A magnetic resonance compatible surgical manipulator: part of a unified support system for the diagnosis and treatment of heart disease

A prototype of a magnetic resonance (MR)-compatible surgical manipulator was designed and evaluated. The manipulator is designed so as to fit into vertical magnetic field open-configuration MR imagers. Moreover, it is designed to work without being fixed to an MR imager, and its electrical circuits and lines of actuators and sensors are independent of the room shield so that it could be installed in various kinds of settings at many MR imager sites without any additional construction. The MR compatibility of the manipulator was evaluated: no noticeable deformation was observed in the MR images even when the manipulator was in motion. Although the signal-to-noise deterioration ratio was higher than that previously reported, the MR images were thought to be good enough for recognizing the whole structure of a targeted organ and for following the relative position of the manipulator tip with regard to the target, i.e. MR tracking.     

配电网单相接地故障选线的一种新方法

为了提取故障暂态信号的相位谱特征以进行配电网单相接地故障选线，引入了零序电流解析信号的小波包分解。信号的解析表达形式可以更好地描述信号的幅值、相位和瞬时频率随时间变化的规律；它有着比原信号增大一倍的幅值谱、和原信号相同的相位谱和瞬时频率。小波包克服了小波变换不能对高频序列进一步分解的缺陷，在时域和频域上皆具有良好的局部化特性，可以聚焦被分析信号不同频带的时域特性。通过对构造的零序电流解析信号进行小波包分解，提取在适当频带的信号相位谱，依据故障线路和非故障线路相位谱的不同进行故障选线。理论分析和仿真结果表明：这种基于零序电流解析信号的小波包分解所提取暂态信号相位谱的选线判据具有很高的灵敏性和可靠性。     

半导体厂超净供气系统

介绍了集成电路制造用的电子气体中粒子控制规范,供气系统用的材料及其处理技术,供气管路和配件技术,粒子过滤及计数技术。     

Development of a remote fault diagnosis system applicable to autonomous mobile robots

In this paper, the novel idea of using an autonomous mobile robot as a virtual local network is proposed as a system for the remote diagnosis of faults of a mobile robot. There are two primary points in this research. First, an objective of the research is to achieve a system with good expandability, i.e. one in which the diagnosis can be achieved whether there are many or few mobile robots to be diagnosed. To accomplish this goal by simply changing some system settings, a simple network management protocol (SNMP) structure is utilized. Within the developed diagnosis system, a mobile robot is taken as one of the management objects of the network management system (NMS). The SNMP, which is widely used in the NMS, is applied and adapted to the developed system as the communication protocol for exchanging diagnosis information. Moreover, by taking advantage of the active moving and sensing ability of autonomous mobile robots, an effective method of fault inference called a 'run-test' is discussed. By using this method, the accuracy of the diagnoses is improved. In the best-case scenario, the certainty of an accurate diagnosis increased from 20% (without using the 'run-test') to 93%. On the other hand, in some cases, the accuracy of the diagnosis results barely improved. On average, among the cases discussed in this paper, the accuracy of the diagnosis results improved about 2.1 times by the proposed method.     

Global Fault Detection in Adhesively Bonded Joints Using Artificial Intelligence

In general, non-destructive evaluation is applied to detect and localize structural faults using a signal with a wavelength smaller than the detected fault. But the method requires analyzing the object in numerous small sections to detect the damage. Non-invasive diagnosis methods for fault detection are used in different industrial sectors. In this work, the main focus is on global fault detection for structural mechanical components such as a bonded beam using artificial intelligence, i.e., neural nets. Therefore, the fault detection procedure requires only a global measurement in the structural component in operational conditions. An experimental setup using two aluminum beams bonded with an adhesive was used to simulate a bonded joint. Different sizes of adhesive surface simulate faults in the original adhesive joint. Thereafter, resonance frequency shifts in the Frequency Response Functions (FRFs) were used to detect structural faults. Damage in structures causes small changes in the structural resonances. Then, the FRFs were used as an input into an artificial supervised neural network. This work considers global non-destructive tests focused only on the soundness estimation of the system. The neural network involved is a supervised feed-forward network with Levenberg–Marquardt backpropagation algorithm, which classifies the beams in four clusters. The classification consists in beam damaged or not damaged. If the beam is damaged the intensity of the fault is established.     

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.     

Design of Robust Stabilization and Fault Diagnosis for an Auto-balancing Two-Wheeled Cart

This paper investigates robust fault diagnosis strategies for the auto-balancing of an ergonomically designed two-wheeled cart which is inherently unstable and has a non-minimum phase. To endow the rider with robust stabilization, the normalized coprime factorization for steering is employed for allowing maximum model uncertainties and the driving orientation is achieved with an electronic differential steering control. A model-based fault-detection filter is designed to detect sensor faults. The observer gain obtained by solving an algebraic Riccati equation in the normalized coprime factorization approach offers some design convenience associated with the fault diagnosis filter. In order to promptly alert the rider for safety purposes in the event of a malfunction, the decision-making process to identify a critical failure is also investigated. Finally, evaluation examples are given to illustrate the performance of the proposed robust fault diagnosis strategies.