JPL Publication 87-40 

Predictive Monitoring Research: 

Summary of the PREMON System 

Richard J. Doyle 

Suzanne M.3ellers 

David J. Atkinson 

( bAS A-CB- 1826& 7) 

EkSEARCB: SUU&ABI GP THE PBEClCb SYSTEfl (Jet 

EtGFUkiCn tab.) 16 I: CSCL 09B 

PRE ClCTl VE EC L ITORING Ii88-2C9C5 

Unclas 

63/63 0134753 

December 15,1987 

\ 

National Aeronautics and 

Space Administration 

Jet Propulsion Laboratory 

California Institute of Technology 

Pasadena, California 
JPL Publication 87-40 

Predictive Monitoring Research : 

Summary of the PREMON System 

Richard J. Doyle 

Suzanne M. Sellers 

David J. Atkinson 

December 15,1987 

NASA 

National Aeronautics and 

Space Administration 

Jet Propulsion Laboratory 

California Institute of Technology 

Pasadena, California 
The research described in this publication was carried out by the Jet Propulsion 

Laboratory, California Institute of Technology, under a contract with the National 

Aeronautics and Space Administration. 

Reference herein to any specific commercial product, process, or service by trade 

name, trademark, manufacturer, or otherwise, does not constitute or imply its 

endorsement by the United States Government or the Jet Propulsion Laboratory, 

California Institute of Technology. 
ABSTRACT 

Traditional approaches to monitoring are proving inadequate in the 

face of two important issues: 

expectations about sensor values when the behavior of the device is 

too complex to enumerate beforehand, and the selective but effective 

interpretation of sensor readings when the number of sensors 

becomes overwhelming. Our system addresses these issues by 

building an explicit model of a device and applying common-sense 

theories of physics to model causality in the device. The resulting 

causal simulation of the device supports planning decisions about 

how to efficiently yet reliably utilize a limited number of sensors to 

verify correct operation of the device. 

the dynamic adjustment of 

iii 
Table of Contents 

Overview ...................................................................................................................... 1 

S y s tern Arc hit ec ture ............................................................................................... 1 

Research Issues ......................................................................................................... 3 

Qualitative Simulation ................................................................................ 3 

Sensor Planning ............................................................................................. 4 

Sensor Interpretation ................................................................................ 4 

Choice of Initial Domain ......................................................................................... 5 

Reference ..................................................................................................................... 6 

Bibliography ...............................................................................................................6 

Approach to Causal Modeling and Qualitative Simulation.. ................... .2 

.. 

PRECEDING PAGE BLANK NOT FILMED 

v 
~~~ 

TECHNICAL REPORT STANDARD TITLE PAGE 

7. Author($) 

9. Performing Organization Name ond Addross 

Richard J. Doyle, Suzanne M. Sellers, David J. Atkinson 

JET PROPULSION LABORATORY 

California Institute of Technology 

4800 Oak Grove Drive 

Pasadena, California 91109 

12. Sponsoring hncy NUM ond Addross 

4. Title ond Subtitle 

PREDICTIVE MONITORING RESEARCH: 

SUMMARY OF THE PREMON SYSTEM 

8. Performing Organization Report No, 

IO. Work Unit No. 

11. Contract or Grant NO. 

13. Type of Report and Period Covered 

RE65 CY-549-01-00-05-59 

NAS7-9 18 

5. Report Date 

6. Performing Organization Code 

9. Security Clcruif. (of this roport) 20. Security Clorrif. (of this pge) 

UNCLASSIFIED UNCLASSIFIED 

21. No. of Pages 22. Rice 

14 

14. Sponsoring Agency &de NATIONAL AERONAUTICS AND SPACE ADMINISTRATION 

Washington, D.C. 20546 I 

15. Supplementary Notes .. 

16. Abrtract 

Traditional approaches to monitoring are proving inadequate in the 

face of two important issues:- the dynamic adjustment of expectations 

about sensor values when the behavior of the device is too complex to 

ennumerate beforehand, and the selective but effective interpretation 

of sensor readings when the number of sensors becomes overwhelming. 

Our system addresses these issues by building an explicit model of 

a device and applying common-sense theories of physics to model 

causality in the device, 

device supports planning decisions about how to efficiently yet 

reliably utilize a limited number of sensors to verify correct 

operation of the device. 

The resulting causal simulation of the 

18. Distribution Statement I 17. Key Words (SeIockdby A&m(s)) 

Spacecraft testing 

UNCLASSIFIED--UNLIMITED 2uali tat ive simulit ion 

sensor planning 

:ausal modeling Artificial intelligence 
Overview 

PREMON is a predictive monitoring system being developed by JPL's 

Artificial Intelligence Group in the Computer Science and 

Applications Section. The system is funded through the NASA 

Systems Autonomy Core Research Program of the Office of 

Aeronautics and Space Technology. Our overall, long-range goal is to 

design and implement a software prototype that will demonstrate 

cooperation (via a blackboard architecture) between knowledgebased systems for the problem of monitoring a complex device. The 

prototype will consist of three modules: a causal simulator, a sensor 

verification planner, and a sensor interpreter. 

System Architecture 

Currently, PREMON consists of the causal simulator only. The causal 

simulator maintains a causal model of the device being monitored. 

Our initial device domain is the 25-foot space simulator at JPL, a 

vacuum chamber used for testing spacecraft in an environment 

similar to that found in space. The causal model is based on a 

representation designed by Richard Doyle at the Massachusetts 

Institute of Technology. Doyle's representation is an extension to one 

developed by Kenneth Forbus [Forbus 841. The causal model 

simulates the future behavior of the device based on the device's 

current state or a hypothetical state. A valuable attribute of the 

causal simulator is that it can be used to generate and reason about 

alternative scenarios of future device behavior. The need to explore 

alternative scenarios arises from the desire to resolve uncertainty 

about the device's observed state, the desire to investigate the 

effects of potential operator inputs to the device, or the desire to 

detect possible inaccuracies in the causal model itself. 

The sensor verification planner will have the capability to use causal 

simulator output, in the form of device behavior envisionments, to 

generate a plan to utilize sensor resources during device operation. 

The development of this module is motivated by limitations in 

current monitoring systems with respect to sensors, e.g.: limitations 

on the bandwidth of data that may be acquired, limitations on the 

available sensors or their configuration, and limitations on human 

attention to relevant sensor data. Current systems lack a way to 

automatically maximize acquisition of the most important sensor 

data. The most important sensor data is that which provides the most 

feedback on the processes critical to evaluating the functionality of 

the device in the current oDerating context. By careful analysis of 

1 
the active dependencies at each stage in the operation of the device, 

we can establish which physical components are most directly 

supporting the stability of the device state, or are contributing to 

changes in the device state. Sensor data reflecting the state of those 

components would be the most important. A theory of relative 

importance of sensor data with respect to operating context will be 

developed and tested in the implementation of the sensor 

verification planner. 

The sensor interpreter will use output from the sensor verification 

planner, in the form of a sensor plan, qualitative parameter value 

expectations, and observed parameter values, to determine whether 

predictions about the behavior of the device being monitored match 

its actual behavior. The process will be model-driven. 

Approach to Causal Modeling and Qualitative Simulation 

In building the PREMON's causal simulator module, we are concerned 

directly with the distinguishable states of physical systems and how 

they change from one state to another. We take a process-oriented, 

rather than a device-oriented, approach to determining the causality 

in physical systems. Device-orientation considers first the behaviors 

of the components of a device, then how those behaviors are 

propagated through the device. Process-orientation, on the other 

hand, considers first what processes support causality in a device, 

then which components participate in those processes. 

The qualitative simulation technique we are using is based on 

research by Kenneth Forbus. It is known as limit analysis, it 

determines which processes are active at any given time in a 

physical system, and it propagates values accordingly. As they 

change, the values of quantities may reach thresholds ("limits"), 

resulting in state changes in the system where active processes 

become inactive or vice versa. 

The causal modelling capability that we are developing is driven by 

knowledge of the kinds of mechanisms existing in physical systems. 

Examples of these causal mechanisms are fluid flow, mechanical 

coupling, expansion, gravity, and physical contact. 

2 
As part of this effort, Richard Doyle has developed a representation 

for causality in physical systems and a vocabulary of causal 

mechanisms describable within it. The representation distinguishes 

four categories of causal mechanisms: 

(1) ProDagations: causally related events at different sites, 

structurally linked through media and potentially disrupted 

by barriers. Example: Fluid Flow 

causally related events at a single site, 

typically involving some kind of energy transformation. 

Example: Expansion 

Example: Gravity 

in physical or geometrical relations. Example: Contact 

(2) Transformations: 

(3) Field Interactions: events due to interaction with a field. 

(4) Thresholds: continuous changes which lead to abrupt changes 

The current causal simulator works as follows: Given an initial state 

of a physical device and a specification of state changes to simulate 

input from device sensors (we are not currently connected to the 

space simulator sensors), the causal simulator uses a common-sense 

theory of causal mechanisms in physical devices to generate a 

simulation of the future behavior of the device. 

To support simulation, a qualitative calculus has been developed 

which encodes knowledge of how qualitative values combine under 

negation, addition, and multiplication. 

Research Issues 

Qualitative Simulation 

Doyle's representation for causality directly describes physical 

relations which enable and disable processes. A research goal for 

PREMON is to give this causal modeling system and simulator the 

capability to identify and reason about thresholds that establish and 

remove media and barriers and thus alter the active process 

structure of a device. 

Most of the existing qualitative simulators use a simplified notion of 

temporal integration. They merely note the sign of the derivative of 

a quantity and find the next qualitative value in the indicated 

direction. Forbus goes a bit further by using the magnitude, as well 

as the sign of the derivative. Our simulator uses a more powerful 

notion of temporal integration that includes the duration of the 

3 
interval during which a quantity's derivative is non-zero. Therefore, 

our system can reason about the result of turning on a heating or 

chilling component based on the length of time the component is 

active. This is a seemingly straightforward inference, yet the more 

complete notion of temporal integration embedded in a simulator is 

needed to make it. We will continue this line of research. 

Currently, PREMON simulates future behavior of the device deducible 

from singular causation, e.g., when the chiller is turned on, a negative 

temperature rate of change is propagated through the gaseous 

nitrogen cooling circuit for the space simulator mirror. We are 

researching methods of integrating multiple causes of events into the 

simulation of future device behavior. For example, it is desirable to 

integrate changes in the mirror temperature resulting from heat loss 

to the liquid-nitrogen-cooled walls, heat gain from the ceiling of the 

chamber, as well as temperature changes internal to the mirror 

cooling circuit. 

Sensor Planning 

The key to determining which components are most appropriate for 

monitoring is the establishment of the relative importance of the 

operation of that component at each instant of time in the dynamic 

behavior of the device as a whole. We believe that a deep, qualitative 

model of the behavior of the device yields information about the 

causal dependencies between different active processes. 

Development of methods for elaborating these dependencies and 

including them in a planning process are key objectives of our 

research. 

Sensor Interpretation 

Substantial domain knowledge is required to detect departures from 

nominal behavior when there are no active expectations; knowledge 

about how other events or data may differ from what is nominally 

true is needed. We suspect that this knowledge is fundamentally the 

same as the underlying qualitative causal model. Identifying this 

knowledge and devising indexing and accessing strategies are 

objectives of our research. 

In addition, we have the problem that the data being sensed can 

have a variety of features that make the process of symbolically 

matching it against expectations difficult. The computational 

complexity of interpreting sensor data increases with the variety and 

number of events that must be recognized. A match against 

4 
expectations is fundamentally an inexact process called "partial 

matching." We propose to investigate heuristic techniques for 

resolving uncertainty in the matching process and to investigate 

improved approaches to knowledge organization. 

It is the responsibility of the Sensor Interpreter to use the sensor 

plan to configure the use of sensors during device operation. 

Mechanisms for sequencing the execution of a sensor plan with the 

corresponding expectations about sensor data need to be developed. 

This is an additional focus of our research. 

Choice of Initial Domain 

Although we found the 25-foot space simulator to be the best initial 

domain for our research, the following also have merit as future 

candidate domains: Deep Space Network Tracking Station, Mars 

Rover, and Space Station. We sought a domain with the following 

characteristics: 

Numerous and diverse mechanisms and sensors. We want to 

address research issues in the context of a problem domain of 

significant complexity to demonstrate the efficacy of new, as 

well as extant, theories. 

Multiple operating modes. Multiple modes present a problem 

for current monitoring systems in the form of false alarms. 

False alarms result from the inability of pre-defined nominal 

operating ranges for sensors to correctly represent alarm states 

within each oDeratinP co ntext. We wish to demonstrate the 

ability to reduce false alarms by dynamically shifting alarm 

ranges of sensors to reflect what is and is not an alarm state 

specific to each operating mode. 

Requirement for real-time response. Our performance goal is to 

simulate the device state, plan sensor usage, and interpret 

sensor data in complex systems such that anomalies are 

detected in real time. 

Burden of interpretation on human operators. PREMON is 

intended to automate the global interpretation of sensor 

readings and, in some cases, ameliorate the need for roundthe-clock human supervision. 

Difficulty of comprehensive sensor interpretation. When 

sensors become too numerous, the task of synthesizing a global 

picture of the state of the system becomes overwhelming. We 

are exploring sensor planning--sampling some subset of the 

available sensors based on the evaluation of the relevance of 

particular sensor readings. 

5 
The space simulator matches our current criteria better than the 

other domains we considered. It contains a diverse set of 

mechanisms and sensors. There are several different environments 

achievable within the chamber. These target environments represent 

the various operating modes of the simulator. Nominal sensor 

readings for each mode are different. Detection of anomalies during 

the process of reaching a target environment, and while maintaining 

it, must occur in real time. Round-the-clock human supervision is 

required. Sensor planning is appropriate for the space simulator and 

is implicit in the established procedures. These procedures require 

operators to monitor only the sensors relevant to the current 

operating mode. 

The three domains other than the space simulator represent 

excellent longer-term targets. The DSN tracking stations offer the 

best support for testing and gradually phasing in new technology. 

The Mars Rover scenario represents the most challenging monitoring 

task, and almost demands a new approach to monitoring. Finally, the 

Space Station Thermal Management System, with its greater 

complexity, would prove the most thorough test of our proposed 

approach to monitoring. 

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