Investigation of a troubleshooting procedure by assessing fault tracing algorithms
The thesis delves into the area of troubleshooting procedures, an interesting area
for industry. Many products in industry tend to be complex, which in turn makes
troubleshooting procedures trickier. A fast and efficient repair process is often
desired, since customers want the product to be repaired as fast as possible.
The purpose of a troubleshooting procedure is to find a fault in a broken product,
and to choose proper repair actions in a workshop. Such a procedure can be
simplified by diagnosis tools, for example software programs that make fault
conclusions based on fault codes. These tools can make such conclusions with
the help of algorithms, i.e. fault tracing algorithms.
Before a product release, it is hard to specify all faults and connections in the sys-
tem. New unknown fault cases are likely to arise after release, and somehow this
need to be taken into account in the troubleshooting scenario. The troubleshoot-
ing procedure can be made more robust, if new data could be easily incorporated
in the current structure. This work seek to answer how new data can be incorpo-
rated in trouble shooting procedures.
A good and reliable fault tracing algorithm is essential in the process of finding
faults and repair actions, which is the reason behind the focus of this thesis. The
presented problem asks how a fault can be identified from fault codes and symp-
toms, in order to recommend suitable repair actions. Therefore, the problem is
divided into two parts, finding the fault and recommending repair actions. In
the first part, three candidate algorithms for finding the faults are investigated,
namely Bayesian networks, neural networks, and a method called matrix correla-
tion inspired from latent semantic indexing. The investigation is done by training
each algorithm with data, and evaluating the results. The second part consists of
one method proposal for repair action recommendations and one example. The
theoretical investigation is based on the Servo unit steering (SUS), which reside
in the IPS system of Volvo Penta.
The primary contribution of the thesis is the evaluation of three different al-
gorithms and a proposal of one strategy to recommend suitable repair actions.
In this study Bayesian networks are found to conform well with the desired at-
tributes, which in turn lead to the conclusion that Bayesian networks is well
suited for this problem.
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Last updated: 2021-11-10