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The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness. [ABSTRACT FROM AUTHOR]

This work is dedicated to theoretical and experimental research on constructing a digital twin for a 24‐story building located in a seismic zone with the possibility of seismic vibrations of up to eight on the Richter scale. A digital model of the building was developed considering the changes in its geometry (damage or absence of structural elements). This requires detailed field inspections of partially constructed buildings and consideration of the inspection results when constructing a digital model. The boundary conditions, in addition to the geometric characteristics of the building, include the physical and mechanical characteristics of all its structural elements obtained through nondestructive testing methods. Discrepancies between the structural elements' design and actual concrete classes were identified. The initial conditions included real accelerograms at the location of the building, which served as input data for the direct dynamic analysis. Thus, there is a need to develop a hybrid system for modeling the stress–strain state of a 24‐story building that uses information technologies (Internet of Things) with sensors during research. Such an IoT system, combined with one information network using cloud technologies, allows us to obtain the necessary information in real‐time, highlighting the features of the building (the second stage of constructing the digital twin). The bar and spatial models of buildings with distributed parameters of structural stiffness at bending and shear and the mass and mass moment of inertia were considered. The results of numerical studies of the first form of natural vibrations for the building rod (according to the expressions obtained by the authors) and spatial models (performed using the LIRA‐CAD software package developed in Ukraine based on the finite element method) are presented. A comparison with the first experimental form of natural vibrations confirmed that considering the moment of inertia of the building mass makes it possible to determine the ordinates of the self‐form of vibrations and seismic horizontal loads more accurately. The experimental first frequency and form of natural vibrations of a 24‐story residential building in Odesa were obtained from the results of vibration monitoring at the construction stage (16 floors were erected) and after all 24‐floor erection. The direct dynamic method of building calculations for seismic effects specified by earthquake accelerograms was used along with the method proposed by the authors to consider damping during forced building vibration calculations and the actual physical and mechanical characteristics of concrete and reinforcement obtained by non‐destructive testing methods. The calculated vibration amplitudes at the top of the building cantilever model were compared with the damping parameters obtained using the Voicht model and the authors' method under the influence of an accelerogram recorded during the powerful 1977 Bucharest earthquake with an epicenter in the Vrancea area (Romanian Carpathians). [ABSTRACT FROM AUTHOR]

Mechanical equipment is ubiquitous in industrial production, and the vibration energy generated by its operation usually cannot be effectively harvested, resulting in huge energy waste. Meanwhile, real‐time monitoring of machine operation can be achieved by collecting vibration information. Indeed, vibration energy harvesting and vibration monitoring are of great significance for the development of green energy and machine fault diagnosis. As an emerging power generation technology, triboelectric nanogenerator (TENG) has shown extraordinary potential in the field of vibration energy harvesting and vibration monitoring. First, the theoretical basis, working modes, and triboelectric materials are described. Then, TENG devices for vibration energy harvesting are classified and introduced based on the structural characteristics, and the main advantages and disadvantages are compared. Furthermore, the current research progress of a self‐powered vibration monitoring system based on TENG is introduced. Finally, the shortcomings of triboelectric nanogenerators in this field are analyzed and summarized, and future research directions and application scenarios are prospected. [ABSTRACT FROM AUTHOR]

The implementation of Structural Health Monitoring (SHM) offers the prospect for sustainable and safe service‐life extension of existing bridges, a large portion of which is approaching the end of their nominal life. Many SHM frameworks for civil infrastructure address timely damage detection and identification. However, the scarcity of case studies on real damaged bridges hinders the generalized application of SHM in practice. In this contribution, monitoring data from a four‐day campaign on the Ponte‐Moesa bridge, a three‐span concrete box‐girder bridge, is presented as a benchmark for data‐driven damage diagnosis schemes. The monitoring data, covering accelerations from ambient and forced vibrations, contains the reference state after concluding the service life along with several gradually increasing damage states, including drilling holes and cutting reinforcement rebars and prestressed cables. The potential of damage‐sensitive features to identify damage is presented and the uncertainties, resulting from the environmental and operational conditions and sensor malfunctioning, pertaining to robust damage detection are discussed. Drawing from real bridge monitoring data, a range of prospects and open challenges of vibration‐based SHM for bridges are reviewed. [ABSTRACT FROM AUTHOR]

Summary: Operators want to maintain awareness of the structure being tested while observing sensor data. Normally, the human's gaze shifts to a separate device or screen during the experiment for data information, missing the structure's physical response. The human–computer interaction provides valuable data and information but separates the human from the reality. The sensor data does not collect experiment safety, quality, and other contextual information of critical value to the operator. To solve this problem, this research provides humans with real‐time information about vibrations using an augmented reality (AR) application. An application is developed to augment sensor data on top of the area of interest, which allows the user to perceive real‐time changes that the data may not warn of. This paper presents the results of an experiment that show how AR can provide a channel for direct sensor feedback while increasing awareness of reality. In the experiment a researcher attempts to closely follow a moving sensor with their own sensor while observing the moving sensor's data with and without AR. The results of the reported experiment indicate that augmenting the information collected from sensors in real time narrows the operator's focus to the structure of interest for more efficient and informed experimentation. [ABSTRACT FROM AUTHOR]

Summary: Precast buildings have been becoming popular in China in these 5 years since they have many merits such as effectiveness, safety, high quality, and environmental friendliness. In some cities of China, it is required to make the proportion of precast buildings account for 30% in newly built construction areas. Following the rapid development, their vibration behaviors are attracting more attention since it may be quite different from the cast‐in‐situ building. To investigate the difference, this paper studies the modal properties of two kinds of buildings, i.e., new‐type precast building and cast‐in‐situ building. They were built in the same area, while the structural forms are also similar. The main difference is the construction manner. Ambient vibration tests of these two structures were conducted respectively. Since the number of sensors is limited, the locations of interest were measured by a multiple‐setup technique. The difficulty encountered during the field test was discussed. A frequency domain Bayesian approach was used to conduct operational modal analysis. Both the most probable value and posterior uncertainties of modal parameters were studied, including translational, torsional, and wrapping modes. The difference of the modal parameters of the two kinds of buildings are compared and discussed. Finite element models were also built to perform a comparison. It is seen that although the structural forms and appearances of the two buildings are similar, some obvious differences were observed regarding the dynamic characteristics, which provide valuable information for popularizing and future assessment of the new‐type precast structure. [ABSTRACT FROM AUTHOR]

Summary: Large‐scale, long‐term structural health monitoring systems have become more feasible in recent years as the required data acquisition and analysis systems are more affordable to deploy. These long‐term systems must process and store vast amounts of data without wasting computational power and storage capacity with redundant or poor quality data. While not a primary system for damage detection, large‐scale, long‐term vibration monitoring systems aim to leverage changes in the dynamic signature of a structure to assess global structural changes. Although the ability to continually collect vibration data at high rates exists, it is not always feasible to store all these data long term. As more long‐term monitoring systems are deployed, efficient methods need to be developed to quickly and efficiently analyze large quantities of vibration data so that only the most pertinent information is archived. Previous researchers have used scheduled approaches, eg, taking data every hour, or triggered sensing systems. A monitoring system on the I‐35W St. Anthony Falls Bridge, which crosses the Mississippi River in Minneapolis, Minnesota, has been collecting vibration and temperature data since the structure's opening in 2008. This provides a uniquely large data set to establish the characteristics of a good signal for output‐only system identification to consistently and efficiently capture natural frequencies and mode shapes. To this end, a system identification routine using a novel signal selection approach and modal sorting routine that leverages NExT‐ERA/DC is proposed to analyze this large data set. The resulting information allows long‐term and temperature‐based trends to be identified. [ABSTRACT FROM AUTHOR]

Abstract: With the diverse development in electronic devices, high cost, low reliability, and the need for battery power have been severe obstacles to restrict the wide application of sensors. In this work, we present a highly reliable, real‐time, self‐powered vibration sensor based on an enhanced piezoelectric nanogenerator (NG), which is heat resistant, waterproof, relatively low cost, and with a long lifetime. The enhanced piezoelectric NG is constructed into a double‐arched structure: the upper arched layer is the poly(vinylidene fluoride) (PVDF) film with designed optimum size, which can utilize the excellent piezoelectric potential of the d31 mode; the lower arched layer is the polydimethylsiloxane (PDMS) film covered by uniform trapezoidal microstructures and thereby increases the charge density of the piezoelectric NG. The piezoelectric NG output voltage with the double‐arched structure increased by 35.1 % upon applying a mechanical force of 5 N. Relating to improved steady voltage output performance, the enhanced piezoelectric NG was used as the sensitive element of a sensor with sensitivity of 2.21 V g−1 and linearity error of 5.91 % under a vibration amplitude of 6 mm. Moreover, the sensor not only exhibited stable repeatability and stability over long times, but it also steadily produced output performances after soaking in water temperatures up to 70 °C. Therefore, the highly reliable self‐powered vibration sensor presents prominent prospect for future application. [ABSTRACT FROM AUTHOR]

Challenges concerning the automation of modal identification and tracking procedures in permanent monitoring systems for Structural Health Monitoring purposes are discussed. In this context, an automated procedure based on parametric identification methods that involve the interpretation of stabilization diagrams is proposed. The methodology comprehends two key points: (i) automatic analysis of stabilization diagrams, performed through a first check of reasonable damping ratio, a subsequent modal complexity check and a final clustering of structural modes; (ii) automated tracking of the evolution in time of the identified modal properties. The proposed modal clustering and tracking steps exploit the introduction of self-adaptable dynamic thresholds, that do not require any a priori manual tuning for the different recorded data set. Finally, the proposed approach was successfully validated using real data collected on a historic iron arch bridge. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper examines the influence of fire damage on experimentally measurable modal parameters to explore the potential role of vibration-based structural identification in post-fire condition assessment. Experimental modal analysis of a precast, prestressed concrete roof comprised of modular double-tee joists was performed following exposure to an accidental fire. Empirical evidence of localized structural degradation is identified in the mode shapes, which suggests that modal parameters are sensitive to fire-induced damage in prestressed concrete members. Additionally, measured out-of-plane global modes are correlated with finite element simulations using a model of the as-built roof as well as a model updated using a large set of impact-echo measurements across the double-tee roof to spatially map localized reductions in stiffness. The comparisons support the experimental conclusions and further demonstrate that, by coupling nondestructive techniques, it is possible to construct a validated analytical model of structural components with sufficient fidelity to assess suitability for rehabilitation and reuse. With further development, vibration-based monitoring and structural identification may be an attractive alternative to physical load testing, particularly when the risk of structural collapse under proof testing loads is high. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Multihop communication is an essential functionality of wireless smart sensor networks used for full-scale civil infrastructure monitoring. It allows sensors to cooperate and deliver data between nodes outside the direct communication range. Multihop communication that can reliably collect a large amount of data generated by the entire monitoring system to a single base-station can be made efficient with the consideration of the specific communication characteristics of civil infrastructure monitoring as well as radio interference and channel contention. This paper proposes an efficient multihop communication service for large static wireless sensor networks in the application to civil infrastructure monitoring. The proposed protocols include a flooding-based routing method that is able to efficiently generate stable static routes and a theoretically efficiency-optimal data collection protocol to promptly retrieve data from the entire network. Additionally, crucial enhancements are introduced for the stable implementation of the proposed methods. Experiments and a full-scale instrumentation are also included to demonstrate the protocols' applicability. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the field of structural health monitoring or machine condition monitoring, most vibration based methods reported in the literature require to measure responses at several locations on the structure. In machine condition monitoring, the number of available vibration sensors is often small and it is not unusual that only one single sensor is used to monitor a machine. The aim of this paper is to propose an extension of fault detection techniques that may be used when a reduced set of sensors or even one single sensor is available. Fault detection techniques considered here are based on output-only methods coming from the Blind Source Separation (BSS) family, namely Principal Component Analysis (PCA) and Second Order Blind Identification (SOBI). The advantages of PCA or SOBI rely on their rapidity of use and their reliability. Based on these methods, subspace identification may be performed by using the concept of block Hankel matrices which make possible the use of only one single measurement signal. Thus, the problem of fault detection in mechanical systems can be solved by using subspaces built from active principal components or modal vectors. It consists in comparing subspace features between the reference (undamaged) state and a current state. The angular coherence between subspaces is a good indicator of a dynamic change in the system due to the occurrence of faults or damages. The robustness of the methods is illustrated on industrial examples. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an extended constitutive relation error (ECRE)‐based method for detecting damage in structural systems. Although most ECRE‐based methods for damage detection have relied on an iterative model calibration process, the approach advocated in this study uses residual energy to identify structural damage. Residual energy represents the spatial distribution of discrepancies between model predictions of the undamaged system and experimental measurements obtained from the damaged system. The current study calculates this residual energy by tightly linking experimentally measured vibration data to an associated numerical model. The approach implemented in this paper, referred to as the M‐K ECRE‐based approach, differs from previous ECRE‐based damage detection methods in that it incorporates both unbalanced elastic and unbalanced inertial forces in the calculation of residual energy. The rationale for including unbalanced inertial forces is that damage can also alter the mass distribution of a structure in addition to its influence on the distribution of structural stiffness. By considering unbalanced inertial forces, the proposed approach improves the calculation of residual energy, which in turn leads to an improvement in the method's ability to identify damage. At the end of this study, the efficiency of the M‐K ECRE‐based approach to identify damage is demonstrated on a scaled two‐story steel frame that is subjected to varying types and severities of structural damage. [ABSTRACT FROM AUTHOR]