888 results about "Diagnostic model" patented technology

The invention provides a system and method for computer-aided detection (“CAD”). The invention relates to computer-aided automatic detection of abnormalities in and analysis of medical images. Medical images are analyzed, to extract and identify a set of features in the image relevant to a diagnosis. The system computes an initial diagnosis based on the set of identified features and a diagnosis model, which are provided to a user for review and modification. A computed diagnosis is dynamically re-computed upon user modification of the set of identified features. Upon a user selecting a diagnosis based on system recommendation, a diagnosis report is generated reflecting features present in the medical image as validated by the user and the user selected diagnosis.

A diagnostic system and method are provided that include an interface for receiving input relating to observed symptoms indicative of one or more failed components, a processing element for correlating the input relating to the observed symptoms with at least one suspect component that is capable of causing the observed symptoms upon failure, and a display for presenting information relating to the suspect components. The processing element correlates the input relating to the observed symptoms with the suspect components in accordance with a diagnostic model constructed based upon systemic information, experiential information and factual information. The processing element generally presents the suspect components in a listing prioritized based upon the relative likelihood that the respective suspect components caused the observed symptoms. Additionally, the processing element can present a prioritized listing of tests that can be conducted to refine the identification and prioritization of the suspect components.

Methods are provided for predicting the presence, subtype and stage of ovarian cancer, as well as for assessing the therapeutic efficacy of a cancer treatment and determining whether a subject potentially is developing cancer. Associated test kits, computer and analytical systems as well as software and diagnostic models are also provided.

A method for troubleshooting a problem with a device includes acquiring device data for the device, receiving a user's query concerning a device in a natural language, presenting possible refinements to at least a portion of the user's query for defining a problem statement, presenting candidate solutions that are associated with the defined problem statement in a knowledge base, at least one of the presentation of possible refinements and the presentation of candidate solutions being informed by device data that is linked through a diagnostic model of the device to at least one of the problem statements and candidate solutions.

The invention discloses a pile-up noise reduction own coding network bearing fault diagnosis method based on particle swarm optimization. The bearing fault diagnosis method provides an improved pile-up noise reduction own coding network SADE bearing fault diagnosis method, SDAE network hyper-parameters, such as cyber hidden layer nodes, sparse parameters, input data random zero setting ratio, are selected adaptively by particle swarm optimization PSO, a SADE network structure is determined, top character representation of malfunction inputting a soft-max classifier is obtained and a classification of defects is discerned. The bearing fault diagnosis method has better feature in learning capacity and more robustness than feature of learning of ordinary sparse own coding device, and builds a SDAE diagnostic model having multi-hidden layer by optimizing the hyper-parameters of noise reduction own coding network deepness network structure with the particle swarm optimization, accuracy of the classification of defects is improved at last.

A diagnostic system and method for enabling multistage decision optimization in aircraft preflight dispatch. The diagnostic system includes an interface for receiving one or more inputs relating to one or more observed symptoms indicative of a failed component in an aircraft. The diagnostic system extends an entropy-based value of information (VOI) diagnostic model by adding an explicit value function into the VOI diagnostic model to accommodate various variables associated with the aircraft preflight dispatch problem. The construction of the entropy-based VOI diagnostic model and thus the extended VOI diagnostic model are both based upon at least one of systemic information relating to aircraft components and input-output relationships of the aircraft components, experience-based information relating to direct relationships between aircraft component failures and observed symptoms, and factual information relating to aircraft component reliability.

The invention discloses a fault diagnosis method of a high-voltage circuit breaker based on a depth belief network, which comprises the following steps: step 1, selecting a data sample required by anexperiment, and dividing the unified standardized sample data into a test sample and a training sample according to a specific proportion; Step 2: building and initializing the DBN deep belief networkfault diagnosis model; Step 3, inputting a large number of unlabeled samples or unlabeled samples in the pre-training set from the bottom of the model, and pre-training the RBM in the model by usinglayer-by-layer unsupervised greedy learning; Step 4: the whole model being fine-tuned by genetic algorithm; Step 5, the fault diagnosis model of the high-voltage circuit breaker obtained by training being classified to the fault samples of the test set in step 1, so as to obtain the fault classification result, and the diagnosis accuracy rate of the model being counted. The invention discloses a fault diagnosis method of a high-voltage circuit breaker based on a depth belief network, which can train a large amount of data samples to realize the fault diagnosis function of the high-voltage circuit breaker.

The invention discloses a construction method and a construction system for a clinical diagnosis model, and a clinical diagnosis system, and relates to the technical field of medical treatment. The construction method for a clinical diagnosis model comprises: step S1, acquiring data, data acquisition sources being a plurality of electronic medical records; step S2, processing the acquired data; step S3, according to known typical medical history in the processed data, establishing a case analysis model, and training and evaluating the case analysis model; step S4, applying the trained and evaluated model in the clinical diagnosis system, to form a the clinical diagnosis model. The method and the systems are used to improve accuracy for a doctor to diagnose diseases.

The invention discloses a method for constructing a cardiovascular disease diagnosis model, a system and the diagnosis model. The cascade classifier is trained to get the ear detection model. VGG, GoogleNet and ResNet neural network models are used to extract ear features. The spatial pyramid is used to integrate the features of ear extracted from neural network model, and the depth heterogeneousfeature map of each neural network model is obtained. Feature preprocessing of depth isomerism feature map; Training to obtain SVM classifier model; The SVM classifier model and the three trained neural network models are integrated by Bagging learning to obtain the cardiovascular disease diagnosis model. The cardiovascular disease diagnosis model constructed by the invention can comprehensively and scientifically carry out cardiovascular disease diagnosis and prediction, has high precision, and can be widely applied in the field of automatic processing of medical data.

The invention relates to a transformer fault diagnosis method based on a Bayesian network. According to the method, gas dissolved in oil of a transformer is analyzed by adopting a three-ratio method; data about gas is obtained in a real operation environment; study of structures and parameters of the Bayesian network is accomplished by adopting a TAN (Tree Augmented Naive) algorithm; a fault diagnostic model is established, and an expert system is utilized for correcting the fault diagnostic model; and the fault diagnostic model is used for diagnosing real-time operation states of the transformer. The method has the benefits that the problem about fault diagnosis for the transformer under the condition of uncertainty and lacking given information is solved, and meanwhile, an importance analytical method based on the Bayesian network is introduced to play a certain assistant role in analysis of the fault mechanism. The method can quickly and accurately diagnose the fault of the transformer, provide support for establishment of a maintenance decision for the transformer, effectively improve the maintenance efficiency, and lower the operation cost of a power system.

The invention discloses a clustering analysis-based intelligent fault diagnosis method for an antifriction bearing of a mechanical system. A diagnosis model is trained firstly, comprising the following steps: collecting standard vibration signal samples of five fault and normal bearing states of an outer ring, an inner ring, a rolling body and a holding frame; decomposing signals, extracting original vibration signals as well as time domain and frequency domain characteristics of decomposed components to obtain an original characteristic set; removing redundancy by means of a self-weight algorithm and an AP (Affinity Propagation) clustering algorithm to obtain Z optimal characteristics; classifying sample statuses by means of the AP clustering algorithm to obtain a well-trained diagnosis model. A fault diagnosis is performed by the following steps: collecting real-time vibration information of a bearing, decomposing the signals, extracting the optimal characteristics determined by the model, importing the AP clustering algorithm to cluster parameters based on the diagnosis model, comparing with the Z characteristics known in the model to obtain a category of a current unknown signal, so as to complete the fault diagnosis. According to the clustering analysis-based intelligent fault diagnosis method disclosed by the invention, both EEMD (Ensemble Empirical Mode Decomposition) and WPT are utilized to decompose the vibration signals, more refined bearing status information can be acquired, the self-weight algorithm and the AP clustering algorithm increase intelligence of the diagnosis, and therefore accurate diagnosis is ensured.

The invention discloses a feature extraction method for a switch action current curve. The method comprises the following steps: obtaining current data of a switch action to generate the switch action current curve; converting the switch action current curve into a projection coordinate system; determining subsections of various switch action sections in the projection coordinate system; and outputting starting points and end points of current value ranges. The method is simple and efficient; the extracted features can provide bases for feature extraction of a diagnosis model; and the selected features can be taken as input parameters of the model. According to a switch fault diagnosis method employing the feature extraction method disclosed by the invention, maintenance information can be timely and accurately provided when a switch is broken down; and on-site maintenance personnel are guided to carry out a maintenance on the faulty switch in a targeted manner, so that the maintenance cost is reduced; the service efficiency of the switch is improved; various adverse effects caused by faults can be reduced; a fault time delay is compressed; a transport delay is further reduced; and the feature extraction method and the switch fault diagnosis method are of important and practical significance in improvement of the safety and the efficiency of a transportation system.

The invention discloses an analog circuit fault diagnosis method based on a Bayes-KFCM (Kernelized Fuzzy C-Means) algorithm, which comprises the following steps of: carrying out fault diagnosis by adopting a kernelized fuzzy C-means clustering algorithm and firstly judging whether a new fault exits in a test sample, if YES, a diagnostic model of a new fault sample is trained to join a diagnosis system, or else, the fault positioning is carried out on the test sample according to a Bayes fault classification standard. In the invention, the wavelet transform pretreatment is carried out on the fault sample, and the multi-feature fusion is carried out on the wavelet coefficient energy value and the wavelet coefficient fractal dimension value of the sample to extract fault characteristics; and the frequency of an optimal measurable node and / or a test signal is selected through taking a maximum class inter-class distance as a basis. Compared with the prior art, the analog circuit fault diagnosis method realizes that the analog circuit fault diagnosis method, the new fault of an analog circuit can be effectively diagnosed, and the diagnosis accuracy can be improved.

The invention discloses a rolling bearing fault diagnosis method based on improved variational model decomposition and an extreme learning machine. The method comprises: vibration signals of a rollingbearing under different types of faults are collected, the vibration signals are filtered by means of maximum correlation kurtosis deconvolution, parameter optimization is carried out on the maximumcorrelation kurtosis deconvolution method by using a particle swarm algorithm, and an enveloped energy entropy after signal deconvolution is used as a fitness function; the mode number of variationalmodel decomposition is improved by an energy threshold and improved variational model decomposition of the filtered vibration signals is realized to obtain mode matrixes of the corresponding vibrationsignals; singular value decomposition is carried out on the mode matrixes to obtain a singular value vector and a rolling bearing fault feature set is constructed; and the fault feature set is trained by using an extreme learning machine and a rolling bearing fault diagnosis model is established. Therefore, stable feature extraction of the complex vibration signal of the rolling bearing is realized, so that the diagnostic accuracy is improved.

The invention relates to a computer vision-based rice disease, pest and weed diagnostic method. The method comprises the following steps: collecting different varieties of rice leaf disease samples at different morbidity periods in different growth environments, and letting a plant disease expert identify the diseases infecting all the samples; then acquiring images of disease symptoms of the samples; realizing the automatic segmentation of disease spots by utilizing an image processing technology; extracting all characteristic indexes of the disease spots, and establishing a characteristic database corresponding to the diseases; on the basis, selecting proper disease spot characteristic parameters and an effective pattern recognition algorithm so as to establish a machine vision-based rice disease, pest and weed real-time diagnostic model.

The invention discloses a device fault diagnosis method, a device fault diagnosis device, and a device fault diagnosis system, and relates to a device detection technology field. A problem of a conventional automobile fault diagnosis way of low efficiency is solved. The device fault diagnosis method comprises steps that an audio signal of a to-be-diagnosed device is acquired, and is a sound signal generated by the to-be-diagnosed device during an operation process; a fault source signal is extracted from the audio signal of the to-be-diagnosed device; a preset fault characteristic representing the device fault is extracted from the fault source signal; the preset fault characteristic is input in a preset fault diagnosis model, and the type of the fault is determined. The device fault diagnosis method, the device fault diagnosis device, and the device fault diagnosis system are used for an automobile fault diagnosis process.

An apparatus and a method for diagnosis are provided. The apparatus for diagnosis lesion include: a model generation unit configured to categorize learning data into one or more categories and to generate one or more categorized diagnostic models based on the categorized learning data, a model selection unit configured to select one or more diagnostic model for diagnosing a lesion from the categorized diagnostic models, and a diagnosis unit configured to diagnose the lesion based on image data of the lesion and the selected one or more diagnostic model.

A method for diagnosing and classifying faults in a system is provided. The method comprises acquiring operational data for at least one of a system, one or more subsystems of the system or one or more components of the one or more subsystems. Then, the method comprises analyzing the operational data using one or more diagnostic models. Each diagnostic model uses the operational data to determine a probability of fault associated with at least one of the one or more components or the one or more subsystems. Finally, the method comprises deriving an overall probability of fault for at least one of the system, the one or more subsystems, or the one or more components using the one or more probabilities of fault determined by the one or more diagnostic models and one or more hierarchical relationships between the subsystems and components of the system.

The invention provides a lead-acid power battery system fault diagnosis method. The method involves an off-line part and an on-line part. The method includes the specific steps that in the off-line state, data are collected through a simulation model, the data are preprocessed by using a normalization method, a data classification training set and a testing set of a power battery system of a support vector machine are obtained, parameter adaptive optimization is conducted through a GA algorithm, a one-to-one method is used for training to obtain a diagnostic model of the support vector machine, and SVM decision classification is conducted; in the on-line state, a fault generating device is used for simulating fault signals, the signals are collected through a collection module, the data are preprocessed by using the normalization method, the data are further input into an SVM module in off-line training, and fault online classification based on an SVM algorithm is conducted. According to the lead-acid power battery system fault diagnosis method, intelligent off-line and on-line diagnosis of faults of the battery system can be achieved, and meanwhile the fault diagnosis recognition rate is increased.

A method for troubleshooting a problem with a device includes acquiring device data for the device, receiving a user's query concerning a device in a natural language, presenting possible refinements to at least a portion of the user's query for defining a problem statement, presenting candidate solutions that are associated with the defined problem statement in a knowledge base, at least one of the presentation of possible refinements and the presentation of candidate solutions being informed by device data that is linked through a diagnostic model of the device to at least one of the problem statements and candidate solutions.

The invention provides a mechanical equipment intelligent fault diagnosis method based on a partial migration convolutional network. The method comprises: collecting operation data of mechanical equipment under different operation working conditions; constituting data sets, taking part of data in the data set X as a source domain training sample set and a target domain test sample set; performingdata standardization on each piece of sample data; training two one-dimensional convolutional neural network models with the same structure and different initialization parameters by using the sourcedomain training sample set, and correcting the two trained convolutional neural network models based on the target domain test sample set to obtain a convolutional neural network mechanical equipmentfault diagnosis model; and performing fault diagnosis on the mechanical equipment based on the real-time operation data by using the fault diagnosis model to output a fault type. The method can be effectively used in more real mechanical fault diagnosis, that is to say, the label-free property of the target domain is considered, so that the trained diagnosis model can better diagnose faults of mechanical equipment.

The present invention provides an adjuvant disease diagnosis method based on patient test results. The method comprises: cleaning original data, and constructing sample data according to the test category; using the current xgboost framework with high speed and high accuracy to improve the algorithm and reduce the error rate, designing the model loss function and optimizing the number of iterations of the model, and training the multiple diagnostic models according to the type of the sample; and outputting some diseases with relatively large probability that patients could be suffered from. The present invention provides the disease diagnosis method with high reliability for assisting doctors to improve the diagnosis rate of the disease and reduce the misdiagnosis rate.

An electric energy measuring fault intelligent diagnostic method based on an S-shaped curve function comprises the following steps: S1: extracting historical data of real-time load, daily electric quantity, terminal alarm, and master station alarm from a measurement automation system; S2: extracting measurement fault evaluation indexes from the historical data; S3: establishing a training sample set based on the S-shaped curve function; S4: establishing a measurement fault diagnostic model; S5: performing intelligent diagnosis of measurement faults; and S6: optimizing parameters of the measurement fault diagnostic model, and reestablishing the measurement fault diagnostic model. The method is based on the electric energy measurement automation system, and can automatically make an intelligent diagnostic analysis of massive electric power data. A measurement fault diagnostic system can perform automatic adjustment and optimization of a model structure and parameters according to applied environment, the latest real data and system feedback, thereby achieving better diagnostic effects.

A method for diagnosing faults in a particular device within a fleet of devices is provided. The method comprises receiving performance data related to one or more parameters associated with a fleet of devices and processing the performance data to detect one or more trend shifts in the one or more parameters. The method then comprises detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device. The method further comprises generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices. The fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices. The method then comprises computing one or more scaling factors for the particular parameter associated with the particular device and scaling the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors, to generate a personalized diagnostic model for the particular parameter associated with the particular device. The method finally comprises evaluating the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the particular device.

A deep learning based hand-foot-and-mouth disease detection system comprises a neural network model training module and a hand-foot-and-mouth disease detection module. A convolutional neural network model is constructed through the neural network model training module on the basis of a hand-foot-and-mouth disease sample set, and a hand-foot-and-mouth disease neural network diagnosis model is obtain by analysis of images in the hand-foot-and-mouth disease sample set. On the basis of input images, the hand-foot-and-mouth disease detection module judges to obtain hand-foot-and-mouth disease diagnosis results through the neural network diagnosis model. By application of the deep learning technology to automatic diagnosis of the hand-foot-and-mouth disease, a hand-foot-and-mouth disease detection core problem is transformed into a target detection problem, and detection result accuracy is improved by autonomous optimization.

The invention provides a power grid fault diagnostic model and a diagnostic method thereof and belongs to the technical field of power grid fault diagnosis. A mathematical expression of the traditional fault diagnostic analytic model can be abstracted as the formula; analysis on the action state of protecting a switch is converted into description on the probability of protecting the switch; the description is information transmission uncertainty description based on an information theory; an objective function is established as an optimal solving function; mutual information between an information sink and an information source under every failure mode is calculated when multiple optimal solutions, namely multiple failure modes exist; corresponding failure modes are most likely to occur if the quantity of condition self-information is smallest; and a principle that the mutual information of the information sink and the information source is maximum is utilized to determine a fault sorting result when the plurality of condition self-information is similar. The power grid fault diagnostic model and the diagnostic method thereof have the advantages of enabling the uncertainty of fault diagnosis to be integrated in an analytic model, enabling the fault tolerance of the model to be improved and the dimension of the model to be greatly reduced, being high in diagnostic speed and diagnostic accuracy, being capable of being well applied to a scheduling terminal and playing a positive and important role in the field of the power grid fault diagnosis.