Tangled metal wire (TMW) devices can be used as damping elements in extreme environments where traditional materials such as viscoelastic polymers deteriorate or become ineffective. Dynamic properties of TMW devices are highly nonlinear because the microstructure consists of coiled metal wires that are compressed together. This paper examines the sensitivity of their dynamic stiffness and damping to loading conditions, in particular, pre-compression, dynamic amplitude and frequency of excitation. Using displacement-controlled experiments, it is shown that properties depend strongly on pre-compression and dynamic amplitude as would be expected in a structure comprising many frictional contact points. Frequency dependence is shown to be negligible over a broad frequency range that encompasses the region of interest for typical machine applications. This work identifies slow dynamic effects, with timescales of the order of around 10 seconds, which show that quasi-static testing, which is sometimes used for these materials, will not provide accurate estimates of dynamic properties.

The application of reliable structural health monitoring (SHM) technologies to operational wind turbine blades is a challenging task, due to the uncertain nature of the environments they operate in. In this paper, a novel SHM methodology, which uses Gaussian Processes (GPs) is proposed. The methodology takes advantage of the fact that the blades on a turbine are nominally identical in structural properties and encounter the same environmental and operational variables (EOVs). The properties of interest are the first edgewise frequencies of the blades. The GPs are used to predict the edge frequencies of one blade given that of another, after these relationships between the pairs of blades have been learned when the blades are in a healthy state. In using this approach, the proposed SHM methodology is able to identify when the blades start behaving differently from one another over time. To validate the concept, the proposed SHM system is applied to real onshore wind turbine blade data, where some form of damage was known to have taken place. X-bar control chart analysis of the residual errors between the GP predictions and actual frequencies show that the system successfully identified early onset of damage as early as six months before it was identified and remedied.

The direct-methods procedure for single isomorphous

No abstract

No abstract

Occupancy detection is essential in smart buildings from energy management and comfort management perspectives. Information on the occupancy also plays an important role in the successful execution of the rescue plan by the first responders in emergency situations. Emergency situations demand proactive, real-time, low latency, and accurate occupancy detection mechanisms. Several occupancy detection mechanisms based on the CO 2 levels, camera images, RF signals, etc. are discussed in the literature. However, practical realization and deployment of these mechanisms, specifically concerning emergency scenarios, needs exploration. In this paper, a proactive open-source client-server architecture of a Wi-Fi localization-based occupancy detection system is presented. This system can be deployed in smart buildings for emergency management. The architectural overview, design details, implementation, and testing procedures for this system are discussed in detail. The details of a simulator used for testing, based on random walk mathematical model are also presented. The results proving the functionality and performance of the system are shown in detail.

No abstract

No abstract