System Identification Technology Research Articles

Impact monitoring on complex structure using VMD-MPE feature extraction and transfer learning

Impacts are common damage events in aviation scenarios that can cause damage to the structural integrity ofan aircraft and pose a threat to its safe operation. Therefore, it is crucial to monitor impact events. A region-to-point monitoring method is proposed to address the challenges posed by the large area of monitored aircraft structures and the long distance between sensors. Firstly, to fully use the information in the original impact signal and reduce the aliasing effect caused by the reinforced structure, the original signal is decomposed into several modes with different frequency bands by Variational Mode Decomposition (VMD). The Multi-scale Permutation Entropy (MPE) value is then calculated to reflect the various characteristics of each mode, which is used as a basis for classification. Secondly, Transfer Component Analysis (TCA) is selected as a transfer learning method to reduce the difference between the features of the source domain and the target domains' features. Thirdly, the TCA-transformed source domain data are used to train the Probabilistic Neural Network model (PNN), and the unfamiliar target domain data are used to verify the impact area identification. Finally, based on regional location, the system identification technology and weighted centroid algorithm can be used to obtain the history of impact force and the precise coordinates of the impact location.

Modal identification of building structures under unknown input conditions using extended Kalman filter and long-short term memory

Various system identification (SI) techniques have been developed to ensure the sufficient structural performance of buildings. Recently, attempts have been made to solve the problem of the excessive computational time required for operational modal analysis (OMA), which is involved in SI, by using the deep learning (DL) algorithm and to overcome the limited applicability to structural problems of extended Kalman filter (EKF)-based SI technology through the development of a method enabling SI under unknown input conditions by adding a term for the input load to the algorithm. Although DL-based OMA methods and EKF-based SI techniques under unknown input conditions are being developed in various forms, they still produce incomplete identification processes when extracting the identification parameters. The neural network of the developed DL-based OMA method fails to extract all modal parameters perfectly, and EKF-based SI techniques has the limitations of a heavy algorithm and an increased computational burden with an input load term added to the algorithm. Therefore, this study proposes an EKF-based long short-term memory (EKF-LSTM) method that can identify modal parameters. The proposed EKF-LSTM method applies modal-expanded dynamic governing equations to the EKF to identify the modal parameters, where the input load used in the EKF algorithm is estimated using the LSTM method. The EKF-LSTM method can identify all modal parameters using the EKF, which is highly applicable to structural problems. Because the proposed method estimates the input load through an already trained LSTM network, there is no problem with computational burden when estimating the input load. The proposed EKF-LSTM method was verified using a numerical model with three degrees of freedom, and its effectiveness was confirmed by utilizing a steel frame structure model with three floors.

An Integrated Test-Based Nonlinear Aeroelastic Analysis Method to Freeplay-Induced Limit-Cycle Oscillations

The nonlinear limit-cycle oscillations induced by aileron rotational freeplay are investigated by numerical simulation and flutter wind tunnel tests. An integrated nonlinear aeroelastic analysis method and procedure based on the nonlinear system identification technology is proposed. Based on the characteristics of the structure, a low-speed nonlinear flutter wing-aileron model that considers both the linear flutter test and the nonlinear limit-cycle oscillation test with aileron rotational freeplay is designed. The wind tunnel tests are conducted with the model, and the stable limit-cycle oscillation is observed by accelerometers under the linear flutter boundary with a 0° angle of attack. Higher-order harmonic vibration is also observed with a low proportion compared to the fundamental oscillation, and the limit-cycle oscillation grows gradually with the increase of airspeed until flutter occurs. The comparison of numerical and experimental investigation shows high consistency.

A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies

Tobacco products are an addictive commodity. According to the World Health Organization’s (WHO) latest statistics data, tobacco kills more than eight million people each year. In 2003, the WHO proposed the Framework Convention on Tobacco Control (FCTC) to provide an effective framework for the control of tobacco products to governments around the world. In the field of tobacco products, the hardest problem is how to prevent counterfeit tobacco products and smuggling. To solve the problems, we proposed a blockchain-based traceable and verifiable logistics system for tobacco products with global positioning system (GPS) and radio-frequency identification (RFID) Technologies. In this research, we provide an overview of system architecture, and also define the protocol and the smart contract in every phase that stores data into the blockchain center. We realized a decentralized database and authentication system that uses blockchain and smart contract technology; every protocol in every phase was designed to achieve the integrity of data and non-repudiation of message. Every tobacco product’s shipping record will be completed by scanning the RFID tag and retrieving the GPS with a mobile reader, where the record will be updated and validated in the blockchain center. In the end, the security and costs of the system were analyzed, and a comparison was made with the EU’s (European Commission) method. Our system is more flexible for transportation, more secure in the communication protocol, and more difficult to tamper and forge data. In general, the proposed scheme solved the problem of tobacco products counterfeiting and tracking issues.

Development of the structural health record of containment building in nuclear power plant

The main objective of this work is to propose a reliable routine standard operation procedures (SOP) for structural health monitoring and diagnosis of nuclear power plants (NPPs). At present, NPPs have monitoring systems that can be used to obtain the quantitative health record of containment (CTMT) buildings through system identification technology. However, because the measurement signals are often interfered with by noise, the identification results may introduce erroneous conclusions if the measured data is directly adopted. Therefore, this paper recommends the SOP for signal screening and the required identification procedures to identify the dynamic characteristics of the CTMT of NPPs. In the SOP, three recommend methods are proposed including the Recursive Least Squares (RLS), the Observer Kalman Filter Identification/Eigensystem Realization Algorithm (OKID/ERA), and the Frequency Response Function (FRF). The identification results can be verified by comparing the results of different methods. Finally, a preliminary CTMT healthy record can be established based on the limited number of earthquake records. It can be served as the quantitative reference to expedite the restart procedure. If the fundamental frequency of the CTMT drops significantly after the Operating Basis Earthquake and Safe Shutdown Earthquake (OBE/SSE), it means that the restart actions suggested by the regulatory guide should be taken in place immediately.

Real-Time Monitoring of Thermal Response and Life-Time Varying Parameters in Power Modules

This article introduces new technologies for monitoring thermal response and life-time varying parameters of power electronic modules in real time without interrupting normal converter operation. The technologies are embedded in an adaptive thermal observer, which integrates temperature measurements and electrothermal models to estimate device losses and temperatures at reliability-critical locations of the power module. The observer structure includes a model reference adaptive system for adaptively calibrating device switching-loss models. This improves thermal monitoring accuracy, avoids time-consuming offline calibrations, and creates the opportunity for efficiency estimations during power module operation. The adaptive observer together with small-signal loss-excitation and system-identification technologies extract the thermal impedance of the power module in magnitude and phase over multiple frequency decades. This in situ thermal impedance spectroscopy has the unique feature of continuously providing information on the thermal interface of the power devices without interrupting normal inverter operation. The extracted information, i.e., life-time varying electrical and thermal parameters, enables diagnosing power module state-of-health with improved accuracy compared to prior technologies. It even allows separating different degradation effects, such as bond wire lift-off, device and base-plate solder delamination as well as deterioration of the convection process. With these new features, next generation monitoring systems can extract thermal response and degradation and thus protect power modules from severe thermal stress and overload. Furthermore, the obtained state-of-health information can be selectively used to reduce thermal stress and to schedule predictive maintenance leading to a safer and more reliable operation of future converter systems.

Identification of Ploidy Variation of Ponkan Embryo Rescue Plants

China has abundant of local citrus resources, which had many seeds. The Seedless is the aim of the local citrus breeding. Triploid citurs resources usually obtained by crosssing breeding between the diploid and tetraploid plants. However, the resources of tetraploid citrus are limited. It has been reported that the hybrid between diploid and diploid plants can produce triploid citrus, which not only saves the breeding time, but also shortens the breeding cycles. In this project, flow cytometry detection assisted by embryo rescue system and SSR identification technology were used to improve the probability of obtaining polyploidy from the hybrid offspring, and provide technical support for obtaining polyploidy from large-scale hybridization between diploid and diploid. plants. Three diploid pollen cultivars were crossbred with Hunan local cultivar 8306, immature seeds were collected for embryo rescue to obtain regenerative plants, plant ploidy was detected by flow cytometry and stoma electron microscope, and offspring genotypes were identified by SSR. After 100 d hybrid between three diploid pollens and Ponkan 8 306, 405 plants were obtained by embryo rescue technique. And 70 plants survived in the greenhouse after transplantation. Through flow cytometry instrument and stomatal electron microscope inspection, the results showed that SSR analysis validated that there are 13 polyploid plants, including 1 tetraploid plant and 12 triploid plants. The ratio of polyploidy was 18.57%. This study is obtained triploid and tetraploid plants by Ponkan embryo rescue from diploid interspecific hybridization. In this study, the embryo rescue technology system and the early identification system for polyploid hybridization were established, and a lot of new polyploid germplasm were obtained.

Expert System Insemination

Insemination is a method used as a program by injecting sperm directly into the cervix so fertilization can occur. According to world health, the success rate of this program ranges from 20-30%, allowing several couples to choose this alternative. The process of applying artificial insemination requires the right pregnancy results, which require proper identification. System identification technology can be done quickly to assist in the identification of fertility that must be performed by an obstetrician. System development in this research is an expert system to determine the success of insemination programs for pregnancy. This system makes it easier for couples to consult and provide information to couples about insemination activities based on their partner’s condition. This expert system is made with a Hybrid method, where reading data from this method is done by forward chaining and certainty factors. The forward chaining method aims to read the cycle of couples who will carry out insemination and certainty factors, namely the method used to determine the level of success in the pregnancy program that will be obtained by the couple.

Internet of Things (IOT) for Railways

The biggest problems of the Indian railways are accidents and more delay time. The Internet of Things (IOT) can be used to reduce the effect of above two problems. Many accidents occur because of un-attended railway gates, over speed and improper signaling. The information of trains is not so clear to the railway stations, which leads to delay in trains. In this paper some solutions are proposed, to make the railways better, like automatic gate control, train tracking using Global Positioning System (GPS) and track switching. This can be achieved by IOT along with database management system (DBMS) and Radio Frequency Identification (RFID) technology. Each train is tracked using RFID and information is updated into the DBMS.

A High Efficient Fluid-Structure Interaction Method for Flutter Analysis of Mistuned

The time cost is very high by direct fluid-structure interaction method for mistuned bladed disk structures, so aerodynamic loads generally are ignored or treated as small perturbations in traditional flutter analysis. In order to analyze the flutter characteristics of mistuned blade rapidly and accurately, this paper presents an efficient fluid-structure interaction method based on aerodynamic reduced order model. system identification technology and two basic assumptions are used to build the unsteady aerodynamic reduced order model. Coupled the structural equations and the aerodynamic model in the state space, the flutter stability of mistuned bladed disk can be obtained by changing the structural parameters. For the STCF 4 example, the response calculated by this method agrees well with the results obtained by the direct CFD, but the computational efficiency is improved by nearly two orders of magnitude. This method is used to study the stiffness mistuned cascade system, and the stability characteristics of the system are obtained by calculating the eigenvalues of the aeroelastic matrix. The results show that the stiffness mistuning can significantly improve the flutter stability of the system, and also lead to the localization of the mode. The mistuning mode, mistuning amplitude and fluid structure interaction can influence the flutter stability obviously.

Identification of unknown operating system type of Internet of Things terminal device based on RIPPER

Due to the vast popularity of sensors, cloud computing, mobile computing, and intelligent devices, the Internet of Things has seen tremendous growth in recent years. Operating system type recognition is the core technology of network security assessment. Due to inherit security problems of Internet of Things such as the situation of risk and threat of information, the operating system recognition seeks research attention for Internet of Things network security. In view of the current identification method of active operating system, it is prone to be detected by intrusion detection system. The operating system identification technology based on transmission control protocol/Internet protocol fingerprint library is more complicated than to distinguish the operating system types of unknown fingerprints. In this work, a passive operating system identification method based on RIPPER model is proposed. Also, it is compared with the existing support vector machine and C45 decision tree classification algorithms. Experiments reveal that RIPPER-based algorithm has better recognition accuracy and recognition efficiency.

Probabilistic Approach for Damping Identification Considering Uncertainty in Experimental Modal Analysis

The system identification technology is essentially an inverse procedure, starting from the experimentally measured response, to construct mass, stiffness, and damping matrices of the structure. However, the measurement inevitably contains uncertainties, which significantly impact the identified system characteristics, especially for damping terms. In the presence of experimental uncertainty, the aim of damping identification in this paper is not a single deterministic solution with maximum fidelity to a single experiment, but rather a set of optimized solutions with acceptable robustness to multiple uncertain experiments. To achieve this objective, an integrated approach combining deterministic identification and probabilistic calibration techniques is proposed. This approach starts from the properness condition of modes in a deterministic identification. A probabilistic estimation technique is performed on the preliminary identified data so that an uncertainty boundary is available for the calibration procedure where the genetic algorithm and classical optimization techniques are used. A comprehensive comparison metric for two continuous quantities is proposed as the objective function in the calibration procedure. Finally, a probabilistic validation metric is proposed to assess the stability of the calibrated damping matrix. In both simulated and experimental examples, the finally obtained matrices exhibit their robustness with regard to the experimental uncertainty.

An Adaptive Anti-collision Algorithm Based on Tracking Tree Search

In the radio frequency identification (RFID) system, the tag collision is the key to improve the efficiency of system identification technology. In order to improve the recognition efficiency of the system, this thesis proposed a new anti-collision algorithm: Adaptive Slots Collision Tracking Tree algorithm (ASCTTA). This algorithm used the track information of collision to calculate collision factor, make it adaptive selection timeslot number for efficient identification tags. From the experimental results, the proposed algorithm is compared with the other three algorithms; the number of communications and traffic between reader and tag are decreased significantly, and has high recognition efficiency.

Outlier-Tolerance RML Identification of Parameters in CAR Model

The measured data inevitably contain abnormal data under the normal operating conditions. Most of the existing algorithms, such as least squares identification and maximum likelihood estimation, are easily affected by abnormal data and appear large indentation deviation. It is a difficult task needed to be addressed that how to improve the sensitivity of the existing algorithm or build a new parameter identifying algorithm with outlier-tolerance ability to abnormal data in system identification technology application. In this paper, the sensitivity of the RML to the sampled abnormal data was analyzed and a new improvement algorithm of CAR process is established to improve outlier-tolerance ability of the RML identification when there are outliers in the sampling series. The improved algorithm not only effectively inhibits the negative impact of the abnormal data but also effectively improve the quality of the parameter identification results. Some simulation given in this paper shows that the improved RML algorithm has strong outlier-tolerance. This paper’s research results play an important role in engineering control, signal processing, industrial automation and aerospace or other fields.

Using CubeSat/micro-satellite technology to demonstrate the Autonomous Assembly of a Reconfigurable Space Telescope (AAReST)

Future space telescopes with diameter over 20m will require new approaches: either high-precision formation flying or in-orbit assembly. We believe the latter holds promise at a potentially lower cost and more practical solution in the near term, provided much of the assembly can be carried out autonomously. To gain experience, and to provide risk reduction, we propose a combined micro/nano-satellite demonstration mission that will focus on the required optical technology (adaptive mirrors, phase-sensitive detectors) and autonomous rendezvous and docking technology (inter-satellite links, relative position sensing, automated docking mechanisms). The mission will involve two “3U” CubeSat-like nanosatellites (“MirrorSats”) each carrying an electrically actuated adaptive mirror, and each capable of autonomous un-docking and re-docking with a small central “15U” class micro/nano-satellite core, which houses two fixed mirrors and a boom-deployed focal plane assembly. All three spacecrafts will be launched as a single ~40kg micro-satellite package. The spacecraft busses are based on heritage from Surrey׳s SNAP-1 and STRaND-1 missions (launched in 2000 and 2013 respectively), whilst the optics, imaging sensors and shape adjusting adaptive mirrors (with their associated adjustment mechanisms) are provided by CalTech/JPL. The spacecraft busses provide precise orbit and attitude control, with inter-satellite links and optical navigation to mediate the docking process. The docking system itself is based on the electromagnetic docking system being developed at the Surrey Space Centre (SSC), together with rendezvous sensing technology developed for STRaND-2. On orbit, the mission profile will firstly establish the imaging capability of the compound spacecraft before undocking, and then autonomously re-docking a single MirrorSat. This will test the docking system, autonomous navigation and system identification technology. If successful, the next stage will see the two MirrorSat spacecraft undock and re-dock to the core spacecraft in a linear formation to represent a large (but sparse) aperture for high resolution imaging. The imaging of stars is the primary objective, but other celestial and terrestrial targets are being considered. Teams at CalTech and SSC are currently working on the mission planning and development of space hardware. The autonomous rendezvous and docking system is currently under test on a 2D air-bearing table at SSC, and the propulsion and precision attitude control system is currently in development. Launch is planned for 2016. This paper details the mission concept; technology involved and progress to date, focussing on the spacecraft buses.

AR時系列解析を用いた非線形振動特性同定法

In this paper, we discussed the geometrically nonlinear system identification technology using auto regressive time series analysis. We focused on the transient response from large amplitude to small amplitude. In addition, we assumed that the transient response contains structural non-stationary. We derived the regression formula of instantaneous frequencies and amplitudes using method of averaging. We formulated estimation method of instantaneous frequencies using Kalman filter and auto-regressive model. In numerical simulation, we conducted the identification of load-deformation relationship. In the result, our proposed method could identify the load-deformation relationship in a high degree of accuracy. On the other hand, we conducted the verification of accuracy using least-square method. In this case, accuracy of our proposed method was higher than accuracy of least-square method. We conducted the identification experiment using geometrically nonlinear system. In tensile test, we observed the hard spring characteristic in our experimental system. In addition, we got the free damping oscillation. In the result of an identification experiment, our estimation result was in good agreement with experimental data. On the other hand, we tried the identification test using least-square method. But, we could not reconstruct load-deformation relationship. Therefore, we confirmed availability of our proposed method in low length data.

Health parameter monitoring via a novel wireless system

This study develops a novel remote healthcare system based on Wireless Sensors Network System (WSNs) and Radio Frequency Identification (RFID) technologies. Cloud equipment is used as sensing cloud architecture to create the system database, and Improved Particle Swarm Optimization (IPSO) is applied to build a personal physiological signal sensing system. The collected personal physiological signals are analyzed, and RFID technology is used to create an administrator identity and database. The integrated physiological instrument measures/monitors blood pressure, heart rate, blood oxygen content, body weight, BMI and cardiogram. This system can be applied to, say, employees, nursing-home residents and the elderly. Physiological changes are identified at any time via a self-health examination, promoting early diagnosis and treatment. The current ZigBee technology, which has many advantages, is used in medical institutions, industry, and agriculture, and for automated control and building monitoring. This study uses WSNs technology to transfer physiological data to the cloud for analysis, processing, and storage. The client-side and appropriate medical personnel are notified by e-mail and short messages via the Internet, such that they can provide timely diagnosis and deploy treatment. The IPSO scheme is used to increase the efficiency and accuracy when searching for at-risk groups, searching data, and defining and summing the weights of physiological data. If the first 10% of users with high weight values are a risky population that must be treated immediately, this system informs medical personnel immediately, potentially improving medical service quality and application of medical resources.

Robust Cubic-Based 3-D Localization for Wireless Sensor Networks

The rapid progress of wireless communication and the availability of many small-sized, light-weighted and low-cost communication and computing devices nowadays have greatly impacted the development of wireless sensor network. Localization using sensor network has attracted much attention for its comparable low-cost and potential use with mon- itoring and targeting purposes in real and hostile application scenarios. Currently, there are many available approaches to locating persons/things based on global positioning system (GPS) and radio-frequency identification (RFID) technologies. However, in some application scenario, e.g., disaster rescue application, such localization devices may be damaged and may not provide the location information of the survivors. The main goal of this paper is to design and develop a robust localization technique for human existence detection in case of disasters such as earthquake or fire. In this paper, we propose a 3-D localization technique based on the hop-count data collected from sensor anchors to estimate the location of the activated sensor mote in 3-D coordination. Our algorithm incorporates two salient features, cubic-based output and event-triggering mechanism, to guarantee both improved accuracy and power efficiency. Both simulation and experimental results indicate that the proposed algorithm can improve the localization precision of the human existence and work well in real environment.

A Virtual Model for Civil Building Electric Control Based on Matlab Controller

The purpose of this study is correct and effective model predictive control (MPC), in reduce energy demand and cost of buildings in power network and use pricing -- a demand and charges. A virtual model, for a single floor, the article commercial buildings equipped with variable air volume (VAV) based on Energyplus cooling system. Real-time data between the realization of the switch Energyplus and Matlab into building control virtual test bed (BCVTB) as a middleware. System identification technology is to realize get zone temperature and the power function model, which is used in the MPC framework. With economic target, target function is formulated as a linear programming problem and solves the problem. In the rush hour pre-cooling effect and independent cooling unloaded from building thermal mass in supply and demand can be observed during a consecutive weekly simulation. The MPC brings the cost comparison, the control strategy and other are preprogrammed baseline.

Determination of applicable input range for approximating a nonlinear FGR furnace around the design point

In this paper, the nonlinear dynamic characteristics of a FGR furnace have been analysed around the furnace design point. Based on the steady-state results of full-scale nonlinear CFD simulations, the maximal allowable range on the variations of the furnace inputs can be determined, once for the maximal error bound between nonlinear system and its linear counterpart is specified. It is interesting to note that for a reheating furnace, the nonlinearities associated with the heat load are less severe than that associated with NO emission. With due consideration of the established input signal linear ranges, the linearized dynamic models of the furnace are derived by applying system identification technologies using the data generated from the CFD simulations. Analysis and validation of the models are also carried out. It is concluded that this technique is applicable to weak nonlinear systems around the design point. The results of the analysis provide additional insights on the nature of the nonlinearities as well as guidelines for selecting the input amplitude if system identification techniques are used. So long as the amplitudes of the probing signals satisfy the respective input constraints, the obtained linearized models will be applicable around the design point. Subsequently, these models can be used to design feedback controllers to maintain the furnace operated around the design point.