In case of mechanical system health monitoring, a need to develop normal-knowledge based novelty detection techniques is increasing. The negative selection algorithm, which is inspired from the operation mechanism of human immune system, is one of such approaches. Our approach is to apply the idea for the anomaly detection in the vibration time series of the rotor system. A real-valued negative selection algorithm based on Euclidean distance, as well as cosine similarity, has been implemented. By means of adding the corresponding coverage radius to each antibody elements, the detection efficiency of each antibody element is increased. The detection efficiency is evaluated with simulated data as well as vibration signal sampled from one rotor system. The results indicate that the algorithm can efficiently detect the anomaly in time series data. Moreover, the number of detectors in antibody set is less enough for potential application in online signal monitoring.