To replace current legacy inspection/maintenance methods with autonomous real-time health status tracking , the paper proposes a smart robotic system with integrated remaining useful life (RUL) prediction tailored for complex components, structures and systems (CSSs). Capabilities like artificial intelligence (AI)/machine learning (ML) utilizing sensing data along with other monitoring data assist in maintenance optimization. The designed system is based on the state-of-the-art reinforcement learning (RL) and deep learning (DL) framework, which consists of an input, modeling, and decision layer. To achieve better prediction accuracy with higher autonomy, a novel active robot-enabled inspection/maintenance system is deployed in the input layer to collect whole-field infrastructure sensing data and inspect critical CSSs. The deep RL approach is integrated with failure diagnostic and prognostic algorithms to train a risk-informed AI-based agent for controlling the robots. With the data collected from the input layer, the modeling layer first conducts data fusion and predicts RUL of components using an efficient Bayesian convolutional neural network (BCNN) algorithm. In the decision layer, a resilience-driven probabilistic decision-making framework will be developed to control the robot for automatically detecting local damage, e.g. defects, degradation, and recommend mitigation/recovery actions for the health management of infrastructure under uncertainty. The combined layers comprise a AI-risk-driven sensing system (AIRSS) which was tested on an Aero-Propulsion System turbofan engine.
There is an increasing need for both governments and businesses to discover latent anomalous activities in unstructured publicly-available data, produced by professional agencies and the general public. Over the past two decades, consumers have begun to use smart devices to both take in and generate a large volume of open-source text-based data, providing the opportunity for latent anomaly analysis. However, real-time data acquisition, and the processing and interpretation of various types of unstructured data, remains a great challenge. Recent efforts have focused on artificial intelligence / machine learning (AI/ML) solutions to accelerate the labor-intensive linear collection, exploitation, and dissemination analysis cycle and enhance it with a data-driven rapid integration and correlation process of open-source data. This paper describes an Activity Based Intelligence framework for anomaly detection of open-source big data using AI/ML to perform semantic analysis. The proposed Anomaly Detection using Semantic Analysis Knowledge (ADUSAK) framework includes four layers: input layer, knowledge layer, reasoning layer, and graphical user interface (GUI)/output layer. The corresponding main technologies include: Information Extraction, Knowledge Graph (KG) construction, Semantic Reasoning, and Pattern Discovery. Finally, ADUSAK was verified by performing Emerging Events Detection, Fake News Detection, and Suspicious Network Analysis. The generalized ADUSAK framework can be easily extended to a wide range of applications by adjusting the data collection, modeling construction, and event alerting.
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