Owing to the rapid development of Industry 4.0, new sensing and communication technologies have made vast amounts of untapped process data available. In order to transform such data assets into strong insights and knowledge that support manufacturing decisions, condition-based maintenance (CBM) and fault detection and diagnosis (FDD) have become effective ways to enhance equipment reliability and reduce costs. A successful data-driven FDD method must not only be capable of identifying the types of known faults, but also in detecting unseen or uncharacterized events during manufacturing system operation. To this end, this paper presents a Transformer-based classifier that can efficiently identify different known types and severity levels of fault conditions, in addition to novel fault detection. In this method, time-frequency spectrograms transformed from raw vibration signals are input to the classifier for known fault classification. Utilizing the advanced feature extracting performance of the classifier, a simple yet effective technique based on Mahalanobis distance is adopted to detect whether the fault comes from a previously unseen fault condition. When a novel condition is detected, the model is subsequently retrained using the novel data in an incremental learning manner. The proposed method is verified by an experimental case study with data collected from a testbed that has many features representative of common manufacturing equipment. The results demonstrated that the proposed method has superior performance in both fault diagnosis and novelty identification when compared with the baseline models and a cutting-edge model.