UNSUPERVISED DOMAIN ADAPTATION VIA SUBSPACE INTERPOLATING DEEP DICTIONARY LEARNING: A CASE STUDY IN MACHINE INSPECTION

With the advent of industry 4.0, data-driven techniques have gained a lot of popularity for machine condition monitoring, ensuring reliable and safe operation of the machines. In most practical application scenarios, domain discrepancy may arise between the training (source domain) and test (target domain) data due to various factors like changes in the operating conditions, different sensor locations, etc. Traditional data-driven techniques fail to address this domain shift, and hence domain adaptation techniques are required to ensure reliable performance. This work presents an unsupervised domain adaptation method where labeled data is available only in the source domain via subspace interpolation using deep dictionary learning. Deep dictionaries learn rich representations from the data and hence are used for subspace interpolation to capture the domain shift and form a shared feature space for cross-domain analysis. The proposed method is evaluated for the challenging scenario of adaptation between different but related machines. Experimental results obtained with two publicly available bearing fault datasets are promising; the proposed method significantly outperforms all the state-of-the-art methods.