# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
## 1 Introduction  
A key problem in many engineering applications is the control of vibrations, because if the damping is too low, they lead to noise and loss of performance. Moreover, if the dynamic load which causes vibrations exceeds the fatigue limit, the safety operation of the structure is undermined [1]. Vibration damping and control systems are commonly found in industrial machines, vehicles, buildings, turbomachinery blades, and so forth. In particular, turbomachinery is extensively deployed for electric power generation, nautical and aeronautical propulsion, and wind turbines for renewable energy extraction; however, the pursuit of the most successful methods for vibration control in turbine blades is still under extensive investigation. In this context, passive vibration damping systems, such as friction dampers and shrouded blades, are still the most popular [2, 3]. Nevertheless, several resonance conditions may be involved throughout the engine's operations,and the possibility of adapting the damping effectiveness to each critical condition is ensured by active damping systems. Several efforts are still being made to implement such systems [4; 5]. In this regard, the advent of smart materials, such as piezoelectrics, has provided designers with new solutions. Several studies have focused on the implementation of active piezoelectric damping and control in beams and blades, both in fixed and rotating frameworks [6; 7; 8; 9; 10; 11; 12; 13]. For example, in Refs. [5; 14], it was demonstrated that an active damping system based on PZT actuator arrays can always operate at its maximum effectiveness if the voltage electrically is optimized according to the excited mode. However, the proposed active controls require vibration detection systems to be effectively implemented, and both the accuracy of the measurement device exploited to detect the excited eigenmodes and the corresponding vibration amplitude play a key role. In this regard, the development of online vibration monitoring equipments in rotating structures is a topic of particular interest. Nowadays, there is an increasing demand for remotely controllable devices, and thanks to the Internet of Things (IoT) paradigm, it is possible to extend Internet connectivity to common objects. The availability of billions of connected devices scattered around the world, each capable of acquiring and analysing data, opens up new possibilities for the development of new data gathering systems and the development of methods for the automatic management of such large data sets, enabling new digital services [15]. In Refs. [16; 17] the IoT for vibration monitoring and fault diagnostics of rotating machinery was analyzed by using vibration analysis, and seemed very promising. Traditional vibration monitoring techniques involve the deployment of accelerometers to detect the modal characteristics of the blade, that is, eigenfrequencies, mode shapes, and so forth [18; 19; 20]. Recently, contact vibration monitoring systems, such as strain gauges, were investigated; however, due to their complex implementation, limited operating life, and low reliability, their practical application remains unfeasible [21; 22; 23; 24]. A wireless vibration monitoring system based on acceleration measures in an operating wind turbine was proposed in Ref. [25], while in Ref. [26] a wireless inertial measurement unit capable of sensing angular speed and tri-axial accelerations was experimentally investigated on a 3 MW wind turbine. Lately, a contactless measurement method denoted as the blade tip timing (BTT) has been proposed. The BTT detects the deflection of the blade tip by means of optical probes located in the machine case [27; 28]. However, the probes' placement around the casing internal surface is non-trivial, since the surface is also filled with structural elements and nozzles. Moreover, the number of deployable optical probes is usually limited, thereby the vibration data acquired with the BTT method showed non-uniform and sub-Nyquist sampling [29].