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.
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. The use of in-situ sensors for real-time health monitoring of aircraft structures are 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 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 are able to detect incipient damage before catastrophic failures occurs. Condition-based maintenance practices could be substituted for the current time-based maintenance approach. 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 report presents a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. 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. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Boeing, Delta Air Lines, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp. and the Federal Aviation Administration (FAA) carried out a certification program to formally introduce Comparative Vacuum Monitoring (CVM) as a structural health monitoring solution to a specific aircraft wing box application. 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 into an airline maintenance program and the document revisions necessary to allow for routine use of CVM as an alternate means of performing periodic structural inspects. To carry out the validation process, knowledge of aircraft maintenance practices was coupled with an unbiased, independent evaluation. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically-relevant experimental effort to quantify the reliability of the CVM system applied to the Boeing 737 Wing Box fitting application. 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 CVM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and also is acceptable to both the aviation industry and regulatory bodies. This report presents 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 discusses how they can converge to produce a confident quantification of SHM performance An important element in developing SHM validation processes is a clear understanding of the regulatory measures needed to adopt SHM solutions along with the knowledge of the structural and maintenance characteristics that may impact the operational performance of an SHM system. This report describes the major elements of an SHM validation approach and differentiates the SHM elements from those found in NDI validation. The activities conducted in this program demonstrated the feasibility of routine SHM usage in general and CVM in particular for the application selected. They also helped establish an optimum OEM-airline-regulator process and determined how to safely adopt SHM solutions. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM technology. It will also streamline the regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring
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.
Wind energy is quickly becoming a significant contributor to the United States' overall energy portfolio. Wind turbine blades pose a unique set of inspection challenges that span from very thick and attenuative 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. To address this growing need, Sandia and SkySpecs collaborated to evaluate NDI methods that are suitable for integration on an autonomous drone inspection platform. A trade study of these NDI methods was performed, and thermography was selected as the primary technique for further evaluation. Based on the capabilities of SkySpecs' custom inspection drone, a miniature microbolometer IR camera was successfully selected and tested in a benchtop setting. After identifying key operating parameters for inspecting wind blade materials, hardware and software integration of the IR camera was performed, and Sandia and SkySpecs conducted initial field testing. Finally, recommendations for a path forward for drone-deployed thermography inspections were provided.
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).
Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges - Proceedings of the 9th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2018
Economic barriers to the replacement of bridges and other civil structures have created an aging infrastructure and placed greater demands on the deployment of effective and rapid health monitoring methods. To gain access for inspections, structure and sealant must be removed, disassembly processes must be completed and personnel must be transported to remote locations. Reliable Structural Health Monitoring (SHM) systems can automatically process data, assess structural condition, and signal the need for specific maintenance actions. They can reduce the costs associated with the increasing maintenance and surveillance needs of aging structures. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board SHM systems were used to regularly, or even continuously, assess structural integrity. A research program was completed to develop and validate Comparative Vacuum Monitoring (CVM) sensors for crack detection. Sandia National Labs, in conjunction with private industry and the U.S. Department of Transportation, completed a series of CVM validation and certification programs aimed at establishing the overall viability of these sensors for monitoring bridge structures. Factors that affect SHM sensitivity include flaw size, shape, orientation and location relative to the sensors, along with operational environments. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Flaw Detection (POD) levels for typical application scenarios. Complimentary, multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft and bridges. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual operating environments, and the prospects for structural health monitoring applications on a wide array of civil structures.
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 .