  Attribute Oriented Induction (AOI) is a data mining algorithm used for
extracting knowledge of relational data, taking into account expert knowledge.
It is a clustering algorithm that works by transforming the values of the
attributes and converting an instance into others that are more generic or
ambiguous. In this way, it seeks similarities between elements to generate data
groupings. AOI was initially conceived as an algorithm for knowledge discovery
in databases, but over the years it has been applied to other areas such as
spatial patterns, intrusion detection or strategy making. In this paper, AOI
has been extended to the field of Predictive Maintenance. The objective is to
demonstrate that combining expert knowledge and data collected from the machine
can provide good results in the Predictive Maintenance of industrial assets. To
this end we adapted the algorithm and used an LSTM approach to perform both the
Anomaly Detection (AD) and the Remaining Useful Life (RUL). The results
obtained confirm the validity of the proposal, as the methodology was able to
detect anomalies, and calculate the RUL until breakage with considerable degree
of accuracy.
