  Due to the incapability of one sensory measurement to provide enough
information for condition monitoring of some complex engineered industrial
mechanisms and also for overcoming the misleading noise of a single sensor,
multiple sensors are installed to improve the condition monitoring of some
industrial equipment. Therefore, an efficient data fusion strategy is demanded.
In this research, we presented a Denoising Multi-Modal Autoencoder with a
unique training strategy based on contrastive learning paradigm, both being
utilized for the first time in the machine health monitoring realm. The
presented approach, which leverages the merits of both supervised and
unsupervised learning, not only achieves excellent performance in fusing
multiple modalities (or views) of data into an enriched common representation
but also takes data fusion to the next level wherein one of the views can be
omitted during inference time with very slight performance reduction, or even
without any reduction at all. The presented methodology enables multi-modal
fault diagnosis systems to perform more robustly in case of sensor failure
occurrence, and one can also intentionally omit one of the sensors (the more
expensive one) in order to build a more cost-effective condition monitoring
system without sacrificing performance for practical purposes. The
effectiveness of the presented methodology is examined on a real-world private
multi-modal dataset gathered under non-laboratory conditions from a complex
engineered mechanism, an inline four-stroke spark-ignition engine, aiming for
spark plug fault diagnosis. This dataset, which contains the accelerometer and
acoustic signals as two modalities, has a very slight amount of fault, and
achieving good performance on such a dataset promises that the presented method
can perform well on other equipment as well.
