This paper presents a simulation model environment for the popular and low-cost remotely operated vehicle (ROV) BlueROV2 implemented in Simulink™ which has been designed and experimentally validated for benchmark control algorithms for underwater vehicles. The BlueROV2 model is based on Fossen’s equations and includes a kinematic model of the vehicle, the hydrodynamics of vehicle and water interaction, a dynamic model of the thrusters, and, lastly, the gravitational/buoyant forces. The hydrodynamic parameters and thruster model have been validated in a test facility. The benchmark model also includes the ocean current, modeled as constant velocity. The tether connecting the ROV to the top-site facility has been modeled using the lumped mass method and is implemented as a force input to the ROV model. At last, to show the usefulness of the benchmark model, a case study is presented where a BlueROV2 is deployed to inspect an offshore monopile structure. The case study uses a sliding mode controller designed for the BlueROV2. The controller fulfills the design criteria defined for the case study by following the provided trajectory with a low error. It is concluded that the simulator establishes a benchmark for future control schemes for position control and trajectory tracking under the influence of environmental disturbances.

This paper describes the development and application of a model-based scheme for detecting the early signs of coil failure in solenoid valves. Contrary to other works, the proposed method simply isolates the fault-induced change in the coil resistance from thermal effects and provides a cost-effective solution with no comprehensive equipment demand. The model-based detection method employs a simple thermal model of the solenoid and an Extended Kalman Filter (EKF) for generating coil current residuals. The EKF utilizes measurements of coil voltage, coil current, ambient, and fluid temperature. The CUmulative SUM (CUSUM) of the current residual is found to be sensitive to the early signs of coil failure. An experimental setup is utilized for evaluating the robustness of the detection method in the event of changing ambient temperature, convection, and fluid flow conditions. Results show that the method is able to detect the early signs of coil failure, and robust behavior and high detection probabilities are achieved for realistic fault sizes. The realistic fault sizes and the number of fault instances before failure are determined through extensive accelerated tests where solenoids are operated at high temperature until failure.

Marine growth challenges the structural integrity of offshore facilities due to increased hydro dynamical loads. As a consequence, marine growth cleaning on offshore structures has been performed for many years. While the industry has shifted from diver-assisted to cleaning driven by remotely operated vehicles, the process remains costly and ineffective. This paper explores the possibilities for introducing an increased level of automation for marine growth inspection and classification. Specific attention is given to sensor technologies and methods for constructing a 3D representation of the offshore structures in order to assess the thickness and composition of marine growth. While optical-based methods show positive potential further work is needed to investigate the robustness to flicking sunlight and turbidity issues experienced in areas close to the water surface. The review of classification methods reveals several promising approaches where deep learning is applied for the categorization of marine growth. The training relies on large databases of relevant images which are not currently available for marine growth on offshore structures. Further work is needed for investigating if virtual images can be used in combination with a reduced set of real images.

Oil & gas operators spend tens of million USD every year on removing marine growth, using manually operated remotely operated vehicles (ROV) with water jets. This study investigates the benefits of automating the ROV used for cleaning by demonstrating a sliding mode control (SMC) algorithm on a reconfigured BlueROV2 with an attached water jet. A nonlinear SMC was designed for the cleaning task. SMC was able to stabilize the orientation of the ROV while following a trajectory in depth. Regular SMC could not stabilize the ROV in front of the member, with the water jet activated. To accommodate for the delay, integral action was added to the SMC (IxSMC) in the surge direction, which stabilized the ROV. From the research presented in this paper, it can be concluded that automation of a marine growth removing ROV can be achieved by applying IxSMC.