Learning rules for temporal fault diagnosis in satellites
Contents and links
Introduction to temporal fault diagnosis
We have applied ILP to a problem within the aerospace industry, namely
the diagnosis of power-supply failures in a communications satellite.
The satellite recharges its batteries using solar energy. As the it
orbits the Earth, its position relative to the Sun changes, taking its
power-supply subsystem through four distinct stages: battery charging,
solstice, eclipse, and battery reconditioning. Using qualitative
modelling, we constructed a simulation with which the behaviour of
the power supply in each stage could be predicted. By provoking simulated
faults in the components, we used the simulation to generate examples of
how each fault would affect the supply's behaviour. From these examples,
the ILP program Golem induced a set of rules for diagnosing power supply
failures. In generating the examples, faults were provoked in all possible
components, thus guaranteeing that the rules are complete and correct for
all single faults.
Because the power-supply's behaviour changes with time, the formalism
used to describe the examples must be able to express a fault's dependence
on time. We have developed such a formalism, which is based on temporal
logic and which is suitable for ILP learning.
Using qualitative models to learn diagnostic rules was first demonstrated
by Kardio [Bratko I., Mozetic I. and Lavrac N.(1989)].
The basic idea is simple. Use a qualitative model of a system to generate
behaviours of the system. If there is a fault in the system, it will be
reflected in the behaviour generated by the model. This ``faulty'' behaviour
can be used to provide examples for a learning program, which can then
learn diagnostic rules for the particular fault. The resulting rules are
thus guaranteed complete and correct for all single faults since all examples
of these were generated from the original model. This technique is applicable
to problems other than the original one considered in Kardio. An example
is the power subsystem of a satellite. The basic structure of one such
system is shown below:
The arrows indicate the flow of supply. The qualitative model of this
system consists of about 40 components and 29 sensors [Pearce
An important feature of this system is that the diagnosis of some faults
is not possible with simple classification rules. These faults usually
require the history of related components. For example, a simple check
of power from the battery is not sufficient to determine if it is faulty.
We need to know the history of other components in the circuit before this
can be decided. Although many different faults can be simulated, the concern
here is with battery faults. The information used is as follows:
The sensors in the power subsystem are as follows:
Examples are are recorded in time points. So, a positive example fault(32)
states that there was a fault at time instance 32.
Background knowledge records the status of the 29 sensors at each time
point. The list of sensors is attached.
During orbit, the basic operation of the power subsystem is divided into
phases. The time instant that each phase commences is also provided as
The succession of time instants is represented by the predicate succ/2.
tm040_switch : system switch status indicator.
tm018_switch : system switch status indicator.
tm031_switch : system switch status indicator.
m038_switch : system switch status indicator.
tm022_switch : system switch status indicator.
tm043_switch : system switch status indicator.
tm013_switch : system switch status indicator.
tm042_switch : system switch status indicator.
tm007_switch : system switch status indicator.
tm222_charging : battery charging status indicator.
tm071_asr_or_switch_20: system switch status indicator.
tm070_supply_3c : power supply from solar.
tm058_asr_or_switch_10: system switch status indicator.
tm057_supply_2c : power supply from solar.
tm257_battery_voltage : system battery status indicator.
tm017_switch : system switch status indicator.
tm009_switch : system switch status indicator.
tm220_supply_1c : power supply from solar.
tm055_supply_1b : power supply from solar.
tm054_supply_1a : power supply from solar.
tm029_ovt_disabled : battery over-temperature blocking signal.
tm021_eoc_disabled : battery end-of-charging indicator.
tm004_eoc_signaled : battery end-of-charging blocking signal.
tm002_battov_temp : battery over-temperature sensor.
tm039_eod_disabled : battery end-of-discharging blocking signal.
tm015_eod_signaled : battery end-of-discharging indicator.
tm011_eod_override : battery end-of-discharging overwriting signal.
tm001_eod_relay : battery end-of-discharging relay switch.
tm211_bus_voltage : system power bus status.
The Golem dataset
The dataset is stored as one
compressed TAR file. Within that, the data files are as used in the
original Golem experiments. That is, background knowledge files have a
``.b'' suffix, positive example files have a ``.f'' suffix, and negative
example files have a ``.n'' suffix.
Inducing Temporal Fault Diagnostic Rules from a Qualitative Model
In Proceedings Eighth International Workshop on Machine Learning
pp 403 - 406, Morgan Kaufmann, San Mateo, C.A..
Bratko I., Mozetic I. and Lavrac
Kardio: a Study in Deep and Qualitative Knowledge for Expert Systems.
MIT Press, Cambridge Mass.
Pearce D.A. (1988).
The induction of fault diagnosis systems from qualitative models.
In Proceedings Seventh National Conference on Artificial Intelligence,
Saint Paul, Minnesota.
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