Structural Health Monitoring (SHM) has reached a high importance in numerous fields of civil and mechanical engineering. Promising damage detection approaches like the Damage Index Method, Gapped Smoothing Technique and Modal Strain Energy Method require the structure's mode shapes [1].Long term modal data acquisition on real life structures requires a computational efficient system based on a measuring method that can easily be installed. Systems using the Random Decrement Method (RDM) are composed of a decentralized network of smart acceleration sensors applied for both, triggering and pure measuring. They allow the reduction of cabling effort and computational costs to a minimum.In order to design a RDM measuring network efficiently, an approved procedure for defining hardware as well as measuring settings is required. In addition, optimal sensor positions have to be defined. However, today those decisions are mostly based on expert's knowledge. In this paper a systematic and analytical procedure for defining the hardware requirements and measuring settings as well as optimal sensor positions is presented. The proposed routine uses the outcome of an Experimental Modal Analysis (EMA).Due to different requirements for triggering and non-triggering sensors in the RDM network a combination of two approaches for sensor placement has to be used in order to find the best distribution of measurement points over the structure. A controllability based technique is used for placing triggering sensors, whereas the Effective Independence (EI) is utilized for the placement of non-triggering sensors.The combination of these two techniques selects the best set of measuring points for a given number of sensors out of all possible sensor positions.Damage detection itself is not considered within the scope of this paper.
Over recent decades, cars have become larger and heavier with every new generation. The main drivers of such a weight increase have been the improved safety and comfort requirements. Decades of R&D investments to tackle this tendency have resulted in a substantial increase in the weight-specific performance of components and assemblies in terms of cost, strength and stiffness. However, the need for weight reduction in future electric vehicles, without unduly compromising performance and safety, is even stronger since additional weight translates into either reduced driving range or in larger, heavier and more expensive batteries. Within this context, the European Green Vehicle project ENLIGHT developed highly innovative lightweight material technologies for application in structural vehicle parts of future volume produced electric vehicles. Among others, ENLIGHT developed thermoplastic matrix composite and associated manufacturing technologies to a stage that they were applicable at least in medium volume production. The material development was complemented by investigating the required manufacturing and assembly technologies as well. In this paper, a summary of the major results obtained during the four-year project year is presented. A special focus is given to a semi-active composite control arm with significant reduced weight but enhanced NVH properties.
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