Internet of things for disaster management: state-of-the-art and prospects

Disastrous events are cordially involved with the momentum of nature. As such mishaps have been showing off own mastery, situations have gone beyond the control of human resistive mechanisms far ago. Fortunately, several technologies are in service to gain affirmative knowledge and analysis of a disaster's occurrence. Recently, Internet of Things (IoT) paradigm has opened a promising door toward catering of multitude problems related to agriculture, industry, security, and medicine due to its attractive features, such as heterogeneity, interoperability, light-weight, and flexibility. This paper surveys existing approaches to encounter the relevant issues with disasters, such as early warning, notification, data analytics, knowledge aggregation, remote monitoring, real-time analytics, and victim localization. Simultaneous interventions with IoT are also given utmost importance while presenting these facts. A comprehensive discussion on the state-of-the-art scenarios to handle disastrous events is presented. Furthermore, IoT-supported protocols and market-ready deployable products are summarized to address these issues. Finally, this survey highlights open challenges and research trends in IoT-enabled disaster management systems. © 2013 IEEE

Applications of Wireless Sensor Networks in the Oil, Gas and Resources Industries

The paper provides a study on the use of Wireless Sensor Networks (WSNs) in refineries, petrochemicals, underwater development facilities, and oil and gas platforms. The work focuses on networks that monitor the production process, to either prevent or detect health and safety issues or to enhance production. WSN applications offer great opportunities for production optimization where the use of wired counterparts may prove to be prohibitive. They can be used to remotely monitor pipelines, natural gas leaks, corrosion, H2S, equipment condition, and real-time reservoir status. Data gathered by such devices enables new insights into plant operation and innovative solutions that aids the oil, gas and resources industries in improving platform safety, optimizing operations, preventing problems, tolerating errors, and reducing operating costs. In this paper, we survey a number of WSN applications in oil, gas and resources industry operations

Development of maintenance framework for modern manufacturing systems

Modern manufacturing organizations are designing, building and operating large, complex and often ‘one of a kind’ assets, which incorporate the integration of various systems under modern control systems. Due to such complexity, machines failures became more difficult to interpret and rectify and the existing maintenance strategies became obsolete without development and enhancement. As a result, the need for more advanced strategies to ensure effective maintenance applications that ensures high operation efficiency arise. The current research aims to investigate the existing maintenance strategies, the levels of machines complexity and automation within manufacturing companies from different sectors and sizes including, oil and gas, food and beverages, automotive, aerospace, and Original Equipment Manufacturer. Results analysis supports in the development of a modern maintenance framework that overcome the highlighted results and suits modern manufacturing assets using systematic approaches and utilisation of pillars from Total productive maintenance (TPM, Reliability Centred Maintenance (RCM) and Industry 4.0

An intelligent monitoring system for online induction motor fault diagnostics

For more than a century, the induction motor (IM) has been the powerhouse industrial applications such as machine tools, manufacturing facilities, pumping stations, and more recently, in electric vehicles. In addition, IMs account for approximately 40%- 45% of the annual global electricity consumption. Therefore it is a critical issue to improve IM operation efficiency and reliability. In applications, unexpected failures of IMs can result in extensive production loss and increased costs. The classical preventive maintenance procedures involve periodic stoppages of IMs for inspection. If such procedures result in no faults found in the machine, as is common in practice, the unnecessary downtimes will increase operational costs significantly. This inefficiency can be addressed by condition monitoring, whereby sensors relay information about the IM in real-time, allowing for incipient IM fault diagnosis. Such a process involves three general stages: • Data acquisition: A process to collect data using appropriate sensors. • Fault detection: A means to process collected data, extract representative fault features, and determine the condition of the motor components. • Fault classification: A means to automatically classify fault data to allow decision-making on whether or not the motor is healthy or damaged. However, there are challenges with the above stages that are at present, barriers to the industrial adoption of condition monitoring, such as: • Implementation limitations of traditional wired sensors in industrial plants. • The restrictive memory and range capabilities of existing commercial wireless sensors. • Challenges related to misleading representative fault signals and means to quantify the fault features. • A means to adaptively classify the data without prior knowledge given to a fault classification system. To address these challenges, the objective of this work is to develop a smart sensor-based IM fault diagnostic system targeted for real industrial applications. Specific projects pertaining to this objective include the following: Smart sensor-based wireless data acquisition systems: A smart sensor network including current and vibration sensors, which are compact, inexpensive, lowpower, and longer-range wireless transmission. • Fault detection: A new method to more reliably extract the representative fault features, applicable under all IM loading conditions. • Fault quantification: A new means to transform fault features into a monitoring fault index. • Fault classification: An evolving classification system developed to track and identify groups of fault index information for automatic IM health condition monitoring. Results show that: (1) the wireless smart sensors are able to effectively collect data from the induction motor, (2) the fault detection and quantification techniques are able to efficiently extract representative fault features, and (3) the online diagnostic classifier diagnoses the induction motor condition with an average accuracy of 99.41%

Intermittent fault diagnosis and health monitoring for electronic interconnects

Literature survey and correspondence with industrial sector shows that No-Fault-Found (NFF) is a major concern in through life engineering services, especially for defence, aerospace, and other transport industry. There are various occurrences and root causes that result in NFF events but intermittent interconnections are the most frustrating. This is because it disappears while testing, and missed out by diagnostic equipment. This thesis describes the challenging and most important area of intermittent fault detection and health monitoring that focuses towards NFF situation in electronics interconnections. After introduction, this thesis starts with literature survey and describes financial impact on aerospace and other transport industry. It highlights NFF technologies and discuss different facts and their impact on NFF. Then It goes into experimental study that how repeatedly intermittent fault could be replicated. It describes a novel fault replicator that can generate repeatedly IFs for further experimental study on diagnosis techniques/algorithms. The novel IF replicator provide for single and multipoint intermittent connection. The experimental work focuses on mechanically induced intermittent conditions in connectors. This work illustrates a test regime that can be used to repeatedly reproduce intermittency in electronic connectors whilst subjected to vibration ... [cont.]

Methods and Systems for Fault Diagnosis in Nuclear Power Plants

This research mainly deals with fault diagnosis in nuclear power plants (NPP), based on a framework that integrates contributions from fault scope identification, optimal sensor placement, sensor validation, equipment condition monitoring, and diagnostic reasoning based on pattern analysis. The research has a particular focus on applications where data collected from the existing SCADA (supervisory, control, and data acquisition) system is not sufficient for the fault diagnosis system. Specifically, the following methods and systems are developed. A sensor placement model is developed to guide optimal placement of sensors in NPPs. The model includes 1) a method to extract a quantitative fault-sensor incidence matrix for a system; 2) a fault diagnosability criterion based on the degree of singularities of the incidence matrix; and 3) procedures to place additional sensors to meet the diagnosability criterion. Usefulness of the proposed method is demonstrated on a nuclear power plant process control test facility (NPCTF). Experimental results show that three pairs of undiagnosable faults can be effectively distinguished with three additional sensors selected by the proposed model. A wireless sensor network (WSN) is designed and a prototype is implemented on the NPCTF. WSN is an effective tool to collect data for fault diagnosis, especially for systems where additional measurements are needed. The WSN has distributed data processing and information fusion for fault diagnosis. Experimental results on the NPCTF show that the WSN system can be used to diagnose all six fault scenarios considered for the system. A fault diagnosis method based on semi-supervised pattern classification is developed which requires significantly fewer training data than is typically required in existing fault diagnosis models. It is a promising tool for applications in NPPs, where it is usually difficult to obtain training data under fault conditions for a conventional fault diagnosis model. The proposed method has successfully diagnosed nine types of faults physically simulated on the NPCTF. For equipment condition monitoring, a modified S-transform (MST) algorithm is developed by using shaping functions, particularly sigmoid functions, to modify the window width of the existing standard S-transform. The MST can achieve superior time-frequency resolution for applications that involves non-stationary multi-modal signals, where classical methods may fail. Effectiveness of the proposed algorithm is demonstrated using a vibration test system as well as applications to detect a collapsed pipe support in the NPCTF. The experimental results show that by observing changes in time-frequency characteristics of vibration signals, one can effectively detect faults occurred in components of an industrial system. To ensure that a fault diagnosis system does not suffer from erroneous data, a fault detection and isolation (FDI) method based on kernel principal component analysis (KPCA) is extended for sensor validations, where sensor faults are detected and isolated from the reconstruction errors of a KPCA model. The method is validated using measurement data from a physical NPP. The NPCTF is designed and constructed in this research for experimental validations of fault diagnosis methods and systems. Faults can be physically simulated on the NPCTF. In addition, the NPCTF is designed to support systems based on different instrumentation and control technologies such as WSN and distributed control systems. The NPCTF has been successfully utilized to validate the algorithms and WSN system developed in this research. In a real world application, it is seldom the case that one single fault diagnostic scheme can meet all the requirements of a fault diagnostic system in a nuclear power. In fact, the values and performance of the diagnosis system can potentially be enhanced if some of the methods developed in this thesis can be integrated into a suite of diagnostic tools. In such an integrated system, WSN nodes can be used to collect additional data deemed necessary by sensor placement models. These data can be integrated with those from existing SCADA systems for more comprehensive fault diagnosis. An online performance monitoring system monitors the conditions of the equipment and provides key information for the tasks of condition-based maintenance. When a fault is detected, the measured data are subsequently acquired and analyzed by pattern classification models to identify the nature of the fault. By analyzing the symptoms of the fault, root causes of the fault can eventually be identified

Fault detection system for internal combustion engines

Dissertação de mestrado, Engenharia Eléctrica e Electrónica, Instituto Superior de Engenharia, Universidade do Algarve, 2017O desenvolvimento de dispositivos para Internet das Coisas abre novas oportunidades na área de manutenção preditiva. A ideia geral da Internet das Coisas é ligar tudo à Internet, permitindo criar assim sistemas escaláveis suportados pelo processamento remoto, centralizado ou decentralizado. Com a utilização de módulos de baixa potência e de baixo custo, esses sistemas são também eficazes em termos de custo. Alguns estudos indicam que nos próximos anos a manutenção preditiva das máquinas será a principal aplicação da análise de dados na indústria, baseada em Internet das Coisas. Apesar de existirem vários tipos de máquinas, as mais importantes são os motores elétricos e os de combustão interna. Os motores elétricos são amplamente utilizados em diversos setores. Já os motores de combustão interna, por sua vez, são principalmente utilizados nas indústrias automóvel e marítima. Tipicamente, os motores modernos de combustão interna têm uma unidade de controlo computadorizado com capacidade de autodiagnóstico. Contudo, ao contrario da indústria automóvel que utiliza o sistema de On-board diagnostics, a indústria marítima não possui uma norma definida, ou pelo menos uma norma dominante. Isso acarreta a utilização de equipamentos de preço elevado para extrair informação sobre o estado do motor da unidade de controlo. Além disso, a monitorização é intermitente, obrigando ao proprietário de uma embarcação a efetuar a extração dos dados periodicamente para posterior análise. A monitorização das características de operação de um motor um processo fundamental numa manutenção preditiva, sendo que, pela monitorização de um ou mais parâmetros de uma máquina (incluindo a vibração, temperatura, etc.), tenta identificar uma mudança significativa na mesma, que, por sua vez, possa indicar o aparecimento de uma falha. A estratégia de monitorização preditiva assegura que as atividades de manutenção são executadas apenas quando são realmente necessárias, mas para tal é necessário monitorizar periodicamente ou constantemente o equipamento, processar os dados e analisar os resultados. Este método tem, no entanto, as suas desvantagens. O custo do equipamento portátil de monitorização, ou de um sistema estacionário que esteja permanentemente instalado, depende do tipo de variáveis monitorizadas, da precisão de medida, do ambiente de trabalho, ou até do nível de desenvolvimento do sistema. Para além disso, o defeito pode não ser detetado, ou então ser detetado um defeito não existente (um falso positivo, ou uma falsa detecção). Para efectuar uma monitorização preditiva, podem ser utilizados um ou mais métodos de monitorização. Os métodos principais são: análise de vibração, análise de óleo, análise de desempenho, termografia, ferrografia ou análise dos sinais acústicos. De entre todos, a análise de vibração é particularmente interessante, por ser um método não intrusivo e por permitir não só detetar as falhas, mas também classificá-las. Para monitorizar as vibrações de uma máquina utilizam-se transdutores de deslocamento, de velocidade ou de aceleração (acelerómetros). Os transdutores de deslocamento utilizados na indústria permitem medir apenas o deslocamento relativo, o que nem sempre é conveniente. Os preços dos transdutores industriais de velocidade são relativamente altos. Os acelerómetros piezoelétricos industrias também têm um preço elevado. Se as condições ambientais não forem exageradas (por exemplo, temperaturas elevadas) podem ser utilizados acelerómetros MEMS (Microelectromechanical system). Os acelerómetros do tipo MEMS não têm a resposta em frequência tão ampla como os piezoelétricos, no entanto a diferença de preço em relação às restantes opções é muito significativa, e os acelerómetros MEMS, hoje em dia, são praticamente todos digitais, o que simplifica o sistema. Outra alternativa é a película piezoelétrica feita a partir de polyvinylidene difluoride (PVDF). A resposta em frequência desta pelicula é por vezes melhor do que a dos acelerómetros piezoelétricos, com frequências de ressonância acima de 10 MHz. O preço de uma película PVDF depende das dimensões da película, mas é normalmente maior do que o preço de um acelerómetro MEMS, sendo ainda assim muito menor do que um acelerómetro piezoelétrico. É importante referir que há modelos de acelerômetros piezoelétricos que incluem amplificador, mas no caso da película PVDF requerem um circuito de condicionamento do sinal. Para além disso, no caso da película PVDF, tem que ser considerada a sensibilidade à interferência eletromagnética. Dado este contexto, o trabalho realizado nesta Tese resulta de uma proposta feita por uma empresa interessada em automatizar este processo, que entrou em contato com universidade para encontrar uma possível solução. O sistema desenvolvido baseia-se no conceito Internet das Coisas, efetuando a monitorização autónoma da condição do motor, de forma independente do sistema de autodiagnóstico que possa existir. Este trabalho tem, genericamente, três objetivos: (i) desenvolver um módulo de contabilização do número de horas de funcionamento do motor; (ii) desenvolver um módulo de monitorização de ocorrência de falhas num motor de combustão interna através da medição e análise de vibrações; (iii) investigar e analisar os resultados de monitorização, para identificar desvios de parâmetros de funcionamento e detetar de forma preditiva a ocorrência de falhas (antes que as mesmas ocorram realmente). O sistema, que compreende os módulos (i) e (ii), será montado no motor, comunicando com um dispositivo do cliente (por exemplo, um smartphone ou tablet) através de uma interface sem fios. Neste caso optou-se por utilizar uma ligação sem fios baseada na tecnologia Bluetooth Low Energy. Para implementação do módulo de contabilização do número de horas de funcionamento, recorreu-se a um sensor do tipo MEMS, por ter um consumo muito baixo. Já o módulo de módulo de monitorização de ocorrência de falhas recorre a um sensor do tipo PVDF, por permitir elevadas definições temporais na captura do sinal de vibração. Para efectuar a análise de dados que permite a identificação da ocorrência de falhas, foram testados e comparados um conjunto de 16 algoritmos de análise conjunta de dados no tempo e na frequência, suportando a identificação de falhas e a diferenciação entre tipos de falhas. Os algoritmos foram avaliados com base em dados reais, obtidos por falhas induzidas num motor de combustão interna, permitindo por essa via encontrar os algoritmos que melhor servem o objetivo proposto

Data Integrity Protection For Security in Industrial Networks

Modern industrial systems are increasingly based on computer networks. Network- based control systems connect the devices at the field level of industrial environments together and to the devices at the upper levels for monitoring, configuration and management purposes. Contrary to traditional industrial networks which axe con­ sidered stand-alone and proprietary networks, modern industrial networks are highly connected systems which use open protocols and standards at different levels. This new structure of industrial systems has made them vulnerable to security attacks. Among various security needs of computer networks, data integrity protection is the major issue in industrial networks. Any unauthorized modification of information during transmission could result in significant damages in industrial environments. In this thesis, the security needs of industrial environments are considered first. The need for security in industrial systems, challenges of security in these systems and security status of protocols used in industrial networks are presented. Furthermore, the hardware implementation of the Secure Hash Algorithm (SHA) which is used in security protocols for data integrity protection is the main focus of this thesis. A scheme has been proposed for the implementation of the SHA-1 and SHA-512 hash functions on FPGAs with fault detection capability. The proposed scheme is based on time redundancy and pipelining and is capable of detecting permanent as well as transient faults. The implementation results of the proposed scheme on Xilinx FPGAs show small area and timing overhead compared to the original implementation without fault detection. Moreover, the implementation of SHA-1 and SHA-512 on Wireless Sensor Boards has been presented taking into account their memory usage and execution time. There is an improvement in the execution time of the proposed implementation compared to the previous works