Structural health monitoring is an important problem of interest in many civil infrastructure and aerospace applications. In the last few decades, many techniques have been investigated to address the detection, estimation, and classification of damage in structural components. One of the key challenges in the development of real-world damage identification systems, however, is variability due to changing environmental and operational conditions. Conventional statistical methods based on static modeling frameworks can prove to be inadequate in a dynamic and fast changing environment, especially when a sufficient amount of data is not available. In this paper, a novel adaptive learning structural damage estimation method is proposed in which the stochastic models are allowed to perpetually change with the time-varying conditions. The adaptive learning framework is based on the use of Dirichlet process (DP) mixture models, which provide the capability of automatically adjusting to structure within the data. Specifically, time–frequency features are extracted from periodically collected structural data (measured sensor signals), that are responses to ultrasonic excitation of the material. These are then modeled using a DP mixture model that allows for a growing, possibly infinite, number of mixture components or latent clusters. Combined with input from physically based damage growth models, the adaptively identified clusters are used in a state-space setting to effectively estimate damage states within the structure under varying external conditions. Additionally, a data selection methodology is implemented to enable judicious selection of informative measurements for maximum performance. The utility of the proposed algorithm is demonstrated by application to the estimation of fatigue-induced damage in an aluminum compact tension sample subjected to variable-amplitude cyclic loading.

Discussion of big data (BD) has been about data, software, and methods with an emphasis on retail and personalization of services and products. Big data also has impacted engineering and manufacturing and has resulted in better and more efficient manufacturing operations, improved quality, and more personalized products. A less apparent effect is that big data have changed problem solving: the problems we choose to solve, the strategy we seek, and the tools we employ. This paper illustrates this point by showing how the big data style of thinking enabled the development of a new quality assurance philosophy called process monitoring for quality (PMQ). PMQ is a blend of process monitoring and quality control (QC) that is founded on big data and big model (BDBM), which are catalysts for the next step in the evolution of the quality movement. Process monitoring (PM) for quality was used to evaluate the performance of the ultrasonically welded battery tabs in the new Chevrolet Volt, an extended range electric vehicle.

The analysis, detection, and classification of damage in complex bolted structures is an important component of structural health monitoring. In this article, an advanced signal processing and classification method is introduced based on time-frequency techniques. The time-varying signals collected from sensors are decomposed into linear combinations of highly localized Gaussian functions using the matching pursuit decomposition algorithm. These functions are chosen from a dictionary of time-frequency shifted and scaled versions of an elementary Gaussian basis function. The dictionary is also modified to use real measured data as the basis elements in order to obtain a more parsimonious signal representation. Classification is then achieved by matching the extracted damage features in the time-frequency plane. To further improve classification performance, the information collected from multiple sensors is integrated using a Bayesian sensor fusion approach. Results are presented demonstrating the algorithm performance for classifying signals obtained from various types of fastener failure damage in an aluminum plate.

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