Fault detection on automotive engine components is an important feature that motivates research from different engineering areas due to the interest of automakers on its potential to increase safety, reliability and lifespan and to reduce pollutant emissions, fuel consumption and maintenance costs. The fault detection can be applied to several types of maintenance strategies, ranging from finding the faults that generated a component failure to find them before the failure occurs. This work is focused on predictive maintenance, which aims to constantly monitor the target component to detect a fault at the beginning, thus facilitating the prevention of target component failures. Due to production costs, it is not possible to have sensors installed in all engine components, which makes it difficult to apply predictive maintenance for all of them. One way to work around that problem is to use predictors based on machine learning paradigms, which take signals from indirect sensors from other components and predict a fault. To accomplish that, it is necessary to acquire data that contains both normal and faulty behaviors from the target component in order to train a machine learning method to recognize the defective behavior prior to embedding it into the software of the engine electronic control unit. Data acquisition can be an expensive process as well, as it may require several rounds of destructive testing for different driving cycles, which must be performed in real-time on instrumented vehicles in a dynamometer. Since machine learning methods are capable of handling a certain amount of noise, the data to train them can be generated by simulating the model of the respective engine in which the target component is installed. That process does not require real-time executions and vehicle instrumentation in a dynamometer lab, which decreases the cost of the data acquisition as a whole. This work presents the results of different machine learning methods implemented as classification predictors for fault detection tasks including Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Network (ANN), and Gaussian Process (GP). The data used for training was generated by a simulation testbed of an engine system, whereby its operation was modeled using industrial-standard driving cycles, such as the Worldwide Harmonized Light Vehicle Test Procedure (WLTP), New European Driving Cycle (NEDC), Extra-Urban Driving Cycle (EUDC), and U.S. Environmental Protection Agency Federal Test Procedure (FTP-75).

A current goal in microsystem research is to overcome small working ranges, typically resulting from mechanical connections and restoring forces such as for cantilevers. In the case of predefined resting positions and unidirectional motion, pseudo-levitation as in magnetic bearings is a promising solution.

In order to investigate concepts for energy efficient, cooperative microactuators, which allow free motion of small objects, we present a bistable levitation setup. The system consists of a magnetic proof mass within a glass tube, a piezoelectric staple actuator, two permanent magnets and a solenoid used as an electromagnet. The movable mass is mechanically unconstrained in its upper vertical motion and is intended to switch between two predefined equilibrium positions, namely on the staple actuator and levitating at a defined upper position. The transition is accomplished by an impulse-like kick force by the staple actuator, and subsequent feedback controlled following of a trajectory via electromagnetic actuation.

The goal of this work consists of both adapting the system parameters, guaranteeing stable and preferably robust equilibrium positions for the unactuated system, and finding optimal trajectories with a short settling time and minimum input effort for the controlled transition. These design and control objectives are combined within a co-design. In this approach, the system and controller optimizations are not performed consecutively, but within a single optimization, taking into account the coupling between the design and control. Here, we apply flatness-based control combined with feedback linearization, allowing for trajectories to be tracked without error in case of the undisturbed system. Thus, the controller is solely used for disturbance compensation and the problem can be simplified by optimizing only the trajectory without the controller parameters. We show that the combined optimization process is of advantage in comparison to the sequential approach and proficiently exploits the design parameters to improve the generated trajectories.

In recent years several legged/wheeled robots have been developed and proved their effective functionality in locomotion on uneven terrains. Many robotics researchers have been focusing on improving the locomotion speed, as well as the stability and robustness of such robots. High-speed locomotion of robots is however subject to various design challenges, especially in the development of actuators. The robotic applications which require high-speed motion in high torque operations along with the ability to manage dynamic physical interactions are not satisfied by the conventional robotics actuators deploying high reduction gearings. In this work, we present a quasi-direct drive actuator designed for continuous high-speed motions in high torque such as wheeled motions in mobile robot or joint motion in dynamic legged robots. The presented actuator exploits low reduction gearing so that it can render over 25Nm continuous torque while the actuator speed can exceed 20 rad/s. Such characteristics enable exhibiting dynamic motions and dealing with large external impacts. The selection of motor and design of the gearing unit was carried out iteratively so that commercial items with minimum customization are employed and the outer diameter of the motor and the gearbox can be matching. A single-level planetary gearbox is devised for the reduction unit to ensure highly back drivability and transparency of the actuator thereby making the actuator robust against external impacts and allowing for accurate torque control using motor current measurement. The gear set design was carried out based on the AGMA gear torque calculation. Given the radial space required for the gearbox dealing with the torque requirements, the actuator motor was chosen to be small in height (pancake type), which ensures high torque density within smaller dimensions at high-speed operation. The mechanical design of the actuator is presented in this paper and the FEM analysis conducted for the design of critical parts is reported. Finally, actuator specifications in terms of size and performance are compared with similar state-of-the-art actuators.

This paper focuses on the electromechanical properties of novel sub-micron compliant metallic thin film electrodes for dielectric elastomer membranes. Electrodes with thicknesses between 10-20 nm and different residual stress states are explored. Either pure nickel films or sandwiches of nickel (Ni) and carbon (C) are deposited by DC magnetron sputtering onto pre-stretched silicone elastomer membranes. Both, 37.5 % biaxial pre-stretch and 57.5% pre-stretch under pure shear condition (PSC) are considered in the conducted investigation. After the coating process is completed, the elastomer is allowed to relax. In the contracted configuration, it exhibits a wrinkled surface. After this state is reached, the electromechanical characterization is performed. In the conducted experiments, all types of films reveal a low initial resistance (around 100 Ω/square). Depending on the kind of pre-stretch and the electrode material, a strain of 100 % without any major degradation is achieved. It is also shown how the residual stress of the layers can be influenced by suitable sputtering parameters. As a result, low residual film stress significantly improves the electromechanical properties of PSC pre-stretched elastomers, but have only minor influence on the biaxially pre-stretched ones, regarding the Ni and the Ni+C thin films. This phenomenon is directly connected to the failure mechanisms observed on the two types of pre-stretched membranes. For C+Ni electrodes, the residual stress state of Ni does not influence the electromechanical properties for both, the biaxially pre-stretched and the PSC pre-stretched coated membranes. Nevertheless, the results are of fundamental importance for understanding the role of residual stresses for the creation of electromechanically stable and highly conductive electrode films, to be used in DE applications.

Soft actuators are the compliant-material based devices capable of producing large deformation upon external stimuli. Dielectric elastomer actuators(DEA) are a type of soft actuators that operate on voltage stimuli. Apart from soft robotics, these actuators can serve many novel applications, for example, tunable optical gratings, lens, diffusers, smart windows and so on. This talk presents our current work on tunable smart windows which can regulate the light transmittance and the sound absorption. This smart window can promote daylighting while maintaining privacy by switching between transparent and opaque. As a tunable optical surface scatters, it turns transparent with smooth surfaces like a flat glass; but it turns ‘opaque’ (translucent) with the micro-rough surface. The surface roughness is varied by means of surface micro-wrinkling or unfolding using dielectric elastomer actuation. In addition, this smart window is equipped with another layer of transparent micro-perforated dielectric elastomer actuator (DEA), which acts like Helmholtz resonators serving as a tunable and broader sound absorber. It can electrically tune its absorption spectrum to match the noise frequency for maximum acoustic absorption. The membrane tension and perforation size are tuned using DEA activation to tune its acoustic resonant frequency. Such novel smart window can be made as cheap as glass due to its simple all-solid-state construction. They are used to green building and could potentially enhance the urban livability.