A new approach for structural health monitoring by applying anomaly detection on strain sensor data

Structural Health Monitoring (SHM) systems help to monitor critical infrastructures (bridges, tunnels, etc.) remotely and provide up-to-date information about their physical condition. In addition, it helps to predict the structure’s life and required maintenance in a cost-efficient way. Typically, inspection data gives insight in the structural health. The global structural behavior, and predominantly the structural loading, is generally measured with vibration and strain sensors. Acoustic emission sensors are more and more used for measuring global crack activity near critical locations. In this paper, we present a procedure for local structural health monitoring by applying Anomaly Detection (AD) on strain sensor data for sensors that are applied in expected crack path. Sensor data is analyzed by automatic anomaly detection in order to find crack activity at an early stage. This approach targets the monitoring of critical structural locations, such as welds, near which strain sensors can be applied during construction and/or locations with limited inspection possibilities during structural operation. We investigate several anomaly detection techniques to detect changes in statistical properties, indicating structural degradation. The most effective one is a novel polynomial fitting technique, which tracks slow changes in sensor data. Our approach has been tested on a representative test structure (bridge deck) in a lab environment, under constant and variable amplitude fatigue loading. In both cases, the evolving cracks at the monitored locations were successfully detected, autonomously, by our AD monitoring tool

Self-optimisation of Vertical Sectorisation in a realistic LTE network

Vertical Sectorisation (VS) is a feature that exploits the capabilities of Active Antenna Systems (AAS) to improve service performance and network capacity, by splitting a single cell into an inner and outer sector. The attained gains primarily depend on the suitable and timely (de)activation of VS in response to the observed/estimated traffic distribution within the cell, while further gains can be achieved by properly adjusting VS parameters such as the transmit power distribution. Considering a realistic LTE (Long Term Evolution) network environment, in this paper a SON (Self Organizing Network) algorithm is proposed and evaluated, which dynamically (de)activates the VS feature and sets the transmit power split. The results show that the (de)activation of VS through a suitably tuned SON algorithm achieves significant throughput gains, while the dynamic adaptation of the transmit power distribution is mostly beneficial when operating in interference-limited environments