5results about How to "Reduce diagnostic costs" patented technology

The invention provides an electric power system main equipment defect diagnosis method and system based on a large language model, and relates to the technical field of electric power system operation and maintenance and intelligent diagnosis, and the method comprises the steps: collecting defect description information of electric power system main equipment; preliminarily understanding the defect description information by using a pre-constructed natural language representation model, obtaining corresponding embedded representation, executing a defect level classification task and a defect basis classification task, and outputting a defect level classification result and a defect basis classification result; and summarizing by utilizing a large language model, fusing the summarized classification information into a prompt text, exciting the reasoning ability of the large language model through a prompt project, and finally obtaining a diagnosis basis and a diagnosis result corresponding to the defect description information. According to the method, excellent results are obtained in defect level classification tasks and question and answer tasks, and high-quality classification evidences and guidance information can be generated.

One aspect of the present disclosure relates to a method for determining an operational state of a vehicle component of a vehicle. The method comprises providing a first knowledge graph containing information about a plurality of vehicle components of a vehicle, wherein each vehicle component provides one or more signals. The method further comprises determining an operational state of a vehicle component from the plurality of vehicle components, wherein the determining comprises selecting, using the first knowledge graph, a plurality of signals and one or more state parameters required for determining the operational state of the vehicle component. The determining further comprises calculating the one or more state parameters of the vehicle component based on the selected plurality of signals. Finally, the determination of the operational state of the vehicle component comprises determining the operational state of the vehicle component in dependence on the information contained in the first knowledge graph and based on the calculated one or more state parameters.

A kind of permanent magnet synchronous motor bearing fault diagnosis method based on motor current analysis method, first acquisition permanent magnet synchronous motor stator U, V phase current signal, calculate W phase current;Three-phase current is obtained by space vector dimension reduction two-phase current under rectangular coordinate system, and two-phase current is carried out vector normalization processing;Based on the mechanism of action of bearing fault to current signal and the characteristics of current signal, the signal after space vector dimension reduction is carried out variation modal decomposition, the approximate entropy of modal component is calculated and the feature matrix is formed;The improved standard artificial bee colony algorithm makes the bee colony type mutually transform according to the optimal solution, and changes the bee colony initialization population generation rule, the optimal classification model is obtained by using the optimized adaptive variable type algorithm to optimize machine learning model parameter;Using optimal model, the test set in sample set is classified according to fault type, and compared with label to obtain test set accuracy rate;The present application has the advantages of high accuracy, high diagnostic efficiency and the like.

The invention discloses a motor fault diagnosis method based on voiceprint feature extraction and transfer learning, and belongs to the technical field of equipment fault diagnosis, and the method comprises the following steps: S1, obtaining a sound signal of a working state of a motor, and extracting voiceprint features through an empirical mode decomposition and Mel-frequency cepstral coefficient method; s2, constructing a voiceprint feature data set according to the extracted voiceprint features; and S3, inputting the voiceprint feature data set into a CNN + ResNet network to obtain a motor fault diagnosis result. According to the method, the limitation of a traditional contact type method can be overcome, the maintenance cost is reduced, more accurate diagnosis information can be provided at the early stage of a motor fault, and the operation safety and reliability of motor equipment are improved. Data analysis and fault classification are carried out through a voiceprint feature extraction technology in combination with a deep learning model, a new direction is developed for motor fault diagnosis, and the method has important research value and application prospect.

The invention provides a voice voiceprint analysis and numeralization method and system for representing a human body qi deficiency state. The method comprises the steps of defining voice feature parameters corresponding to a human body qi deficiency state according to a traditional Chinese medicine theory, and establishing a qi deficiency state voice feature knowledge base; acquiring voice data of the qi-deficiency crowd and the non-qi-deficiency crowd; extracting specific voice global features of the qi-deficiency crowd from the preprocessed voice data, wherein the global voice features include but are not limited to a fundamental frequency mean value, a fundamental frequency variance, an energy mean value, an energy variance, a speech speed and a formant parameter of voice; respectively constructing differentiated voiceprint models of the qi-deficiency crowd and the voiceprint comparison crowd by using the extracted global voice features, establishing a quantitative mapping relation between the global voice features and the human body qi-deficiency state through regression calculation, and generating a qi-deficiency state evaluation model; and verifying the qi deficiency state evaluation model by using a test sample, and optimizing model parameters according to a verification result.