Remote Condition Monitoring System Research Articles

Development of Remote Condition Monitoring System for Panauti Hydropower Station

Abstract Condition monitoring is the process of predicting faults in machine through the measurement and analysis of pre-identified parameters. This paper aims to present the features and process of development of a real time condition monitoring system installed at Panauti Hydropower station. In total, seven different sensors including Pressure, Temperature, Guide Vane Position and Tri-axial Vibration sensor were installed at the power plant. The locations of the pressure sensors are at the inlet of the turbine and the outlet of the draft tube. Tri-axial vibration sensor was installed at the turbine shaft to capture the turbine vibrations at different operating conditions. The temperature sensors provide the temperature data from the bearings and housing of a generator. The sensors were connected to the NI-DAQ (data acquisition system) hardware including current measuring module and sound and vibration module integrated with the LabVIEW program. The data acquisition system is capable of recording the pressure pulsations at the outlet of draft tube. The monitoring system processes the data from sensors and uploads the relevant information to the Kathmandu University’s central server. A remote monitoring program in LabVIEW has been developed which uses an Application Programming Interface (API) to retrieve the information from the central server in the real time, enabling the monitoring of the power plant from any location.

Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System

Remote condition monitoring (RCM) aims to ensure the availability of railway assets. Previous work has indicated the importance of a user-centred RCM design approach based on cognitive principles, but there has been no known demonstration of the application of these principles. The following paper takes this theory-based approach and applies it to the design of an RCM system for the rail pantograph/Overhead Line (OHL) system. The paper first presents a high-level conceptual architecture, based on four stages of cognitive decision-making (notification, acceptance, analysis and clearance), linked to the wider monitoring architecture. Second, the paper uses cognitive principles to propose demonstration Human–Machine Interface designs for the OHL system. These HMIs were presented in an evaluation with subject matter experts. The outcomes of the process generated user-centred design recommendations for RCM. Furthermore, the evaluation suggested the importance of multiple paths through the HMI dependent on the type and urgency of fault. Finally, the outcomes of the evaluation also highlighted the importance of considering context when deploying user-centred RCM.

Application of Industry 4.0 in LPG condition monitoring and emergency systems using IoT approach

Purpose The Purpose of this research is to initiate the “Fourth Industrial Revolution” by using the Internet of things (IoT), which can be applied to flammable gas condition monitoring and detection of gas leakage and activate fire extinguisher in case of fire accidents. Liquefied petroleum gas (LPG) leakage and explosions cause many injuries and death each year. By developing an automated and remote LPG ppm condition monitoring and fire extinguisher activation system with the help of a cyber-physical system, the rate of accidents and injuries can be reduced to a significant amount. Design/methodology/approach The IoT enabled the sensors to transmit LPG concentration value reading to a mobile app or cloud server and control actuators by connecting all in the same network. In case of a fire accident, the solenoid valve automatically or can be activated by an android application manually, which will be pre-installed in mobile phones. Another advantage of this system is that the gas cylinder or flammable particle source can be closed by closing the solenoid valves attached to their outlets. The first challenge of Industry 4.0 is to develop a cyber-physical system where all physical entities can be monitored and controlled over the internet or another way remotely or from a single point. Findings This fire extinguisher system can be used everywhere and in all types of firefighting because all types of fire extinguishers are commercially available in cylinders where solenoid valves can be used instead of conventional valves. This system will reduce human effort in the fire safety system and reduce the number of losses owing to fire accidents by taking all actions automatically and from a safe distance. The reliability analysis of this system indicated that the working condition for the best outcome is 20–35°C and the baud rate of the controller should be 11.5 kHz. Originality/value The study of pieces of the literature summarizes that this work is unique in terms of the application of Industry 4.0 in the fire safety system and reliability analysis of this system helped to determine the operating condition for the best performance of this system. Some LPG condition monitoring system was developed using IoT before but had many limitations such as working capability during load shading or emergency cases.

Detection and evaluation of rolling stock wheelset defects using acoustic emission

Acoustic emission (AE) can be employed for the early fault detection of rolling stock wheelset components. Research has been carried out on the development of a remote condition monitoring (RCM) technology for monitoring online rolling stock wheelset defects. Railway axle bearings and wheels are critical components that can develop faults at any time when in service. AE is a reliable passive RCM technique that can be employed for the quantitative evaluation of the structural integrity of rolling stock wheelsets. The emphasis of this study is placed on the results obtained from experimental work performed under laboratory and field testing conditions. Several laboratory tests were carried out using different axle bearing defects. In addition, a customised online RCM system installed on the Chiltern Rail Line, adjacent to a hot box axle detector, was used for comparison purposes. Using this, AE signal analysis was carried out in order to detect potential rolling stock faults. Defect type evaluation and quantification can also be achieved, leading to effective diagnosis of the structural rolling stock integrity of rolling stock wheels and axle bearings.

Compressive Sensing-Based Missing-Data-Tolerant Fault Detection for Remote Condition Monitoring of Wind Turbines

Compared with traditional onsite wind turbine condition monitoring systems (CMSs), the remote CMSs can use better computational resources to process data with more advanced algorithms and, thus, can provide more advanced condition monitoring capabilities, but may suffer from a data loss problem, especially when wireless data transmission is used. To solve this problem, this article proposes a compressive sensing-based missing-data-tolerant fault detection method for remote condition monitoring of wind turbines. First, the condition monitoring signals collected from wind turbines are conditioned to increase their sparsity. Then, a compressive-sensing-based sampling algorithm is designed to sample the conditioned signals. The resulting data samples, called measurements of the conditioned signals are transmitted wirelessly during which some data samples are possibly lost. At the data receiving end, the conditioned signals are reconstructed from the received data samples, which might be incomplete, via a compressive-sensing-based signal reconstruction algorithm. Finally, spectrum analysis is performed on the reconstructed signals for wind turbine fault detection via fault characteristic frequency identification. The proposed method is validated for bearing fault detection of a Skystream 3.7 wind turbine and an Air Breeze wind turbine by using the data of a generator current signal collected from each wind turbine remotely while considering different data loss rates.

Selection of a rational authentication method for the remote systems of interaction with the vessels

Scientific and technical progress in the development and creation of software and hardware systems has a positive effect on improving the efficiency of water transport operation. The developed systems solve both general and particular tasks. Remote technical condition monitoring systems are used to solve particular problems, for example, to control fuel consumption. The vessels remote control systems are considered as larger ones. Obviously, only wireless means of communication between the remote user (operator) and the vessel are provided in them. The purpose of the paper is to determine a rational approach to ensure the information security for the remote systems of interaction with vessels. The main approaches to ensure the safe use of resources (Password authentication and authentication with one-time passwords, biometric authentication, authentication with a public key, authentication protocols and cryptographic methods) are considered and evaluated by the expert assessments method. Information security threats for the above-mentioned systems of remote interaction with the vessels are highlighted. It has been determined that for remote control systems it is highly expediently to use the cryptography methods with encryption, and for the remote technical condition monitoring such protection is not expedient, but the password authentication (including authentication with one-time password) is sufficient. Further directions of researches will be the elaboration of the specific solutions for selected means of security providing.

Distributed Inference Condition Monitoring System for Rural Infrastructure in the Developing World

Remote condition monitoring systems for rural infrastructure lack “intelligent” analysis and advanced insights offered by recent Internet of Things devices. This is because the extreme and inaccessible operating locations necessitate the conservative use of limited resources, such as battery life and data transmission. Present implementations are often limited to usage data loggers, which are informative of general usage but post-processed advanced insights lag real-time system changes. A lightweight novelty filter is implemented onboard rural handpumps to identify subsets of data as potential infrastructure failure. The “intelligent” summaries of these data subsets are sent to a cloud-based system, where more advanced machine learning approaches are applied to increase the fidelity of potential failure predictions. The proposed method was tested on three independent data sets and found that the on-pump novelty filter could predict failure with up to 61.6% in situ . Incorporating more advanced machine learning methods on the cloud-based platform increased the classifiers’ positive predictive value by at least an additional 10%–73%. This novel method has proven that it is possible for rural operating, resource-constrained devices to use lightweight, onboard machine learning approaches to perform anomaly detection in the embedded system. Distributed inference between the embedded system at the rural node and powerful cloud-based machine learning algorithms offers robust information without the need for expensive hardware or sensors embedded in situ —making the possibility of a large-scale (and perhaps even continent-wide) monitoring system feasible.

Recognition of Wheel Polygon Based on W/R Force Measurement by Piezoelectric Sensors in GSM-R Network

The influence of polygonal wheels on vehicle dynamic responses and condition monitoring measurement, are a focal point of research to ensure operation safety and improve service quality of high speed railway transportation. Wheel defects are commonly identified by the detection of wheel–rail (W/R) contact forces based on installing strain gauge on wheelsets or rails. Compared with strain gauge, PVDF piezoelectric sensor has the advantages of high sensitivity, wide frequency response, vast dynamic range, and perfect electromagnetic-immune property. Hence, a measure system to recognize wheel polygon based on W/R force measurement by PVDF strain sensors in Railroad network is built, which has long distance and high stability. On the basis of W/R contact forces derived from wheel polygon impacts, the measured PVDF strain sensor response is processed to generate an index that reflects the condition of wheel polygon. An automatic remote condition monitoring system is designed under GSM-R transmission lines. The preliminary experimental results showed that this system is suitable for complex electromagnetic environment and stable demands of high speed railways.

From preventative to predictive maintenance: The organisational challenge

Introduction of new technology (technology implementation) within an organisation can have wide reaching implications, beyond the effectiveness and efficiency savings that are typically the aim of such an endeavour. The ‘Health and Prognostic Assessment of Railway Assets for Predictive Maintenance’ project developed a prognostic tool, which aimed to support enhancement of the London underground’s remote condition monitoring system to support change from reactive and preventative to predictive maintenance, in order to increase effectiveness and efficiency and reduce lost customer hours. This paper investigates the organisational challenges associated with the introduction of such a tool. The paper describes the approach adopted to model the extant maintenance processes (focusing on role mapping) and associated organisational structures which revealed issues such as unclear processes, poor communication and data sharing links and problems with delineation of responsibility for decision making. It also describes the development of a new maintenance process model that incorporates the additional functionality of the new prognostic tool, taking in to account changes of roles, responsibilities, organisational processes and activities.

Development and testing of an automatic remote condition monitoring system for train wheels

The safe guidance of railway vehicles is dependent on the contact geometry between the wheel and the rail as defects in the running surface of the wheels can increase the risk of derailments and wheel catastrophic failures, and significantly increase the damage done by the vehicle to the track. An automatic optical based system for detecting wheel defects of rail vehicles passing in service has been developed and tested. The system is described, detailing the process of capturing consistent high resolution and quality images of the wheel tread and flange suitable for processing to detect defects. Tests of the system have been carried out with a wide variety of different rail vehicles, some with defects created artificially on the test wheel to test the image capturing capability of the system and its ability to automatically detect the defects. The prototype system was successfully installed in an operational railway site and captured high-resolution images of the tread and flanges of the entire circumference of rail vehicle wheels as they passed through the system. The system was also shown to be successful at automatically analyzing the captured images to detect wheel surface defects.

Optimal Maintenance Management of Offshore Wind Farms

Nowadays offshore wind energy is the renewable energy source with the highest growth. Offshore wind farms are composed of large and complex wind turbines, requiring a high level of reliability, availability, maintainability and safety (RAMS). Firms are employing robust remote condition monitoring systems in order to improve RAMS, considering the difficulty to access the wind farm. The main objective of this research work is to optimise the maintenance management of wind farms through the fault probability of each wind turbine. The probability has been calculated by Fault Tree Analysis (FTA) employing the Binary Decision Diagram (BDD) in order to reduce the computational cost. The fault tree presented in this paper has been designed and validated based on qualitative data from the literature and expert from important European collaborative research projects. The basic events of the fault tree have been prioritized employing the criticality method in order to use resources efficiently. Exogenous variables, e.g., weather conditions, have been also considered in this research work. The results provided by the dynamic probability of failure and the importance measures have been employed to develop a scheduled maintenance that contributes to improve the decision making and, consequently, to reduce the maintenance costs.

Internet-based remote monitoring system of thermo-electric-generations with mobile communication technology

A remote online condition monitoring system for thermo-electric-generations (TEGs) is reported in this paper, which is applied in sustainable buildings. The system monitors the electrical power and heat energy generated by TEGs and to regulate the temperature of cold side of thermoelectric (TE) units used in the TEGs. The system provides the following major functions: data acquisition, data processing, system control and remote communication. The data acquisition module is built up within this system based on sensor technology and data I/O communication to acquire the data of working conditions of TEGs. System control is implemented by sending the commands to the control unit of the water valve for adjusting the temperature of cold side of TE units. The system has been validated by carrying out a case study of remote monitoring the working conditions of TEGs.

Research on Remote Condition Monitoring System in Coal Mine

In this paper, the signal sources of communication system are described in coal mine firstly, and then a kind of remote condition monitoring system based on the OFDM technology is presented. This system makes wire communication combined mutually with wireless communication by tandem adapter. Wireless communication is used in mining area or in the time of sudden accident, Power line or fiber-optic is used in main lane. This system makes use of the advantage of OFDM technology to provide many kinds of access service in mine. There are different grades and different rates for many consumers at the same time in these access services. The different modulation methods of sub-carries in this system increase flexibility and adaptability. The system not only fulfils the usual demand of produce communication monitoring, but also considers sudden accident, attains the state where the mining area arrives where the signal transports. This paper analyzed feasibility of the system based on some simulation tests finally.

Design of Remote Real-Time Environmental Condition Monitoring System Based on Multi-Network Mergence

t is a hot issues about how to the environmental condition conveniently and economically. A kind of remote real-time environmental condition monitoring system merging ad hoc network, GPRS/GSM and PSTN is designed. Ad hoc wireless sensor network (WSN) is used to detect environmental temperature, humidity, illuminance, gas and carbon monoxide concentration etc. Wireless gateway is constructed with GPRS/GSM module and embedded Modem. It realizes seamless connection of ad hoc network, GPRS/GSM and PSTN and transmits data collected by WSN to mobile and remote computer. This system has the characteristics of small, remote, dispread and economical, it can realize multipoint distribution, central management, multiple alarm and remote monitoring.

STUDY on the RIA Based Remote Monitoring System

This paper presents a method to design the Rich Internet Application (RIA) based remote condition monitoring system. The contents include the structure of RIA based remote condition monitoring system, the design method of RIA based client program, and the design method of server program. The movability and cross-platform ability are two main advantages of this method. Two examples are also shown in the paper. One is to use a computer as the remote monitoring terminal, and another is to use a smart phone as the remote monitoring terminal.

A remote condition monitoring system for wind-turbine based DG systems

In this paper, a remote condition monitoring system is proposed, which fundamentally consists of real-time monitoring modules on the plant side, a remote support centre and the communications between them. The paper addresses some of the key issues related on the monitoring system, including i) the implementation and configuration of a VPN connection, ii) an effective database system to be able to handle huge amount of monitoring data, and iii) efficient data mining techniques to convert raw data into useful information for plant assessment. The preliminary results have demonstrated that the proposed system is practically feasible and can be deployed to monitor the emerging new energy generation systems.

Study on Remote Condition Monitoring and Fault Diagnosis System for Rotating Machinery Based on LabVIEW

In order to suit the demand of monitoring and fault diagnosis of modern small and medium machinery devices better, this paper discusses the development of machinery condition monitoring and fault diagnosis system of good universality and strong expansibility using LabVIEW. Mainly illuminates vibration signal, temperature signal and electric current signal acquisition module using NI data acquisition hardware; signal analysis module in time domain, frequency domain and joint time–frequency domain using signal processing technology. DataSocket, database and fuzzy diagnosis technique have been utilized enabling this system to monitor and diagnose machinery fault remotely.

Remote Machine Condition Monitoring Using Wireless Web Technology

A remote real-time machine condition monitoring system is reported in this paper, which is applied for diagnosis and prognosis of gearboxes’ working condition. Within the system, the diagnostic classification is performed by pattern recognition using statistic parameters, and remote diagnostic capability is enhanced by applying Wireless Web technology. An online signal-processing scheme is adopted based on time-frequency analysis, digital filtering and statistic parameter algorithm to detect early fault signals of gears and to provide expert advice for decision making for maintenance. The effectiveness of the developed remote diagnostic system is verified via experimental investigation of monitoring a gearbox on a test rig under different conditions.

SOA-based remote condition monitoring and fault diagnosis system

This paper describes the development of the service-oriented architecture (SOA)-based Remote Condition Monitoring and Fault Diagnosis System in the field of equipments remote monitoring and fault diagnosis. The architecture is based on Web Services, Smart Client, and Extensible Markup Language (XML) technologies etc. More specifically, the paper analyzes the structures and the requirements in equipments remote monitoring and fault diagnosis. Finally, the SOA-based Enterprise Monitoring and Fault Diagnosis System has been developed and realized with Visual Studio 2005, which has been applied to industrial field. It verifies the effectiveness and practicality of the system.

Remote online machine condition monitoring system

Abstract The study aims at realizing a remote online machine condition monitoring system built up in the architecture of both the Borland C++ Builder (BCB) software-developing environment and Internet transmission communication. Various signal-processing computation schemes such as time–frequency analysis and order tracking for signal analysis and pattern recognition purposes are implemented based upon the Borland C++ Builder graphical user interface. Thus machine fault diagnostic capability can be extended by using the socket application program interface as the transmission control protocol/Internet protocol (TCP/IP). In the study, the effectiveness of the developed remote diagnostic system is justified by monitoring a transmission-element test rig. A complete monitoring cycle including data acquisition, signal-processing, feature extraction, pattern recognition through the artificial neural networks, and online video surveillance, is demonstrated.