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The aim of Predictive Maintenance, within the field of Prognostics and HealthManagement (PHM), is to identify and anticipate potential issues in theequipment before these become critical. The main challenge to be addressed isto assess the amount of time a piece of equipment will function effectivelybefore it fails, which is known as Remaining Useful Life (RUL). Deep Learning(DL) models, such as Deep Convolutional Neural Networks (DCNN) and LongShort-Term Memory (LSTM) networks, have been widely adopted to address thetask, with great success. However, it is well known that this kind of black boxmodels are opaque decision systems, and it may be hard to explain its outputsto stakeholders (experts in the industrial equipment). Due to the large numberof parameters that determine the behavior of these complex models,understanding the reasoning behind the predictions is challenging. This workpresents a critical and comparative revision on a number of XAI methods appliedon time series regression model for PM. The aim is to explore XAI methodswithin time series regression, which have been less studied than those for timeseries classification. The model used during the experimentation is a DCNNtrained to predict the RUL of an aircraft engine. The methods are reviewed andcompared using a set of metrics that quantifies a number of desirableproperties that any XAI method should fulfill. The results show that GRAD-CAMis the most robust method, and that the best layer is not the bottom one, as iscommonly seen within the context of Image Processing.

On the Soundness of XAI in Prognostics and Health Management (PHM)