Incremental Novelty Identification from Initially One-Class Learning to Unknown Abnormality Classification

In industrial applications, different abnormal conditions of a machine or a machinery fleet typically occur sequentially instead of becoming initially known. Therefore, one of the most challenging tasks for the machinery fault diagnosis is to incrementally classify unknown abnormal patterns. For this reason, an incremental novelty identification (INI) method is proposed, on the strength of deep learning-based representation learning, novelty detection and incremental learning. In the INI framework, a convolutional neural network autoencoder is first developed for high-level feature extraction from multiple monitoring signals. An online sequential-extreme learning machine (OS-ELM) is then proposed to learn healthy features of the normal condition initially known. The newly collected data are employed for updating the OS-ELM with the quantification of the model modification for novelty detection. An incremental learning procedure is implemented by adding a new OS-ELM model representing the novel abnormal pattern. The repository of OS-ELMs, each of which represents a specific condition, is employed for incrementally diagnosing known abnormal patterns with the identification of unknown ones. The performance of the proposed INI is validated under experimental scenarios considering both a benchmark bearing case study and a delta three-dimensional printer, and the results are compared with other state-of-the-art approaches.