INTELLIGENT BUILDING RESEARCH

Incorporating Expert Knowledge andReal-Time Data in Smart House/Building

Chia Y. HanECECS Department

University of Cincinnati

• Components are live – each has its own life cycle

• Ideal design conditions are assumed to be throughout the entire life cycle

• Interrelationships change with both time and events

• No monitoring for detection of deterioration

• Preventive maintenance – often wasteful & harmful

• Experts in the field are few

• Digital devices are still proprietary

• Interoperability across AEC/FM

• New open systems are coming

Introduction

Building

Work control

Application Application

Data Storage

ConsoleConsole

DevicesDevices

Client/Server Architecture

Old

Interoperability

IFC_2X

BLISBuilding Lifecycle Interoperable Software Project

BACnet

• Analog Input

• Analog Output

• Analog Value

• Averaging Object

• Binary Input

• Binary Output

• Binary Value

• Calendar • Command

• Device

• Event Enrollment

• File Object

• Group Object

• Life Safety Point

• Life Safety Zone

• Loop Object

• Multi-state Input Object

• Multi-state Output Object

• Multi-state Value Object

• Notification Class Object

• Program Object

• Schedule Object

• Trend Object

BACnet Objects

BACnet device

BACnet Communication Protocol

Web Services

Web-based open systems

Intelligent behavior

• Automatic model configuration

- based on the currently existing components and

their respective forms and functions. • Operation adaptive to dynamic environment

- real-time data search and access

- model-based and rule-based control

How smart can it be?

Console

System Modeling

PPCL ProgramsCAD DXF/DWG

VISIOEXPRESS

BLIS-XML

Layout/Layer Reasoner

DB

DB

KB

Service &

BuildingSelection

Servicerequest

Building Services

OperatingMaintenanceRepairMonitoringReportingFDD

AEC/Vendor/Regulatory Documents

Rule Generator

IEMonitoring

Diagnosis

Message RoutingReport Generation

Work ControlCenter

Energy

IBS Architecture

Major building systems

• HVAC

• Lighting

• Energy/power

• Communication

• Plumbing

• Fire Safety

• Security

HVAC Systems

Background

• Rule-based expert systems for fault detection and diagnostics (FDD) applications have been implemented

• Rules can be defined by carefully analyzing the logic of a DDC program in terms of both the normal and abnormal states of the affected subsystem on a given input.

• Existence of expert knowledge in mature engineering and technology fields.

FDD Decision Tree

• Portability The rules developed in the past are usually created

with the assistance of the maintenance personnel, and they often reflect the specifics of a particular HVAC, such as containing hard coded device names.

• Scalability The rules are dependent on the target system

configuration, but there are many differences in system configuration from building to building.

• Webability The captured domain knowledge not accessible

(interoperable or exchangeable) in networked and distributed environment such as the Internet.

Motivation for new solution

To provide a framework for automatically

extracting knowledge and generating expert systems rules for any particular process related to building systems

and

integrating the expert systems into the current web-based environment.

The Main Goal

To use the web services technology, such as the Extensible Markup Language (XML) and the Resource Description Framework (RDF) and the industry standards, such as the Industry Foundation Classes (IFC) from the International Alliance for Interoperability (IAI).

Proposed Solution

FDD XML/RDFFile Server

Expert System Engine &Knowledge BaseServer

Database Server

FDD App

FDD XML/RDFFile Server

Expert System EngineKnowledge Base

Server

FDD Client

DB Servers

Distributed computing

Console

System Modeling

PPCL ProgramsCAD DXF/DWG

VISIOEXPRESS

BLIS-XML

Layout/Layer Reasoner

DB

DB

KB

Service &

BuildingSelection

Servicerequest

Building Services

OperatingMaintenanceRepairMonitoringReportingFDD

AEC/Vendor/Regulatory Documents

Rule Generator

IEMonitoring

Diagnosis

Message RoutingReport Generation

Work ControlCenter

Energy

Physical systemRT data

System Modeler

FDD Module

Desired procedure:1. The operator requests information about an HVAC from the BLIS-

XML/RDF file server. The server responds with two files: (a) BLIS-XML file of a specified HVAC system and (b) RDF file.

2. From the operator’s computer, the BLIS-XML and RDF file are automatically sent for parsing and storing on the expert system engine server.

3. An agent on the expert system engine server uses the two files to parse and generate expert system rules for each HVAC process based on the process model.

4. The operator uses a browser to make a connection to the expert system engine server to specify for which HVAC system FDD should be performed immediately or according to a schedule.

5. The expert system engine server performs FDD immediately or according to a schedule and sends the results to a computer display, pager, SMTP server, etc.

The necessary components for implementing the above procedure of

generating expert system rule automatically:

(1) A generic fault model for each HVAC process;

(2) RDF, as a method for associating an HVAC element with its real-time operational value;

(3) IFC, as a standard capable of describing an HVAC system;

(4) A software agent that will apply the process model to a given HVAC system to generate expert system rules; and

(5) An expert system engine to parse the rules and generate inference results in the form of warnings if faults are present.

Control loop model

VAV System with constant volume return

 

RDF structure

Resource Property Value

Mixed_Air/Damper1 Percent open URL_1

URL 1 Location www.uc-dbserver/URL_1

Return_Air/Fan Rotation speed URL_2

URL_2 Location www.uc-dbserver/URL_2

… … … 

XML-IFC description of the sample process model

<IfcSensor XMLID="i19" PredefinedType="HVACSENSOR" ConnectedTo="i20" ObjectType="Humidity sensor for measuring the temperature of the air after it

has been mixed, warmed or cooled" />

<IfcController XMLID="i20" ControlElementID="5" ConnectedTo="i21" ObjectType="controls the humidifier valve actuator" />

<IfcActuator XMLID="i21" PredefinedType="PNEUMATICACTUATOR" ConnectedTo="i22" ObjectType="humidifier valve 1 actuator" />

<IfcValve XMLID="i22" ValveType="GATE" ObjectType="humidifier valve 1" />

<IfcActuator XMLID="i23" PredefinedType="PNEUMATICACTUATOR" ConnectedTo="i24" ObjectType="humidifier valve 2 actuator" />

<IfcValve XMLID="i24" ValveType="GATE" ObjectType="humidifier valve 2" />

<IfcRelAssemblesElements XMLID="i25" RelatingElement="i20" RelatedElements="i21 i23" />

Implementation

• The system is implemented with the Java-based open source solution.

• The expert system component consists of Jess (Java Expert System Shell), which is in its core a collection of Java classes

• Jess provides easy to use mechanisms for creating Java objects, accessing their variables and calling their methods.

• In addition, it is possible to start the Jess engine from Java code, which gives the programmer the ability to redirect the output of the engine to any valid Java output stream (e.g., TCP/IP socket).

• Jess also takes advantage of the Java Bean™ technology, a form of inter-process communication, which enables a Java application to implement the event driven model.

• A Java Bean is a Java object that generates (or “fires” in Java terminology) predetermined events.

• If a Java Bean has one or more “listening” objects attached to it, they will be able to “hear” the events and take appropriate actions.

• All “listeners” must first register with the Java Bean to receive fired events. Jess can easily register to become a “listener” with such a class and update the changed information in the appropriate slot of its template (a named entity that contains a list of facts).

• As soon as Jess modifies a fact on its fact list, it re-runs all the rules in its knowledge base.

• The FDD rules from the prior expert system shell, M4, were converted into the Jess language and a Java Bean based template was created to retrieve parameters relevant to the process from the database.

• The connection to the database was established with the help of the JDBC-ODBC Bridge, which provides the necessary API for Java programs to make connection to ODBC sources on Windows based computers and use SQL for querying the tables in question.

A simple message exchange protocol of a client-server model was implemented:

- A TCP/IP server is launched and listens on a port for applet connections.

- As soon as one connection is established, it spawns an object of the class “processor” passing the obtained communication socket as the parameter.

- The processor immediately enters a new thread waiting for commands from the applet.

- The server meanwhile is able to accept new connections creating a processor object for each case.

GUI

The last step is to create a Java applet to allow the Internet user to view the output of the FDD. After the server receives the request from the incoming connections from the applet, the output of the expert system engine would be re-routed to the client side of the connection into a text area located in the applet.

• A real-world application of HVAC FDD system was used.

• The main steps and the major components needed for design and deployment of web-based intelligent systems are highlighted.

• For an application in other domains, expert knowledge needs to be extracted, modeled, and represented in knowledge base. The knowledge of experienced domain workers is critical.

• Overall, we have considered and demonstrated successfully that practical configuration mapping, expert system rule generation and real-time data access can be accomplished with the off-the-shelf web tools and today’s component technologies.

Conclusion

Conclusion

• There is a need for a practical solution to implement intelligent behavior of systems that are networked and digitally controlled.

• It is important to use expert knowledge and salvage the knowledge in the existing expert systems and readapting it into the distributed computing model of the present time.

• Expert system technology is still used to let the computer “reason” about what it “sees” based on a generic model for each of the modeled processes and real-time data.

• Application software development for upcoming building open systems is coming of age.

Thank You!