Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

Advanced techniques in reliability model representation and solution

The current tendency of flight control system designs is towards increased integration of applications and increased distribution of computational elements. The reliability analysis of such systems is difficult because subsystem interactions are increasingly interdependent. Researchers at NASA Langley Research Center have been working for several years to extend the capability of Markov modeling techniques to address these problems. This effort has been focused in the areas of increased model abstraction and increased computational capability. The reliability model generator (RMG) is a software tool that uses as input a graphical object-oriented block diagram of the system. RMG uses a failure-effects algorithm to produce the reliability model from the graphical description. The ASSURE software tool is a parallel processing program that uses the semi-Markov unreliability range evaluator (SURE) solution technique and the abstract semi-Markov specification interface to the SURE tool (ASSIST) modeling language. A failure modes-effects simulation is used by ASSURE. These tools were used to analyze a significant portion of a complex flight control system. The successful combination of the power of graphical representation, automated model generation, and parallel computation leads to the conclusion that distributed fault-tolerant system architectures can now be analyzed

Reliability Evaluation of Common-Cause Failures and Other Interdependencies in Large Reconfigurable Networks

This work covers the impact of Interdependencies and CCFs in large repairable networks with possibility of "re-configuration" after a fault and the consequent disconnection of the faulted equipment. Typical networks with these characteristics are the Utilities, e.g. Power Transmission and Distribution Systems, Telecommunication Systems, Gas and Water Utilities, Wi Fi networks. The main issues of the research are: (a) Identification of the specific interdependencies and CCFs in large repairable networks, and (b)Evaluation of their impact on the reliability parameters (load nodes availability, etc.). The research has identified (1) the system and equipment failure modes that are relevant to interdependencies and CCF, and their subsequent effects, and (2) The hidden interdependencies and CCFs relevant to control, supervision and protection systems, and to the automatic change-over systems, that have no impact in normal operation, but that can cause relevant out-of-service when the above automatic systems are called to operate under and after fault conditions. Additionally methods were introduced to include interdependencies and CCFs in the reliability and availability models. The results of the research include a new generalized approach to model the repairable networks for reliability analysis, including Interdependencies/CCFs as a main contributor. The method covers Generalized models for Nodes, Branches and Load nodes; Interdependencies and CCFs on Networks / Components; System Interdependencies/CCFs; Functional Interdependencies/CCFs; Simultaneous and non-simultaneous Interdependencies/CCFs. As an example detailed Interdependency/CCFs analysis and generalized model of an important network structure (a "RING" with load nodes) has been analyzed in detail

Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach

الطاقة الشمسية هي واحدة من الطاقة المتجددة التي لا حصر لها في توليد الطاقة لبيئة خضراء ونظيفة وصحية. تمتص الألواح الشمسية المكونة من طبقة السيليكون طاقة الشمس وتتحول إلى كهرباء بواسطة عاكس خارج الشبكة. نقل الكهرباء يتم إما من هذا العاكس أو من المحول، التي تستهلكها وحدة (وحدات) الاستهلاك المتاحة للأغراض السكنية أو الاقتصادية. الشبكة العصبية الاصطناعية هي أساس الذكاء الاصطناعي وتحل العديد من المشاكل المعقدة التي يصعب من خلال الأساليب الإحصائية أو من قبل البشر. في ضوء ذلك، فإن الغرض من هذا العمل هو تقييم أداء نظام الطاقة الشمسية - المحولات - الاستهلاك (STC). قد يكون النظام في حالة انهيار كامل بسبب فشل كل من النظام الفرعي لأتمتة الطاقة الشمسية والمحول في وقت واحد أو وحدة الاستهلاك ؛ وإلا فإنه يعمل بكفاءة كاملة أو أقل. يتم النظر في حالات الفشل والإصلاحات المستقلة إحصائيًا. يتم استخدام ظاهرة الاحتمالات الأولية المدمجة مع المعادلات التفاضلية لفحص موثوقية النظام ، للنظام القابل للإصلاح وغير القابل للإصلاح، ولتحليل دالة التكلفة الخاصة به. يمكن تحسين دقة واتساق النظام من خلال نهج الشبكة العصبية للانتشار الأمامي والخلفي (FFBPNN). يمكن لآلية تعلم النسب المتدرجة أن تقوم بتحديث الأوزان العصبية وبالتالي النتائج تصل إلى الدقة المطلوبة في كل تكرار، وبغض النظر عن مشكلة تلاشي التدرج في الشبكات العصبية الأخرى، مما يزيد من كفاءة النظام في الوقت الفعلي. تم تصميم كود MATLAB لخوارزمية FFBP لتحسين قيم الموثوقية ووظيفة التكلفة من خلال تقليل الخطأ إلى الحد الأدنى حتى 0.0001. يتم النظر في الرسوم التوضيحية العددية مع جداول البيانات والرسوم البيانية الخاصة بهم، لتوضيح النتائج وتحليلها في شكل الموثوقية ووظيفة التكلفة، والتي قد تكون مفيدة لمحللي النظام.Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both solar power automation subsystem and transformer simultaneously or consumption unit; otherwise it works with fully or lesser efficiency. Statistically independent failures and repairs are considered. Using the elementary probabilities phenomenon incorporated with differential equations is employed to examine the system reliability, for repairable and non-repairable system, and to analyze its cost function. The accuracy and consistency of the system can be improved by feed forward- back propagation neural network (FFBPNN) approach. Its gradient descent learning mechanism can update the neural weights and hence the results up to the desired accuracy in each iteration, and aside the problem of vanishing gradient in other neural networks, that increasing the efficiency of the system in real time. MATLAB code for FFBP algorithm is built to improve the values of reliability and cost function by minimizing the error up to 0.0001 precision. Numerical illustrations are considered with their data tables and graphs, to demonstrate and analyze the results in the form of reliability and cost function, which may be helpful for system analyzers

Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry

A Method for Evaluating Aircraft Electric Power System Sizing and Failure Resiliency

With the More Electric Aircraft paradigm, commercial commuter aircraft are increasing the size and complexity of electrical power systems by increasing the number of electrical loads. With this increase in complexity comes a need to analyze electrical power systems using new tools. The Hybrid Power System Optimizer (HyPSO) developed by Airbus SAS is a simulator designed to analyze new aircraft power systems. This thesis project will first provide a method to assess the reliability of complex aircraft electrical power systems before and after failure and reconfiguration events. Next, an add-on to HyPSO is developed to integrate the previously developed reliability calculations. Proof-of-concepts including new data visualizations are performed and provided

TIDAL STREAM DEVICES: RELIABILITY PREDICTION MODELS DURING THEIR CONCEPTUAL & DEVELOPMENT PHASES

Tidal Stream Devices (TSDs) are relatively new renewable energy converters. To date only a few prototypes, primarily horizontal-axis turbine designs, are operational; therefore, little reliability data has accumulated. Pressure to develop reliable sources of renewable electric power is encouraging investors to consider the technology for development. There are a variety of engineering solutions under consideration, including floating tethered, submerged tethered, ducted sea-bed bottom-mounted and sea-bed pile-mounted turbines, but in the absence of in-service reliability data it is difficult to critically evaluate comparative technologies. Developing reliability models for TSDs could reduce long-term risks and costs for investors and developers, encouraging more feasible and economically viable options. This research develops robust reliability models for comparison, defining TSD reliability block diagrams (RBD) in a rigorous way, using surrogate reliability data from similarly-rated wind turbines (WTs) and other relevant marine and electrical industries. The purpose of the research is not to derive individual TSD failure rates but to provide a means of comparison of the relative reliabilities of various devices. Analysis of TSD sub-assemblies from the major types of TSDs used today is performed to identify criticality, to improve controllability and maintainability. The models show that TSDs can be expected to have lower reliability than WTs of comparable size and that failure rates increase with complexity. The models also demonstrate that controls and drive train sub-assemblies, such as the gearbox, generator and converter, are critical to device reliability. The proposed developed models provide clear identification of required changes to the proposed TSD system designs, to raise availability, including duplication of critical systems, use of components developed for harsh environments and migration of equipment onshore, wherever practicable

A coloured Petri net framework for modelling aircraft fleet maintenance

The aircraft fleet maintenance organisation is responsible for keeping aircraft in a safe, efficient operating condition. Through optimising the use of maintenance resources and the implementation of maintenance activities, fleet maintenance management aims to maximise fleet performance by, for example, ensuring there is minimal deviation from the planned operational schedule,that the number of unexpected failures is minimised or that maintenance cost is kept at a minimum. To obtain overall fleet performance, the performance of individual aircraft must first be known. The calculation of aircraft performance requires an accurate model of the fleet operation and maintenance processes. This paper aims to introduce a framework that can be used to build aircraft fleet maintenance models. A variety of CPN (coloured Petri nets) models are established to represent fleet maintenance activities and maintenance management, as well as the factors that have a significant impact on fleet maintenance including fleet operation, aircraft failure logic and component failure processes. Such CPN models provide an ideal structured framework for Monte Carlo simulation analysis, within which calculations can be performed in order to determine numerous fleet reliability and maintenance performance measures

Reliability and fault tolerance modelling of multiprocessor systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Reliability evaluation by analytic modelling constitute an important issue of designing a reliable multiprocessor system. In this thesis, a model for reliability and fault tolerance analysis of the interconnection network is presented, based on graph theory. Reliability and fault tolerance are considered as deterministic and probabilistic measures of connectivity. Exact techniques for reliability evaluation fail for large multiprocessor systems because of the enormous computational resources required. Therefore, approximation techniques have to be used. Three approaches are proposed, the first by simplifying the symbolic expression of reliability; the other two by applying a hierarchical decomposition to the system. All these methods give results close to those obtained by exact techniques.Consejo Nacional de Ciencia y Tecnologia" (National Council for Science and Technology of Mexico) and "Instituto de Investigaciones Electricas" (Institute for Electrical Research