Nikolaos Papakonstantinou

Fault detection in the hyperspace: Towards intelligent automation systems

This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associative memory and vector symbolic architectures, commonly used for modeling cognitive abilities of human brain. Compared to classical methods of artificial intelligence used in the context of fault detection the proposed methodology shows an unprecedented performance, while featuring zero configuration and simple operations.

Change request management in model-driven engineering of industrial automation software

Change request management and Model Driven Engineering (MDE) are two key concepts for industrial automation software in todays competitive and fast changing environment. However, although there exist frameworks on general change management, they do not exploit the capabilities of MDE. This paper proposes a workflow to combine these two technologies, enabling the engineer responsible for the change to quickly and efficiently create analysis on the impact of the change, as well as the feasibility of a proposed solution. This result of the analysis is presented with concrete SysML model diagrams to better support decision making for a change request. One advantage of performing change management in SysML is the possibility to automate parts of the change analysis process; one step is automated as a proof-of-concept.

Modeling of function failure propagation across uncoupled systems

The design of modern complex engineered systems must rapidly and accurately be developed to satisfy customer needs while accomplishing required functions with a minimum number of failures. Failure analysis in the conceptual stage of design, including the propagation of failures, has expanded in recent years to account for failures in functional modeling. However, function failure propagation across uncoupled functions and subsystems has not been fully addressed; failures are known to cross these boundaries in complex systems. To address this research gap, a functional model based geometric method of predicting and mitigating functional failure propagation across systems, which are uncoupled during nominal use cases, is presented. Geometric relationships including function location and physical properties are established between uncoupled functions to serve as failure propagation flow paths. Mitigation options are developed based upon the geometric relationships and a path toward physical functional layout is provided to limit failure propagation across uncoupled subsystems. The model-based geometric method of predicting and mitigating functional failure propagation across uncoupled engineered systems guides designers toward improved protection and isolation of cross-subsystem failure propagation. The proposed method is validated using the case study of a pressurized water nuclear reactor modeled using APROS, a first principal simulator. Results identified that the top 10 failures exceeded those of PRA in importance based on the probability of failure.

District heating temperature control algorithm based on short term weather forecast and consumption predictions

District heating grids are complex systems where energy production has to match the consumption load while key system parameters like temperatures and pressures through the grid have to be kept within limits. The choice of a control strategy for the grid depends on the selected key performance indicators. The scientific contribution of this paper is a methodology for controlling the supply water temperature setpoint of a heat power plant using heat consumption predictions. The proposed algorithm aims to provide more heat energy to the difficult consumers when they need it the most. The required input information are the short term weather forecast, the supply hot water temperature propagation delays of the district heating grid as a function of the grid load level and consumption profiles based on historical data or heat consumption models. The methodology is applied on a simplified case study of a 120MW district heating grid. The results showed that within a specific supply water temperature range the performance of the grid in terms of minimum pressure difference at the consumers over a year was significantly better using the proposed proactive algorithm compared to simple reactive and constant temperature control strategies.

A model-driven approach for incorporating human reliability analysis in early emergency operating procedure development

Current Probabilistic Risk Assessment (PRA) methods analyze operator actions in accident scenarios using Human Reliability Analysis (HRA) methods after Emergency Operating Procedures (EOPs) and complex system design are largely complete. This paper proposes the early Model-based HRA (eMHRA) method that couples PRA, HRA, and EOP development together and shifts analysis earlier into the complex system design process. By moving the development of these related and important steps in complex system design earlier in the design process, significant modifications to the complex system can be made much more inexpensively and consume much less time to address critical issues found in PRA, HRA, and EOP development. Further, EOP developers can benefit from rapid and early feedback from the HRA and PRA information. A software tool was developed to implement the eMHRA method presented in this paper and is demonstrated in the paper. A case study is presented of a subsystem of a generic Pressurized Water Reactor (PWR) civilian nuclear power plant. The case study shows that HRA and EOP insights can be incorporated into PRA models early in the design process to better inform system designers of potential high likelihood failure events in operator actions. The eMHRA method presented in this paper provides a new tool for risk analysts to better predict and understand failure scenario outcomes early in the design process. With this information, engineers will be better able to develop new EOPs and operator interfaces to reduce failure likelihood in due to missed operator recovery actions.

An auction-based smart district heating grid

A district heating grid is a hot water pipeline grid used to transmit waste heat from CHP (Combined Heat and Power) production plants to buildings, where it is used for service water and space heating. Currently, broad use of district heating is limited to Northern Europe and some regions in Central Europe, but the European Union Energy Efficiency Directive 2012/27/EU and Energy Performance of Buildings Directive 2010/31/EU drive the widespread adoption of this technology in member states. Further, these directives push the introduction of significant renewable local energy production into these grids, creating a need for a smart district heating grid that is in many ways analogous to the smart electric grid. One major difference is that in a hot water pipeline grid, there are significant delays in energy propagation, so algorithms developed for the smart electric grid are not directly applicable. In this paper, an auction based heat trade mechanism is proposed between an auctioneer agent representing the CHP plant operator and prosumer agents representing end users with local solar thermal generation capacity. A predictive simulation is used by an auctioneer to account for the said delays in energy production and to determine the market price that best satisfies the performance indicators defined by the CHP plant operator. The proposed multi-agent system is demonstrated by co-simulating it against a model of a part of a Finnish municipalitys district heating grid.

A SysML profile supporting change orders in model driven engineering

One important capability for manufacturing enterprises is the ability to manage change orders, which are changes after the customer has placed the order. The change can be due to several reasons, such as misunderstanding of requirements or the inability of the supply chain to deliver parts, materials or subassemblies at an acceptable cost and lead time. The effective management of the latter kind of change requires linking design model elements to parts, which can be obtained from the supply chain or made in-house. In this paper, SysML is extended to support the said linkage, and based on these extensions a workflow is defined for handling the change. The model repository that underpins model driven engineering is exploited to partially automate the workflow and to relieve the change engineer from the need to understand the product structure or SysML.

Risk modeling of variable probability external initiating events

As components engineering has progressively advanced over the past 20 years to encompass a robust element of reliability, a paradigm shift has occurred in how complex systems fail. While failures used to be dominated by ‘component failures,’ failures are now governed by other factors such as environmental factors, integration capability, design quality, system complexity, built in testability, etc. Of these factors, environmental factors are difficult to predict and assess. While test regimes typically encompass environmental factors, significant design changes to the system to mitigate any failures found is not likely to occur based on the cost. The early stages of the engineering design process offer a significant opportunity to evaluate and mitigate risks due to environmental factors. Systems that are expected to operate in a dynamic and changing environment have significant challenges for assessing environmental factors. For example, external failure initiating event probabilities will change with respect to time and new types of external initiating events can be expect with respect to time. While some of the well exercised methods such as Probabilistic Risk Assessment (PRA) [Error! Reference source not found.] and Failure Modes and Effects Analysis (FMEA) [Error! Reference source not found.] can partially address a time-dependent external initiating event probability, current methods of analyzing system failure risk during conceptual system design cannot. As a result, we present our efforts at developing a Time Based Failure Flow Evaluator (TBFFE). This method builds upon the Function Based Engineering Design (FBED) [Error! Reference source not found.] method of functional modeling and the Function Failure Identification and Propagation (FFIP) [Error! Reference source not found.] failure analysis method that is compatible with FBED. Through the development of TBFFE, we have found that it can provide significant insights into a design that is to be used in an environment with variable probability external initiating events and unique external initiating events. We present a case study of the conceptual design of a nuclear power plants spent fuel pool undergoing a variety of external initiating events that vary in probability based upon the time of year. The case study illustrates the capability of TBFFE by identifying how seasonally variable initiating event occurrences can impact the probability of failure on a month timescale that otherwise would not be seen on a yearly timescale. Changing the design helps to reduce the impact that time-varying initiating events have on the monthly risk of system failure.

A Plant-Wide and Function-Specific Hierarchical Functional Fault Detection and Identification (HFFDI) System for Multiple Fault Scenarios on Complex Systems

The development of Fault Detection and Identification (FDI) systems for complex mechatronic systems is a challenging process. Many quantitative and qualitative fault detection methods have been proposed in past literature. Few methods address multiple faults, instead an emphasis is placed on accurately proving a single fault exists. The omission of multiple faults regulates the capability of most fault detection methods. The Functional Failure Identification and Propagation (FFIP) framework has been utilized in past research for various applications related to fault propagation in complex systems. In this paper a Hierarchical Functional Fault Detection and Identification (HFFDI) system is proposed. The development of the HFFDI system is based on machine learning techniques, commonly used as a basis for FDI systems, and the functional system decomposition of the FFIP framework. The HFFDI is composed of a plant-wide FDI system and function-specific FDI systems. The HFFDI aims at fault identification in multiple fault scenarios using single fault data sets, when faults happen in different system functions. The methodology is applied to a case study of a generic nuclear power plant with 17 system functions. Compared with a plant-wide FDI system, in multiple fault scenarios the HFFDI gave better results for identifying one fault and also was able to identify more than one faults. The case study results show that in two fault scenarios the HFFDI was able to identify one of the faults with 79% accuracy and both faults with 13% accuracy. In three fault scenarios the HFFDI was able to identify one of the faults with 69% accuracy, two faults with 22% accuracy and all three faults with 1% accuracy.Copyright