208results about How to "Fast diagnosis" patented technology

The invention belongs to the field of maintenance decision-making of hydraulic equipment, and relates to a method for the optimal maintenance decision-making of hydraulic equipment with risk control. The method mainly comprises three steps: 1) judging whether a system is in a status of defect by using a variable-weight association rule algorithm, if so, calculating the probability values of occurrences of latent faults of the system; 2) calculating the comprehensive evaluation value for the consequence of each latent fault by using a BP neural network; and 3) multiplying the probability values obtained in step 1 by the comprehensive evaluation values obtained in step 2 so as to obtain the VaRs (values-at-risk) of the latent faults, judging whether the VaRs are more than a specified threshold, if so, ranking the VaRs in descending order so as to determine the maintenance sequence; otherwise, returning to the step of monitoring. The method can judge whether a device is in a status of defect, judge the type of the latent fault and calculate the probability values of occurrences of latent faults only through a calculation; and compared with traditional risk maintenance methods, the method of the invention improves the accuracy of fault diagnosis, speeds up the diagnosis speed, and provides a better reference for online decision-making.

The invention discloses an open-circuit fault diagnosis method for the drive system of a double-winding fault-tolerant permanent-magnet motor. According to the method, the collected phase current of a motor is respectively subjected to the converting and processing process by a low-pass filter, a Clarke convertor, a Park vector processor, a normalization processor, an average value calculator, an absolute value calculator and an absolute value averaging processor, so as to obtain the average value of the normalized phase current of the motor and the average value of the absolute value of the normalized phase current of the motor. With the average value of the normalized phase current of the motor and the average value of the absolute value of the normalized phase current of the motor as a basis, fault diagnostic variables for the system are constructed, so that the open-circuit fault of the drive system of the double-winding fault-tolerant permanent-magnet motor can be detected and positioned in real time. Based on the above method, no extra current sensor is required, so that the method is simple, feasible and high in reliability. Based on the combined utilization of the average value of the normalized current and the average value of the absolute value of the normalized current, the incorrect diagnosis caused by load sudden change and other reasons during the conventional diagnosis process can be avoided. Meanwhile, the diagnosis time is greatly reduced. Therefore, the open-circuit fault of the drive system can be effectively detected and positioned in real time.

The invention discloses an intelligent train traction fault big data abnormality detection and recognition method. The method includes: collecting working signals of traction equipment of an intelligent train in real time; sampling the working signals to acquire a sampling dataset; using an isolated forest abnormal value quick detector to quickly detect the sampling dataset, and acquiring remarkable abnormal point in the working signals; building a Hampel sliding window identifier filter according to the remarkable abnormal point, and processing the sampling dataset to acquire a sampling dataset after being processed; acquiring a training sample set, and training a classifier based on a slack supporting vector machine by taking processed sampling data of a training sample as input and corresponding fault type as output; using a traction fault diagnosis classifier acquired by training to diagnose the sampling dataset after being processed and acquired in the step 3, and detecting faulttype of tiny and suspicious fault in the working signal S. By the method, fault detection efficiency and diagnosis accuracy of the train traction equipment are improved.

The invention discloses a method and a device of car emission fault detection and diagnosis based on fuzzy reasoning and self-learning and belongs to the field of car maintenance and diagnosis. The method of the car emission fault detection and diagnosis based on the fuzzy reasoning and the self-learning comprises collecting all data values of a to-be-tested car exhaust gas under different working conditions, comparing the data values with standard values, if the data values reach the standard values, then the diagnosis is finished, if the data values do not reach the standard values, then confirming the possible corresponding causes of the problem according to a built fuzzy diagnosis matrix through a fuzzy reasoning method and outputting a diagnosis result, implementing maintenance according to the diagnosis result until the data value reaches the standard value through staff, and at last correspondingly updating vehicle profile information and the right fault diagnosis information of the diagnosis result to a data base to be used for self-learning and adjusting the fuzzy diagnosis matrix. According to the method and the device of the car emission fault detection and diagnosis based on the fuzzy reasoning and the self-learning, a car emission fault example is studied through the fuzzy reasoning, fault features are extracted from a large amount of samples, detection accuracy is high, dependency of the technical merit of the operators is low, and operation is convenient.

The invention discloses an open circuit fault diagnosis method for an NPC three-level photovoltaic inverter. The method comprises the following steps of (1) building an NPC three-level inverter simulation model for simulating a fault condition; (2) collecting phase voltage time domain signals of three phases; (3) performing wavelet packet transform on the phase voltage signals to obtain frequencyband energy after the transform, and taking the frequency band energy after the transform as signal eigenvectors; (4) performing dimension reduction processing on the extracted eigenvectors by using aprincipal component analysis method to obtain low-dimensional data matrix samples, and classifying the samples into two parts including training samples and test samples; (5) establishing a radial basis neural network, training the radial basis neural network by using the training samples, and storing the trained neural network; and (6) testing the trained radial basis neural network by using thetest samples, and analyzing a fault diagnosis result. The method is high in diagnosis speed and high in diagnosis accuracy.

The invention discloses a rolling bearing fault diagnosing method and equipment based on CEEMDAN and CFSFDP, belonging to the field of fault diagnosis of rotary machines. The method comprises the steps of acquiring vibration signals of a bearing in a normal state and different fault mode states so as to obtain sample points of the vibration signals of different states, decomposing by using CEEMDANto obtain time and frequency domains characteristics of bearing diagnosis and screening out bearing state representation parameters along with the time domain and frequency domain characteristics, dividing the representation parameters into training samples and a test samples, then using a table CFSFDP algorithm as a bearing fault diagnosis model, inputting the training samples into the bearing fault diagnosis model, clustering output results, obtaining clustering amount, clustering center points of each type, and state types corresponding to the clustering center points, and inspecting the trained diagnosis model by using test samples. The method and equipment can identify different bearing fault types and fault degrees accurately and effectively.

A distribution transformer fault diagnosis device based on multi-index comprehensive evaluation includes a sound acquisition unit, an oil temperature acquisition unit, a unit for analyzing gas dissolved in oil of a transformer, a fault diagnosis unit and a battery power supply module; the fault diagnosis unit includes an FM receiver module (7) whose output end is connected with an AD converter (8) and an ARM single-chip microcomputer (9) in sequence; and the sound acquisition unit includes a fixed support (2) on which a strong magnet (1) and a pickup head (3) are fixed, the pickup head is connected with a pickup (4), an output end of the pickup is connected with a signal conditioning circuit (5) and an FM transmitter module (6) in sequence, and the FM transmitter module is connected with the FM receiver module through FM electromagnetic waves. The distribution transformer fault diagnosis device based on multi-index comprehensive evaluation can satisfy requirements for performing fault detection on a distribution transformer in operation on site and judging the fault type, does not need a power supply to be provided on site, and is convenient to carry, low in signal acquisition and transmission cost, fast in diagnosis speed and convenient to use.

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 discloses a diagnosis method for an active power filter switching device open-circuit fault based on model reference analysis. The diagnosis method includes the steps that on the basis of building an actual operation model of active power filter main circuits, a reference model is constructed; measurable active power filters in all sampling periods are used for outputting three-phase compensating currents, and voltage residual errors are generated according to reference analysis of the model; by setting a threshold value and two judgment flag bits, the residual errors are evaluated to detect whether the open-circuit fault happens on an APF switching device or not in real time; if the fault happens, the specific position where the open-circuit fault of the switching device happens is diagnosed according to the two judgment flag bits. The diagnosis method is effectively used for monitoring the active power filters on line and diagnosing faults, and electric, electronic and electric energy quality control devices such as the active power filters and reactive compensators operate reliably and safely.

The invention relates to a fault treatment system of a numerical control machine tool. The fault treatment system of the numerical control machine tool comprises a fault detecting module, a fault diagnosing module and an alarming module. The fault detecting module is used for sending fault feature information to the fault diagnosing module when the numerical control machine tool breaks down; the fault diagnosing module is used for receiving the fault feature information and comparing the fault feature information in a fault database, so that a diagnosing result is generated and a control order is sent to a numerical control system of the numerical control machine tool. The alarming module is used for conducting acousto-optic display on the fault information and sending generation time of a fault, the fault diagnosing result and the control order to an operator of the numerical control machine tool. The fault treatment system of the numerical control machine tool is used for conducting fault monitoring and diagnosing on various numerical control machine tools, the diagnosing speed is high, and the diagnosing result is reasonable, so that the fault time of the numerical control machine tool is shortened, the operation efficiency of machining of the machine tool and the economical benefits of an enterprise are improved, and the fault treatment system of the numerical control machine tool is suitable for various numerical control machining fields.

The invention discloses a three-phase power grid voltage short-circuit fault diagnosis method, which is characterized in that a short-circuit fault type judging unit is used for judging whether a fault belongs to a three-phase ground short-circuit fault, a two-phase fault or a single-phase ground short-circuit fault according to a phase voltage amplitude; a k value calculating unit is used for calculating a ratio between a real-time line voltage amplitude and a line voltage rated value; and a two-phase fault type diagnosis unit us used for diagnosing whether the two-phase fault belongs to the ground short circuit or the inter-phase short circuit according to the situation that whether a theoretical value of the verified line voltage amplitude is equal to a real detection value or not. By utilizing two voltage sensors, the type of the short-circuit fault can be diagnosed within a half voltage cycle of a power grid, so that the hardware cost is low, the diagnosis speed is fast, the practicability is strong, and the application value is promising in market prospect.

The invention discloses a gearbox fault diagnosis method based on a bird flock algorithm and a hidden Markov model. The gearbox fault diagnosis method comprises four steps of: feature extraction, model parameter initialization, parameter training, and output probability calculation. The step (1) feature extraction is implemented by selecting a wavelet function for performing 3-layer wavelet packetdecomposition and reconstruction on vibration signals, and analyzing wavelet decomposition coefficient signals of each frequency band so as to realize the extraction of feature information of different fault states represented by the vibration signals from each frequency band respectively. The step (2) model parameter initialization is implemented by taking frequency band energy of the vibrationsignals as eigenvectors for modeling. The step (3) parameter training is implemented by adopting the bird flock algorithm for re-estimation according to the parameters initialized in the second step.The steps (4) output probability calculation is implemented by extracting monitored vibration data features after models are constructed in the previous step, substituting the monitored vibration datafeatures into different fault state models, calculating an output probability by adopting a forward-backward algorithm, and regarding the maximum probability as a corresponding fault type.

The invention discloses an adaptive fault diagnosis method based on a one-dimensional convolutional neural network. According to the adaptive fault diagnosis method, the one-dimensional convolutionalneural network is used as a main model structure; a high-precision diagnosis result can be output immediately by directly inputting a one-dimensional time sequence signal; compared with a traditionalmode of diagnosis by manual feature extraction, the adaptive fault diagnosis method provided by the invention is more convenient and efficient and has lower requirements for professional knowledge andexperience of diagnosis personnel; meanwhile, compared with the mode of fault diagnosis based on neural network structures, the one-dimensional convolutional neural network model is simpler and faster in calculation while maintaining the diagnosis precision, and can provide good guarantee for real-time fault diagnosis. The real-time and rapid adaptive fault diagnosis method provided by the invention can deal with the diversity of fault types easily, and can be widely applied to the fields of machine manufacturing, aerospace, electricity, metallurgy and the like.