Mechanisms for space applications

This paper presents a new method that provides the means to detect sunspots on full-disk solar images recorded by the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite. The method is a totally automated detection process that achieves a sunspot recognition rate of 97.6%. The number of sunspots detected by this method strongly agrees with the NOAA catalogue. The sunspot areas calculated by this method have a 99% correlation with SOHO over the same period, and thus help to calculate the filling factor for wavelength (W.L.) 607nm.

Context. In 2015, the International Astronomical Union (IAU) passed Resolution B3, which defined a set of nominal conversion constants for stellar and planetary astronomy. Resolution B3 defined a new value of the nominal solar radius (R⊙N = 695 700 km km) that is different from the canonical value used until now (695 990 km). The nominal solar radius is consistent with helioseismic estimates. Recent results obtained from ground-based instruments, balloon flights, or space-based instruments highlight solar radius values that are significantly different. These results are related to the direct measurements of the photospheric solar radius, which are mainly based on the inflection point position methods. The discrepancy between the seismic radius and the photospheric solar radius can be explained by the difference between the height at disk center and the inflection point of the intensity profile on the solar limb. At 535.7 nm (photosphere), there may be a difference of ∼330 km between the two definitions of the solar radius. Aims. The main objective of this work is to present new results of the solar radius in the near-ultraviolet, the visible, and the near-infrared from PICARD space-based and ground-based observations. Simulations show the strong influence of atmosphere effects (refraction and turbulence) on ground-based solar radius determinations and highlight the interest of space-based solar radius determinations, particularly during planet transits (Venus or Mercury), in order to obtain more realistic and accurate measurements. Methods. Solar radius observations during the 2012 Venus transit have been made with the SOlar Diameter Imager and Surface Mapper (SODISM) telescope on board the PICARD spacecraft. We used the transit of Venus as an absolute calibration to determine the solar radius accurately at several wavelengths. Our results are based on the determination of the inflection point position of the solar limb-darkening function (the most common solar radius definition). A realistic uncertainty budget is provided for each solar radius obtained with the PICARD space-based telescope during the 2012 Venus transit. The uncertainty budget considers several sources of error (detection of the centers of Venus and Sun in PICARD images, positions of Sun and Venus from ephemeris (planetary theory), PICARD on-board timing, PICARD spacecraft position, and optical distortion correction from PICARD images). Results. We obtain new values of the solar radius from the PICARD mission at several wavelengths and in different solar atmosphere regions. The PICARD spacecraft with its SODISM telescope was used to measure the radius of the Sun during the Venus transit in 2012. At 535.7 nm, the solar radius is equal to 696 134 ± 261 km (combined standard uncertainty based (ξ) on the uncertainty budget). At 607.1 nm, the solar radius is equal to 696 156 ± 145 km (ξ), and the standard deviation of the solar radius mean value is ±22 km. At 782.2 nm, the solar radius is equal to 696 192 ± 247 km (ξ). The PICARD space-based results as well as PICARD ground-based results show that the solar radius wavelength dependence in the visible and the near-infrared is extremely weak. The differences in inflection point position of the solar radius at 607.1 nm, 782.2 nm, and 1025.0 nm from a reference at 535.7 nm are less than 60 km for the different PICARD measurements.

A failure mode and effect analysis (FMEA) helps to identify potential failure modes which can occur in any industry. These failures can lead to major quality defects in the product. In this study, we used a process FMEA approach in a maize flake producing company to enhance the quality and efficiency of the process. A field study approach was used to understand the process, potential failure modes present, and its causes in the process. We developed an FMEA model to evaluate the impact of these failure modes on the process. According to the effects of the potential failure mode on the process and product, a severity ranking was assigned to the respective potential failure mode. Using this severity ranking, detectability in the company and occurrences of the failure modes, a risk priority number (RPN) was assigned to these failure modes. By prioritizing the failure modes, some corrective actions were suggested to the company. These actions reduced the occurrences of the failure modes leading to a reduction in the RPN associated with the potential failure modes. These RPN reductions led to enhance the quality of product and overall increment in productivity and efficiency of the process.KeywordsFood industryFailure mode and effect analysis (FMEA)Risk priority number (RPN)Potential failure modeQuality enhancement

Upgrading seismic performance of underground frame structures based on potential failure modes

Purpose- Quality activities should include all product life processes, starting from the determination of customers' demands and prejudices to the service activities provided to customers. By integrating quality-oriented practices, conducting FMEA, and employing Pareto analysis, the research aims to enhance the system's reliability, efficiency, and overall performance. This, in turn, can lead to improved customer satisfaction, reduced costs, increased productivity, and a more sustainable and successful operation in the packaging department. Methodology- In order to keep up with the rapidly developing technology, to be open to innovations with developing technologies, to be in an advantageous position against competitors and ultimately to satisfy customers and fulfil their satisfaction; it is necessary to meet the demands and needs of customers without error and at low cost. Failure Modes and Effect Analysis is applied to provide the sustainability of the production system without the failures. This study focuses on failure analysis, defining failure modes, and conducting error prevention studies within the context of businesses. The aim is to achieve zero failure and zero error by implementing effective prevention techniques. By identifying the root causes of failures and errors, businesses can develop strategies and procedures to prevent them from occurring in the first place. This proactive approach helps ensure a higher level of quality, reliability, and customer satisfaction. By emphasizing the importance of error and failure prevention, businesses can strive for continuous improvement, minimize disruptions in the production system, reduce costs associated with rework or product recalls, and maintain a strong competitive position. Implementing zero failure-error and zero error-failure prevention techniques is crucial for sustaining a reliable and efficient production system. Findings- This research shows that quality is a science that people meet in reality, not simply a theoretical concept. All organizations in the universe should take their quality management systems one step further with their approach to quality processes. In this context, the concept of zero failure-error in production or service lines has become important. By applying FMEA and considering sustainability aspects, the research aims to enhance the manufacturing process, minimize risks and failures, and contribute to sustainable practices in the food industry. Conclusion- The mentioned sample study on Failure Modes and Effects Analysis (FMEA) in food packaging within the production department of a food sector enterprise in the Marmara region highlights the importance of preventing possible failure modes and their impact on the sustainability of production. One key aspect emphasized in the study is the potential reduction of food and packaging waste by minimizing packaging or substituting oil-based materials with renewable resources. By utilizing environmentally friendly food packaging, various advantages can be achieved, including a decrease in food loss and waste, as well as a reduction in the negative environmental impact associated with managing packaging materials and waste. To accomplish this, it is crucial to address food demands throughout the entire food supply chain, with particular attention to transportation and consumption stages. In order to optimize the sustainability of food and packaging systems as a whole, it is necessary to consider the product and its packaging as an integrated system. By applying FMEA and considering the potential failure modes in food packaging, the study aims to identify areas for improvement and implement measures that enhance sustainability throughout the production process. Keywords: Failure prevention, failure mode and effect analysis, quality, sustainable production. JEL Codes: M10, L15, O14

Interpretation of chromatographic data recovered from space missions: decoding of complex chromatograms by Fourier analysis

Failure modes and effects analysis (FMEA) is a popular reliability tool in petroleum engineering. In FMEA, potential failure modes or corrective actions are evaluated, each assigned a Risk Priority Number (RPN) score, and prioritized for decision making. FMEA is also known as Failure Modes, Effects, and Criticality Analysis (FMECA), while focuses on failure modes prioritization. Despite of the popularity of FMEA and FMECA, it is not clear, how potential failure modes and corrective actions could be represented systematically, for effective decision making. In this paper, two new representations (i.e., a tree representation and a vector representation), for potential failure modes and corrective actions, are proposed. The tree representation for a potential failure mode allows its root cause(s), effect(s) and corrective action(s), together with their severity, occurrence and detection rating(s), to be represented as a three-layer tree model. The tree representation for a corrective action with similar contents is outlined too. The RPN model, together with its score, is represented as a node of the tree model. These tree models can also be represented as their associated equivalence layered-vector representations. In this paper, the usefulness of the proposed approaches is illustrated with benchmark FMEA worksheets pertaining to petroleum engineering.

The completeness and accuracy of the front pages of electronic medical records (EMRs) are crucial for evaluating hospital performance and for health insurance payments to inpatients. However, the quality of the first page of EMRs in China's medical system is not satisfactory, which can be partly attributed to deficiencies in the EMR system. Failure mode and effects analysis (FMEA) is a proactive risk management tool that can be used to investigate the potential failure modes in an EMR system and analyze the possible consequences. The purpose of this study was to preemptively identify the potential failures of the EMR system in China and their causes and effects in order to prevent such failures from recurring. Further, we aimed to implement corresponding improvements to minimize system failure modes. From January 1, 2020, to May 31, 2022, 10 experts, including clinicians, engineers, administrators, and medical record coders, in Zhejiang People's Hospital conducted FMEA to improve the quality of the front page of the EMR. The completeness and accuracy of the front page and the risk priority numbers were compared before and after the implementation of specific improvement measures. We identified 2 main processes and 6 subprocesses for improving the EMR system. We found that there were 13 potential failure modes, including data messaging errors, data completion errors, incomplete quality control, and coding errors. A questionnaire survey administered to random physicians and coders showed 7 major causes for these failure modes. Therefore, we established quality control rules for medical records and embedded them in the system. We also integrated the medical insurance system and the front page of the EMR on the same interface and established a set of intelligent front pages in the EMR management system. Further, we revamped the quality management systems such as communicating with physicians regularly and conducting special training seminars. The overall accuracy and integrity rate of the front page (P<.001) of the EMR increased significantly after implementation of the improvement measures, while the risk priority number decreased. In this study, we were able to identify the potential failure modes in the front page of the EMR system by using the FMEA method and implement corresponding improvement measures in order to minimize recurring errors in the health care services in China.

Knowledge of potential failure modes during design is critical for prevention of failures. Currently industries use procedures such as Failure Modes and Effects Analysis (FMEA), Fault Tree analysis, or Failure Modes, Effects and Criticality analysis (FMECA), as well as knowledge and experience, to determine potential failure modes. When new products are being developed there is often a lack of sufficient knowledge of potential failure mode and/or a lack of sufficient experience to identify all failure modes. This gives rise to a situation in which engineers are unable to extract maximum benefits from the above procedures. In this work we report on a new failure identification scheme and integrate it with a function-based failure identification methodology, which would act as a storehouse of information and experience, providing useful information about the potential failure modes for the design under consideration, as well as enhancing the usefulness of procedures like FMEA. As an example, the method is applied to 41 products and the benefits are illustrated.

The solar wind, a stream of charged particles originating from the Sun, poses significant risks to technology and astronauts. It is driven by large structures on the solar surface like coronal holes and active regions, which can be identified in extreme ultra-violet (EUV) solar images several days before they become geoeffective. In this work, we propose to use a distributional regression algorithm to forecast the solar wind speed at the Lagrange 1 point from solar images. Instead of predicting a single value, this method models the entire conditional distribution as a function of input features. It allows computing the uncertainty of predictions and specifying the probability of the solar wind speed exceeding certain thresholds, which is especially useful for extreme event predictions like coronal mass ejections and high-speed solar wind streams. We employ a convolutional neural network to encode solar images from multiple wavelength channels into unstructured low-dimensional representations. Using a semi-structured distributional regression approach, we couple the deep learning encoder with structured physical input parameters, such as past solar wind properties and solar cycle information. Thereby, we incorporate physical knowledge into the model and enhance explainability. We predict the solar wind speed distributions with a one-hour cadence four days in advance. We train and evaluate our method using cross-validation on 15 years of data and compare it to current state-of-the-art models. We find that it provides an accurate forecast and especially models the heavy-tailed solar wind speed distribution well. We further show the advantages over standard regression approaches and how to use the predicted conditional quantiles to improve extreme event predictions, highlighting the potential for operational space weather forecasts.

The Bacharach Hi Flow® Sampler (BHFS) has been widely used to monitor methane leaks from industrial sources, however results have been challenged due to possible instrument performance issues. This study focused on improving the understanding of the BHFS performance by investigating its characteristics and potential failure modes. BHFS operation was split into three modes: catalytic oxidation (CO), thermal conductivity (TC) and a transition region. Good linear performance was observed in CO and TC modes (R2 > 0.992), however, the calibration factor changed between experiments highlighting the importance of regular calibration. Measurements in the middle region were dominated by noise with poor linearity. Instrument failure due to high non-methane hydrocarbons occurred sometimes; a hypothesis to explain this has been established. We found the BHFS to be a suitable instrument for measuring methane emissions if operated correctly and with knowledge of its limitations. Some key operational guidelines are provided in the conclusions.

Background: Neonatal intensive care units are prone to a variety of errors due to their special conditions. Failure mode and effects analysis (FMEA) is a method for risk assessment and management, which assesses the safety of patient care processes through its system approach. Objectives: The present study aimed to identify and assess common medical errors at Amirkola Children’s hospital NICU in 2016. Methods: This was a cross-sectional study conducted from September 2015 to February 2016 in the NICU of Amirkola Children’s Hospital in the city of Babol to identify and assess the medical errors and their effects qualitatively and quantitatively using FMEA through direct observations of the NICU processes, brainstorming, and focus group discussions (FGD). The FMEA standard worksheet was used for data collection. The collected data were analyzed using Excel 2010. Results: In this study, 4 key processes were selected through studying the care methods and brainstorming including drug administration, infection control, medical equipment use, and laboratory tests; 27 activities and 50 potential failure modes, as well as their impacts were detected and recorded in the final worksheet of FMEA. According to the calculated PRNs, 27 potential failure modes with PRN > 65 were determined as high-risk failures. The highest and lowest PRNs were, respectively, related to improper and incomplete washing and disinfecting the hands (PRN = 127) and illegibility of the lab requests for laboratory tests (PRN = 32). Conclusions: Based on the findings of this study, 57 potential failure modes in 4 key processes of the studied NICU were determined, among which 27 potential errors and failures with high risks were recognized. Therefore, it can be suggested that the senior managers and administrators should create multidisciplinary teams for patient safety at the organizational and unit levels

Proposal of a risk analysis model to diminish negative impact on patient care by preanalytical errors in blood gas analysis (BGA). Here we designed a Failure Mode and Effects Analysis (FMEA) risk assessment template for BGA, based on literature references and expertise of an international team of laboratory and clinical health care professionals. The FMEA identifies pre-analytical process steps, errors that may occur whilst performing BGA (potential failure mode), possible consequences (potential failure effect) and preventive/corrective actions (current controls). Probability of failure occurrence (OCC), severity of failure (SEV) and probability of failure detection (DET) are scored per potential failure mode. OCC and DET depend on test setting and patient population e.g., they differ in primary community health centres as compared to secondary community hospitals and third line university or specialized hospitals. OCC and DET also differ between stand-alone and networked instruments, manual and automated patient identification, and whether results are automatically transmitted to the patient's electronic health record. The risk priority number (RPN=SEV×OCC×DET) can be applied to determine the sequence in which risks are addressed. RPN can be recalculated after implementing changes to decrease OCC and/or increase DET. Key performance indicators are also proposed to evaluate changes. This FMEA model will help health care professionals manage and minimize the risk of preanalytical errors in BGA.

In constructing hard rock mine deep shafts, the failure caused by high stress is a significant problem. A construction section extending from the -930 m to -1271 m level of a 1527 m deep shaft was selected as a case study. Q, RMR, and GSI rock mass quality classifications of the surrounding rock were obtained to determine the shaft’s maximum unsupported sinking cycle height in different construction sections. The potential failure mode, failure zone shape, and failure depth of the surrounding rock shaft under high stress were then analyzed theoretically, empirically, and by numerical simulation, sequential stress control and delayed support technology was proposed according to the results. The temporary and permanent support timing parameters were calculated and verified by the empirical chart, numerical simulation, convergence-confinement theory, and theoretical formula. Results show that the surrounding rock in all the construction sections from -930 m to -1271 m belongs to the mild or severely pressure squeezed strata. The potential failure mode is stress-controlled, and the failure zone of the surrounding rock is “ear” shaped. Temporary support strengthens the rock mass, improves the shear capacity of surrounding rock, and prevents the broken rock from falling but does not release stress. Permanent support, conversely, cannot bear the stress of surrounding rock. The stress and elastic strain energy in rock mass should be released to the greatest extent possible before installing permanent support; it is not advisable to install it too early. The time and cycle height of delayed permanent shaft support were comprehensively determined to be four days and 16 m, respectively. The numerical simulation and safety factor proof show that the sequential stress control process effectively minimizes stress in surrounding rock and ensures long-term shaft stability.

We describe the scientific motivations, the mission concept and the instrumentation of SPACE, a class-M mission proposed for concept study at the first call of the ESA Cosmic-Vision 2015–2025 planning cycle. SPACE aims to produce the largest three-dimensional evolutionary map of the Universe over the past 10 billion years by taking near-IR spectra and measuring redshifts for more than half a billion galaxies at 0 < z < 2 down to AB~23 over 3π sr of the sky. In addition, SPACE will also target a smaller sky field, performing a deep spectroscopic survey of millions of galaxies to AB~26 and at 2 < z < 10 +. These goals are unreachable with ground-based observations due to the ≈500 times higher sky background (see e.g. Aldering, LBNL report number LBNL-51157, 2001). To achieve the main science objectives, SPACE will use a 1.5 m diameter Ritchey-Chretien telescope equipped with a set of arrays of Digital Micro-mirror Devices covering a total field of view of 0.4 deg2, and will perform large-multiplexing multi-object spectroscopy (e.g. ≈6000 targets per pointing) at a spectral resolution of R~400 as well as diffraction-limited imaging with continuous coverage from 0.8 to 1.8 μm. Owing to the depth, redshift range, volume coverage and quality of its spectra, SPACE will reveal with unique sensitivity most of the fundamental cosmological signatures, including the power spectrum of density fluctuations and its turnover. SPACE will also place high accuracy constraints on the dark energy equation of state parameter and its evolution by measuring the baryonic acoustic oscillations imprinted when matter and radiation decoupled, the distance-luminosity relation of cosmological supernovae, the evolution of the cosmic expansion rate, the growth rate of cosmic large-scale structure, and high-z galaxy clusters. The datasets from the SPACE mission will represent a long lasting legacy for the whole astronomical community whose data will be mined for many years to come.