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\section*{Temporal fault diagnosis}
Using qualitative models to learn diagnostic rules was first
demonstrated by Kardio \cite{bratko:kardio}. 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 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 as follows
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The arrows indicate the flow of supply. The qualitative model of this
system consists of about 40 components and 29 sensors \cite{pearce:aaai88}.
An important feature of this system is that the diagnosis of some faults
are 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:
\begin{itemize}
\item Examples are are recorded in time points. So, a positive example
	{\bf fault(32).} states that there was a fault at time instance 32.
\item Background knowledge records the status of the 29 sensors at each time
	point. The list of sensors is attached.
\item During orbit, the basic operations of the power subsystem is divided
	into phases. The time instant that each phase commences is
	also provided as background knowledge.
\item The succession of time instants is represented by the predicate {\bf succ/2}.
\end{itemize}

The sensors in the power subsystem are as follows
\begin{tabbing}
\ \ \ \ \= {\bf tm040\_switch}: system switch status indicator.\\
\> {\bf tm018\_switch}: system switch status indicator.\\
\> {\bf tm031\_switch}: system switch status indicator.\\
\> {\bf m038\_switch}: system switch status indicator.\\
\> {\bf tm022\_switch}: system switch status indicator.\\
\> {\bf tm043\_switch}: system switch status indicator.\\
\> {\bf tm013\_switch}: system switch status indicator.\\
\> {\bf tm042\_switch}: system switch status indicator.\\
\> {\bf tm007\_switch}: system switch status indicator.\\
\> {\bf tm222\_charging}: battery charging status indicator.\\
\> {\bf tm071\_asr\_or\_switch\_20}: system switch status indicator.\\
\> {\bf tm070\_supply\_3c}: power supply from solar.\\
\> {\bf tm058\_asr\_or\_switch\_10}: system switch status indicator.\\
\> {\bf tm057\_supply\_2c}: power supply from solar.\\
\> {\bf tm257\_battery\_voltage}: system battery status indicator.\\
\> {\bf tm017\_switch}: system switch status indicator.\\
\> {\bf tm009\_switch}: system switch status indicator.\\
\> {\bf tm220\_supply\_1c}: power supply from solar.\\
\> {\bf tm055\_supply\_1b}: power supply from solar.\\
\> {\bf tm054\_supply\_1a}: power supply from solar.\\
\> {\bf tm029\_ovt\_disabled}: battery over-temperature blocking signal.\\
\> {\bf tm021\_eoc\_disabled}: battery end-of-charging indicator.\\
\> {\bf tm004\_eoc\_signaled}: battery end-of-charging blocking  signal.\\
\> {\bf tm002\_battov\_temp}: battery over-temperature sensor.\\
\> {\bf tm039\_eod\_disabled}:  battery  end-of-discharging blocking signal.\\
\> {\bf tm015\_eod\_signaled}: battery end-of-discharging indicator.\\
\> {\bf tm011\_eod\_override }:  battery   end-of-discharging overwriting signal.\\
\> {\bf tm001\_eod\_relay}: battery end-of-discharging relay switch.\\
\> {\bf tm211\_bus\_voltage}: system power bus status.\\
\end{tabbing}

The dataset is stored as a compressed TAR file.
Within that, the data files are as used in the original {\em Golem\/} \cite{mugfeng: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.

\begin{thebibliography}{}


\bibitem{bratko:kardio}
Bratko I., Mozetic I. and Lavrac N. (1989).
{\sl Kardio: a Study in Deep and Qualitative Knowledge for Expert Systems}.
MIT Press, Cambridge Mass.

\bibitem{mugfeng:golem}
Muggleton S.H. and Feng C. (1990).
Efficient induction of logic programs.
{\sl Proceedings of the First Conference on Algorithmic Learning Theory},
Tokyo.

\bibitem{pearce:aaai88}
Pearce D.A. (1988).
The induction of fault diagnosis systems from qualitative models.
{\sl Proceedings Seventh National Conference on Artificial Intelligence},
Saint Paul, Minnesota.

\end{thebibliography}

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