196 research outputs found
A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies
CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two
main circumstances: rapid evolution, without precedents, in the capture and analysis of data and
significant cost reduction of supporting technologies. CBM programs in industrial systems can become
extremely complex, especially when considering the effective introduction of new capabilities provided
by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM
solution involves the management of numerous technical aspects, that the maintenance manager needs
to understand, in order to be implemented properly and effectively, according to the companyâs strategy.
This paper provides a comprehensive representation of the key components of a generic CBM solution,
this is presented using a framework or supporting structure for an effective management of the CBM
programs. The concept âsymptom of failureâ, its corresponding analysis techniques (introduced by ISO
13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software
application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the
development of the framework. An original template has been developed, adopting the formal structure
of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure
mode behaviour and to manage maintenance. Finally, a case study describes the framework using the
referred template.Gobierno de AndalucĂa P11-TEP-7303 M
Maintenance models applied to wind turbines. A comprehensive overview
ProducciĂłn CientĂficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models
Micro Smart Micro-grid and Its Cyber Security Aspects in a Port Infrastructure
Maritime ports are intensive energy areas with a plenty of electrical systems that require an average power of
many tens of megawatts (MW). Competitiveness, profits, reduction of pollution, reliability of operations, carbon emission
trading are important energy related considerations for any port authority. Current technology allows the deployment of a local
micro-grid of the size of tenths of MW, capable of islanded operation in case of emergency and to grant an increasing energy
independency. Ownership of the grid permits a large flexibility on prices of energy sold inside the port, trading on local electric
market and reduction of pollution. Renewable energy generation has a large impact on costs since features a low marginal cost.
Unfortunately the smart grid is a critical asset within the port infrastructure and its intelligence is a high-level target for cyberattacks.
Such attacks are often based on malicious software (malware), which makes use of a controlling entity on the network
to coordinate and propagate. In this document, we will outline some features of a port smart grid and typical characteristics of
cyber-attacks including potential ways to recognize it and suggestion for effective countermeasures
Towards securing SCADA systems against process-related threats
We propose a tool-assisted approach to address process-related threats on SCADA systems. Process-related threats have not been addressed before in a systematic manner. Our approach consists of two steps: threat analysis and threat\ud
mitigation. For the threat analysis, we combine two methodologies (PHEA and HAZOP) to systematically identify process-related threats. The threat mitigation is supported by our tool, MELISSA, that helps to detect incidents (attacks or user mistakes). MELISSA uses SCADA system logs and visualization techniques to highlight potential incidents. A preliminary case study suggests that our approach is effective in detecting anomalous events that might alter the regular SCADA process work-flow
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
Operational expenditure optimisation utilising condition monitoring for offshore wind parks
There is a strong desire to increase the penetration of renewable energy sources inthe UK electricity market. Offshore wind energy could be a method to achieve this. However, there are still issues, both technical and economical, that hinder the development and exploitation of this energy source.A condition based maintenance plan that relies on fully integrating the input from condition monitoring and structural health monitoring systems could be the method to solve many of these issues. Improved maintenance scheduling has the potential to reduce maintenance costs, increase energy production and reduce the overall cost of energy. While condition monitoring systems for gearboxes, generators and main bearings have become common place over the last few years, the deployment of other monitoring systems has been slower. This could be due to the expense and complication of monitoring an entire wind farm. Wind park operators, correctly, would like to see proof that their investment will be prudent.To assist wind park operators and owners with this decision, an offshore wind operations and maintenance model that attempts to model the impacts of using monitoring systems has been developed. The development of the model is shown in this analysis: multiple methodologies are used to capture deterioration and the abilities of monitoring systems. At each stage benchmarks are shown against other models and available data. This analysis has a breadth and scope not currently addressed in literature and attempts to give insight to industry that was previously unavailable.There is a strong desire to increase the penetration of renewable energy sources inthe UK electricity market. Offshore wind energy could be a method to achieve this. However, there are still issues, both technical and economical, that hinder the development and exploitation of this energy source.A condition based maintenance plan that relies on fully integrating the input from condition monitoring and structural health monitoring systems could be the method to solve many of these issues. Improved maintenance scheduling has the potential to reduce maintenance costs, increase energy production and reduce the overall cost of energy. While condition monitoring systems for gearboxes, generators and main bearings have become common place over the last few years, the deployment of other monitoring systems has been slower. This could be due to the expense and complication of monitoring an entire wind farm. Wind park operators, correctly, would like to see proof that their investment will be prudent.To assist wind park operators and owners with this decision, an offshore wind operations and maintenance model that attempts to model the impacts of using monitoring systems has been developed. The development of the model is shown in this analysis: multiple methodologies are used to capture deterioration and the abilities of monitoring systems. At each stage benchmarks are shown against other models and available data. This analysis has a breadth and scope not currently addressed in literature and attempts to give insight to industry that was previously unavailable
Health monitoring of renewable energy systems
The offshore wind energy industry has grown exponentially; globally, there is 12GW of
installed capacity of offshore wind, of which over 95% has been installed in the past ten years.
Access and maintenance in offshore wind farms can be difficult and considerably more
expensive than onshore wind farms. Additionally, with low availability levels and greater
downtime due to failures, there is a growing interest in the optimisation of operation and
maintenance (O&M) activities to maximise profitability.
Traditionally, maintenance activities on critical components and subsystems have deployed
two maintenance approaches; time-based preventative or corrective. Time-based
preventative or scheduled maintenance approaches are based on intervening at fixed
intervals, determined in advance for each component. Scheduling is based on failure statistics
such as mean time between failures (MTBF), mean time to repair (MTTR) or mean time to
failure (MTTF). These come either from publicly available databases or operational
measurements. As part of preventive maintenance activities, there are annual services of the
turbine to replace and maintain any component or assembly based on manufacturersâ
indications. On the other hand, the corrective maintenance approach involves operating
equipment until it fails and then restoring it, repairing it, or replacing it.
Due to conservative estimates regarding the probability of failure, preventive and corrective
maintenance approaches have financial implications associated with them. In the preventive
approach, components are frequently replaced before they reach the end of their working
life. In contrast, corrective maintenance guarantees that the serviceable life of a component
is maximised, but it is subjected to long downtime, which is expensive regarding energy
generation loss. Additionally, failure of the component may cause consequential damage to
other parts of the wind turbine system, resulting in even greater repair costs, downtime and
loss of revenue.
A comprehensive literature review has been undertaken in the areas of maintenance, turbine
reliability, turbine failure modes and causes, physics of failure, condition monitoring
techniques, and costs. The limitations and disadvantages of current operation and
maintenance practices are identified, and new approaches combining the knowledge of the
condition of components and historical data are proposed and compared to achieve optimal
turbine availability and maintenance cost reduction.
A Failure Modes and Effects Analysis (FMEA) was performed for the functional modes of each
system, subsystem, assembly and component following the British standard BS EN
60812:2006. Currently, the most common offshore wind turbine uses three blades, a 3-stage
gearbox, induction generator and a fully rated power converter. The Siemens 3.6MW -120
turbine is selected for this project as an example of this configuration. The main objectives
of undertaking this comprehensive FMEA are to identify critical components and their failures
with significant impact on the wind turbine operation in terms of maintainability, safety and
availability. The assessment identified 500 components and almost 1000 failure causes. The
most critical assemblies identified in terms of severity, occurrence and undetectability of the
failure are; the frequency converter, pitch system, yaw system and gearbox.
The implementation of a condition-based maintenance philosophy, including the
development of real predictive approaches which estimate the remaining useful life of
degrading critical components has been analysed by the recent literature. However,
developing such capabilities for the critical assemblies identified is a significant technical
challenge. This study aims to develop and demonstrate the implementation of a
methodology and appropriate algorithms to optimise O&M of offshore wind farms, by
estimating the remaining useful life of critical components with greater accuracy using a
combination of physics-based models, statistical-based models and data mining approaches.
A register of trends and likely the main causes of failures of the power converter, gearbox,
yaw system and pitch system was generated through a thorough literature search and
participation in conferences and workshops during the project. The main sources of failure of
the power converter and gearbox have been represented by algorithms and physics-based
models developed in Python and proprietary software, respectively. These algorithms
comprise two phases: diagnosis or learning phase using historical data (such as SCADA or
digital information recorded by condition monitoring systems) and prognosis phase using
simulated data (using as a basis the wind turbine aero-elastic software FASTv8). The pitch
system failure mechanisms were explored using a combination of data mining approaches
and subject matter expert knowledge. Examples of approaches investigated and
implemented include: Support Vector Machine (SVM) to define normal behaviour and K
Nearest Neighbour (KNN) to classify new observations regarding operation state (green for
normal operation, amber for abnormal operation, red for failure). New observations with
amber or red colours need to be analysed further, to diagnose potential failure modes using
a decision tree algorithm with more variables related to the pitch system.
The goals of developing a well-defined strategy for maintenance interventions and optimised
management of wind farm logistics are required to effectively improve wind farm availability
while reducing the cost of operations. Additionally, a clear identification of uncertainties
inherent in stochastic processes, necessary for estimating access, failure prognosis and failure
probabilities is required for operators to make informed decisions. The final output of this
work is an O&M cost model which analyses and compares a conventional O&M strategy using
a combination of preventive and reactive maintenance against an O&M strategy using the
approaches described above for failure prognosis and diagnosis. The analysis is performed
for a fictitious offshore wind farm with one-year operational data. The results include
availability, downtime, the cost of repair, loss of production, revenue losses and the hidden
CO2 emissions of the maintenance activities taking into account a combined probability level
to account for the uncertainties
Critical Infrastructure Protection Approaches: Analytical Outlook on Capacity Responsiveness to Dynamic Trends
Overview: Critical infrastructures (CIs) â any asset with a functionality that is critical to normal societal functions, safety, security, economic or social wellbeing of people, and disruption or destruction of which would have a very significant negative societal impact. CIs are clearly central to the normal functioning of a nationâs economy and require to be protected from both intentional and unintentional sabotages. It is important to correctly discern and aptly manage security risks within CI domains. The protection (security) of CIs and their networks can provide clear benefits to owner organizations and nations including: enabling the attainment of a properly functioning social environment and economic market, improving service security, enabling integration to external markets, and enabling service recipients (consumers, clients, and users) to benefit from new and emerging technological developments. To effectively secure CI system, firstly, it is crucial to understand three things - what can happen, how likely it is to happen, and the consequences of such happenings. One way to achieve this is through modelling and simulations of CI attributes, functionalities, operations, and behaviours to support security analysis perspectives, and especially considering the dynamics in trends and technological adoptions. Despite the availability of several security-related CI modelling approaches (tools and techniques), trends such as inter-networking, internet and IoT integrations raise new issues. Part of the issues relate to how to effectively (more precisely and realistically) model the complex behavior of interconnected CIs and their protection as system of systems (SoS). This report attempts to address the broad goal around this issue by reviewing a sample of critical infrastructure protection approaches; comprising tools, techniques, and frameworks (methodologies). The analysis covers contexts relating to the types of critical infrastructures, applicable modelling techniques, risk management scope covered, considerations for resilience, interdependency, and policy and regulations factors. Key Findings: This research presents the following key findings: 1. There is not a single specific Critical Infrastructure Protection (CIP) approach â tool, technique, methodology or framework â that exists or emerges as a âfit-for-allâ; to allow the modelling and simulation of cyber security risks, resilience, dependency, and impact attributes in all critical infrastructure set-ups. 2. Typically, two or more modelling techniques can be (need to be) merged to cover a broader scope and context of modelling and simulation applications (areas) to achieve desirable highlevel protection and security for critical infrastructures. 3. Empirical-based, network-based, agent-based, and system dynamics-based modelling techniques are more widely used, and all offer gains for their use. 4. The deciding factors for choosing modelling techniques often rest on; complexity of use, popularity of approach, types and objectives of user Organisation and sector. 5. The scope of modelling functions and operations also help to strike the balance between âspecificityâ and âgeneralityâ of modelling technique and approach for the gains of in-depth analysis and wider coverage respectively. 6. Interdependency and resilience modelling and simulations in critical infrastructure operations, as well as associated security and safety risks; are crucial characteristics that need to be considered and explored in revising existing or developing new CIP modelling approaches. Recommendations: Key recommendations from this research include: 1. Other critical infrastructure sectors such as emergency services, food & agriculture, and dams; need to draw lessons from the energy and transportation sectors for the successive benefits of: i. Amplifying the drive and efforts towards evaluating and understanding security risks to their infrastructure and operations. ii. Support better understanding of any associated dependencies and cascading impacts. iii. Learning how to establish effective security and resilience. iv. Support the decision-making process linked with measuring the effectiveness of preparedness activities and investments. v. Improve the behavioural security-related responses of CI to disturbances or disruptions. 2. Security-related critical infrastructure modelling approaches should be developed or revised to include wider scopes of security risk management â from identification to effectiveness evaluations, to support: i. Appropriate alignment and responsiveness to the dynamic trends introduced by new technologies such as IoT and IIoT. ii. Dynamic security risk management â especially the assessment section needs to be more dynamic than static, to address the recurrent and impactful risks that emerge in critical infrastructures
ATA-BASED SECURITY ASSESSMENT OF SMART BUILDING AUTOMATION SYSTEMS
The information and control system of smart building is considered as a set of subsystems including building automation system (BAS). BAS security and availability during its life cycle are assessed using the technique Attack Tree Analysis (ATA), and Failure Modes and Effects Analysis (FMECA). The FMECA is applied at the initial stage of analysis to assess criticality of BAS hardware/software failures and failed connections between components on the different levels of system design. Modification of FMECA is IMECA allowing to analyze modes and effects of attacks/intrusions. The ATA is applied to investigate any intrusions into the BAS by analyzing system probability of a failure caused by faults and vulnerabilities during operation time. The ATA is applied for different BAS subsystems and results of analysis are combined
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