227results about How to "Accurate fault diagnosis" patented technology

A diagnostic system and method for a home appliance is provided. When the home appliance outputs product information as a sound signal, a service center remotely performs fault diagnosis of the home appliance by receiving the sound signal, detecting the product information from the sound signal, checking the state of the home appliance using diagnostic data included in the product information to determine whether the home appliance is out of order, diagnosing, when water supply malfunction including a water supply error or a water level sensor error has occurred, a cause of the water supply malfunction, and deriving a solution to the water supply malfunction. Upon deriving a diagnosis result through the fault diagnosis of the home appliance, the service center immediately notifies the user of the diagnosis result and may dispatch a service technician or may provide the user with a solution to allow the user to easily fix the fault without dispatching a service technician.

A diagnostic system and method for a home appliance is provided. When the home appliance outputs product information as a sound signal, a service center remotely performs fault diagnosis of the home appliance by receiving the sound signal, detecting the product information from the sound signal, checking the state of the home appliance using diagnostic data included in the product information to determine whether the home appliance is out of order, determining, when an unbalance error associated with a balance abnormality of the home appliance has occurred, a cause of the unbalance error, and deriving a solution to the unbalance error. Upon deriving a diagnosis result through the fault diagnosis of the home appliance, the service center immediately notifies the user of the diagnosis result and may dispatch a service technician or may provide the user with a solution to allow the user to easily fix the fault without dispatching a service technician.

The invention discloses battery system monitoring method and device based on OBD-II, which relate to the technical field of monitoring of batteries for vehicles. The method is characterized in that a data acquiring module, an OBD-II failure diagnosing module, a safety monitoring module, a heat monitoring module, a communication module, an SOC estimating module and an interactive display module are provided, wherein the OBD-II failure diagnosing module comprises a signal acquiring unit, a failure recognizing unit and a failure processing unit. The device comprises a data acquiring interface, a signal adjusting circuit, a microprocessor, a data memory and a program memory. Tests prove that the battery system monitoring method and device can construct and monitor all parameters of a battery system and carry out failure diagnosis, improves the safety of the system, is convenient for daily maintenance and failure repair and has an important function on the development of the current finished vehicle distributed control network.

The invention provides a photovoltaic module fault diagnosis method of a deep convolution generative adversarial network. The method comprises the steps of establishing a mathematical model of a photovoltaic module; carrying out fault image acquisition on the photovoltaic module; setting a part of fault data as a training sample; constructing a training model of the deep convolutional adversarialnetwork; the generator G inputting a noise vector and outputting a pseudo image through a deconvolution layer; the discriminator D inputting a real sample and a pseudo sample, extracting convolution features through convolution operation, and obtaining the probability of the real sample; optimizing a weight parameter through a back propagation algorithm, then starting the next cycle, and outputting a test image every 300 cycles; and inputting the real sample and the obtained test sample into a classifier to classify fault types, thereby realizing fault diagnosis. According to the fault diagnosis method, a large number of fault pictures are generated by using the deep convolutional network, and a fault image database is expanded, so that fault classification is more detailed, and fault diagnosis is more accurate.

The invention discloses a gear fault diagnosis method based on a part mean decomposition cycle frequency spectrum. The part mean decomposition method vibrating signal is decomposed as the sum of a plurality of amplitude-modulation frequency-modulation signals with single component, obtaining the instantaneous frequencies of the components and the changing situation of the instantaneous frequencies of the components, which is suitable for processing AM-FM signals with a plurality of components. When the gear has faults, the vibrating signal is generally AM-FM signal; the part mean decomposition method is used for obtaining the changing situation of the instantaneous frequency of the gear vibrating signal along time, further analyzing the instantaneous frequencies to obtain the circulated frequency spectrum to identify the gear state and the faults.

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 present invention is directed an ultrasonic frequencies fault detecting apparatus. The present invention uses high frequency ultrasonic energy signals to analyze bearings and determine the presence of faults therein. The ultrasonic return signals are heterodyned (by amplitude demodulation) into the audio spectrum for purposes of audio detection. In addition, a FFT spectrum of the signal is displayed on a monitor for more accurate results. According to the present invention software is used to automatically analyze the FFT spectrum by comparing the current spectrum with stored spectrums of known bearing conditions as modified based on the rotary speed of the bearing and the number of balls. Once a defect in a bearing has been located in this manner using automated FFT analysis, an operator can then use a device, such as a calibrated lubricant dispenser, to effectuate maintenance and / or repair.

An automotive passenger restraint and protection apparatus includes an electric retractor having a motor for retracting and protracting a seatbealt. The apparatus also includes an airbag driving device, a pretensioner, a speed detecting device, and a controller. The controller controls at least one of the actuation time or expansion pressure of an airbag by the airbag driving device, actuation time of the pretensioner, a force of the pretensioner for retracting the seatbelt, and a force of the seatbelt driving device for retracting the seatbelt, based upon the speed of protraction of the seatbelt. The controller controls the airbag driving device to shorten the actuation time of the airbag when the speed of protraction of the seatbelt is greater than a predetermined value, and prolongs the actuation time of the airbag when the detected speed of protraction of the seatbelt is lower than the predetermined value.

The invention relates to a method for diagnosing and processing faults of an accelerator pedal of a medium hybrid electric vehicle, which is required to be protected. The method comprises the following steps of: processing signals of the accelerator pedal; performing overrun diagnosis and reasonable diagnosis on a hybrid control unit; and setting different zonebits according to different faults of the accelerator pedal, processing the signals differently and performing different limitations on the functions of the whole vehicle. The method has the advantages of performing accurate fault diagnosis on the signals of the accelerator pedals and greatly reducing the influence of a pedal mechanism on the safety of the whole vehicle in acceleration faults.

The invention discloses a neural network-based subway train fault diagnosis device and a neural network-based subway train fault diagnosis method. A lower computer classifies acquired subway train state information according to a functional unit, concentrates and summarizes data, and sends the data to an upper computer; the upper computer receives, processes and stores the subway train state data summarized by the lower computer; the upper computer comprises a data acquisition module and a neural network module; the data acquisition module finishes the classification of the acquired subway train state data; the lower computer acquires real-time data information of a subway train; and when the subway train fails or is to fail, the neural network module of the upper computer performs corresponding judgment and pre-judgment according to the output of a radial basis function neural network established during training and outputs fault information to a fault diagnosis result module. By the embodiment of the invention, the technical problems of insufficient acquired and analyzed data volume, low data processing efficiency and a complicated calculation process in the prior art can be well solved.

An automotive passenger restraint and protection apparatus for an automotive vehicle having doors, and a seatbelt, for restraining an occupant of the automotive vehicle by the seatbelt to protect the occupant includes an electric retractor having a motor for retracting and protracting the seatbelt, a controller for controlling the motor, and a door opening / closing detector for detecting opening and closing of a predetermined one of the doors. When the door opening / closing detector detects the opening of the predetermined one of the doors, the controller controls the motor to carry out retraction of the seatbelt at a higher speed than when the door opening / closing detector detects the closing of the predetermined one of the doors.

The invention provides a method for diagnosing faults of a multi-model flight control system based on expected model expansion, comprising the following steps: making a statistic of various faults of the flight control system, and building a basic model collection; forecasting the probability of the multiple fault models at the current time, and building an expected model collection; combining the basic model collection with the expected model collection to build a fault model collection at the current time; filtering each fault model in the model collection at the current time, and updating the probability; if the probability of certain fault model in the model collection at the current time is more than or equal to the preset probability threshold value, judging that the flight control system has the fault corresponding to the fault model. The invention further provides a device for diagnosing the faults of the multi-model flight control system based on expected model expansion, comprising a basic model collection building module, an expected model collection building module, a model collection at the current time building module, a filtering and probability updating module and a fault judging module. The invention further provides a flight control system.

The invention discloses a power grid resource fault diagnosis method based on association rules. The method comprises the following steps: A, drawing off fault data, converting the fault data, loading the fault data to a data warehouse, and establishing a multidimensional data model and a fault fact table; B, performing cluster analysis on the fault data; C, performing association rule analysis on the fault data; and D, abstracting actual fault events as nodes, if it is compared that a high-credibility fault power grid device accords with a calculated aggregation degree, obtaining a diagnosis result, and giving assistance advice for processing of power grid faults. According to the invention, correlation data of power equipment can be comprehensively acquired, the multidimensional data model and the fault fact table can be established, a data base is provided for data mining, data is preliminarily analyzed by use of the cluster analysis, data association analysis is performed by integrating various data, comparative determining is carried out, integration of multiple intelligent criteria is formed, the faults are accurately determined, and the application prospect is good.

The invention provides an analog circuit fault diagnosis method based on wavelet packet analysis and the Hopfield network. The method includes data obtaining, feature extraction and fault classification, wherein data obtaining includes performing data sampling for output response of an analog circuit respectively through simulation program with integrated circuit emphasis (SPICE) simulation and a data collection plate connected at a practical circuit terminal so as to obtain an ideal output response data set and an actually-measured output response data set; feature extraction includes performing wavelet packet decomposition with ideal circuit output response and actually-measured output response respectively serving as a training data set and a test data set, and leading energy values obtained by decomposed wavelet coefficient through energy calculating to form feature vectors of corresponding faults; and fault classification includes leading the feature vectors of all samples to be subjected to Hopfield coding and then submitting the coded feature vectors to the Hopfield network to achieve accurate and fast fault classification. The analog circuit fault diagnosis method is good in fault feature pretreatment effect aiming at hard faults with weak amplitude response and soft faults with large amplitude response, and the newly defined energy function and the newly defined coding rule are remarkable in influence on fault diagnosis accuracy of the analog circuit.

A portable apparatus for determining testing the condition of a machine or device using ultrasonic signals with an array of ultrasonic sensors for receiving an ultrasonic signal transmitted from the machine or device. The apparatus possesses a heterodyne circuit coupled to receive the output signals from the ultrasonic sensors and convert the output signals to a heterodyned audio signal to be analyzed via a digital spectrum analyzer integral to the portable apparatus. The digital spectrum analyzer performs real-time fast fourier transformations on the heterodyned audio signal. After analyzing the signals, the hand held device uses a variety of audiovisual cues to direct a user to portions of the machine in need of repair or monitoring.

The invention relates to a method for mechanical fault diagnosis based on information entropies and an evidence theory. The method comprises the following steps of step 1 adopting four typical mechanical fault types to construct a recognition framework; step 2 taking four information entropies of a vibration signal as fault characteristics; step 3 computing to obtain fault characteristic reference values of the four typical mechanical fault types through analogue simulation; step 4 obtaining a fault vibration signal received by a sensor, and computing to obtain fault characteristic values thereof through the information entropies; step 5 utilizing a fault characteristic extraction method based on weighted information entropies to obtain sensor vibration signals to be distributed to basic probability assignment functions of the four typical mechanical fault types; step 6 utilizing an improved evidence synthesizing method based on a conflict between revised evidences to carry out evidence synthesizing on the obtained basic probability assignment functions in order to obtain a synthesized result; and step 7 obtaining a final result for fault diagnosis according to a decision rule.

A diagnostic system and method for a home appliance is provided. When the home appliance outputs product information as a sound signal, a service center remotely performs fault diagnosis of the home appliance by receiving the sound signal, detecting the product information from the sound signal, checking the state of the home appliance using diagnostic data included in the product information to determine whether the home appliance is out of order, determining, when a temperature error associated with a temperature abnormality has occurred, a cause of the temperature error, and deriving a solution to the temperature error. Upon deriving a diagnosis result through the fault diagnosis of the home appliance, the service center immediately notifies the user of the diagnosis result and may dispatch a service technician or may provide the user with a solution to allow the user to easily fix the fault without dispatching a service technician.

Disclosed in the invention is a remote fault analysis and feedback system of an urban rail transit vehicle. The system comprises a vehicle-mounted device system and a ground device system. The vehicle-mounted device system includes a data acquisition module, a data transmitting-receiving module, and an intelligent display terminal; and the ground device system contains a data transmitting-receiving module, a fault analysis module, and a fault knowledge base. According to the invention, the vehicle-mounted device system collects vehicle data and the ground device system receive the data to carry out analyses continuously; diagnosis is carried out based on a fault tree and fault diagnosis steps; and a diagnosis result can be fed back to a vehicle intelligent terminal in real time. Therefore, accuracy and reliability of the fault diagnosis can be improved.

A diagnostic system and method for a home appliance is provided. When the home appliance outputs product information as a sound signal, a service center remotely performs fault diagnosis of the home appliance by receiving the sound signal, detecting the product information from the sound signal, checking the state of the home appliance using diagnostic data included in the product information to determine whether the home appliance is out of order, determining, when a motor error associated with a motor operation has occurred, a cause of the motor error, and deriving a solution to the motor error. Upon deriving a diagnosis result through the fault diagnosis of the home appliance, the service center immediately notifies the user of the diagnosis result and may dispatch a service technician or may provide the user with a solution to allow the user to easily fix the fault without dispatching a service technician.

The invention discloses a diagnosis method of a rolling bearing fault. The method comprises the following steps of S1, collecting an acceleration signal of a rolling bearing; S2, using a discrete wavelet transform method and a soft threshold method in a combination mode and carrying out de-noising processing on the acceleration signal; S3, carrying out segmentation on a time sequence of the acceleration signal after the de-noising processing and extracting a sample; S4, through more than two self-encoding networks, constructing a stack self-encoding network framework, and extracting characteristic information of the sample; S5, using the characteristic information of the sample to train at least one BP nerve network classifier; and S6, according to a fault diagnosis model established through using known fault data to train the at least one BP nerve network classifier, determining fault information of the rolling bearing. By using the diagnosis method of the rolling bearing fault, accuracy of the fault diagnosis is greatly increased.

The invention discloses a fault diagnosis method for a PWM inverter of a motor drive system. The fault diagnosis method is characterized in that an inverter fault diagnosis model based on both wavelet packet decomposition and an RBF neural network is designed, wavelet packet transformation is utilized to extract a feature vector of a fault signal of the inverter, and the feature vector is taken as the input quantity of the RBF neutral network; a wolf pack-simulated annealing algorithm is adopted to optimize structural parameters of the RBF neutral network; and 22 groups of learning samples and 6 groups of test samples are utilized to train and examine the RBF neutral network. Simulation experiment analysis shows that when the fault diagnosis method is used for an open-circuit fault of the PWM inverter of a three-phase motor drive system, the fault position of an inverter TGBT power tube can be accurately positioned, fault diagnosis is quick, accurate and efficient, and the fault diagnosis method contributes to improving the running reliability of the motor drive system.

The invention provides an analog circuit fault diagnosis method based on cross-wavelet characteristics. The method comprises the following steps: inputting an excitation signal to a tested analog circuit, collecting time-domain response output signals and constructing an original data sample set; dividing the original data sample set into a training sample set and a test sample set; carrying out cross wavelet decomposition on the training sample set and the test sample set to obtain a wavelet cross spectrum of the training sample set and the test sample set; carrying out processing on the wavelet cross spectrum of the training sample set and the test sample set through bidirectional two-dimensional linear discriminant analysis, and extracting fault feature vectors of the training sample set and the test sample set; submitting the fault feature vectors of the training sample set to a support vector machine for training an SVM classifier, and constructing a support vector machine fault diagnosis model; and inputting the fault feature vectors of the test sample set to the model and carrying out fault classification. The method can effectively identify the fault of the analog circuit,and obviously improve fault diagnosis precision of the analog circuit.

A diagnostic system and method for a home appliance is provided. When the home appliance outputs product information as a sound signal, a service center remotely performs fault diagnosis of the home appliance by receiving the sound signal, detecting the product information from the sound signal, checking the state of the home appliance using diagnostic data included in the product information to determine whether the home appliance is out of order, determining, when a drying error has occurred, a cause of the drying error, and deriving a solution to the drying error. Upon deriving a diagnosis result through the fault diagnosis of the home appliance, the service center immediately notifies the user of the diagnosis result and may dispatch a service technician or may provide the user with a solution to allow the user to easily fix the fault without dispatching a service technician.

The invention discloses a time series data intelligent fault diagnosis method based on deep learning. The method comprises the following steps: 1) collecting one-dimensional time series health state data of electromechanical equipment, wherein fault data is the fault data arranged at a preset measuring point of the electromechanical equipment; 2) constructing a one-dimensional time series originalfault data set according to the acquired N health state data; 3) preprocessing the data, wherein the preprocessing includes normalization and data truncation; 4) constructing a two-dimensional feature map fault data set; 5) carrying out data set division; 6) constructing a deep learning fault diagnosis model; 7) training the deep learning fault diagnosis model to obtain parameters; and 8) performing fault diagnosis on the input to-be-diagnosed sample data by using the deep learning fault diagnosis model, and outputting a final fault diagnosis result. By constructing the two-dimensional feature map and improving a traditional convolutional neural network model structure, a fault diagnosis speed and fault diagnosis accuracy are improved.

The invention relates to a principal element degree of association sensor fault detection method and apparatus based on density clustering. The method comprises steps of: determining the operating condition data of monitoring sensors by using a unit multi-operating condition model and classifying the monitoring sensors to obtain an operating condition cluster, wherein the operating condition information of all monitoring sensors in the operating condition cluster forms a matrix X; forming a matrix K by using the operating condition information of any two monitoring sensors in the operating condition cluster; analyzing the principal element of the matrix K to obtain the major characteristic T of the matrix K; determining the degree of association of the any two monitoring sensors by using the normalized matrix X and the major characteristic T; and comparing the degree of association with a threshold value and detecting the fault of the monitoring sensor corresponding to the degree of association greater than the threshold value.

A diagnostic system and method for a home appliance is provided. When the home appliance outputs product information as a sound signal, a service center remotely performs fault diagnosis of the home appliance by receiving the sound signal, detecting the product information from the sound signal, checking the state of the home appliance using diagnostic data included in the product information to determine whether the home appliance is out of order, determining, when a drainage error has occurred, a cause of the drainage error, and deriving a solution to the drainage error. Upon deriving a diagnosis result through the fault diagnosis of the home appliance, the service center immediately notifies the user of the diagnosis result and may dispatch a service technician or may provide the user with a solution to allow the user to easily fix the fault without dispatching a service technician.

A fault diagnosis method of a hydraulic submerged pump system comprises three steps of data acquisition and storage, data processing and analysis as well as data diagnosis and alarming. According to the method, fault information in the system are accurately reflected with a multivariate data analysis method, coupling of multiple variates in the system are analyzed, and a new comprehensive index is formed and can comprehensively reflect faults of the original system. With the adoption of the method, fault types of a submerged pump can be accurately reflected, the reliability of the submerged pump system can be effectively improved, the usage range is wide, and manufacturing and operation costs are low.

An automotive passenger restraint and protection apparatus for an automotive vehicle has a seatbelt and operates to restrain an occupant of the automotive vehicle by the seatbelt to protect the occupant. An electric retractor has a DC motor for retracting and protracting the seatbelt. An MPU applies voltage having a predetermined waveform to the DC motor, and then detects a waveform of current flowing to the DC motor. The MPU carries out fault diagnosis of the DC motor, based upon the detected waveform of current when the voltage having the predetermined waveform is applied to the DC motor. Thus, accurate fault diagnosis of the apparatus can be achieved.

The invention discloses a bearing fault classification method based on CNN and Adaboost. A bearing signal is collected, the bearing signal is preprocessed, and a time domain signal and a time-frequency domain signal are extracted; a time-domain weak classification module and a time-frequency-domain weak classification module are constructed based on the time domain signal and the time-frequency domain signal; and then the time-domain weak classification module and the time-frequency-domain weak classification module are integrated and a membership probability value of a to-be-detected unmannedaerial vehicle bearing signal is predicted by using the integrated classification model. Therefore, the classification of UAV bearing faults is realized.