A data-driven temperature compensation approach for Structural Health Monitoring using Lamb waves

Abstract

This paper presents a temperature compensation method for Lamb wave structural health monitoring. The proposed approach considers a representation of the piezo-sensor signal through its Hilbert transform that allows one to extract the amplitude factor and the phaseshift in signals caused by temperature changes. An ordinary least square (OLS) algorithm is used to estimate these unknown parameters. After estimating these parameters at each temperature in the operating range, linear functional relationships between the temperature and the estimated parameters are derived using the least squares method. A temperature compensation model is developed based on this linear relationship that allows one to reconstruct sensor signals at any arbitrary temperature. The proposed approach is validated numerically and experimentally for an anisotropic composite plate at different temperatures ranging from 16• C to 85 • C. A close match is found between the measured signals and the reconstructed ones. This approach is interesting as it needs only a limited set of piezo-sensor signals at different temperatures for model training and temperature compensation at any arbitrary temperature. Damage localization results after temperature compensation demonstrate its robustness and effectiveness.

A scalable data-driven approach to temperature baseline reconstruction for guided wave structural health monitoring of anisotropic carbon-fibre-reinforced polymer structures

“…Figure 13 shows the normalised residual amplitude of four signal paths on panel B (Figure 8) for the pristine state and the damaged state under multiple temperature conditions. To distinguish between the pristine state and the damaged state, the normalised residual amplitude at two states must be at different value range [16].…”

Influence and Compensation of Temperature Effects for Damage Detection and Localization in Aerospace Composites

“…The effects of temperature on the propagation of Lamb waves is determined by its influence on the transducers and the mechanical properties of the structure material [26]. These effects produce the variation, mainly, in the amplitude of the wave packets and the instantaneous phase, affecting their envelope and, therefore, their energy distribution ( Figure 5).…”

“…In this paper, the SHM traditional point of view was applied, using baseline datasets information, and a data-driven method was used for the evaluation and correction of temperature effects [26], assuming that the temperatures distribution field on the sample was homogeneous [27], as well as based on minimizing the difference between several types of baseline tests, adjusting each of them to a reference temperature and, with the use of polynomial regression, being able to estimate the parameters used for compensating a signal recorded at the current damaged state, at a certain temperature at which the baseline data is not available. Finally, to check the effectiveness of the method applied to damage detection and location, the data from the current state were adapted to several available baselines at several different temperatures, and an artificial damage was detected and located, using an imaging algorithm (RAPID, Reconstruction Algorithm for Probabilistic Inspection of Damage) [28] and, based on previous experience [29], a modification of this algorithm (RAPID-G).…”

Application and Challenges of Signal Processing Techniques for Lamb Waves Structural Integrity Evaluation: Part A-Lamb Waves Signals Emitting and Optimization Techniques

“…This approach needs to train with prior data, which are always unavailable. Fendzi et al [98] presented a data-driven temperature compensation approach, which considers a representation of the piezo-sensor signal through its Hilbert transform that allows one to extract the amplitude factor and the phase shift in signals, while its compensation accuracy depends on the length of the time window that should be considered in the temperature compensation parameters estimation. Liu et al [99] proposed a baseline signal reconstruction technique in which the Hilbert transform is used to compensate the phase of baseline signals and the orthogonal matching pursuit is used to compensate the amplitude of baseline signal.…”