of meso (5–25 mm) and microplastics (1–5 mm) in Playa Grande beach (Tenerife, Canary Islands, Spain) was monitored during a complete moon cycle on the different moon phases between 17th June and 16thJuly 2019. A total of 10 points were sampled each day finding an average content of mesoplastics of 18 g/m2 (0.36 g/L) and of microplastics of 13 g/m2 (1277 items/m2 or 1.6 g/L). Polypropylene and polyethylene accounted for 19% and 76% of the total, respectively. Tar was also found in the 1–5 mm fraction (2% of the total). Among the particles found, 83% were fragments, 11% pellets, 4% fibres and 2% films. The obtained results revealed that microplastic presence could not be related in this case with the tides but with the orientation and strength/speed of the wind.

Underwater and airborne monitoring of marine ecosystems and debris

Advancing the sustainable use and conservation of marine environments is urgent. Tons of debris including macro- and microplastics generated on land are entering the oceans, marine resources are decreasing, and many species are facing extinction. Though satellite remote sensing techniques are commonly used for global environmental monitoring, it is still difficult to detect small objects such as floating debris on the vast ocean surface, and the ecosystems deep in the oceans where light does not reach are unobservable. An autonomous monitoring system consisting of optimally controlled robots is required for acquiring spatiotemporally rich marine data. However, object detection in marine environments, which is a necessary function the robots should have for underwater and aerial monitoring, has not been extensively studied. Here, we argue that state-of-the-art deep-learning-based object detection works well for monitoring underwater ecosystems and marine debris. We found that by using the deep-learning object-detection algorithm YOLO v3, underwater sea life and debris floating on the ocean surface can be detected with mean average precision of 69.6% and 77.2%, respectively. We anticipate our results to be a starting point for developing tools for enabling safe and precise acquisition of marine data to elucidate and utilize this last frontier.

Review on online inductive wear debris monitoring technology

Online oil monitoring technique is now an important development means to monitor wear condition and diagnose wear fault real time. Inductive wear debris monitoring is the research hotspot in the current online oil monitoring for its unique characteristics. Based on the current domestic and international online inductive wear debris monitoring, the relevant comments to the online inductive wear debris monitoring technology were given. Then, the positive and inverse problems of numerical analysis of inductive wear debris monitoring are analysed. Based on the above, the difficulties and shortages of online inductive wear debris monitoring are studied and the development trend and research focus of online inductive wear debris monitoring are discussion to provide for the oil online technology research.

Global Review of Beach Debris Monitoring and Future Recommendations.

Marine debris is distributed worldwide and constitutes an increasing threat to our environment. The exponential increase in the level of plastic debris raises numerous concerns and has led to an intensification in plastic monitoring and research. However, global spatial and temporal patterns and knowledge gaps in debris distribution, both on land and at sea, are relatively poorly understood, mainly due to a lack of comprehensive data sets. Here, we critically review the quality of the available information about beach plastic debris worldwide to highlight where the most urgent actions are required and to promote the standardization of reporting metrics and sampling methods among researchers. From a total of 174 studies evaluated, 27.0% reported marine debris densities in metrics that were not comparable. Some studies failed to report basic parameters, such as the date of the sampling (9.8%) or the size of the collected debris (19.5%). Our findings show that current research regarding beach debris requires significant improvement and standardization and would benefit from the adoption of a common reporting framework to promote consensus within the scientific community.

BEACH DEBRIS ON LABUANGE BEACH, BARRU DISTRICT, SOUTH SULAWESI PROVINCE, INDONESIA

To support the efforts of the Indonesian government in managing marine debris that has a broad potential negative impact in various aspects, beach debris monitoring activities are urgently required in various locations. This research was carried out from June to August 2018 in the west coast region, on Labuange beach, Barru Regency, South Sulawesi Province, Indonesia, which was exposed by the Indonesian Through-Flow. The study aimed to determine the amount, weight and composition of marine debris on Labuange beach. Marine debris was sampled from a permanent line transect with an area of 150 m2 (three replications). Samples were then cleaned, sorted, counted, and weighed. The average amount of marine debris was 5.98 + 2.13 items/m2 per month, with a total weight was 138.69 ± 91.32 g/m2 per month. Plastic and rubber category dominated every sampling period in the location. The abundance of marine debris, both regarding quantity and weight, fluctuated in high amount during the sampling period, which indicated the magnitude of the role of oceanographic factors, including the direction of waves, and lack of community awareness on marine debris impacts and management. The great amount and weight of marine debris and the type of waste, which was dominated by plastic and rubber is a potential threat to humans, marine biota, and the local or global environment.

An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network

Debris flow disasters pose a serious threat to public safety in many areas all over the world, and it may cause severe consequences, including losses, injuries, and fatalities. With the emergence of deep learning and increased computation powers, nowadays, machine learning methods are being broadly acknowledged as a feasible solution to tackle the massive data generated from geo-informatics and sensing platforms to distill adequate information in the context of disaster monitoring. Aiming at detection of debris flow occurrences in a mountainous area of Sakurajima, Japan, this study demonstrates an efficient in-situ monitoring system which employs state-of-the-art machine learning techniques to exploit continuous monitoring data collected by a wireless accelerometer sensor network. Concretely, a two-stage data analysis process had been adopted, which consists of anomaly detection and debris flow event identification. The system had been validated with real data and generated favorable detection precision. Compared to other debris flow monitoring system, the proposed solution renders a batch of substantive merits, such as low-cost, high accuracy, and fewer maintenance efforts. Moreover, the presented data investigation scheme can be readily extended to deal with multi-modal data for more accurate debris monitoring, and we expect to expend addition sensory measurements shortly.

Online wear characterisation of rolling element bearing using wear particle morphological features

Rolling element bearings are commonly used in rotatory machines and their operation conditions need to be monitored to prevent catastrophic failures. This research aims at developing an effective online monitoring method to analyse wear severity and wear mechanisms of bearings. For this purpose, morphological features of wear debris from bearings, including quantity, colour, size and shape, are extracted from videos of moving particles carried in lubrication oil. Moving particles are detected and tracked based on the Gaussian background mixture model and the blob detection algorithm, and the target particles are separated from the image background by background subtraction. Then particle shape and dimensional features both in two- and three-dimensions are extracted from multiple images captured from different views. At the same time, the numbers of moving particles are determined based on the tracking results. The developed techniques for online particle feature extraction are applied to a rolling element bearing test rig. The particle quantity is used to indicate the trend of wear state evolution, while the colour, size and shape features of wear debris are used to classify particles into different types for wear mechanism analysis. The relationship between wear mechanisms and the trend of wear states in the time domain is established. Based on the gathered information and wear analyses, a comprehensive characterisation is obtained for online monitoring the wear state evolution as well as evaluating the wear severity and wear modes. The proposed method is able to provide wear mechanism information for root cause analysis and thus enhance the capabilities of existing online wear debris monitoring techniques.

Multi-satellite Universat-SOCRAT mission for monitoring technogenic and natural hazards

The natural and man-made space environment generates serious risks for the implementation of space missions, both automatic and human. The main natural and technogenic risk factors that limit or pose a threat to the implementation of space-based automatic and manned space missions in the near-Earth space are cosmic radiation and space debris. In the upper layers of the atmosphere, natural transient electromagnetic phenomena associated with significant energy release are also risk factors for suborbital flights. It is planned to create a system of spacecraft in the proposed Universat-SOCRAT project that makes it possible, in a mode close to real-time, to determine the radiation conditions in a significant part of the area of trapped radiation, up to the orbits of global navigation satellite systems or the geostationary orbit. It is also planned to create a space segment of monitoring space debris and electromagnetic transients in the upper atmosphere. Monitoring of space debris will allow all-weather and global tracking of near-Earth objects and, thereby, increase the efficiency of analyzing data and making necessary decisions. In some cases it will improve the accuracy of determining the coordinates of objects for their subsequent cataloging. Successful realization of the project will make it possible to create a space system for monitoring and preventing space hazards for both ongoing and planned space missions for the first time in the world.

Marine debris visual identification assessment

Estimates of marine debris are often based on beach surveys. Few studies have documented the veracity of these observations and the factors that may affect accuracy. Our laboratory-scale experiment identified potential sources of error associated with visual identification of marine debris (1–2 cm long) during shoreline surveys of sand beaches. Characteristics of the survey site (beach characteristics), observer (personal characteristics), and debris (color and size) may be important factors to consider when analyzing data from shoreline surveys. The results of this study show that the ability of individuals to accurately identify plastic fragments depends on the plastic and sand color, and density of shell fragments. Most suggestively, the high accuracy of blue plastic counts (95%) and the under-counting of white (50%) and clear plastic counts (55%) confirmed the hypothesis that a significant amount of clear and white plastic fragments may be missed during shoreline surveys. These results highlight the need for further research and possible modifications of visual shoreline survey methodologies in order to optimize this cost-effective method of marine debris monitoring.

Мониторинг техногенного засорения космического пространства. Проблемы и решения

Мониторинг техногенного засорения космического пространства. Проблемы и решения

Method of constructing a space-time grid of variable size images and its application in image processing of landfills

The paper proposes a method of slicing space images into areas of varying sizes to automate the image processing, beginning from the receipt of raw data (download pictures) up to mapping of the underlying surface indices. Indices calculation are based on one image (the surface and the vegetation indices temperature, indices of objects detection on the ground and etc.) and their time series (conducting temporal processing). The downloading of the sources of satellite images algorithm from the sites archives of geodata from the initial data download (the observation area, stage, time period, etc.) is also represented. The procedure of cutting images into segments of varying sizes is described. It allows to use the ?contents? of image both in general and for each pixel of the earth's surface to have a whole time series of the spectral brightness coefficients. Examples of time series of geo-referenced source images and the individual indices of the underlying surface are automatically generated by the algorithm method, for specific problems of space debris monitoring (vegetation indices reaction, the degree of soil degradation, detection of indices).

Debris Recognition Methods in the Lubrication System with Electrostatic Sensors

The oil-line electrostatic sensor (OLS) is a developing debris monitoring sensor. Previous work has shown that electrostatic charge signals can indicate the debris by calculating the Root Mean Square (RMS) value or the correlation-based indicator, but the precision of these methods is not high. This paper further developed the more accurate methods to obtain detailed debris information. Firstly, to interpret the monitoring principle of OLS and provide the guidance for developing the debris recognition methods, this paper analyzed the possible charge sources in the lubrication system and obtained the characteristics of the OLS by establishing its mathematical model. Further, a new OLS test rig was designed and verified the correctness of the sensor’s characteristics and its mathematical model. Based on the characteristics of the sensor, two new debris recognition methods were proposed. Finally, the effects of the new debris recognition methods were verified by the practical industrial gearbox bench test. Results showed that, compared to the traditional methods, the new methods could recognize the debris effectively and provide more detailed information of the debris.

On Increasing the Accuracy of Star Trackers to Subsecond Levels

Modern star trackers are based on photodetector arrays such as CCD or CMOS arrays. The accuracy of commercially available devices is ~1–3 arcseconds. However, the development of the space industry calls for higher orientation accuracies, which are needed in laser space communications, monitoring of near-Earth space and space debris, high-precision global mapping, and remote sensing of the Earth. The problems associated with enhancing the accuracy of modern star trackers are discussed.

Magnetic-Capacitive Wear Debris Sensor Plug for Condition Monitoring of Hydraulic Systems

This paper presents a new and efficient sensor for monitoring the presence of wear debris received by the storage tank in hydraulic systems. The proposed sensor plug employs a magnetic approach to attract the ferrous debris in the storage tank. A capacitive sensing scheme is employed in the same sensor plug to sense the presence of the debris accumulated on the plug. This combination of the magnetic and capacitive features in a single sensor for debris monitoring is new and it helps to realize a very sensitive sensor. The proposed sensor is designed in an ingenious way that the permanent magnet that is used to form the magnetic field in the vicinity of the sensor, to attract the debris towards the sensor, itself serves as one of the electrodes of the capacitive sensor. This approach improves the sensitivity and keeps the number of components in the sensor minimum. A prototype of the proposed sensor has been developed and tested. In the prototype, the change in capacitance of the sensor is measured using a high-resolution capacitance-to-digital converter. The sensor was tested after integrating it in a hydraulic test bed which has a controlled debris injection facility to the hydraulic circuit. The output of the sensor was found to be very sensitive to the typical ferrous debris that was introduced in the system. The proposed low-cost sensor will find applications in debris monitoring of underwater and land-based hydraulic systems.

Marine Debris Trends: 30 Years of Change on Ventura County and Channel Island Beaches

The persistence of plastics in marine ecosystems and the physical hazards marine debris pose to wildlife have become issues of global concern. The Santa Barbara Channel is home to a number of important marine and coastal ecosystems, including the 5 islands of Channel Islands National Park, and has a variety of factors that influence marine debris accumulation. We examined the spatial variation of marine debris density and composition across the Santa Barbara Channel by quantifying marine debris on beaches of Santa Rosa and Santa Cruz Islands and the Ventura County mainland. Debris from surveyed beaches was cataloged, weighed, and measured to compare differences in island and mainland marine debris abundance, density, and composition. Derelict fishing gear accounted for a higher proportion of marine debris on island beaches compared to mainland beaches, and marine debris items on island beaches were significantly heavier compared to debris items on mainland beaches. The majority of debris on mainland beaches comprised smaller plastic fragments and single-use plastic items, and debris accumulation rates varied by season and location. Microplastics (plastic fibers and particles 25 mm) that were collected during marine debris cleanups. We compared our data from 2015 and 2016 with historical surveys performed from 1989 to 1994 to examine the temporal variation of marine debris on Santa Rosa Island. We found that there has been a significant increase in the amount of derelict fishing gear found on Santa Rosa Island over the past 27 years, which mirrors expansion of the California spiny lobster (Panulirus interruptus) fishery. This study highlights the importance of marine debris monitoring, as local changes in policy, fisheries, and consumer culture are reflected in accumulations of marine debris found on the California mainland and the uninhabited Channel Islands. Monitoring marine debris can provide insight into anthropogenic impacts and is a useful mechanism in monitoring the health of coastal and marine ecosystems.

Inductive debris sensor using one energizing coil with multiple sensing coils for sensitivity improvement and high throughput

Abstract Inductive oil debris monitoring has been widely used in the prognosis of machinery faults. This work proposes a new structure of inductive debris sensor that provides sensitive debris-sensing capability. This structure, which includes multiple sensing coils inside an energizing coil, considerably differs from traditional structures. Theoretical analysis shows that the proposed structure can significantly improve sensitivity. The sensor with two semicircular sensing coils is preliminarily designed and tested. The performance of the sensor is evaluated using a simple and efficient test platform. Experimental results show that the designed sensor can identify 120 μm (D) ferrous particles and 210 μm (D) non-ferrous particles in a pipe with an inner diameter of 34 mm.

An online de-noising method for oil ultrasonic wear debris signal: fuzzy morphology component analysis

PurposeIn real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection.Design/methodology/approachFor completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function.FindingsAccording to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor.Research limitations/implicationsThe rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further.Practical implicationsThis paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.Originality valueThis paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.

Review on Condition Monitoring of Bearings using vibration analysis techniques.

A Bearing is one of the important components in the Rotary machines and has been widely used in various industries in many of the applications such as shaft mountings, to reduce friction as well as facilitate relative motion between the two components etc. It is therefore very essential to determine the early faults conditions from bearings. There are various methods to detect faults in the bearings, such as vibration monitoring, wear debris monitoring, temperature monitoring, soap techniques, non destructive test etc. Vibration signal analysis may be one of the commonly used techniques for checking the condition and finding faults in bearings. Vibration analysis has been used as a predictive maintenance procedure in the machine maintenance. By adopting appropriate signal processing techniques, changes in vibration signals due to faults can be detected to aid in maintaining the bearings health condition. By detecting and analyzing the machine vibration, it is possible to determine and predict the machine failure. Early fault detection of the bearings is possible by analyzing the vibration signal using different techniques. This paper give a relative of various techniques used for finding the fault in the bearings based on vibration analysis method.