In this study, an adaptive scheme for autonomous underwater vehicle systems is developed that utilizes a model of the complex nonlinear dynamics and control of the vehicle to enable detection of sensor faults and failures. Our framework for design of fault identification and risk management, incorporates a neural network-based nonlinear observer to monitor the input and output of the control system for detection of a variety of faults in the sensors. The training occurs online and parameters of the recurrent neural network are updated by an extended Kalman filter. The fault detection and identification system was developed and integrated for a nonlinear model of a Remus-100 underwater vehicle. The results obtained from the numerical simulation shows the system's ability for prompt detection and isolation of a variety of sensor faults. Further study is needed for development of experimental validation and verification and computational efficiency of the proposed algorithm.
In this paper, the design, development, and laboratory testing of a pipe crawling robot for autonomous piping maintenance is presented. The robot consists of a modular design with four cylindrical modules for navigation which uses worm-type locomotion. Two gripping modules at a given sequence alternate gripping action simultaneously to create forward motion along with the other two modules holding the robot in place between gripping sequences. The gripping modules are designed for light-weight, optimized radial traction and to provide the maximum pull force. Then an active inspection mechanism equipped with a computer vision camera is used in the design of a conceptual nondestructive evaluation module. The bio-mimic design of the robot not only provides significant traction with pipe walls to carry NDE equipment, but it also allows conducting multi-scale mechanism tasks. Inspired by peristaltic locomotion, the proposed pipe inspection crawler can perform gripping action using radial motions to adjust to variations of pipes diameter within 4-5 inches inside pipes sloped from 0 to 180 degrees. The initial crawler’s prototype is manufactured using an additive manufacturing process. A laboratory scale test set-up is manufactured for experimentation. Testing performance of the crawler shows that the robot can accomplish horizontal and vertical motions in both upward and downward directions with adjustable gripping force. It also, demonstrated fitting and T-joint compatibility for pipe transitioning.
KEYWORDS: Composites, General packet radio service, Structural health monitoring, Sensors, Reliability, Calibration, Data modeling, Ultrasonics, Electromagnetic coupling, Data analysis
In this study, the surface response to excitation method (SuRE) is investigated using a data-driven method for diagnostics and prognostics of applied load on a plate structure. The SuRE method is an emerging approach in ultrasonic wave-based structural health monitoring (SHM). In this method, high-frequency, surface-guided waves are excited on the structure using piezoceramic elements. The waves propagate and interact with internal or surface damages on the structure. State of heath is evaluated by monitoring changes in the measured frequency transfer functions. Reliability and computational efficiency of the SuRE method has been verified for several diagnostic and structural health monitoring applications. In this paper, the effectiveness of the SuRE method for prognostics and health management (PHM) of a composite plate under applied load is studied. Two piezoelectric elements are attached on the surface of a carbon fiber reinforced polymer (CFRP) composite plate. Sweep excitation-generated (150-250 kHz), surface-guided waves and the transmitted waves were monitored at the sensory position. The reference data set comprised of characteristic transfer functions was generated. SHM data using the SuRE method was captured for eleven locations of applied load between the sensor and exciter. Four data-driven prognostic models, using Gaussian Processes Regression (GPR), were qualified by interval-averaging features extracted from the spectrums and predicted the location of load. During this study, a new approach based on SuRE method is proposed for identifying the location of applied load on a composite and the optimum parameters of the study were evaluated to enhance the performance of GPR identified the optimum parameters number of SuRE method and selected features for most accurate predictions.
KEYWORDS: Composites, Sensors, Structural health monitoring, Machine learning, Data modeling, Data processing, Data acquisition, Signal generators, Ultrasonics, Wave propagation
In this study, the surface response to excitation method (SuRE) is investigated using a data-driven method for load monitoring on a laminated composite plate structure. The SuRE method is an emerging approach in ultrasonic wavebased structural health monitoring (SHM) field. In this method, a range of high-frequency, surface-guided waves are excited on the structure using piezoceramic elements. The waves propagate on the structure and interact with internal or surface damages. Initially, a baseline data of the intact structure is created by measuring the frequency transfer function between the excitation and sensing point. The integrity of structure is evaluated by monitoring changes in the frequency spectrums. The SuRE method has effectively been used for a variety of SHM applications including the detection of loose bolts, delamination in composite structures, internal corrosion in pipelines, and load and impact monitoring. Data obtained using the SuRE method was used for identifying the location of the applied load on a laminated composite plate using Support Vector Machine (SVM). A set of two piezoelectric elements were attached on the surface of the plate. A sweep excitation (150-250 kHz) generated surface-guided waves, and the transmitted waves were monitored at the sensory positions. The reference data set was measured simultaneously from the sensors. The plate was subjected to static loads while health monitoring data was being captured using the SuRE method. The confusion matrix indicated that the model classified correctly with up to 99.8% accuracy.
Conference Committee Involvement (5)
Health Monitoring of Structural and Biological Systems XVII
13 March 2023 | Long Beach, California, United States
Health Monitoring of Structural and Biological Systems XVI
7 March 2022 | Long Beach, California, United States
Health Monitoring of Structural and Biological Systems XV
8 March 2021 | Online Only, California, United States
Health Monitoring of Structural and Biological Systems XIV
27 April 2020 | Online Only, California, United States
Health Monitoring of Structural and Biological Systems XIII
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