In the monitoring of high-pressure servo motor, traditional methods face significant challenges due to the scarcity of fault data, which is difficult to acquire, and the inherent uncertainty of fault occurrences, with normal samples vastly outnumbering fault samples. To address these issues, this paper proposes a state monitoring system for high-pressure servo motor based on Support Vector Data Description (SVDD) for anomaly detection and Support Vector Machine (SVM) for fault diagnosis. Initially, time-domain (T), frequency-domain (F), and wavelet packet subband energy (W) features are extracted from raw data. These features are then fused and normalized to form a new multidimensional feature vector, TFW. Subsequently, a Convolutional Neural Network (CNN) is employed to deeply mine the TFW, generating more expressive features, TFWCNN, which serve as inputs to the SVDD and SVM models. An experimental platform for simulating high-pressure servo motor faults was constructed to collect data and validate the proposed method. The results indicate that on three dynamic datasets with different valve opening positions, the F1 scores for SVDD anomaly detection are 0.9991, 0.9978, and 0.9760, and for SVM fault diagnosis, the F1 scores are 0.9988, 0.9950, and 0.9867, respectively. These findings not only demonstrate the superior performance of the proposed method in the state monitoring of high-pressure servo motor but also highlight the efficacy and accuracy of deep TFWCNN features. Furthermore, this study provides an effective technical solution for similar turbine state monitoring and diagnostic systems.
Key words
high-pressure servo motor /
SVDD anomaly detection /
SVM fault diagnosis /
state monitoring system
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