Fault Monitoring System Research Articles

An Intelligent Fault Monitoring System for Hydraulic Excavator

In this paper, functions of intelligent fault monitoring system of hydraulic excavator are analyzed. Architecture of the fault monitoring system is presented. Key technologies of the monitoring system are discussed detailed. Key parts of the fault monitoring system such as inspection controller, travel controller and programmable monitor, remote monitoring system is developed. As a result, the intelligent monitoring system is of great useful for monitoring the faults of hydraulic excavator.

Acquisition Module Design of Fault Monitoring System of Wind Turbine

In view of the present, the data acquisition system of the fault monitoring system is not perfect, so the data acquisition module of mechanical fault system of wind power generator is developed based on DSP, which better improves the on-line monitoring system. Firstly, the paper introduces the working principle of the module and the selection principle of set measuring point and monitoring point. Then, it introduces the design of hardware system in detail.

A New Method of Distribution Network Fault Location

Proposed a new method establish the line fault monitoring system to achieve the real-time fault monitoring and location because so many power line open fault that caused by the cable theft or the frequent natural disasters such as ice storm and so on today, The system uses the power line carrier technology to locate the power line fault in one transformer area, and uses the Zigbee wireless communication technology to transmit the fault information within the jurisdiction of different transformers.

Key performance aspects of an LTE FDD based Smart Grid communications network

The Smart Grid will enable a new era of electricity generation, transmission, distribution and consumption driven by efficiency, reliability, flexibility and environmental concerns. A key component of the Smart Grid is a communications infrastructure for data acquisition, monitoring, control and protection. In this paper, we evaluate the key performance aspects of an LTE Release 8 FDD network as the wide area communications network for Smart Grid applications. We develop analytical results for latency and channel utilization and discuss the implications for Smart Grid traffic sources, particularly the fact that system capacity is likely to be control channel limited. We also develop an OPNET based discrete event simulation model for a PMU based fault monitoring system using LTE FDD as its communication medium and use it to validate the analytical findings. In particular, we demonstrate how uplink data plane latencies of less than 10ms can only be achieved using small application layer packets. These findings can be used to understand how to best deploy an LTE FDD network in a Smart Grid environment and also in the development of new radio resource management algorithms that are tailored specifically to Smart Grid traffic sources.

基于ZigBee和GPRS技术的高压线故障监测系统设计

本文设计了无线远距离的高压输电线故障监测系统方案，远程传输使用GPRS通信技术将信息发送至监控中心，监测结点间构成ZigBee网络进行短距离数据交换。ZigBee网络使用CC2530射频芯片模块组成，GPRS模块选用SIM900A模块。整个系统故障监测结点和网关结点的能量来源于高压输电线电磁线圈互感取电方式，可长期在户外稳定工作。 This paper introduces design of long-distance fault monitoring system for high-voltage lines. Remote transmission uses GPRS communications technology to send information to the display software on host computer of the monitoring center. The monitoring nodes exchange data within a short distance by forming ZigBee network. CC2530 RF chip modules are used to build up ZigBee network, and SIM900A is selected as GPRS module. The energy of the monitoring node and gateway node is obtained by the mutual inductance of high-voltage lines. They can operate stably outside for a long time.

Design on Distributed Fault Monitoring System for Navigation Lights

For the environment of using the navigation lights and its requirement, a distributed fault monitoring system on the navigation lights is designed. The three-layer structure with slave station and master station and Voyage Data Recorder is employed in the system, which can monitor broken wire fault, short circuit fault and power failure on navigation lights. The mature RS485 communication circuit is adopted in communication interface not only between the master and the slave, but also between the master and VDR (Voyage Data Recorder). The master station gets the information of all navigation lights from the slave stations, and sends these received data to VDR after being processed. The system has been successfully applied in practice, and can effectively monitor the state of the navigation lights and give the alarm information when in fault. In addition, the monitoring system can quickly and accurately trace the fault position and type, so the navigation lights can be maintain timely.

Model-based fault monitoring of a plastic film extrusion process

This study reports the modelling of a plastic film extrusion process and the development and implementation of a model-based fault monitoring system. The model is mainly derived from the first-principles of chemical and mechanical engineering in addition to empirical knowledge related to the behaviour of polymer and designed to provide a safe offline platform for developing monitoring and control systems. The fault monitoring system constructs a residual via parity relations, and a multi-objective optimisation problem must therefore be solved in order that the residual can be sensitive to faults but insensitive to disturbances and modelling errors. In this study, a genetic algorithm is exploited for solving this multi-objective optimisation problem, and the resulting fault monitoring system is applied to the model. Simulation results demonstrate that various types of faults can be detected and diagnosed successfully.

General Fault Monitoring and Diagnosis Expert System Based on Fault Tree and Multi-Sensor Information

Due to the limited generality of traditional fault diagnosis expert system and its low accuracy of extracting failure symptoms, a general fault monitoring and diagnosis expert system has been built. For different devices, users can build fault trees in an interactive way and then the fault trees will be saved as expert knowledge. A variety of sensors are fixed to monitor the real-time condition of the device and intelligent algorithms such as wavelet transform and neural network are used to assist the extraction of failure symptoms. On the basis of integration of multi-sensor failure symptoms, the fault diagnosis is realized through forward and backward reasoning. The simulation diagnosis experiments of NC device have shown the effectiveness of the proposed method.

Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks

Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.

The Research of GPRS-Based Remote Fault Monitoring System for CNC Machine Tools

Based on GPRS network remote monitoring system which uses its two-way transmission performance, can easily monitor various electrical equipments and get information. Compare with the past remote monitoring systems, the system has the advantage of flexible networking, convenient, wide range of data transmission, high reliability, fast response time, and has great significance and value of research in CNC machine tool system with upgrade GPRS in the mobile communication networks, data services expand and data transfer capabilities.

단층감시시스템 기술현황 분석

이 연구는 신월성 원자력발전소 1,2호기(이하 신월성원전) 운영과 함께 국내 원자력관련 이용시설 부지(이하 원자력부지)에 처음 도입될 단층감시시스템의 설치 운영에 필요한 안전요건(즉, 규제 요건, 지침 및 기술기준)을 제공할 목적으로 국내외 단층감시시스템과 그와 유사한 측지시스템의 구축 사례를 수집, 분석하였으며, 단층감시시스템 구축 시 기본적으로 고려하여야 할 기술 사항들을 제시하였다. 단층감시시스템 구축 시 고려해야할 사항들로는 1) 부지고유특성의 이해(잡음 특성 포함), 2) 감시대상에 맞는 설비/설계 선택, 3) 백업시스템 구축, 4) 정례화된 검 교정체제를 통한 계측자료의 품질과 신뢰성 확보, 5) 투명한 운영체계와 정보공개 정책, 6) 장기적으로 안정적인 운영기반 등이 제안되었다. To provide regulatory standards and technical guides that are likely to be required for the Fault Monitoring System (FMS), soon to be installed and operated at the Shin-Wolsong NPP #1, 2 site, technical trend analysis was performed for currently operating geodetic and/or earthquake monitoring systems and activities worldwide, resulting in the identification of several items of concern regarding the design, installation, and operation of FMS at Korean sites. The items are as follows: 1) characterization of site-specific properties (including local noise), 2) selection of measuring instruments/design of appropriate sensitivity for the tectonic behavior of the target, 3) data/electricity backup system, 4) cross-checking and/or cross-referencing system to enhance data quality and credibility, 5) transparent data operation and open-to-the-public policy, and 6) long-term operation in a stable environment.

A New Mechanical Vibration Source Number Estimation Method Based on EMD-SVD-DE

To estimate the number of mechanical vibration sources from the convolution mixtures with fewer sensors than sources, a new method combined with EMD-SVD- DE（Empirical Mode Decomposition, EMD; Singular Value Decomposition, SVD; Dominant Eigenvalue, DE）was proposed. The characteristic forms of vibration sources were obtained through the EMD of the mixtures. The relation matrix of the characteristic forms of different mixtures was used to get the eigenvalues of sources which were used to determine the number of sources by means of SVD and DE. It is verified that the new method yields a correct estimate of source number in the simulation and test. It is help to analyze the mechanical vibrations in the fault monitoring system and to separate the useful mechanical vibrations efficiently from the mixtures.

A hybrid fault diagnosis and monitoring system for precision rotor systems

Rotor and its corresponding system play an important role in a precision machine. However, accuracy deterioration, machine damage, or malfunction could also happen to the machine due to various abnormalities of the rotor system. Thus, a monitoring system that can early detect and identify abnormalities so that proper actions can be taken to ensure the machine works properly is necessary. In this study, fault diagnosis/monitoring method that can online monitor the three major abnormalities (impact by unusual force, abnormal friction and unbalanced rotation) of a rotor system was developed. Both of wavelet transform and fast Fourier transform were employed in the method to analyse the permanent change and transient change of system vibration signals for abnormality diagnosis. Characteristic values of converted vibration signals of a rotor system were calculated and used to form a diagnosis map. Subsequently, occurrence possibility of an abnormality can be predicted with the use of the degree of approach model. To verify the proposed monitoring method and system, both simulation and experiments were conducted. The results have shown high feasibility and reliability of the detection system.

자동차 차체 제조 공정에서 용접 공정 오류 검출을 위한 지능형 모니터링 시스템 개발

In resistance spot welding, regardless of the optimal condition, bad weld quality was still produced due to complicated manufacturing processes such as electrode wear, misalignment between the electrode and workpiece, poor part fit-up, and etc.. Therefore, the goal of this study was to measure the process signal which contains weld quality information, and to develop the process fault monitoring system. Welding force signal obtained through variety experimental conditions was analyzed and divided into three categories: good, shunt, and poor fit-up group. And then a monitoring algorithm made up of an artificial neural network that could estimate the process fault of each different category based on pattern was developed.

Intelligent Data Reduction Algorithm for Sensor Network based Fault Diagnostic System

In the modern life, machines are used for various areas in industries as the advance of science and industrial development has proceeded. In many machines, the rotating machines play an important role in many processes. Therefore, the development of fault diagnosis and monitoring system for rotating machines is required. An ubiquitous sensor network (USN) is a combination of the key computer science and engineering area technology including the wireless network, embedded system hardware and software, communication, real-time system, etc. It collects environmental information to realize a variety of functions. In this work, a data reduction algorithm for USN based remote fault diagnostic system which can be easily applied to previously built factories is proposed. To verify the feasibility of the proposed scheme, some simulations and experiments are executed.

Failure detection and isolation in robotic manipulators using joint torque sensors

SUMMARYReliability of any model-based failure detection and isolation (FDI) method depends on the amount of uncertainty in a system model. Recently, it has been shown that the use of joint torque sensing results in a simplified manipulator model that excludes hardly identifiable link dynamics and other nonlinearities such as friction, backlash, and flexibilities. In this paper, we show that the application of the simplified model in a fault detection algorithm increases reliability of fault monitoring system against modeling uncertainty. The proposed FDI filter is based on a smooth velocity observer of degree 2n where n stands for the number of manipulator joints. No velocity measurement and assumptions on smoothness of faults are used in the fault detection process. The paper focuses on actuator faults and investigates the effect of torque sensor noise on threshold selection. The FDI filter is further improved to become robust against an unknown bias in torque sensor reading. The effect of position sensor noise together with position sensor faults are also investigated. Simulation example on a 6-degrees of freedom manipulator is carried out to illustrate the performance of the proposed FDI method.

In-situ monitoring of the brush/rotor interface in a homopolar motor with Acoustic Emission

Homopolar motors have been proposed as alternative propulsion means for ships. Such motors operate in a very harsh and demanding environment, making in-situ diagnostics and fault-monitoring systems a key issue for extended operation. In a typical homopolar motor, several thousand brushes are used, which are all in sliding contact with a copper rotor at a velocity of about 10 m/s. Wear and degradation of the brush and rotor are key issues that need to be understood in order to design a reliable motor. In this investigation, Acoustic Emission (AE) transducers were used to study the characteristics of the brush/rotor interface of a homopolar motor as a function of speed, load, materials and other design parameters. The AE sensors were directly coupled to the brush in the homopolar motor test rig, and data were obtained over extended periods of time. The sensor signal demonstrated good sensitivity to brush/rotor contact conditions, brush contact load and motor current polarity. The results show that AE is well suited for in-situ monitoring of the brush and rotor conditions.

Fault detection and identification using FIRFMS

This paper deals with two of the main tasks of fault monitoring systems (FMS): fault detection and fault identification. During fault detection, the FMS should recognize that the plant behavior is abnormal, and therefore, that the plant is not working properly. During fault identification, the FMS should conclude which type of failure has occurred. The main goal of this work is to present, in the context of the Fuzzy Inductive Reasoning Fault Monitoring System (FIRFMS), a new fault detection technique called enveloping and an enhancement of the fault identification method based on the model acceptability measure. Both contributions allow a more robust and reliable FIRFMS fault detection and identification processes. The enveloping technique and the model acceptability measure are applied to three applications of quite different areas. The first one corresponds to an electric circuit model previously used for such purpose in the literature. The second one is a biomedical system, the human central nervous system (CNS) control. It is the first attempt to apply the FIRFMS to support medical decisions. The third and last one corresponds to a water demand distribution system. The electric circuit is used to show that the enhanced FIRFMS outperforms the previous FIRFMS. The biomedical and water demand distribution systems are presented to show the good performance of the new FIRFMS.

Automatic Programming Methodologies for Electronic Hardware Fault Monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.

Analytical Redundancy Techniques for Fault Detection in an Active Heavy Vehicle Suspension

This paper describes the design and implementation of an intelligent fault monitoring system for a prototype heavy vehicle active suspension system. A controller architecture has been designed, to facilitate safe start up and shutdown of the active control. This paper develops a method for monitoring the complete system, by dividing it into submodels which are more straightforward to analyse. The aim is to define a practical method for detecting faults, taking into account the nonlinearities in the real vehicle.