FBG based structural health monitoring of engine blades towards intelligent structures and CBM

Abstract

Structural Health Monitoring (SHM) has been gaining increased attention over the past decades as an important step towards Condition Based Maintenance (CBM). Measurements from the SHM systems provide the necessary information to monitor the condition of a (sub)component or structure and enable the use of this knowledge for maintenance task when needed, increasing availability and safety while reducing downtime related costs [1]. On the final level of SHM lie diagnostics and prognostics [2, 3], whose output inform about the current and future state of the (sub)component and their accuracy impact the effectiveness of the CBM decision making, and hence a capable sensor network is important. In this research our focus lies with monitoring the structural integrity of composite aircraft engine blades, through a capable network of SHM technologies. Engine blades are an important part of any aircraft and their integrity is imperative for its safe operation. A common yet critical damage case is impact damage (cause by hail or bird strikes) which can significantly reduce the load bearing capabilities of the blade. The aim is to demonstrate the feasibility, effectiveness and usefulness of different SHM systems in identifying the existence of damage, monitoring the damage and degradation growth and eventually use robust and reliable indicators to estimate the remaining useful life. To accomplish that task, a subpart of the engine blade is manufactured from 3D-woven CFRP preforms via the injection molding technique. The panels are curved and their length is 800mm while their width is 350mm. A secondary adhesively bonded steel edge is also adhered to the entire length of the panel with a width of 50mm. The panels are subjected to a repeated 4-point bending load-unload scheme with increased severity to simulate low frequency fatigue at ~0.02 Hz and introduce controlled and gradual degradation. First, one panel is subjected to 4-point bending quasi-static loading to determine the failure load which was approximated at 22kN through finite element analysis. Experimental collapse was reached at 28 kN and with this a guide the load envelope was decided. The loads include [4, 8, 12, 14, 16, 18, 20, 22, 24, 26, 28] kN and each load is applied for 400 cycles. Impact damage is also introduced to most of the panels in order to create a damage area to monitor. Regarding the SHM systems employed, the panel is equipped with state of the art FBG sensors and piezoelectric sensors. In this work, the data from the optical fibers are studied more in depth. The fibers can either be surface mounted (tensiled side of the panel) or embedded during the layup process or a combination of both. The majority of optical fibers run across the length of the panel at different width locations, two focused in the middle section, and one close to each of the edges. The sensors are used to monitor the strain field and it is attempted to correlate damage formulation and overall degradation with alterations to the strain field [4]. The first indication of degradation can be observed by analyzing the data collected from the hydraulic machine. By processing the load and displacement data, the experimental stiffness can be calculated as the slope of a linear equation between the load and displacement during the loading part. What was observed, is that at first the experimental stiffness slightly increases after the first load case, attributed to the increased robustness of the panel after significant bending. A somewhat constant stiffness follows, until the time close to failure, where rapid stiffness drop can be seen. This is accompanied by the first visible mode of damage in the form of skin tears and fiber cracking at the top surface close to the loading pins. Final collapse is dominated by matrix and fiber breakage close to the loading pin locations which extend across the entire width of the panel. These results are summarized in Figure 1 and Figure 2. FBG data are dependent on the type of FBG. Embedded FBG sensors display a mixture of tension and compression behavior while surface mounted show predominantly tension strains with increased intensity as the max load increases. An example of strains from embedded and surface mounted FBG sensors can be seen in Figure 3. The end goal of analyzing the FBG data is to extract capable indicators, similar to [5], and correlate the online SHM measurements to the degradation, as observed by the stiffness reduction, in a semi-quantitative way. In this research the use of strain based SHM sensors for degradation monitoring is demonstrated. Strain based indicators were used in an attempt to capture degradation evolution in large curved composite panels representative of aircraft engine blades. An unprecedented experimental campaign was launched on engine blade panels, which are subjected to fatigue-like 4-point bending load, and are mounted with state of the art SHM systems. The raw strains are transformed into an informative measure of degradation, demonstrating the feasibility, effectiveness and usefulness of such sensors for diagnostic and prognostic tasks, a first step towards a CBM paradigm.

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