  The problem of detecting and identifying sensor faults is critical for
efficient, safe, regulatory-compliant and sustainable operations of modern
systems. Their increasing complexity brings new challenges for the Sensor Fault
Detection and Isolation (SFD-SFI) tasks. One of the key enablers for any
SFD-SFI methods employed in modern complex sensor systems, is the so-called
analytical redundancy, which is nothing but building an analytical model of the
sensors observations (either derived from first principles or identified from
historical data in a data-driven fashion). In a nutshell, SFD amounts to
generate and to monitor residuals by comparing the sensor measurements with the
model predictions with the idea that the faulty sensors will result in large
residuals (i.e. the defective sensors generate measurement that are
inconsistent with their expected behavior represented by the model). In this
paper we introduce a disentangled Recurrent Neural Network (RNN) with the
objective to cope with the \textit{smearing-out} effect, i.e. the propagation
of a sensor fault to the non-faulty sensors resulting in large misleading
residuals. Moreover, the introduction of a probabilistic model for the residual
generation allows us to develop a novel procedure for the identification of the
faulty sensors. The computational complexity of the proposed algorithm is
linear in the number of sensors as opposed to the combinatorial nature of the
SFI problem. Finally, we empirically verify the performances of the proposed
SFD-SFI architecture using a real data set collected at a petrochemical plant.
