Designing intelligent systems in a dispersed environment with data scarcity: the use case of automatic anomaly detection in combustion engines

The challenges of designing intelligent systems that allow automated classification of problems and decision-making in complex environments are diverse. Particularly difficult are dispersed environments, where the created system must take into account the local nature of the data. Such data may differ in the number of attributes, but also in other aspects, which often requires specific normalization or some other form of information unification. This article contains conclusions from the work on the design of an intelligent system for early detection of problems in truck combustion engines. The environment in this situation is scattered and the data is often heterogeneous. The process included the selection of relevant attributes occurring in different vehicle manufacturers and attempts to automatically detect anomalies. The next phase was to build a classifier based on artificial neural networks. In the end, the system took the form of a rule-based expert system. The work makes one see that less complex expert systems can, in many cases, significantly outperform more advanced techniques that require labeled data sets of very good quality. The advantage of expert systems for local datasets is that they can be customized on the basis of available knowledge, be it expert or catalog data, without affecting other distributed models. However, their greatest advantage is that they can be practically applied even when the initial amount of information and training data is relatively small.