Turbopump condition monitoring using incremental clustering and one-class support vector machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine (LRE). Because of lack of fault samples, a monitoring system cannot be trained on all possible condition patterns. Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods. One-class support vector machine (OCSVM) that has been commonly used for novelty detection cannot deal well with large scale samples. In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump, a monitoring method that integrates OCSVM with incremental clustering is presented. In this method, the incremental clustering is used for sample reduction by extracting representative vectors from a large training set. The representative vectors are supposed to distribute uniformly in the object region and fulfill the region. And training OCSVM on these representative vectors yields a novelty detector. By applying this method to the analysis of the turbopump's historical test data, it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors, and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding, rub-impact and sensor faults. This monitoring method does not need fault samples during training as classical recognition methods. The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.