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.

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 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.

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 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 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.

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.

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.

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.