Improving sensor placement optimisation robustness to environmental variations and sensor failures for structural health monitoring systems

The installation of structural health monitoring (SHM) systems based on machine learning algorithms on structures has been of constant interest. The application of this kind of SHM system can facilitate decisions regarding maintenance and the remaining useful life of the structure in a more automatic and convenient way. As part of the SHM system to collect information, the sensor system can be optimally designed to improve the performance of the final system. In this thesis, the work focuses on how to consider the effects of environment and sensor failures during the sensor placement optimisation (SPO) to build a more robust and effective monitoring system. Since the availability of data during the design phase varies widely from project to project and there are no studies or specifications that provide specific guidance, not much research has been done on the design of such sensor systems, which require reliable simulated or measured data to be available during the design phase. Considering the different levels of data accessibility at the design phase, this thesis proposes a series of strategies for the optimal design of sensor systems for SHM systems from a machine-learning perspective. The first main content of this thesis is hierarchical assessment criteria of designed-system performance to balance the computational feasibility and visualisation of the final system performance. At the stage after data is collected, machine learning model results are often used as a criterion, whose acquisition is usually time-consuming. At the same time, higher data accuracy is required. Therefore, the criteria used in the design of sensor systems are divided into different tiers. The criteria for the initial stage can be abstracted from the purpose of the applied machine learning model to significantly reduce the number of candidate designs. The criteria for the final stage can be similar to those used in the stage after data is collected. Whether or not to use criteria from all tiers depends on the level of data availability. It can be found that more work on the optimisation design of the sensor system can be done at the initial stage of the hierarchical design framework. Therefore, the other three main contents of this thesis are developed at this stage. Considering different levels of data availability, supervised and unsupervised correlation-based strategies to evaluate sensor combinations are proposed, including the evaluation criterion and the fast calculation methods of this criterion. Sensor combinations can be ranked even if only the healthy state data are accessible. To account for the effects of environmental variations, two SPO strategies based on approaches to extracting robust features are proposed, and an appropriate criterion that can be used is also introduced. These two strategies cover both situations where environmental change information is available and not. To consider the sensor-failure effect in the SPO process, another two strategies, namely fail-safe sensor optimisation or fail-safe optimisation with redundancy, are proposed in this thesis, both of which can take into account the performance of the designed system before and after the failure of some critical sensors. Different assessment criteria are adopted to demonstrate the generality of these strategies

No-Tension Sensor Closed-Loop Control Method with Adaptive PI Parameters for Two-Motor Winding System

In a winding system, it is very important to control the tension precisely. Based on the process of rewinding and unwinding, a sensorless tension control method with PI parameters of adaptive speed controllers is proposed in this paper. According to the principle of torque balance, a tension observer is designed to replace the tension sensor, and the observed value instead of the measured value of tension is used as feedback. Then the measurement delay caused by tension sensor is reduced. For the time-variable inertia, Landau discrete-time recursive algorithm is used to estimate the inertias of the rewind and unwind motors. Moreover, the estimated inertias are used to adjust the PI parameters of the speed controllers. As the tension control system has the ability to adapt to the change of inertia, its dynamic performance is improved to some extent. In addition, the proposed sensorless tension control method is simple and easy to implement, which only uses the current and speed signals of the motors without any additional hardware needed. At last, the feasibility and effectiveness of the proposed method are verified by the experimental results

A disease forecast and early warning system based on electronic health records

Conference Name:8th International Conference on Computer Science and Education, ICCSE 2013. Conference Address: Colombo, Sri lanka. Time:August 26, 2013 - August 28, 2013.Disease forecast and early warning have been always important but difficult tasks. Because of the drawbacks of traditional records, the electronic health records, which bring in the ICD-10, are used in our system. Input information are firstly de-duplicated to remove redundancy. After that, the system are used for disease early warning and forecast. The results show that the proposed system has great help for the health sector to prevent and control the diseases. ? 2013 IEEE

High‐frequency square‐wave voltage injection position sensorless control method using single current sensor

Abstract High‐frequency (HF) square‐wave voltage injection position sensorless control method for interior permanent magnet synchronous motor (IPMSM) is widely utilised in zero and low speed range due to its good dynamic performance and easy implementation. However, this method relies on the sampling accuracy of current sensors for rotor position estimation. To overcome this restriction, an HF square‐wave voltage injection position sensorless control method for IPMSM using a single current sensor (SCS) is proposed. Firstly, the impact of current sampling errors on HF square‐wave voltage injection position sensorless control is analysed, and it is concluded that the scaling errors of current sensors will cause the estimated position to oscillate at twice the fundamental frequency. Based on this conclusion, the phase currents reconstruction technology with SCS is adopted to avoid the impact of scaling errors on rotor position estimation. To reconstruct the phase currents containing HF component, a PWM cycle is divided into two parts, sampling stage and injection stage. By this way, the impact of HF square‐wave voltage injection on current reconstruction can be avoided. Then, the rotor position estimation is realised. The experiments are performed on a 20‐kW IPMSM platform and the results verify the effectiveness of the proposed method

A Novel Moment of Inertia Identification Strategy for Permanent Magnet Motor System Based on Integral Chain Differentiator and Kalman Filter

In a motor control system, the parameters tuning of speed and position controller depend on the value of the moment of inertia. A new moment of inertia identification scheme for permanent magnet motor system was proposed in this paper. This is an extension of the existing acceleration deceleration methods, which solves the large moment of inertia identification error caused by variable angular acceleration, large calculation error of inertia torque, and large measurement noise in the acceleration process. Based on the fact that the angular acceleration is not constant and the sampling signal is noisy, the integral chain differentiator was used to calculate the instantaneous angular acceleration at any time and suppress the sampling signal noise at the same time. The error function with instantaneous angular acceleration and inertia torque as parameters was designed to estimate the moment of inertia. In order to calculate the inertia torque accurately, viscous friction torque was considered in the calculation of inertia torque, and Kalman filter was used to estimate the total load torque to solve the problem of under rank of motor motion equation. Simulation and experimental results showed that the proposed method could effectively identify the moment of inertia in both noisy and noiseless environments

An improved SVM method for cDNA microarray image segmentation

Conference Name:8th International Conference on Computer Science and Education, ICCSE 2013. Conference Address: Colombo, Sri lanka. Time:August 26, 2013 - August 28, 2013.Microarray technology, as a revolutionary tool for biomedical research, has been widely used to analyze the gene expression level. Image segmentation is an important step of microarray technology. In this paper, we have presented an improved SVM method, which combined the SVM with the canny algorithm, the morphological algorithm and the fixed circle method, to obtain a better segmentation result. In addition, the initial image was preprocessed by using the image contrast enhancement and median filtering. Intensive experiments on the Stanford Microarray Database (SMD) and the Gene Expression Omnibus (GEO) database indicate that the proposed method is superior to the K-means method and the Gene Pix. Pro. ? 2013 IEEE