Compensation of Environmental Effects on Modal Properties by Second Order Blind Source Separation Techniques

The most recent algorithms for automated identification and tracking of the modal parameters of structures in operational conditions have fostered the spread of vibration-based Structural Health Monitoring in civil engineering. Continuously monitoring damage sensitive features based on the estimated modal properties makes possible the remote assessment of the health state of structures. One of the main drawbacks of modal based damage detection is the sensitivity of natural frequency estimates to environmental and operational conditions that can cause changes of the same order of magnitude of those induced by damage. Thus, the estimates have to be depurated from the effects of environmental factors in order to effectively detect damage. However, a straightforward identification of the factors influencing the estimates and, therefore, their measure are often unfeasible. In this case statistical tools are the sole option to correct the estimates without measuring environmental and operational factors. The present paper deals with an explanatory case study where the influence of environmental factors on dynamic response and tensile load of a cable in operational conditions is investigated. Among the various approaches to compensate the influence of environmental and operational factors, attention has been mainly focused on robust blind source separation techniques. After a qualitative analysis of the influence of environmental factors on the dynamics of the cable, different approaches are tested to compensate environmental effects. The performance of statistical tools not requiring the information about the temperature, such as Principal Component Analysis and Second Order Blind Identification, is compared with that of methods based on temperature measurements, such as regression methods. Quantitative comparisons in terms of predictive performance are presented. The obtained results are definitely partial and additional investigations are necessary. However, they remark the effectiveness of statistical tools such as the Principal Component Analysis for removal of environmental effects in the context of continuous monitoring applications.