FAULT DETECTION AND DIAGNOSIS METHODS FOR RESIDENTIAL AIR CONDITIONING SYSTEMS USING CLOUD-BASED DATA

Buildings account for nearly 40% of total energy consumption and nearly 75% of electrical energy consumption in the United States, and a significant portion of this energy consumption is due to the heating and cooling systems. Both commercial and residential heating, ventilation, and air conditioning (HVAC) systems are prone to faults that degrade performance and increase energy consumption. Furthermore, these systems are robust to faults in that they will operate with faults present for an extended period of time and will often continue to maintain a comfortable indoor environment. While considerable work has been devoted to developing fault detection and diagnosis (FDD) strategies for large and small commercial systems, relatively little has been done specifically for residential systems. This research presents novel FDD methods developed specifically for residential air conditioning systems. By using a novel set of virtual sensing methods, the proposed methodology eliminates the need for installing sensors on the outdoor unit. This is a significant advantage for residential ‘split’ air conditioning systems because installing sensors on both the indoor and outdoor units increases the complexity and cost of the data acquisition system. In addition to the proposed set of virtual sensors, this research provides solutions to two other problems that arise when implementing FDD methods on field-operating systems. (1) While most FDD methods use static models and rely on steady state analysis, field-operating systems often will not achieve steady state operation. This research provides a method for predicting the equilibrium operating point for many air conditioning parameters while the system is still in a transient response. This enables the equilibrium point to be determined before steady state operation has been achieved, and thus a static analysis may be performed without the system reaching steady state. (2) Existing change-point detection methods that could be used for detecting faults are impractical to implement on a large scale because they may require a priori knowledge, extensive tuning, or high computational loads. This research proposes a change-point detection algorithm for the purpose of fault detection which requires minimal assumptions, tuning, and computation. This change-point detection algorithm is suitable for deployment across many different systems simultaneously. In addition to the solutions outlined above for performing FDD using installed sensors, this research also proposes methods for performing fault detection and diagnose using only thermostat data. While a full strategy for thermostat data is not presented, crucial preprocessing methods that more complete methods will be built on are presented in detail. Nearly all of the data analyzed for each method described in this study uses event-based data uploaded in real-time to a cloud-based database and then queried and analyzed to perform FDD

Condition-based maintenance: innovation in building maintenance management

Maintenance is a continuous process implemented by Facilities Management (FM) providers as one their core competences to effectively manage and maintain critical assets throughout the whole life of a building and prevent downtime of essential systems. Maintenance actions are usually categorised into two main streams: corrective (CM) and precautionary (PM). In CM equipment is repaired after a failure occurs (i.e. reactively). In contrast, PM is applied based on a fixed-time or age-schedule (i.e. preventive). However, a subdivision of PM that is widely discussed in literature, yet rarely implemented in practice within FM, is Condition-based Maintenance (CBM), which enables maintenance to be applied predictively. CBM exploits the operating condition of equipment to predict a failure occurrence, thus preventing any unexpected downtime and reducing maintenance cost by avoiding unnecessary preventive actions. The underlining theory of CBM is based on the belief that 99 per cent of equipment will evidence some sort of indicators prior to failure. Therefore, it is possible to identify the fault, determine the cause and establish the severity and longevity of the equipment’s optimum life through monitoring and evaluating data collected through various techniques. Nevertheless, although the theoretical foundations of CBM are relevant to building maintenance management, such data and technology-focused strategies are seldom considered to be a viable and feasible option within the FM strategy. Therefore, this thesis details a mixedmethods, action research project undertaken within this industry sector, which has been significantly suppressed of innovative contributions. The study investigates the viability, practicality and impact of implementing an innovative CBM focused maintenance framework that is inclusive of real-time vibration analysis and enhanced with statistical data analysis. The CBM framework is demonstrated to be economically viable, technically feasible and complimentary to the inadequacies of the existing time-based regime. The framework adds value to the buildings maintenance management objectives

Facilitating teacher participation in intelligent computer tutor design : tools and design methods.

This work addresses the widening gap between research in intelligent tutoring systems (ITSs) and practical use of this technology by the educational community. In order to ensure that ITSs are effective, teachers must be involved in their design and evaluation. We have followed a user participatory design process to build a set of ITS knowledge acquisition tools that facilitate rapid prototyping and testing of curriculum, and are tailored for usability by teachers. The system (called KAFITS) also serves as a test-bed for experimentation with multiple tutoring strategies. The design includes novel methodologies for tutoring strategy representation (Parameterized Action Networks) and overlay student modeling (a layered student model), and incorporates considerations from instructional design theory. It also allows for considerable student control over the content and style of the information presented. Highly interactive graphics-based tools were built to facilitate design, inspection, and modification of curriculum and tutoring strategies, and to monitor the progress of the tutoring session. Evaluation of the system includes a sixteen-month case study of three educators (one being the domain expert) using the system to build a tutor for statics (forty topics representing about four hours of on-line instruction), testing the tutor on a dozen students, and using test results to iteratively improve the tutor. Detailed throughput analysis indicates that the amount of effort to build the statics tutor was, surprisingly, comparable to similar figures for building (non-intelligent) conventional computer aided instructional systems. Few ITS projects focus on educator participation and this work is the first to empirically study knowledge acquisition for ITSs. Results of the study also include: a recommended design process for building ITSs with educator participation; guidelines for training educators; recommendations for conducting knowledge acquisition sessions; and design tradeoffs for knowledge representation architectures and knowledge acquisition interfaces

Semester Courses and Course Equivalents: Graduate Courses Summary

A list comprised of summaries of all graduate courses and course equivalents at Wright State University

Undergraduate and Graduate Course Descriptions, 2008 Summer

Wright State University undergraduate and graduate course descriptions from Summer 2008

Bringing Health Care Online: The Role of Information Technologies

This report identifies key technologies and shows how they are being used to communicate clinical information, simplify administration of health care delivery, assess the quality of health care, inform the decision-making of providers and administrators, and support delivery of health care at a distance