3results about How to "High diagnostic sensitivity" patented technology

The invention provides a flight control sensor electric signal abnormity detection method and system based on electric signal analysis, and the method comprises the steps: collecting the voltage and current signal data of an aircraft control sensor in real time, and carrying out the data preprocessing of the voltage and current signal data; inputting the preprocessed electric signal data into a pre-constructed nonlinear four-rotor kinetic model for analogue simulation, and outputting an electric signal data set containing fault information; based on the real-time feature vector in the electric signal data set and a pre-stored historical feature sample set, constructing a global adjacency graph containing real-time nodes and historical nodes; inputting the global adjacency graph into a pre-constructed graph convolutional network model, and extracting space-time fusion features of the real-time nodes; and carrying out classification processing on the space-time fusion features, and outputting an abnormal state category of a sensor electric signal, so as to achieve the purposes of online high-precision diagnosis and autonomous fault-tolerant control of aircraft sensor faults and guarantee safe and reliable operation of a complex equipment system.

The application discloses a shark-derived nanobody targeting CPV2c-VP2 protein and application thereof. The amino acid sequence of the complementarity determining region 3 of the shark-derived nanobody is selected from the sequence shown in any one of SEQ ID NO. 17 to SEQ ID NO. 32, or a sequence homologous to the sequence shown in any one of SEQ ID NO. 17 to SEQ ID NO. 32; or the amino acid sequence of the shark-derived nanobody is selected from the sequence shown in any one of SEQ ID NO. 1 to SEQ ID NO. 16, or a sequence homologous to the sequence shown in any one of SEQ ID NO. 1 to SEQ ID NO. 16. The shark-derived VNAR nanobody has the advantages of small molecular weight, high affinity, high stability, easy genetic engineering, low production cost and the like.

The application discloses a plunger pump wear fault detection method based on pressure-temperature layered triggering and physical prior mask self-attention, and belongs to the technical field of hydraulic system state monitoring and fault diagnosis. The method comprises the following steps: collecting vibration, pressure and temperature multi-source signals, and setting a default low power consumption of a main controller; realizing layered early warning and deep diagnosis awakening based on four dynamic characteristics of pressure and temperature and an abnormal proportion of a sliding window; performing synchronous compression wavelet transform on the vibration signal and performing temperature-pressure analytical linear real-time correction, block encoding and vibration Token sequence generation; generating an additive physical mask guided self-attention calculation based on a fault frequency band prior, and extracting wear sensitive features; comparing with a working condition grouping fault feature library to determine the fault type and grade. The application runs through the whole chain of signal correction, feature fusion and diagnosis decision with physical knowledge, and under the lightweight design of a model parameter quantity less than 500k, realizes a variable working condition diagnosis accuracy of 94.2% and an early wear false negative rate of 3.5%.