Computer-implemented method, computer program product and system for anomaly detection and/or predictive maintenance

A computer-implemented method for anomaly detection in an entity of interest comprising:

• receiving a new observation said new observation characterizing at least one parameter of the entity;
• inputting the new observation to a deep neural network (100), the deep neural network (100) having a plurality of hidden layers and being trained using a training data set that includes possible observations that can be input to the deep neural network (100);
• obtaining a second set of intermediate output values that are output from at least one of the plurality of hidden layers of the deep neural network (100) by inputting the received new observation to the deep neural network (100);
• mapping, using a latent variable model stored in a storage medium, the second set of intermediate output values to a second set of projected values;
• determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values,
• calculating, by the deep neural network (100), a prediction for the new observation;
• and
• determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier;
• wherein the latent variable model stored in the storage medium is constructed by:
• obtaining first sets of intermediate output values that are output from said one of the plurality of hidden layers of the deep neural network (100), each of the first sets of intermediate output values obtained by inputting a different one of the possible observations included in at least a part of the training dataset;
• and
• constructing the latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space of the latent variable model that has a dimension lower than a dimension of the sets of the intermediate outputs.