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Attribute Oriented Induction (AOI) is a data mining algorithm used forextracting knowledge of relational data, taking into account expert knowledge.It is a clustering algorithm that works by transforming the values of theattributes and converting an instance into others that are more generic orambiguous. In this way, it seeks similarities between elements to generate datagroupings. AOI was initially conceived as an algorithm for knowledge discoveryin databases, but over the years it has been applied to other areas such asspatial patterns, intrusion detection or strategy making. In this paper, AOIhas been extended to the field of Predictive Maintenance. The objective is todemonstrate that combining expert knowledge and data collected from the machinecan provide good results in the Predictive Maintenance of industrial assets. Tothis end we adapted the algorithm and used an LSTM approach to perform both theAnomaly Detection (AD) and the Remaining Useful Life (RUL). The resultsobtained confirm the validity of the proposal, as the methodology was able todetect anomalies, and calculate the RUL until breakage with considerable degreeof accuracy.

An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance