Few-shot Classification of Radar Equipment Fault Based on TF-IDF Feature Date Augmentation and BERT

A Novel Prognostic Model of the Degradation Malfunction Combining a Dynamic Updated-ARIMA and Multivariate Isolation Forest: Application to Radar Transmitter

“…Feature extraction required [15][16][17][18]22,23] Artificial threshold required [19,22] Extensive historical data and fault samples required [19,20,23,24] By analyzing the constraints of the fault-prediction model in Table 1, it can be seen that the need for building a radar transmitter degradation malfunction prognosis model that using less historical data, no artificial thresholds, no features extraction, and no fault samples is urgent. To meet the above requirements, a novel prognostic model combined with dynamic updated-auto-regressive integrated moving average (DU-ARIMA) and multiple isolation forest (M-iForest) is proposed for radar transmitter degradation malfunction.…”

“…The research to date has tended to focus on prognostic methods based on measurement and data-driven models, which have been widely used in transportation [1][2][3], biology [4,5], machinery [6][7][8], energy [9][10][11], market [12][13][14], radar [15][16][17][18][19][20][21][22][23][24], and other applications. Among them, the research results of malfunction prognoses in radar-related fields continue to emerge.…”

“…Wang et al [22] developed a datadriven fault-detection method using principal component analysis to detect the fault of the fans in a radar cooling system. Li et al [23] achieved the classification of radar equipment faults utilizing a data-augmentation method based on TF-IDF features. Zhao et al [24] presented a testable radar system design and demonstration approach based on fault modes and software control actions for improving the self-diagnosis ability of a radar system and reducing the fault rate.…”

“…These are shown in Table 1. It can be seen from the table, References [15][16][17][18]22,23] needed to extract the effective feature or radiation pattern of the degradation fault. In [19,22], an artificial threshold needed to be set to judge the fault.…”

“…In [19,22], an artificial threshold needed to be set to judge the fault. Reference [19,20,23,24] also needed extensive historical data and fault samples for its training sets. Although reference [21] solves the two restrictions of radar transmitter degradation fault, this method is not accurate enough for data prediction, and at least one fault sample is still needed.…”