A Shannon entropy approach for structural damage identification based on self-powered sensor data

© 2019 Elsevier Ltd Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions

Using a moving load to simultaneously detect location and severity of damage in a simply supported beam

This paper demonstrates the feasibility of simultaneously identifying both the location and severity of structural damage in a beam by using two independent moving load experiments. First, a simple but sufficiently accurate single degree of freedom model is presented to simulate the structure efficiently over a wide range of relevant inputs. We then introduce a damage sensitive feature (DSF) based on the integral of the velocity time history of the beam at its midspan when the load moves over the beam. A critical velocity, a function only of the beam’s first natural frequency and length, is obtained for the proposed DSF, upon which the damage can be located more accurately. The only required data for the damage detection is the midspan velocity-time history of the cracked beam subjected to a moving load, and the midspan static deflection of the intact beam subjected to a load of the same magnitude. In the last section of this paper, the capability of the proposed DSF is examined in the presence of noise. The results demonstrate the capability of the proposed method to find both the damage location and severity successfully, and methods for further reducing the effects of noise are suggested

Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data

This paper explores the advantages of Variational Mode Decomposition (VMD) in detecting local damage on beam type structures (bridge) subjected to a sprung mass (vehicle). VMD is used to decompose the acceleration time history of the bridge at its midspan into its constitutive intrinsic mode functions (IMFs). The instantaneous frequency (IF) and instantaneous amplitude (IA) of the first IMF show irregularities at the damage position. We demonstrate through computer simulation that VMD is superior for detecting damage when compared to the well-known Empirical Mode Decomposition (EMD) method. A new damage sensitive feature (DSF) is also introduced that considers synchronisation of peaks between the IA and IF signals. The results show that the new DSF can enhance the peak at the damage positions while suppressing peaks at other locations