Chapter 1
Dynamic Reconfiguration of
Complex Systems to Avoid Failure
Fred M. Discenzo
Francisco P. Maturana
Raymond J. Staron
Kenwood H. Hall
Rockwell Automation, USA
{Fmdiscenzo,Fpmaturana,Rjstaron,Khhal}@ra.rockwell.com
Pavel Tichý
Petr Šlechta
Jan Bezdicek
Vladimír Marík
Rockwell Automation, Czech Republic
{Ptichy,Pslechta,Jbezdicek,Vmarik}@ra.rockwell.com
1.1. Introduction
There are increasing pressures for low cost, reliable automation systems even though system
applications are becoming increasingly complex and deployed in critical applications. The
automation of historically manual or mechanical systems is contributing to the growth in
coupled automation systems while placing a greater demand on machinery reliability. In spite
of the greater focus on maintenance activities, machinery failures do occur causing process
upsets and equipment damage while potentially compromising worker safety and negatively
affecting the environment (Figure 1 Example Failure of a Coupled Process).
The development of intelligent machines is often targeted at providing more reliable
machines, machines that are easier to configure and diagnose, and machines that can be more
readily integrated to help manage the growing complexity of automation systems. Examples
of intelligent, self-diagnosing machines include a self-diagnosing smart valve [Marritt 2001],
an intelligent motor with embedded sensors, processor, and motor diagnostic algorithms
[Discenzo(a) 2000] and an intelligent variable frequency drive with embedded pump
diagnostic algorithms and automatic pump protection
capability [Discenzo(b) 2002].
Distributed intelligent
machines support distributed computing and distributed
control architectures while reducing the requirement for
large complex central controllers. Distributed control
implemented in intelligent machines provides the
framework for highly distributed intelligent agents for
diagnostic and control.
A distributed multi-agent system employs intelligent agents
Figure 1
that encapsulate the core fundamental behavior or function
Example Failure of a
of the intelligent device as an autonomous component.
Coupled Process
These components exhibit primitive, local goal-seeking
capabilities to realize local objectives as well as collaborate with other intelligent devices to
define and realize higher level cluster goals or overarching system-level goals. It is
significant that new goals may emerge dynamically and replace previous goals. The suite of
goals is hierarchical and is dynamic based on changes in machinery condition, predicted
operating states, and changing system goals, objectives, or missions. The emerging suite of
goals and strategies for realizing these goals is determined by collaboration and negotiation
among groups of intelligent agents.
This paper presents the foundation technologies that are essential to realizing an adaptive, reconfigurable automation system. A dynamic agent registry is presented along with a
hierarchical framework for organizing agent clusters. This framework, called cluster
associations, provides the basis for coordinating the dynamic reconfiguration of multiple
subsystems that must be coordinated but are loosely coupled.
1.2. Intelligent Agents
1.2.1. Machine Intelligence
For over 50 years there has been an ongoing effort to understand cognition and intelligence
beginning with the famous Turing Paper. An important objective of these efforts is to provide
a rigorous foundation to enhance the capabilities of machines and to make machines more
useful [Charniak 1985] [Nilsson 1980]. Some of the techniques pursued include a suite of
artificial intelligence (AI) techniques such as expert systems, fuzzy logic, genetic algorithms,
analogic reasoning, artificial neural networks, and related model-based and model-free
techniques [Charniak 1985]. Many of the automation successes reported apply biologically
inspired architectures and techniques to solve well targeted, specific automation problems
such as adaptive control, defect classification, and job scheduling. [Zurada 1994].
The capabilities which may be provided by intelligent machines may be categorized based on
the degree of embedded knowledge with the most capable systems employing real-time goal
adjustment, cooperation, preemption, and dynamic reconfiguration [Discenzo(c)
2000][Discenzo(d) 2002]. These capabilities may be effectively integrated in an agent-based
system employing intelligent machines in a distributed automation system. This architecture
built on a foundation of a society of locally intelligent cooperating machines provides an
effective framework for the efficient and robust automation of complex systems.
1.2.2. Autonomous Agents
The approach taken is to encapsulate the fundamental behavior of intelligent devices as an
autonomous component. The autonomous component employs a model of the primitive
device behavior and enables agents to act on behalf of physical devices or complex processes.
The approach of establishing application-specific agent behavior in a reusable and scalable
manner finds counterparts in other research activities such as Multi-agent Systems (MAS),
Autonomous Agents, Flexible Manufacturing, and Virtual Enterprise [Shen 2001][Vasko
2000][Zhang 1999]. Our focus for intelligent agents is on device prognostics, and
reconfiguration to realize local and system-level goals. This is complementary to previously
reported developments employing autonomous control that incorporates agents for planning,
communication, diagnostics, and control [Vasko 2000][Maturana 2000][Maturana 2002]. The
core capabilities of the intelligent agents are summarized in Table I below [Wooldridge 1995].
Agent collaboration utilizes an agent registry facility
and communicates using open, standard interfaces.
The FIPA (Foundation for Intelligent Physical
Agents) standard is used for multi-agent system
operation. This facilitates system development and
provides a basis for different and outside agents to be
discovered and to participate in control and
reconfiguration planning and execution [FIPA][FIPAOS].
Table I
Typical Agent Characteristics
1. Autonomous
2. Reactive
3. Proactive
4. Social
To collaborate, agents usually need a facility for registering their capabilities and to inquire
about additional capabilities required. We employ a Matchmaker-based architecture which is
based on Directory Facilitators (DF) and consistent with the FIPA standard. All agents
register their capabilities with the DF agent and provide updates dynamically as functioning
changes. With each request for a capability, the DF agent provides a list of agents that match
the requested service or function. The DF agent acts as an information broker and passively
provides information services. The DF agent organization may be hierarchical and agent
capability registration may be made in a breadth-first or depth-first manner. Alternatively, to
avoid propagation, information may only be propagated locally and new capability requests
will be processed by local DF agents who carry out meta-level communications to discover
needed capabilities. DF agents then provide location and service capability information of
remote agents to the initial requesting agent. Relevant information may be stored locally to
enhance organizational learning. Since one DF agent in the system represents a singe point of
failure and also communication bottleneck, it is advantageous to use more than one DF agent.
A structure of DF agents called Dynamic Hierarchical Teams (DHT) has been developed to
insure user defined levels of fault tolerance while preserving scalability [Tichy 2004].
The implementation of self-emerging organizational structures is based on the dynamic
gathering of system capabilities. The mechanism for collaboration and control is dynamic
clustering of agents where transient clusters are representative of system capabilities for short
periods of time. User and system tasks trigger the dynamic clustering. The task complexity
determines the size and configuration of the clusters. Gathering multiple clusters forms a
cooperation domain. An important characteristic of this mechanism is the capability of the
system to aggregate resources as needed into the emergent organizations. This organizational
feature provides an architecture that is robust and survivable. Machinery prognostic and
diagnostics provide the foundation for autonomous agents to define the need for
reconfiguration, the urgency for changing configuration and control, and to prescribe viable
options for dynamic reconfiguration.
1.3. Machinery Prognostics
Many characteristics such as component type, information descriptors, and diagnostic
reference information are similar across components (e.g. motors, pumps, and compressors)
and processes that use these components (e.g. industrial automation processes, aircraft
actuators, shipboard auxiliary systems, and building services). An open, general framework
for describing machinery health information was recently developed. The system, OSA-CBM
(Open Systems Architecture for
Condition-Based
Maintenance)
provides a framework for the real-time
integration of machinery health and
prognostic information with decision
support activities. The scope of this
framework includes legacy analog
sensors and smart transducers (e.g.
IEEE 1451), signal processing, state
assessment, prognostics and decision
support (www.osacbm.org). This
architecture specification is open to
Figure 2
the public and may be implemented in
Intelligent Agent with OSA-CBM Data
a DCOM, CORBA, or HTTP/XML
environment.
Closely coupling diagnostic and prognostics
information with real-time automatic control can
provide important new capabilities. It is possible
to define the expected evolution of a state variable
of interest. The evolution of a state variable,
under specific environmental and operating
conditions assumptions permits tracking the
expected degradation of a critical system
component eventually leading to a device failure.
e.g. PM /
Tank
Empty
Critical
State
Variable
x(t)
Time (t)
Figure 3
Integrated Prognostics and Control
The rate of component degradation may be determined continuously during machinery
operation using general rules of thumb, simulation models, or dynamically updated models.
For example, L10 bearing life may be calculated using speed, temperature, and loading.
Similarly, motor winding lifetime is reported to be reduced by ½ with each 10 degree F rise in
temperature. Alternatively on-line diagnostic and prognostic algorithms such as used for
pumps, motors, or rolling element anti-friction bearings may provide a more accurate estimate
of the degradation rate or lifetime trajectory of critical system components. Changing
operating conditions such as speed, temperature, frequency, or acceleration / deceleration
times may change the stress on critical components and cause the state variable to take a
different time trajectory. The family of possible state trajectories represents the control space
in which we may operate the system and where some of the possible trajectories are better
than others and some states represent critical or unstable states and are to be avoided (Figure 3
Integrated Prognostics and Control).
It is possible to dynamically drive the system to achieve a prescribed trajectory. This
trajectory may represent an improved state than would occur if we did not alter the control
based on machinery health information. Furthermore, the future state we achieve is chosen to
be optimal in some sense such as machinery operating cost, machinery lifetime, or life-cycle
cost. The lifetime curves for many devices or components will be linked dynamically. It is
not uncommon for degradation in one component to cause excessive stress in another
perfectly good component and the premature failure of this previously health component.
Control decisions and reconfiguration options must consider the device stress coupling that
exists and the degraded state of system components when defining the new configuration state
required and the transition plan to this new state. Intelligent distributed agents provide an
effective framework for managing this complexity and for controlling the dynamic
reconfiguration of critical system elements.
1.4. Cluster Associations
Multi-agent collaboration and control are achieved through the dynamic formation of agent
clusters. Agent clusters directly support the collaboration of distributed autonomous devices.
For example, in a chilled water system, agent clusters may be formed representing coupled
pump, valve, chiller, and heat load entities. The cluster facility coupled with the registry
facility enables intelligent agents to identify component or device faults, degraded
components, or system services that, although not critical, will need to be satisfied in the near
future. The cluster facility also supports defining a suite of possible reconfiguration options,
evaluating the potential new configurations, defining a transition plan to the most desirable
configuration, carrying out the prescribed reconfiguration plan, and implementing the
associated new control action.
The same agent cluster facility described above can be applied to other systems such as fluid
handling systems, material handling systems, and power distribution systems. Many
processes employ multiple coupled subsystems working together. For example, the operation
of the chilled water system described above is affected by the supply power provided to the
system components and the demand of the various heat loads such as facility cooling and
equipment cooling requirements.
A structure for coordinating the interface between agent
clusters is needed to provide the required sub-system
coordination
while
not
inducing
excessive
communications and coordination demands.
The
coordination required between loosely coupled systems
may be accomplished by associating clusters that operate
concurrently in separate but coupled domains. This is
shown graphically in Figure 4 Cluster Associations
Across Application Domains. Cluster associations are
represented as agent properties and this information is in
the appropriate agent registry. The agent association
may function as connectors as described by Gladwell
[Barabasi 2002].
MISSION
SUBSYSTEM / AUXILIARY
CHILLER / BALLAST / JP5
POWER DISTRIBUTION
Figure 4 Cluster Associations
Across Application Domains
Intelligent agent negotiation within a cluster will also propagate the reconfiguration option
through the cluster association to agent clusters managing other linked domains such as
power. For example, a decision to operate a pump at full load must be made in concert with
the agent-based power management system to insure that power will be provided and
maintained for the new critical load in spite of possibly diminished overall power capacity.
Power reconfiguration planning will take into account existing and future reconfigured power
requirements to support the various required services. Agent negotiations will necessarily be
performed in parallel in multiple application service domains and coordination achieved
across service domains through cluster associations.
Coordinating the reconfiguration and control of coupled systems using can provide the
capability for maintaining critical system functions in spite of unexpected disturbances,
unforeseen damage, and unique or severe loading requirements. The use of both intelligent
agent virtual clusters and cluster associations provides the framework for managing the
complexity of dynamically reconfiguring coupled systems.
1.5. System Implementation
We have developed autonomous agents for
distributed control of a land based Chilled Water
System (CWS) pilot system. The laboratory system
is a Reduced Scale Advanced Development
(RSAD) model that is a scaled down model of a
chilled water system from a US Navy ship. The
RSAD model employs distributed diagnostic and
control agents deployed on commercially available
controllers (Figure 5).
Figure 5 Chilled Water Pilot System
There are currently two chiller plants in the system
and an infrastructure of pipes, valves, pumps,
sensors, and ship services (i.e. heat loads) both vital and non-vital. This system employs 68
agents running on 23 commercially available programmable logic controllers. Clusters are
formed dynamically and the Contract Net protocol [Smith 1980] is used to establish dynamic
negotiations among the agents to realize ship-level and mission goals and to meet local and
immediate operational objectives. This implementation utilizes a full suite of Directory
Facilitators (DF) that provide matchmaking and Agent Management Services (AMS)
functionality. Group goals emerge dynamically and these are agreed upon by the agents
through negotiation. For example, an agent that detects a water leakage problem in a pipe
section establishes a goal to isolate the leakage and informs adjacent agents to evaluate the
problem according to their data.
A simulation model and associated development tools were developed to facilitate designing
and deploying the intelligent agent system and to validate performance over a wide range of
operating conditions. A screen copy showing the system schematic and the simulation
screens is shown in Figure 6 Intelligent Agent System Simulation. It is significant that this is
a highly distributed autonomous system. There is no central controller and no single point of
failure. This system has been shown to dynamically establish new goals and automatically
reconfigure system operation to minimize damage and to meet critical cooling needs. New
operating goals may emerge based on
equipment prognostics or predicted
component failure to avoid reaching a
predicted or probable state that is
undesirable
(e.g.
catastrophic
component failure).
The potential
undesirable states may be efficiently
avoided while continuing to satisfy
critical system needs (e.g. radar
cooling).
This system serves to
validate the agent methodology to
manage the inherent complexity of
highly distributed systems while
Figure 6 Intelligent Agent System Simulation
responding dynamically to changes in
operating requirements and degraded or failed components.
1.6. Opportunities and Challenges
The software content and complexity of automation systems continue to increase rapidly
while software problems represent a leading cause of production breakdowns [Salimen 1992].
The new paradigm of Intelligent Multi-agent systems can provide significant benefits such as
scalability, reliability, and survivability for complex critical systems. The broadscale
deployment of intelligent devices such as intelligent pumps, fans, drives, valves, and motors
may readily utilize this paradigm. The potential benefits of deploying intelligent devices in an
autonomous multi-agent framework are significant and far surpass those of merely
implementing a collection of intelligent devices. Some of the challenges that remain include
the need for a consistent framework and information model that will encompass integrating
disparate agents, operating constraints, mission planning, dynamic optimization criteria,
adaptive learning, and self-organizing behavior. The technological developments cited above
combined with intelligent devices implemented in an agent-based / Holonic framework
promise to provide unprecedented capabilities for the automation of a broad class of complex
systems.
The unique and important capabilities are provided by integrating prognostics and reconfigurable control in an intelligent agent framework. Laboratory demonstrations and
simulation studies have exhibited unprecedented capabilities for survivability, adaptability,
and dynamic adaptation to changing demands and unexpected faults. The techniques outlined
above promise to change the way future complex automation systems are designed and
deployed. The technologies outlined above represent new and important capabilities that are
broadly applicable across a wide range of industrial and commercial systems.
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