Alarm Forecasting in Natural Gas Pipelines

This thesis examines alarm forecasting methods for a natural gas production pipeline to assure the efficient transportation of high-quality natural gas. Natural gas production companies use pipelines to transport natural gas from the extraction well to a distribution point. Forecasting natural gas pipeline pressure alarms helps control room operators maintain a functioning pipeline and avoid costly down time. As gas enters the pipeline and travels to the distribution point, it is expected that the gas meets certain specifications set in place by either state law or the customer receiving the gas. If the gas meets these standards and is accepted at the distribution point, the pipeline is referred to as being in a steady-state. If the gas does not meet these standards, the production company runs the risk of being shut-in, or being unable to flow any more gas through the distribution point until the poor-quality gas is removed.Sensors are used to collect real-time gas quality information from within the pipe, and alarms are used to alert the control operators when a threshold is exceeded. If operators fail to keep the pipeline’s gas quality within an acceptable range, the company risks being shut¬¬-in or rupturing the pipeline. Predicting gas quality alarms enables operators to act earlier to avoid being shut-in and is a form of predictive maintenance. We forecast alarms by using a 10th-order autoregressive model, autoregressive model with exogenous variable, simple exponential smoothing with drift (Theta Method) and an artificial neural network with alarm thresholds. The alarm thresholds are defined by the production company and are occasionally adjusted to meet current environment conditions. The results of the alarm forecasting method show that we accurately forecast natural gas pipeline alarms up to a 30-minute time horizon. This translates into sensitivity rates that drop from around 100% at one minute to 82.7% at a 30-minute forecast horizon. This means that at 30 minutes, we correctly forecast 82.7% of the alarms. All alarm forecasting models outperform the state-or-the-art forecaster used by the production company, with the artificial neural network performing the best

Embroidered Meteorology

Weathering is a series of embroidered works that explore the symbolic and cartographic language of meteorology. Through research, mentorship and the physical work, my understanding and anxiety around weather has grown. Making art is a learning process for me: the haptic is a means for understanding. From embroidered world maps to animation to painted laundry, I conflate the intricacy of textiles with the complicated nature of the atmosphere

Equatorial scintillations in relation to the development of ionization anomaly

International audienceThe irregularities in the electron density distribution of the ionosphere over the equatorial region frequently disrupt space-based communication and navigation links by causing severe amplitude and phase scintillations of signals. Development of a specification and forecast system for scintillations is needed in view of the increased reliance on space-based communication and navigation systems, which are vulnerable to ionospheric scintillations. It has been suggested in recent years that a developed equatorial anomaly in the afternoon hours, with a steep gradient of the F-region ionization or Total Electron Content (TEC) in the region between the crest and the trough, may be taken as a precursor to scintillations on transionospheric links. Latitudinal gradient of TEC measured using Faraday Rotation technique from LEO NOAA 12/14 transmissions during the afternoon hours at Calcutta shows a highly significant association with L-band scintillations recorded on the INMARSAT link, also from Calcutta, during the equinoxes, August through October 2000, and February through April 2001. The daytime equatorial electrojet is believed to control the development of the equatorial anomaly and plays a crucial role in the subsequent development of F-region irregularities in the post-sunset hours. The diurnal maximum and integrated value (integrated from the time of onset of plasma influx to off-equatorial latitudes till local sunset) of the strength of the electrojet in the Indian longitude sector shows a significant association with post-sunset L-band scintillations recorded at Calcutta during the two equinoxes mentioned earlier. Generation of equatorial irregularities over the magnetic equator in the post-sunset hours is intimately related to the variation of the height of the F-layer around sunset. Ionosonde data from Kodaikanal, a station situated close to the magnetic equator, has been utilized to calculate the vertical drift of the F-layer over the magnetic equator for the period August through October 2000. The post-sunset F-region height rise over the magnetic equator shows a remarkable correspondence with the occurrence of scintillations at Calcutta located near the northern crest of the equatorial anomaly. Existence of a flat-topped ionization distribution over the magnetic equator around sunset has been suggested as a possible indication of occurrence of post-sunset scintillations. Width of the latitudinal distribution of ionization obtained from DMSP satellite shows some correspondence with post-sunset L-band scintillations. During the period of observation of the present study (August through October 2000, and February through April 2001), it has been observed that although the probability of occurrence of scintillations is high on days with flat-topped ion density variation over the equator, there are cases when no scintillations were observed even when a pronounced flat top variation was recorded

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability

An architecture to predict anomalies in industrial processes

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe Internet of Things (IoT) and machine learning algorithms (ML) are enabling a revolutionary change in digitization in numerous areas, benefiting Industry 4.0 in particular. Predictive maintenance using machine learning models is being used to protect assets in industry. In this paper, an architecture for predicting anomalies in industrial processes was proposed in which SMEs can be guided in implementing an IIoT architecture for predictive maintenance (PdM). This research was conducted to understand what machine learning architectures and models are generally used by industry for PdM. An overview of the concepts of the Industrial Internet of Things (IIoT), machine learning (ML), and predictive maintenance (PdM) was provided, and through a systematic literature review, it was possible to understand their applications and which technologies enable their use. The survey revealed that PdM applications are increasingly common and that there are many studies on the development of new ML techniques. The survey conducted confirmed the usefulness of the artifact and showed the need for an architecture to guide the implementation of PdM. This research can be a contribution for SMEs, allowing them to become more efficient and reduce both production and maintenance costs in order to keep up with multinational companies

Using real-time truck transportation information to predict customer rejections and refrigeration-system fuel efficiency in packaged salad distribution

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 139-141).Companies that operate cold supply chains can greatly benefit from information availability and data generation. The abundance of information now available to cold chain operators and harvested from every echelon of the supply chain, ranging from the procurement process to the sales and customer service processes, provides an opportunity for logistics organizations to monitor and improve their operations. It is increasingly imperative to transform data into meaningful information that creates a competitive advantage for early adopters. This thesis attempts to determine how to make best use of and effectively interpret the information generated by trailer mounted temperature sensors and geospatial data collection devices during refrigerated transportation of packaged salads. The study covers only the transportation segment from the manufacturer's distribution center to the customer's (grocery retailer) distribution center. This thesis uses regression analysis in an effort to create a model that effectively uses realtime transportation information to identify the elements that can create a competitive advantage for cold chain operators. The main performance measurements subject to analysis in this thesis are reefer-unit fuel consumption and rejections of salad products at the customer's drop location. Regression yields a formula that can predict more than 70% reefer fuel consumption. However, with the independent variables available in the data at our disposal, it is not possible to build a model the effectively predicts product rejections. The findings of this thesis can help operators of transportation cold chains better manage fuel consumption by isolating and improving the independent variables we identified.by Carlos Seminario and Emmanuel Marks.M.Eng.in Logistic

Development of advanced algorithms to detect, characterize and forecast solar activities

Study of the solar activity is an important part of space weather research. It is facing serious challenges because of large data volume, which requires application of state-of-the-art machine learning and computer vision techniques. This dissertation targets at two essential aspects in space weather research: automatic feature detection and forecasting of eruptive events. Feature detection includes solar filament detection and solar fibril tracing. A solar filament consists of a mass of gas suspended over the chromosphere by magnetic fields and seen as a dark, ribbon-shaped feature on the bright solar disk in Hα (Hydrogen-alpha) full-disk solar images. In this dissertation, an automatic solar filament detection and characterization method is presented. The investigation illustrates that the statistical distribution of the Laplacian filter responses of a solar disk contains a special signature which can be used to identify the best threshold value for solar filament segmentation. Experimental results show that this property holds across different solar images obtained by different solar observatories. Evaluation of the proposed method shows that the accuracy rate for filament detection is more than 95% as measured by filament number and more than 99% as measured by filament area, which indicates that only a small fraction of tiny filaments are missing from the detection results. Comparisons indicate that the proposed method outperforms a previous method. Based on the proposed filament segmentation and characterization method, a filament tracking method is put forward, which is capable of tracking filaments throughout their disk passage. With filament tracking, the variation of filaments can be easily recorded. Solar fibrils are tiny dark threads of masses in Hα images. It is generally believed that fibrils are magnetic field-aligned, primarily due to the reason that the high electrical conductivity of the solar atmosphere freezes the ionized mass in magnetic field lines and prevents them from diffusing across the lines. In this dissertation, a method that automatically segments and models fibrils from Hα images is proposed. Experimental results show that the proposed method is very successful to derive traces of most fibrils. This is critical for determining the non-potentiality of active regions. Solar flares are generated by the sudden and intense release of energy stored in solar magnetic fields, which can have a significant impact on the near earth space environment (so called space weather). In this dissertation, an automated solar flare forecasting method is presented. The proposed method utilizes logistic regression and SVM (support vector machine) to forecast the occurrences of solar flares based on photospheric magnetic features. Logistic regression is used to derive the probabilities of solar flares occurrence, which are then fed to SVM for determining whether a flare will occur. Comparisons with existing methods show that there is an improvement in the accuracy of X-class solar flare forecasting. It is also found that when sunspot-group classification is combined with photospheric magnetic parameters, the performance of flare forecasting can be further lifted

A Quantitative Research Study on Probability Risk Assessments in Critical Infrastructure and Homeland Security

This dissertation encompassed quantitative research on probabilistic risk assessment (PRA) elements in homeland security and the impact on critical infrastructure and key resources. There are 16 crucial infrastructure sectors in homeland security that represent assets, system networks, virtual and physical environments, roads and bridges, transportation, and air travel. The design included the Bayes theorem, a process used in PRAs when determining potential or probable events, causes, outcomes, and risks. The goal is to mitigate the effects of domestic terrorism and natural and man-made disasters, respond to events related to critical infrastructure that can impact the United States, and help protect and secure natural gas pipelines and electrical grid systems. This study provides data from current risk assessment trends in PRAs that can be applied and designed in elements of homeland security and the criminal justice system to help protect critical infrastructures. The dissertation will highlight the aspects of the U.S. Department of Homeland Security National Infrastructure Protection Plan (NIPP). In addition, this framework was employed to examine the criminal justice triangle, explore crime problems and emergency preparedness solutions to protect critical infrastructures, and analyze data relevant to risk assessment procedures for each critical infrastructure identified. Finally, the study addressed the drivers and gaps in research related to protecting and securing natural gas pipelines and electrical grid systems