Smart sensors densely distributed over structures can use their computational and wireless communication capabilities to provide rich information for structural health monitoring (SHM). Though smart sensor technology has seen substantial advances during recent years, implementation of smart sensors on full-scale structures has been limited. Hardware resources available on smart sensors restrict data acquisition capabilities; intrinsic to these wireless systems are packet loss, data synchronization errors, and relatively slow communication speeds. This paper addresses these issues under the hardware limitation by developing corresponding middleware services. The reliable communication service requires only a few acknowledgement packets to compensate for packet loss. The synchronized sensing service employs a resampling approach leaving the need for strict control of sensing timing. The data aggregation service makes use of application specific knowledge and distributed computing to suppress data transfer requirements. These middleware services are implemented on the Imote2 smart sensor platform, and their efficacy demonstrated experimentally.

Globally, civil infrastructures are deteriorating at an alarming rate caused by overuse, overloading, aging, damage or failure due to natural or man-made hazards. With such a vast network of deteriorating infrastructure, there is a growing interest in continuous monitoring technologies. In order to provide a true distributed sensor and control system for civil structures, we are developing a Structural Nervous System that mimics key attributes of a human nervous system. This nervous system is made up of building blocks that are designed based on mechanoreceptors as a fundamentally new approach for the development of a structural health monitoring and diagnostic system that utilizes the recently developed piezo-fibers capable of sensing and actuation. In particular, our research has been focused on producing a sensory nervous system for civil structures by using piezo-fibers as sensory receptors, nerve fibers, neuronal pools, and spinocervical tract to the nodal and central processing units. This paper presents up to date results of our research, including the design and analysis of the structural nervous system.

In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.

Civil infrastructures are always subjected to various types of hazard and deterioration. These conditions require systematic efforts to assess the exposure and vulnerability of infrastructure, as well as producing strategic countermeasures to reduce the risks. This paper describes the needs for and concept of advanced sensor technologies for risk assessment of civil infrastructure in Japan. Backgrounds of the infrastructure problems such as natural disasters, difficult environment, limited resource for maintenance, and increasing requirement for safety are discussed. The paper presents a concept of risk assessment, which is defined as a combination of hazard and structural vulnerability assessment. An overview of current practices and research activities toward implementing the concept is presented. This includes implementation of structural health monitoring (SHM) systems for environment and natural disaster prevention, improvement of stock management, and prevention of structural failure.

A framework for structural health monitoring (SHM) systems is presented utilizing a recent 3.5 generation mobile telecommunication technology, HSDPA (High Speed Downlink Packet Access). It may be effectively applied to monitoring bridges, cut-slopes, and other facilities located in rural areas where the conventional Internet service is not readily available, since HSDPA is currently commercialized in 86 countries to make the Internet access possible in anywhere the mobile phone service is available. The proposed SHM framework is also incorporating remote desktop software to have remote control/operation of the SHM systems. The feasibility of the proposed framework has been demonstrated by field tests on a highway bridge in operation. One can expect that fast advances in the mobile telecommunication technology will further enhance the performance of the SHM network using the proposed framework for bridges and other facilities located in remote areas without the conventional wired Internet service.