10 research outputs found
Risk-Informed Artificial Intelligence for Autonomous Inspection on Subsea Pipelines
Get PDFThe research in this thesis centers on the image data interpretation capabilities of autonomous underwater systems in the offshore oil and gas industry responsible for visual inspection and monitoring of underwater pipelines and detection of hazards on pipeline surfaces. The main contribution of research is a framework that provides the solutions to overcome the vulnerabilities of artificial intelligence methods during the underwater pipeline inspection by autonomous underwater systems through applied safety engineering.
Increased autonomy in autonomous underwater systems require a greater reliance on artificial intelligence technology for executing pipeline inspection tasks. However, the artificial intelligence for pipeline inspection is limited by several data interpretation challenges. The shortcomings of artificial intelligence for autonomous systems during offshore pipeline hazard inspection can result in catastrophic environmental damage and substantial financial losses for the oil and gas industry. Imbalanced and underrepresented data can cause the artificial intelligence methods, such as machine learning, anomaly detection, and computer vision, to form biases in favor of more represented data with atendency to reproduce biases learned from data. Underrepresented data can be disregarded as noise during anomaly detection due to their inclination toward efficiency and sacrificing anomalies as tolerable collateral damage. Current methods focus primarily on data content with no regard for the context behind data, yielding conclusions primarily based on correlation and not causation, further causing the occurrence of false alarms during anomaly detection that are a significant drawback during real-time operations. Furthermore, the acute lack of annotated training image data of offshore pipelines and lack of hazard evidence in the data results in the reliance on inexplainable, unsupervised methods. Therefore, one of the main contributions of this research is using the methods for risk and hazard analysis to semi-supervise anomaly detection methods and generate synthetic images of pipeline hazards for extrapolating and annotating the training data. Risk analysis aids anomaly detection in identify the types of anomalies that are recognized as risks, by analyzing low-probability, high-consequence event detection. Furthermore, due to the acute disorganization of categorization and definition of anomalies in current research, this research proposes a redefined anomaly categorization for autonomous underwater systems operations based on hazard behavior and traditional anomaly classification. Finally, this research examines the complex and connected properties of offshore pipeline inspections and offers future directions in rethinking the artificial intelligence methods for pipeline inspection with autonomous underwater systems. The general theme of this thesis lies in risk-informed approaches to address the fundamental challenge of finding early true anomalies and avoiding false alarms when no label exists to inform us of the anomaly or its properties by giving context to anomaly detection methods to comprehend data points not by their labels but by how they relate to one another
Manually or Autonomously Operated Drones: Impact on Sensor Data towards Machine Learning
Get PDFThe growing need for autonomous systems in offshore industries has contributed to the increased use of machine learning methods. These systems promise to improve safety in operations. However, the methods as enablers of autonomy are susceptible to various failures while interpreting data and making decisions. Several studies have highlighted the lack of research on the reliability and resilience of autonomous systems powered by these standard methods. Recent research provides sets of data interpretation methods. Despite the popularity of machine learning, there is a significant drop in knowledge when these methods result in failures. These failures further support autonomous systems in making wrong decisions. For autonomous systems, resilience and safety management should be an integrated functionality for recovery from risky situations and reporting of incidents. This research proposes an overview of machine learning methods for interpreting sensor data captured by drones operated manually and autonomously. We apply Isolation Forest for anomaly detection analysis and evaluate the Decision tree, Random forest, kNN, Logistic Regression, SVM, and, Naive Bayes for classification analysis. The methods are chosen based on their adequacy and comparative research prevalence. Comparison between the two drone operation modes contributes to understanding the reliability level for autonomously collected data. This research’s results provide an evaluation of machine learning methods’ performance across sensor data.acceptedVersio
Reliable Unmanned Autonomous Systems: Conceptual Framework for Warning Identification during Remote Operations
Get PDFIn the offshore industry, unmanned autonomous systems are expected to have a permanent role in future operations. During offshore operations, the unmanned autonomous system needs definite instructions on evaluating the gathered data to make decisions and react in real-time when the situation requires it. We rely on video surveillance and sensor measurements to recognize early warning signals of a failing asset during the autonomous operation. Missing out on the warning signals can lead to a catastrophic impact on the environment and a significant financial loss. This research is helping to solve the issue of trustworthiness of the algorithms that enable autonomy by capturing the rising risks when machine learning unintentionally fails. Previous studies demonstrate that understanding machine learning algorithms, finding patterns in anomalies, and calibrating trust can promote the system’s reliability. Existing approaches focus on improving the machine learning algorithms and understanding the shortcomings in the data collection. However, recollecting the data is often an expensive and extensive task. By transferring knowledge from multiple disciplines, diverse approaches will be observed to capture the risk and calibrate the trust in autonomous systems. This research proposes a conceptual framework that captures the known risks and creates a safety net around the autonomy-enabling algorithms to improve the reliability of the autonomous operations.Reliable Unmanned Autonomous Systems: Conceptual Framework for Warning Identification during Remote OperationsacceptedVersio
A Novel Warning Identification Framework for Risk-Informed Anomaly Detection
Get PDFCyber-physical systems are taking on a permanent role in the industry, such as in oil and gas or mining. These systems are expected to perform increasingly autonomous tasks in complex settings removing human operators from remote and potentially hazardous environments. High autonomy necessitates a more extensive use of artificial intelligence methods, such as anomaly detection, to identify unusual occurrences in the monitored environment. The absence of data characterizing potentially hazardous events leads to disruptive noise displayed as false alarms, a common anomaly detection issue for hazard identification applications. Contrastingly, disregarding the false alarms can result in the opposite effect, causing loss of early indications of hazardous occurrences. Existing research introduces simulating and extrapolating less represented data to expand the information on hazards and semi-supervise the methods or by introducing thresholds and rule-based methods to balance noise and meaningful information, necessitating intensive computing resources. This research proposes a novel Warning Identification Framework that evaluates risk analysis objectives and applies them to discern between true and false warnings identified by anomaly detection. We demonstrate the results by analyzing three seismic hazard assessment methods for identifying seismic tremors and comparing the outcomes to anomalies found using the unsupervised anomaly detection method. The demonstrated approach shows great potential in enhancing the reliability and transparency of anomaly detection outcomes and, thus, supporting the operational decision-making process of a cyber-physical system.A Novel Warning Identification Framework for Risk-Informed Anomaly DetectionpublishedVersio
Context-based and image-based subsea pipeline degradation monitoring
No full textAbstract This research examines the factors contributing to the exterior material degradation of subsea oil and gas pipelines monitored with autonomous underwater systems (AUS). The AUS have a role of gathering image data that is further analyzed with artificial intelligence data analysis methods. Corrosion and potential ruptures on pipeline surfaces are complex processes involving several competing elements, such as the geographical properties, composition of soil, atmosphere, and marine life, whose eflt in substantial environmental damage and financial loss. Despite extensive research, corrosion monitoring and prediction remain a persistent challenge in the industry. There is a lack of knowledge map that can enable image ausing an AUS to recognize ongoing degradation processes and potentially prevent substantial damage. The main contribution of this research is the knowledge map for increased context and risk awareness to improve the reliability of image-based monitoring and inspection by autonomous underwater systems in detecting hazards and early signs of material degradation on subsea pipeline surfaces
Enhancing Autonomous Systems’ Awareness: Conceptual Categorization of Anomalies by Temporal Change During Real-Time Operations
Get PDFThe Unmanned Autonomous Systems (UAS) are anticipated to have a permanent role in offshore operations, enhancing personnel, environmental, and asset safety. These systems can alert onshore operators of hazardous occurrences in the environment, in the form of anomalies in data, during real-time inspections, enabling early prevention of hazardous events. Time series data, collected by sensors that detect environmental phenomena, enables the observation of anomalous data as dynamic instances of the dataset. Recent research characterizes anomalies in terms of their patterns of occurrence in data. However, there is insufficient research on anomalous temporal change patterns. In this paper, we examine anomalies in relation to one another and propose a conceptual categorization system for anomalies based on their temporal changes. We demonstrate the categorization through a case study of potentially hazardous occurrences observed by UAS during underwater pipeline inspection. Analyzing anomalies based on their behavior can provide further information about current environmental changes and enable the early discovery of unwanted events, simultaneously minimizing false alarms that overwhelm the systems with low-significance information in real-time.publishedVersio
Image-based and risk-informed detection of Subsea Pipeline damage
Get PDFAs one of the most important assets in the transportation of oil and gas products, subsea pipelines are susceptible to various environmental hazards, such as mechanical damage and corrosion, that can compromise their structural integrity and cause catastrophic environmental and financial damage. Autonomous underwater systems (AUS) are expected to assist offshore operations personnel and contribute to subsea pipeline inspection, maintenance, and damage detection tasks. Despite the promise of increased safety, AUS technology needs to mature, especially for image-based inspections with computer vision methods that analyze incoming images and detect potential pipeline damage through anomaly detection. Recent research addresses some of the most significant computer vision challenges for subsea environments, including visibility, color, and shape reconstruction. However, despite the high quality of subsea images, the lack of training data for reliable image analysis and the difficulty of incorporating risk-based knowledge into existing approaches continue to be significant obstacles. In this paper, we analyze industry-provided images of subsea pipelines and propose a methodology to address the challenges faced by popular computer vision methods. We focus on the difficulty posed by a lack of training data and the opportunities of creating synthetic data using risk analysis insights. We gather information on subsea pipeline anomalies, evaluate the general computer vision approaches, and generate synthetic data to compensate for the challenges that result from lacking training data, and evidence of pipeline damage in data, thereby increasing the likelihood of a more reliable AUS subsea pipeline inspection for damage detection.Image-based and risk-informed detection of Subsea Pipeline damagepublishedVersio
Security System Layanan Internet Banking PT BANK MANDIRI (Persero) Tbk.
Get PDFHow many banking services that bank provide to their clients for customer satisfaction. One of the services who begant to demand nowadays is Internet Banking. With this service, customer can perform many kind of banking transaction easier, as simple as browsing they only need Internet Access. It is easier for customer especially for those who are very busy to maintain their finance. In this service, Internet Access must be safe from people whom irresponsible. Therefore, this type of service using a variety of security methods to take cares the privasi and customer data. Security System usually involves Secure Socet Layer (SSL, cryptography Public Key and Digital Signature). However, do all this security service is 100% safe?In this case, the author will analyze the security data and methods that used by Bank Mandiri in Mandiri Internet Banking, so it will be conclude based on theory and analysis, the quality of security is applied to this service
Zeka - Friendy Chatterbot
Get PDFThe idea of chatbots firstly appeared in the 1960s. But only after more than half a century passed the world became ready for their implementation into the real life, this being a result of the rapid progress in natural language processing, artificial intelligence, and the global presence of text messaging applications. Today, specialized chatbots exist in different domains, thus helping organizations handle large amount of inquiries. Idea of this project was to develop one friendly chatbot with whom you can talk about politics, movies, weather, sport, emotions and similar everyday things. Friendly chatbot named Zeka, is a web-based chatbot developed with the help of Chatterbot library. Chatbot relies on different natural processing and machine learning algorithms altered by its developers to increase its performance
