Juan P. Amezquita-Sanchez

Bio: Juan P. Amezquita-Sanchez is an academic researcher from Autonomous University of Queretaro. The author has contributed to research in topics: Induction motor & Structural health monitoring. The author has an hindex of 20, co-authored 86 publications receiving 1575 citations. Previous affiliations of Juan P. Amezquita-Sanchez include Universidad de Guanajuato.

Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures

Abstract: Signal processing is the key component of any vibration-based structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the vibration signals in order to detect, locate and quantify the damage and its severity in the structure. This paper presents a state-of-the-art review of recent articles on signal processing techniques for vibration-based SHM. The focus is on civil structures including buildings and bridges. The paper also presents new signal processing techniques proposed in the past few years as potential candidates for future SHM research. The biggest challenge in realization of health monitoring of large real-life structures is automated detection of damage out of the huge amount of very noisy data collected from dozens of sensors on a daily, weekly, and monthly basis. The new methodologies for on-line SHM should handle noisy data effectively, and be accurate, scalable, portable, and efficient computationally.

Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures

Abstract: A new methodology is presented for (a) detecting, (b) locating, and (c) quantifying the damage severity in a smart highrise building structure. The methodology consists of three steps: In step 1, the synchrosqueezed wavelet transform is used to eliminate the noise in the signals. In step 2, a nonlinear dynamics measure based on the chaos theory, fractality dimension (FD), is employed to detect features to be used for damage detection. In step 3, a new structural damage index, based on the estimated FD values, is proposed as a measure of the condition of the structure. Further, the damage location is obtained using the changes of the estimated FD values. Three different FD algorithms for computing the fractality of time series signals are investigated. They are Katz's FD, Higuchi's FD, and box dimension. The usefulness and effectiveness of the proposed methodology are validated using the sensed data obtained experimentally for the 1:20 scaled model of a 38-storey concrete building structure.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).