ONTOLOGY-ENABLED TRACEABILITY MODELS FOR ENGINEERING SYSTEMS DESIGN AND MANAGEMENT

This thesis describes new models and a system for satisfying requirements, and an architectural framework for linking discipline-specific dependencies through inter- action relationships at the ontology (or meta-model) level. In a departure from state-of-the-art traceability mechanisms, we ask the question: What design concept (or family of design concepts) should be applied to satisfy this requirement? Solu- tions to this question establish links between requirements and design concepts. The implementation of these concepts leads to the design itself. These ideas, and support for design-rule checking are prototyped through a series of progressively complicated applications, culminating in a case study for rail transit systems management

Software Patterns for Traceability of Requirements to Finite State Machine Behavior

AbstractThere is a growing class of engineering applications for which long-term managed evolution and/or managed sustainability is the primary development objective. The underlying tenet of our work is that neither of these trends will become fully mature without: (1) An understanding for how and why system entities are connected together, and (2) Formal procedures for assessing the correctness of system operations, estimating system performance, and understanding trade spaces involving competing design criteria. To address these concerns, during the past few years we have developed methodologies and tools for ontology-enabled traceability; that is, traceability mechanisms where requirements are connected to models of engineering entities by threading the traceability connection through one or more ontologies. In our proof-of-concept work the engineering entities were restricted to elements of system structure. But, of course, real engineering systems also have behaviors. This paper will report on research to understand the role that software patterns (e.g., model-view-controller) and mixtures of graph and tree visualization can play in the implementation of traceability mechanisms from requirements to elements of finite-state machine behavior (e.g., actions, states, transitions and guard conditions). We will present a simple lamp example

Semantic Models and Reasoning for Building System Operations: Focus on Knowledge-Based Control and Fault Detection for HVAC

According to the U.S. Energy Information Administration (EIA), the Building Sector consumes nearly half (47.6%) of all energy produced in the United States. Seventy-five percent (74.9%) of the electricity produced in the United States is used just to operate buildings. At the same time, decision making for building operations still heavily rely on human knowledge and practical experience and may be far from optimal. In a step toward mitigating these deficiencies, this dissertation reports on a program of research to identify opportunities for using semantic models and reason- ing in building system operations. The work focuses on knowledge-based control and fault detection for heating, ventilation and air conditioning (HVAC) systems. Decision-making procedures for building system operations are complicated by the multiplicity of participating domains (e.g., architecture, equipment, sensors, occu- pants, weather, utilities) that need to be considered. The key opportunity of this approach is a means to utilize semantic models for knowledge representation, inte- gration of heterogeneous data sources, and executable processing of semantic graph models in response to external events. The results of this dissertation are con- densed into three case-study applications; (1) Semantic-assisted model predictive control (MPC) for detection of occupant thermal comfort, (2) Semantic-based util- ity description for MPC in a chiller plant operation, and (3) Knowledge-based fault detection and diagnostics for HVAC systems

Framework for Knowledge-Based Fault Detection and Diagnostics in Multi-Domain Systems: Application to HVAC Systems

State-of-the-art fault detection methods are equipment and domain specific and non-comprehensive. As a result, the applicability of these methods in different domains is very limited and they can achieve significant levels of performance by having knowledge of the domain and the ability to mimic human thinking in identifying the source of a fault with a comprehensive knowledge of the system and its surroundings. This technical report presents a comprehensive semantic framework for fault detection and diagnostics (FDD) in systems simulation and control. Our proposed methodology entails of implementation of the knowledge bases for FDD purposes through the utilization of ontologies and offers improved functionalities of such system through inference-based reasoning to derive knowledge about the irregularities in the operation. We exercise the proposed approach by working step by step through the setup and solution of a fault detection and diagnostics problem for a small-scale heating, ventilating and air-conditioning (HVAC) system.NIS

Reinforcement Learning for Building Management Systems

It is increasingly common to design buildings with advanced sensing and control systems to improve energy efficiency, indoor air quality which impacts health and productivity. However, there has been limited progress in making building automation systems “intelligent,” as the performance of such buildings is often limited by reactive control systems, primarily using setpoint limits and fixed operation schedules. The complex nature of building control problems motivates the application of state-of-the-art software engineering methods and techniques. Agent-based models (ABM) are well-suited for controlling complex engineering systems such as those employed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paradigm, a collection of interacting autonomous components (i.e., agents) adapt and make decisions in changing environments. There is a growing body of literature on adaptive agents in ABMs in many industries, but few have looked at the compatibility of ABMs with artificial intelligence (AI) optimization approaches. In most cases, conventional optimization techniques, such as mixed integer linear programming and gradient descent, have been used to find an optimal solution. This paper explores the use of an actor-critic, model-free algorithm based on a deterministic policy gradient that provides continuous control to generate the desired supply air temperature. The case study develops a thermal energy storage (TES) agent that determines the optimal valve position to manage the temperature of the cooling water flow. The case study was developed using the Intelligent Building Agents Laboratory at the National Institute of Standards and Technology. Future work will use multiple agents (i.e., air handling unit, TES, chiller) acting in cooperation or competition