# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 1 Introduction  
Azimuth thrusters are used for propulsion systems and dynamic positioning (DP) of offshore platforms and vessels. In a drill ship, the thrusters are used to maintain the ship position by counteracting environmental forces, such as wind, waves and current, but also for propulsion in transit from site to site. The environmental forces, the impacts with external objects like ice blocks, propeller operation, the pulsation from prime movers and accessory systems together inflict complex vibrations and stress to the system [1]. Over the long haul, the stress caused by the harsh environment may lead to fatigue, fracture and tribological issues in the system [2, 3]. This inflicts damage to gears and bearings, but also other types of issues, such as propeller failures emerge [4]. The measurement technology for the condition monitoring (CM) of the azimuth thrusters must be economically viable, maintenance-free and reliable due to the closed and inaccessible construction. Therefore, the driveline components, such as the rolling element bearings and gears, are commonly monitored with the well-established indirect methods, such as piezoelectric accelerometers or oil condition and wear debris sensors [5]. Although the vibration responses of propulsion shafts are studied regularly based on laboratory experiments and simulations [1, 6], the literature on the practical condition monitoring of azimuth thrusters is rare [2, 3]. At the same time, the CM methods forwind turbines, which also experience excessive loads and system oscillations, are studied extensively [7], and the technology could provide useful benchmarks for the thruster monitoring as well. It is still an industry standard to have a human in the loop when analyzing acceleration sensor data from the noisy marine environment. The common cloud-based approach is limited by the slow data transfer over an offshore satellite network, whereas the fully algorithmic approach enables the analysis on the edge, i.e., locally onboard the offshore vessel. On the edge, the size of the data set can be much larger, enabling the use of data-hungry methods. However, it is possible that the future generations of wireless technologies provide more efficient solutions to data transfer. Currently, a large part of CM approaches that strive for some level of automation utilize the machine learning methodology, such as the classification approach [8; 9]. The research in this field is commonly realized with idealized laboratory experiments, where high accuracies can be reached, as was inferred in [9]. There, the models are typically trained with data from a limited number of operational states, and then tested with samples from the same states. On the contrary, industrial machines may operate in a multitude of operational states rather than a few exclusively. The entire operating area may be so wide that the data used for model training include only a part of the operational states that are possible in practice. New data collected from an operating azimuth thruster, for example, may represent a new operational state influenced by various factors, such as the varying shaft speed and steering angle in the thruster, changing vessel speed and other external factors. This variation is a challenge to the models trained with incomplete information on the actual operation of the system. A model fitted accurately based on few scattered operational states may not correctly recognize new samples which are different from the training samples [10]. Furthermore, the relatively low incidence of faults in industrial machines hinders the proper training of classifiers for the practical applications [11; 12]. The condition monitoring of azimuth thrusters could be done based on anomaly detection algorithms [3; 12; 13] that require training data from the typical operation of the system in a condition without failures.