Publications

The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.

Reliable structural health monitoring (SHM) systems can automatically process data, assess structural condition and signal the need for human intervention. There is a significant need for formal SHM technology validation and quantitative performance assessment processes to uniformly and comprehensively support the evolution and adoption of SHM systems. In recent years, the SHM community has made significant advances in its efforts to evolve statistical methods for analyzing data from in-situ sensors. Several statistical approaches have been demonstrated using real data from multiple SHM technologies to produce Probability of Detection (POD) performance measures. Furthermore, limited comparisons of these methods - utilizing different simplification assumptions and data types - have shown them to produce similar POD values. Given these encouraging results, it is important to understand the circumstances under which the data was acquired. Thus far, the statistical analyses have assumed the viability of the data outright and focused on the performance quantification process once acceptable data has been compiled. This paper will address the array of parameters that must be considered when conducting tests to acquire representative SHM data. For some SHM applications, it may not be possible to simulate all environments in one single test. All relevant parameters must be identified and considered by properly merging results from multiple tests. Laboratory tests, for example, may have separate fatigue and environmental response components. Flight tests, which will likely not include statistically-relevant damage detection opportunities, will still play an important role in assessing overall SHM system performance under an aircraft operator's control. One statistical method, the One-Sided Tolerance Interval (OSTI) approach, will be discussed along with the test methods used to acquire the data. Finally, prospects for streamlining the deployment of SHM solutions will be considered by comparing SHM data needs during what is now an introductory phase of SHM usage with future data needs after a substantial database of SHM data and usage history has been compiled.

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Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today's extensive fleet of aging aircraft. The use of in-situ sensors for real-time health monitoring of aircraft structures, coupled with remote interrogation, provides a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, Structural Health Monitoring (SHM) systems can automatically process data, assess structural condition, and signal the need for human intervention. Prevention of unexpected flaw growth and structural failure can be improved if on-board health monitoring systems are used to continuously assess structural integrity. Such systems can detect incipient damage before catastrophic failures occurs. Other advantages of on-board distributed sensor systems are that they can eliminate costly and potentially damaging disassembly, improve sensitivity by producing optimum placement of sensors and decrease maintenance costs by eliminating more time-consuming manual inspections. This paper presents the results from successful SHM technology validation efforts that established the performance of sensor systems for aircraft fatigue crack detection. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, and the potential inspector population. All factors that affect SHM sensitivity were included in this program including flaw size, shape, orientation and location relative to the sensors, operational and environmental variables and issues related to the presence of multiple flaws within a sensor network. This paper will also present the formal certification tasks including formal adoption of SHM systems into aircraft manuals and the release of an Alternate Means of Compliance and a modified Service Bulletin to allow for routine use of SHM sensors on commercial aircraft. This program also established a regulatory approval process that includes FAR Part 25 (Transport Category Aircraft) and shows compliance with 25.571 (fatigue) and 25.1529 (Instructions for Continued Airworthiness).

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Wind turbine blades pose a unique set of inspection challenges that span from very thick and attentive spar cap structures to porous bond lines, varying core material and a multitude of manufacturing defects of interest. The need for viable, accurate nondestructive inspection (NDI) technology becomes more important as the cost per blade, and lost revenue from downtime, grows. NDI methods must not only be able to contend with the challenges associated with inspecting extremely thick composite laminates and subsurface bond lines, but must also address new inspection requirements stemming from the growing understanding of blade structural aging phenomena. Under its Blade Reliability Collaborative program, Sandia Labs quantitatively assessed the performance of a wide range of NDI methods that are candidates for wind blade inspections. Custom wind turbine blade test specimens, containing engineered defects, were used to determine critical aspects of NDI performance including sensitivity, accuracy, repeatability, speed of inspection coverage, and ease of equipment deployment. The detection of fabrication defects helps enhance plant reliability and increase blade life while improved inspection of operating blades can result in efficient blade maintenance, facilitate repairs before critical damage levels are reached and minimize turbine downtime. The Sandia Wind Blade Flaw Detection Experiment was completed to evaluate different NDI methods that have demonstrated promise for interrogating wind blades for manufacturing flaws or in-service damage. These tests provided the Probability of Detection information needed to generate industry-wide performance curves that quantify: 1) how well current inspection techniques are able to reliably find flaws in wind turbine blades (industry baseline) and 2) the degree of improvements possible through integrating more advanced NDI techniques and procedures. _____________ S a n d i a N a t i o n a l L a b o r a t o r i e s i s a m u l t i m i s s i o n l a b o r a t o r y m a n a g e d a n d o p e r a t e d b y N a t i o n a l T e c h n o l o g y a n d E n g i n e e r i n g S o l u t i o n s o f S a n d i a , L L C , a w h o l l y o w n e d s u b s i d i a r y o f H o n e y w e l l I n t e r n a t i o n a l , I n c . , f o r t h e U . S . D e p a r t m e n t o f E n e r g y ' s N a t i o n a l N u c l e a r S e c u r i t y A d m i n i s t r a t i o n u n d e r c o n t r a c t D E - N A 0 0 0 3 5 2 5 .

The use of in-situ sensors for real-time health monitoring of a wide array of civil structures can be a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. The maturity of Structural Health Monitoring (SHM) sensors has evolved to the point where many networks have demonstrated sensitivities that meet or exceed current damage detection requirements. As a result, there is a growing need for well-defined methods to statistically quantify the performance of sensors and sensor networks. Statistical methods can be applied to laboratory and flight test data to derive Probability of Detection (POD) values for SHM sensors in a fashion that agrees with current nondestructive inspection (NDI) validation requirements. However, while there are many agreed-upon procedures for quantifying the performance of NDI techniques, there are no guidelines for assessing SHM systems. While the intended function of the SHM and NDI systems may be very similar, there are distinct differences in the parameters that affect their performance and differences in their implementation that require special consideration. Factors that affect SHM sensitivity include flaw size, shape, orientation and location relative to the sensors, operational and environmental variables and issues related to the presence of multiple flaws within a sensor network. The FAA Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with the FAA WJH Technical Center, has conducted a series of SHM validation and certification programs aimed at establishing the overall viability of SHM systems and producing appropriate precedents and guidelines for the safe adoption of SHM solutions for aircraft maintenance. This paper will present the use of several different statistical methods, some of them adapted from NDI performance assessments and some proposed to address the unique nature of damage detection via SHM systems, and discuss how they can converge to produce a confident quantification of SHM performance. Comparisons of hit-miss, a versus ?, and One Sided Tolerance Intervals will provide valuable insights into how the characteristics of the collected SHM data affect the formulation of that system's POD curve. Similarities between NDI and SHM assessments will be highlighted in order to provide a foundation in traditional flaw detection performance measures. In addition, considerations of the controlling factors to be considered when collecting SHM response data will be discussed.

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