  The use of machine learning rapidly increases in high-risk scenarios where
decisions are required, for example in healthcare or industrial monitoring
equipment. In crucial situations, a model that can offer meaningful
explanations of its decision-making is essential. In industrial facilities, the
equipment's well-timed maintenance is vital to ensure continuous operation to
prevent money loss. Using machine learning, predictive and prescriptive
maintenance attempt to anticipate and prevent eventual system failures. This
paper introduces a visualisation tool incorporating interpretations to display
information derived from predictive maintenance models, trained on time-series
data.
