This paper presents a complete electronic smart monitoring system dedicated to Structural Health Monitoring (SHM) applications for aeronautics. This monitoring system is called Smart Tag, has the size of a credit card and is completely autonomous, by using a single Li-ion battery for its power supply. The main features of the system include the capability of multi-channel data acquisition, digital processing and data logging, wireless data transmission over a Radio Frequency Identification (RFID) communication interface, the capability to interface different types of analogue and digital sensors, the capability of re-charging its battery with energy harvested from the RF field, the management of multiple power sources and small dimensions. The Smart Tag has been designed to be directly attached on the structure or inserted in the last composite layer of an aircraft and to operate in the harsh environment dedicated to aeronautics.

Modern wind turbines are relatively "immature" in the sense that they have not been fielded for a sufficient amount of time to assess their long-term viability. Availability, the ability of a system to function when it is required, is a major concern for alternative energy systems. Profits and environmental benefits will be lost if the costs and energy required to maintain a system outweigh the benefits obtained. Prognostics and system health management (PHM) methods can have a significant impact on the wind energy community. PHM enables the manufacturers and operators of complex systems to move from traditional time-or cycle-based maintenance to condition-based maintenance, which can significantly improve availability. This paper discusses the challenges in guaranteeing the high availability of wind turbines, and the use of PHM as a methodology to guarantee the high availability. A new sensor system for the health monitoring of turbine blades is proposed and a return on investment analysis for its use is presented.

TRIADE is a European Union project that focuses on the development of technological building blocks for Structure Health Monitoring (SHM) sensing devices in aeronautics. It is funded under the 7th framework program. In terms of objectives, the TRIADE project focuses on providing these technological building blocks and fully integrated prototypes in order to achieve power generation, power conservation, energy management and embedded powerful intelligence for data processing and storage for SHM sensing devices.The principal technological building blocks that the TRIADE project will provide are: - A low profile battery with high energy density which will be able to function in a harsh environment, - An energy harvester from vibration and electromagnetic RF, - Ultra low power sensors which will be designed in SOI technology, and - A neural network for data recording and damage assessment.The smart tag will be the size of a credit card (dimension 85x55x4 mm) and will support remote sensors. It will be embedded under the last composite layer or attached on the structure of the aircraft. It will be autonomous as all the power needed will be provided by the onboard rechargeable battery. It is wireless and the data stored will be transferred to the base station by using the RFID communication protocols through an RFID reader. It will record temperature, relative humidity, pressure, strain, vibration and acoustic emissions whenever the flight domain is overrun.The structure health monitoring capability that the TRIADE project offers is planned to be applied in 2 application domains: on helicopters with Eurocopter and PZL Swidnik as end-users, and on airplanes with EADS Deutschland GmbH and Dassault Aviation as end-users. The different requirements from each end-user have been unified on the smart tag with minimal compromises to accommodate of.

Health and Usage Monitoring Systems (HUMS) have been developed to improve the reliabili ty as well as the functionality and the performance of hi gh cost systems such as helicopters. As electronics is incr easingly integrated into systems, the cost of electronic equ ipment failures is increasing similarly. Thus, HUMS are more and more valuable for diagnostics and prognosis. Thi s trend is supported by aircraft manufacturers and the MoDs as well, which intend to extend the use of prognostics technologies on weapon platforms, vehic les and ammunitions. To assess the feasibility of an onboard Prognostic Health Management (PHM) system, this paper discusse the demonstration of a real time PHM system for electronic interconnect fatigue prediction based on in-situ sensors, data fusion and relevant algorithms proces sing. The study focuses on thermo-mechanical degradation o f electronic assemblies, because a significant percen tage of field failures are related to the high temperature and temperature cycling operating environments of elect ronic equipment. The prototype presented within this study is an autonomous embedded PHM system which integrates an onboard real time methodology for remaining life prediction based on a physics of failure approach. The results of accelerated failure testing applied on a Printed Wired Assembly (PWA) are compared to the modelling and predictions. They validate the identified prognostic features and show good agreement between the actual degradation status and the life expectancy forecast ed for the PWA. Index Terms — Data reduction, electronics, smart sensors, lifetime estimation, monitoring, prediction methods, reliability.

For many years, autonomous electronic devices have been developed to monitor the environment of systems, store their behaviour and send warnings upon exceeded thresholds previously defined. However, the trend is to further analyse these data to take into account the aging and damage accumulation in order to better assess the reliability of the monitored systems, depending of their real environmental life profile. Thus, the aim is to make a diagnostic or health status of the systems and evaluate their remaining life for a predictive maintenance.This paper shows that normalized or standardized Life Assessment Monitoring Systems (LAMS) may be developed and used for very different applications of onboard and real-time PHM (Prognostics Health Management) approaches. The goal is to make use of common electronic components: microcontroller, memories and sensors. However, dedicated embedded software has been developed for environmental data computations to predict the life expectancy of systems. These LAMSs can use field return results or simulation results to make electronic prognostics, assess the length of cracks in hidden metallic structures or track the progression of a moving system with a GPS module and send regular reports and warnings by SMS through a GSM module.

In situ diagnostics or prognostics of electronics are based on environment monitoring and reliability computation associated to experienced loads. As health assessment is a major concern for improving the maintenance process, it has to be known as soon as possible to be able to react according to the health status prediction. Indeed, the speed needed for failure prediction calculation from the stress monitored to the warning reported is essential to prevent ongoing operation of an electronic system on the way to failure. Real time on-board calculation is then the main objective in order to avoid regular data downloads and ground-based analysis. This paper presents a smart and integrated micro-programmable life consumption monitoring system (LCMS). The latter embeds sensors and on-board processing capabilities for advanced prognostics of printed wired assemblies (PWAs). Based on a methodology using physics of failure (PoF), it can provide, in real time, the life consumption prediction of electronics. The reliability of the LCMS is also tackled before presenting the real monitoring experiment. Finally, the future trend of such monitoring tools is discussed. (c) 2007 Elsevier Ltd. All rights reserved.

The MEDEA+ SWANS (Silicon Platform for Wireless Advanced Networks of Sensors) project aims at defining a generic silicon platform, integrating analogue and digital Intellectual Property (IP) blocks for wireless sensor nodes technology. This generic platform will be used in various applications, such as transportation (aeronautics, automotive), homeland security, environmental and health/fitness. In the aeronautical application, the platform monitors, continuously, aircraft structures to detect whether a crack exists or not and process the data in real time, inside the platform. Measurements are provided by an inductive sensor glued on a structure and are acquired during flights. The sensor impedance (real and imaginary parts) varies depending on the state of the part area on which it is stuck. For example, this sensor aims at monitoring the further evolution of the crack too. The data are transmitted from the sensor to an ARM microcontroller through an electronic conditioner. Then, they are analysed and stored in a non volatile memory. Data measurements are collected by a RF transmission, every 2 or 4 months. A 3D stack platform demonstrator that allows the use of different technologies, will be realised, fully tested and characterised.

To improve the reliability, the design and the maintenance of electronic systems, the knowledge of the operational environment in which these systems operate is essential. HUMS (Health and Usage Monitoring System) are specialised in environmental monitoring and provide operating life status according to the experienced loads. Nevertheless, their major drawbacks, which limit their use, come from their size and their ability to provide relevant information for health assessment. Usually performed by a ground-based software, the environmental data are analysed off-board periodically, after regular HUMS downloads. However, the speed needed for failure prediction calculation from the stress monitored to the warning reported is essential to prevent ongoing operation of an electronic system on the way to failure. This paper presents a smart integrated electronic system embedding MEMS sensors for in-situ health assessment associated to the experienced loads. This system uses advanced algorithms processing the measurements for immediate Prognostic Health Monitoring (PHM), based on the Physics of Failure for real-time life consumption prediction of electronic systems.

A Time Stress Measurement Device is an electronic instrument that records environmental or factual measurements (temperature, relative humidity, shocks, vibrations, on/off, open/closed, voltage, pressure, ...). All these measurements are then dated and stored. Typically, a TSMD is a battery-powered device that is equipped with a microcontroller (for electronic management), memory (for data storage) and sensors (for recordings of environmental data). Most TSMDs use turnkey software on a personal computer to set up and initiate the TSMD as well as retrieve and compute the collected data. TSMD technology is based on embedded or remote environmental stress sensors linked to a microcontroller that controls the sensors (e.g. threshold values), the inputs from the sensors (e.g. sampling frequencies) and the memories (e.g. number and type of recordings). A TSMD provides information for enhanced failure analysis, particularly intermittent failures, and permits trend analysis for preventive maintenance. The aim of using such a device is to determine the impact the environment has on a particular system of interest. The TSMD allows a complete recording of environmental data over time. The scope of the CLIO project is to generalise the TSMD concept, The aim is to develop an autonomous, miniaturised and multi-application TSMD that could be used in civil and military domains.

Proven prognostic sensors and in-situ monitoring strategies for cost- effectively recording environmental, operational, and performance parameters of new and legacy systems Proven models and algorithms for "health" assessment and prognostics Methods to integrate cost-effective prognostics with other technolo- gies (RFID, logistics, Net-centric databases) for new and legacy sys- tems Maintenance and logistical support methods that incorporate prognos- tic outputs Solution to the CND, NFF, NTF, intermittent problem Techniques for self-healing and system reconfiguration based on prog- nostics outputs Software to assess the return-on-investment opportunities of prognos- tics Participation in the PHMC will place members at the forefront of electronics prognostics and health management. PHMC members guide the research efforts, have access to all PHMC materials (website and tool access), and receive research results. The annual membership fee is $35K. New PHMC members who are not currently CALCE members will need to complete a membership agreement posted on the PHMC website. Interested companies and organizations can obtain membership information on the PHMC website at http://www.prognostics.umd.edu. For more information, contact Prof. Michael Pecht at pecht@calce.umd.edu, tel. 301-405-5323.