656 results about "Mechanical failure" patented technology

A system and method for ΔI-powered engine diagnostics combine OBD-II data, high-frequency sound, and vibration analysis to detect engine wear and mechanical faults. A detachable puck sensor in the engine bay captures sound and vibration signals, while an OBD-II module collects engine performance metrics. A smartphone app collects and uploads data to a backend AI engine, where Dynamic Time Warping (DTW) aligns event-driven or asynchronous data and time series data from multiple sources, ensuring accurate feature fusion. Machine learning models then detect engine wear, belt degradation, knocking, and bearing faults, generating a diagnostic report with severity assessments and predictive maintenance recommendations. By integrating multi-modal sensor data, this system enhances early-stage fault detection beyond traditional OBD-II diagnostics, offering greater accuracy in assessing physical wear conditions and optimizing vehicle maintenance.

The invention relates to a mechanical equipment fault data identification method based on artificial intelligence, and belongs to the technical field of data processing and artificial intelligence. The problems of insensitive signal processing, insufficient feature extraction, significant noise interference, low model training efficiency and the like in fault diagnosis in the prior art are solved. According to the method, a self-adaptive normalization method based on energy density is provided, the influence of non-stationary signals is effectively inhibited, and key features are reserved; through mixed energy entropy feature extraction and inter-band energy jump penalty terms, the sensitivity to a complex fault mode is significantly enhanced; constructing a fault feature enhancement strategy based on a Gaussian potential well, and reinforcing the response of a fault feature accumulation area; a weighted loss function and a gradient directional correction mechanism are adopted, so that the robustness and accuracy of the model are improved; and in combination with an entropy weighted learning rate and a covariance scaling strategy, adaptive training is realized, and the convergence speed and adaptability are improved. According to the method, the precision and efficiency of mechanical fault diagnosis are improved, and support is provided for industrial intelligent development.

The invention relates to a simulation prediction method for the plugging life of a contact element of an electric connector, and the method comprises the steps: building a three-dimensional simulation model of a plug end and a socket end of the electric connector, and defining the material attributes and contact relationships of all parts in the three-dimensional simulation model; performing finite element grid discretization on the three-dimensional simulation model; applying a fixed constraint to the socket end, applying a displacement load for simulating the plugging process to the plug end, and performing statics solution to obtain plugging force in the plugging process of the plug end and the socket end and contact pressure data of the plug end contact piece and the socket end contact piece; and mapping a statics solution result into a fatigue analysis module, and simulating, calculating and outputting a life distribution result of the plug end contact element and the socket end contact element under repeated plugging and unplugging loads. The method has the effect of analyzing the stress-strain state of the internal contact element of the electric connector and the fatigue damage of the internal contact element under repeated plugging and unplugging so as to help monitoring personnel to discover the potential mechanical failure problem in time.

The invention discloses a wind turbine generator monitoring and early warning system based on the Internet of Things sensing technology, and relates to the technical field of the Internet of Things, and the system comprises a data collection module which collects the operation state information of key parts of a wind turbine generator in real time, and transmits the operation state data to an edge calculation unit; the prediction module is used for identifying an abnormal mode, analyzing time domain and frequency domain characteristics of the vibration signal in combination with fast Fourier transform, detecting a mechanical fault, and obtaining a residual life prediction value according to a residual life prediction model; the preliminary early warning module is used for determining an equipment health state score based on fuzzy reasoning, and performing preliminary early warning locally according to the equipment health state score; and the grading early warning module is used for carrying out grading alarm on the wind turbine generator equipment according to the mechanical fault condition in combination with the equipment health state score, and providing maintenance decision suggestions. By integrating the Internet of Things technology, edge computing and artificial intelligence, intelligent monitoring and early warning of the wind turbine generator are achieved, and the reliability and safety of equipment operation are improved.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a rotating machine fault diagnosis method and system based on a time sequence large model, and the method comprises the following steps: extracting a time sequence value of a vibration signal, measuring the frequency and amplitude fluctuation of a time period, and recognizing an extreme point based on the operation data of a rotating machine; and calculating the fluctuation amplitude difference between the extreme point and the adjacent segment, measuring the time interval between the amplitude peak values, and generating the frequency change segment amplitude. According to the method, the time sequence value of the vibration signal is extracted, frequency and amplitude fluctuation extreme point recognition and adjacent segment amplitude difference calculation are combined, the fine-grained analysis capability of the dynamic characteristics is improved, key frequency point time distribution and amplification value change are detected, and amplitude fluctuation time interval and frequency amplification correlation analysis is combined; deep analysis of frequency and amplitude interaction characteristics, dynamic change characteristic positioning of an interaction ratio and rate quantification are realized, and diagnosis accuracy and evaluation depth are comprehensively improved.

The invention relates to a GIS disconnecting switch mechanical fault identification method and device, and the method comprises the steps: obtaining bus structure parameters of a source side and a load side of a GIS disconnecting switch, and calculating bus electrical parameters according to the bus structure parameters; constructing a travel curve discrete time sequence according to the plurality of motion states of the moving contact of the GIS disconnecting switch; constructing a whole-process VFTO discrete time sequence based on the travel curve discrete time sequence, and tagging time sequence data in the whole-process VFTO discrete time sequence; extracting a plurality of transient characteristics from the whole-process VFTO discrete time sequence, and constructing a labeled sample set with a supervision model according to each transient characteristic and a corresponding label; and through the trained supervised model and the real-time whole-process VFTO discrete time sequence, identifying the mechanical fault of the target GIS isolation switch. According to the method, the supervised model is trained through the discrete time sequence based on the mechanical state of the GIS disconnecting switch, so that the accuracy of fault detection is improved.

The invention discloses a TCN-SVM rolling bearing fault diagnosis method fusing an SE attention mechanism, and belongs to the technical field of mechanical fault intelligent diagnosis. Aiming at the problems of feature redundancy, noise sensitivity, insufficient Softmax classifier generalization and the like existing in a traditional time sequence convolutional network (TCN), the invention provides a solution for collaborative optimization of a deep network and a support vector machine. The method comprises the following steps: acquiring a vibration signal of the rolling bearing; constructing a multi-fault sample set, and processing an original signal; constructing an SE-TCN feature extraction network, capturing multi-scale time sequence features by adopting expansion causal convolution, and embedding an SE module into a residual module to realize channel adaptive weighting; and a support vector machine (SVM) classifier decision function is constructed, and fault classification is completed through the RBF kernel SVM. Experiments show that the method has high fault recognition accuracy and robustness, and the problem of confusion of composite fault features of the rolling bearing is effectively solved.

The invention provides a vibration-temperature correlation fault early warning method based on multi-sensor fusion, and relates to the technical field of rotating machine fault diagnosis, and the method achieves the efficient diagnosis of the rotating machine fault through the three-field coupling analysis of vibration, temperature and torque. Multi-dimensional health indexes are introduced, sensor data of different dimensions are normalized into a monitorable numerical value, and the health state of equipment is reflected in real time through a dynamically optimized weight coefficient. A core depth time sequence neural network model is combined with an attention mechanism and a Transform coding layer, the problem of long-term dependence is effectively solved, and the fault recognition precision is improved. According to the method, future innovation directions such as cross-device collaborative diagnosis, quantum computing acceleration and digital twinborn visualization are also exhibited, and the method has important industrial application value.

The invention relates to the technical field of fault diagnosis, in particular to a rotating machine fault diagnosis method, device and equipment under variable working conditions, and the method comprises the steps: collecting the operation parameters of a target rotating machine under a plurality of preset working conditions, and carrying out the feature normalization processing of the operation parameters, thereby obtaining a sample data set; the sample data set considers a plurality of preset working conditions and minimizes signal characteristic changes caused by working condition changes; a deep multi-scale conditional adversarial network model is constructed, a test data set is input into the deep multi-scale conditional adversarial network model trained by a training data set to output a fault diagnosis result, and domain self-adaption of an output space is achieved on different feature levels by using time sequence features. The accuracy of mechanical fault diagnosis in an actual production scene is improved; meanwhile, as the constructed multi-scale conditional adversarial network model belongs to an unsupervised transfer learning category, the dependency on training label samples is relatively weak, and the time and resources invested into label marking are greatly reduced.

The invention discloses a wind power gear box online fault diagnosis method based on multi-source data fusion, and particularly relates to the field of mechanical fault detection, and the method comprises the steps: S1, collecting high-frequency dynamic, medium-frequency working condition and low-frequency thermal state data, and outputting a standardized data set through time alignment, quality verification and physical constraint verification; s2, performing multi-scale decomposition to retain a fault sensitive frequency band, inverting physical parameters such as gear contact stress and the like, constructing a physical cause and effect graph, and determining fault sensitive characteristics and threshold values; s3, constructing a multi-modal feature tensor, and obtaining a low-dimensional health representation vector through CP decomposition fusion, graph neural network reasoning and variational auto-encoder dimension reduction; s4, calculating a weight by using an entropy weight method, calculating a dynamic health degree in combination with a health benchmark, predicting a trend by using LSTM, and establishing a five-level health system; s5, judging a fault mode through double-layer identification, analyzing a root cause and formulating a hierarchical operation and maintenance suggestion; the method is based on multi-source fusion and data mechanism dual drive, and precise diagnosis and operation and maintenance guidance are achieved.

The invention discloses a mechanical fault diagnosis method and system based on deep fusion of multi-modal monitoring data. The method comprises the following steps: randomly selecting two kinds of modal target monitoring data from multi-modal target monitoring data; a corresponding target mechanical fault diagnosis model is determined according to the two kinds of modal target monitoring data, the target mechanical fault diagnosis model is obtained by training a sample monitoring data set with the same modal as the two kinds of modal target monitoring data, and the sample monitoring data set is acquired multi-modal monitoring data in the operation process of multiple kinds of mechanical equipment; the target mechanical fault diagnosis model comprises a symmetric fusion module and a fault classifier, the symmetric fusion module is used for carrying out bidirectional symmetric interaction on two kinds of modal target monitoring data, and the fault classifier is used for classifying mechanical faults; and inputting the two kinds of modal target monitoring data into the target mechanical fault diagnosis model to obtain a mechanical fault diagnosis result. According to the invention, the accuracy of mechanical fault diagnosis can be improved.

The invention relates to the technical field of power equipment fault diagnosis, and particularly discloses a pole-mounted circuit breaker mechanical fault diagnosis method and system, and the method comprises the steps: firstly, synchronously collecting a dynamic force-displacement signal, a high-frequency acoustic emission signal and a broadband vibration signal in the switching-on and switching-off process of a circuit breaker operating mechanism; then, the acoustic emission and vibration signals are decoupled into an impact source component and a friction source component which are statistically independent through a blind source separation algorithm; performing envelope spectrum analysis on the impact source component to extract impact characteristics, and performing energy calculation on the friction source component to obtain friction noise energy characteristics; finally, on the basis of dynamic force-displacement curve fitting analysis, in combination with impact characteristics and friction noise energy characteristics, distinguishing and delimiting of mechanical wear and lubrication degradation faults are achieved. According to the invention, the problem that the existing diagnosis technology cannot effectively identify and distinguish the coupling fault of mechanical wear and lubrication deterioration is solved, and early warning and accurate diagnosis of the mechanical fault of the pole-mounted circuit breaker are realized.

The invention relates to the technical field of new energy vehicles, and discloses a new energy vehicle air conditioner compressor fault diagnosis system based on deep learning. The system comprises a data acquisition module, a model construction module, a model activation module and a result output module. The data acquisition module collects real-time operation data of the compressor to form an initial fault feature set containing a vibration spectrum feature sequence and a current fluctuation trend graph. The model construction module constructs a first diagnosis model and a second diagnosis model, the first diagnosis model reflects correlation mapping of vibration characteristics and mechanical faults, and the second diagnosis model comprises nonlinear classification rules of current characteristics and electrical faults. And the model activation module starts a corresponding target model and generates a diagnosis result according to the real-time vibration deviation and the current fluctuation abnormal data. And the result output module transmits the result to a vehicle control system to trigger a fault early warning signal or a maintenance suggestion instruction. The system can comprehensively cover mechanical and electrical fault diagnosis through dual-model collaborative operation, and is adaptive to a dynamic operation scene of the compressor.

Autonomous vehicles (AVs) may have processors to perform compute operations. These processors may be susceptible to overheating, due to one or more mechanical failures that may occur in the assembly that encloses the processors. To proactively address potential overheating of these processors, temperature of the processors can be monitored over time, e.g., at launch and during AV operation. A temperature profile line can be fitted to envelop the temperature data samples. The temperature profile line can indicate an extent of overheating, the rate of regression of the assembly's ability to cool the processors and predict when the processor may reach a critical temperature in the future. Based on one or more parameters that define the temperature profile line, it is possible to determine a regression profile of the processor. The regression profile can dictate whether to take an action to compensate for the regression.

The invention discloses an intelligent mechanical fault rapid diagnosis method. The method comprises the steps of multi-source data acquisition, data optimization processing, mechanical abnormity rapid detection, mechanical fault rapid identification, model performance optimization and mechanical fault intelligent diagnosis. The invention relates to the technical field of mechanical fault data processing, in particular to an intelligent mechanical fault rapid diagnosis method, which innovatively proposes a mechanical fault rapid diagnosis technology for abnormal trigger key sequence extraction, and improves the sensitivity and discrimination precision of mechanical fault features. A random convolution disturbance mechanism is introduced, and a multi-equipment adaptability enhanced loss function is designed, so that rapid, accurate and intelligent diagnosis of mechanical faults under complex working conditions and multi-equipment conditions is realized; an optimization algorithm is improved by adopting a disturbance guide extension strategy, a jump drive fine tuning mechanism and a multi-channel fine tuning candidate strategy, a model performance optimal parameter combination is obtained, and the stability and recognition precision of a mechanical fault recognition model are enhanced.

The invention belongs to the technical field of wind turbine generators, and particularly relates to a wind turbine generator fault early warning and abnormal parameter inspection method comprising the following steps: collecting historical operation data of a wind turbine generator, analyzing the data, and establishing a data model; the historical data comprises operation data of a generator, a gear box, a main bearing, a variable pitch system and an electrical system; vibration spectrums of a gearbox and a generator rotating part are monitored through an acceleration sensor to identify whether bearing pitting corrosion, gear tooth breakage mechanical faults and frequency are abnormal or not; an on-line monitoring device is installed in an oil way of the wind turbine generator to monitor the oil state in real time. The temperature of the gearbox and the bearing is detected through the thermal infrared imager, and whether the temperature is abnormal or not is monitored; abnormal sound signals of blade cracks or bearing early damage are captured through an ultrasonic sensor, and the blade is judged.

The invention discloses a motor fault feature extraction and fault diagnosis method. The method comprises the following steps: step 1, collecting an MCAVFCNN training data set, wherein motor fault types comprise two major classes, namely mechanical faults and electrical faults, namely 9 subclasses; 2, an MCAVFCNN model is built, a dynamic random erasure layer, a self-adaptive weighted sound and vibration fusion layer and a self-adaptive POOLING layer are provided, and the data set collected in the step 1 is used for model training till convergence; and step 3, the trained model is used online, and the trained MCAVFCNN model is applied online. According to the method, state monitoring and fault detection can be accurately carried out on the running motor in real time, and unnecessary loss is reduced while safe running of the motor is guaranteed.

The invention discloses a transport robot motor torque evaluation method, and relates to the technical field of torque evaluation, and the method comprises the steps: collecting a motor current signal of a transport robot, an actual torque signal of an output shaft, and a driving wheel vibration signal; constructing a time-frequency matrix of the actual torque signal and the driving wheel vibration signal, and extracting a fault sensitive factor through singular value decomposition; order analysis is carried out on the motor current signal to obtain net abnormal torque; and when the net abnormal torque is larger than a first preset threshold value and the fault sensitive factor is larger than a second preset threshold value, it is judged that a mechanical fault occurs in the motor of the transportation robot. According to the invention, the accuracy of motor torque evaluation of the transportation robot is improved.

The invention discloses a shaftless disc type turbine self-powered flow monitoring device. The device comprises a static assembly, a rotating assembly, a bearing and a data collection module which are coaxially arranged. Magnetic induction coils are distributed in the shell in the circumferential direction; the shell end cover is provided with a guide vane; the turbine comprises shaftless fluid power blades and permanent magnets which are uniformly distributed in the circumferential direction, the magnet yoke is rigidly connected with the impeller assembly, and the permanent magnets are arranged in a Halbach array; the dynamic sealing interface adopts a three-stage labyrinth sealing structure; the blade has an NACA airfoil section; the magnetic induction coil is of a three-layer close winding structure. Self-power-generation is achieved through the electromagnetic induction principle, an external power source is not needed, and cost is reduced; the shaftless design reduces mechanical faults, improves the reliability and prolongs the service life; the data processing module can realize high-precision measurement; a three-stage labyrinth sealing structure prevents fluid leakage; the optimized blade parameters adapt to fluid flow; the design of the permanent magnet assembly and the magnetic induction coil enhances the magnetic field and improves the power generation efficiency and the anti-interference capability.

The invention discloses a rotating machine fault diagnosis method and device and a storage medium in the technical field of mechanical fault diagnosis. The method comprises the following steps: acquiring a discretized demodulation signal; solving the discretized demodulation signal through an augmented Lagrangian multiplier method and an alternating direction multiplier method, and obtaining an iteratively optimized demodulation signal; calculating an instantaneous frequency increment based on the demodulated signal after iterative optimization, applying smoothness constraint and low-rank constraint to the instantaneous frequency increment, and solving through an iterative solution algorithm to obtain an estimated instantaneous frequency; and based on a pre-acquired theoretical fault characteristic frequency and the estimated instantaneous frequency, determining the fault type of the rotating machine according to a matching result. According to the method, the technical problems that the instantaneous frequency presents a non-stationary feature and is easily influenced by background noise, so that the accurate instantaneous frequency is difficult to extract, and the potential fault of the rotating machine cannot be accurately diagnosed can be solved.

The invention relates to the technical field of wind turbine generator system fault diagnosis, and discloses a wind turbine generator system gear box output end bearing comprehensive diagnosis method based on multi-model collaboration, and the method comprises the steps: predicting the bearing temperature trend through a dynamic power-bearing temperature model, and carrying out the temperature early warning through a power-temperature difference model; confidence calculation is carried out in cooperation with multi-dimensional data such as mechanical vibration characteristics (time domain / frequency domain impact energy reflects bearing mechanical damage), bearing temperature trend and temperature difference analysis (eliminating environment and load interference to position real fault temperature rise), and mechanical faults such as roller peeling and inner ring cracks and operation faults such as heat dissipation failure can be distinguished; and a traditional'single-point alarm 'mode is broken through, a'root analysis-unit power reduction / trigger shutdown' whole-process closed loop is constructed, and the safety, reliability and operation and maintenance efficiency of unit operation are remarkably improved.

The invention provides a bearing fault diagnosis method and system based on a twin neural network under a small sample, and relates to the technical field of mechanical fault diagnosis and artificial intelligence. The method comprises the following steps: collecting X-axis, Y-axis and Z-axis vibration signals of a bearing, slicing, and generating a time-frequency grey-scale map through short-time Fourier transform to enhance feature expression; the method comprises the following steps: constructing same-class and different-class sample pair training sets, training by adopting a weight-shared twin neural network model which comprises two same sub-networks, and optimizing model parameters by calculating the Euclidean distance of sample pair features and combining a cross entropy loss function; in the test stage, unknown samples are matched based on One-shot and N-shot strategies, and the fault category is judged with the maximum similarity probability. According to the method, the accuracy of 95% or above is achieved under 70 training samples, the noise immunity and generalization ability under the small sample condition are remarkably improved, and the method is suitable for low-cost intelligent diagnosis of industrial equipment.

The invention discloses a rotating machine fault diagnosis method based on an expansion residual network, and relates to the technical field of mechanical faults, and the method comprises the steps: collecting a continuous time sequence vibration signal of a rotating machine through an acceleration sensor, obtaining a high-order harmonic energy sequence set after fast Fourier transform, and determining an abnormal trend segment; then, obtaining and updating a structural anomaly candidate set based on the anomaly trend segment and the operating environment state of the rotating machine, generating a first drift vector set according to the updated structural anomaly candidate set, and identifying a structural offset section according to the first drift vector set and historical operating data; and backtracking the structural offset section to an original time domain signal to establish a second drift vector set, and finally, inputting the first drift vector set and the second drift vector set into the expansion residual network to analyze whether the rotating machinery is evolved into a fault state.

The embodiment of the invention discloses a transformer mechanical fault positioning method based on a vibration vector difference, and the method comprises the steps: setting a plurality of measurement points on the surface of a transformer oil tank, and determining the coordinate information of the plurality of measurement points; vibration signals at the measurement points are obtained, the vibration signals of any two measurement points are compared, and the vibration vector difference between the measurement points is obtained; determining a global space autocorrelation coefficient between the vibration signals of the two adjacent measurement points according to the vibration vector difference; and determining a mechanical fault point of the transformer according to the global space autocorrelation coefficient between the vibration signals. According to the method, the multiple measurement points are arranged on the surface of the transformer oil tank, the vibration signals of all the points are obtained, the vibration vector difference between any two points is calculated, then the vibration vector difference is analyzed through the global space autocorrelation coefficient, the transformer mechanical fault point is positioned, only the vibration signals need to be analyzed, the calculation efficiency is high, and the method is easy to implement. The method is suitable for positioning various mechanical faults.

The invention relates to the technical field of mechanical fault detection, in particular to a scraper conveyor chain breakage fault detection method and device, and the method comprises the following steps: collecting a picture of a scraper of a scraper conveyor, and building a data set according to the collected picture of the scraper; designing a rotating target detection network model according to the characteristics of the established data set; the invention provides a scraper conveyor chain breakage fault detection method and device, solves the problems that in the prior art, a sensor is prone to abrasion and poor in severe environment resistance, and a visual detection method is poor in real-time performance and not high in applicability, improves the intelligent level of a scraper conveyor, and improves the working efficiency of the scraper conveyor. By means of the detection method and device, faults can be found in advance so as to reduce economic losses, the equipment maintenance period can be shortened, and the method and device have important engineering application value for guaranteeing continuous and safe production of coal, reducing the labor intensity of workers and reducing influences and losses caused by chain breakage faults of the scraper conveyor to the maximum extent.

The invention relates to the technical field of electric power operation and maintenance, in particular to a transformer fault diagnosis method, device and equipment and a storage medium, and the method comprises the steps: collecting a transformer acoustic monitoring signal based on an acoustic microphone array, carrying out blind source separation and decoupling, and constructing an acoustic signal feature set; transformer operation condition parameters are extracted, mechanical state degradation evaluation is carried out according to the acoustic signal feature set, and a mechanical state health degree index is generated; and calculating an acoustic signal receiving timestamp of the acoustic microphone array, performing sound source distribution mapping, and constructing a sound source spatial distribution diagram. According to the method, blind source separation decoupling, mechanical state degradation evaluation, sound source distribution mapping and fault situation diagnosis are performed by acquiring the acoustic signals and the operation condition parameters, so that tiny mechanical faults in the transformer can be accurately identified, intelligent maintenance decision is realized, and the method has the advantages that the tiny mechanical faults in the transformer can be detected earlier; the real-time performance and reliability of fault diagnosis are improved, and the accident risk caused by diagnosis lag is reduced.

The invention relates to an intelligent mechanical fault diagnosis method based on a progressive transfer learning network, and belongs to the technical field of mechanical part monitoring and fault diagnosis. The method comprises the following steps: respectively acquiring original signal data in a laboratory environment and a real industrial environment, and preprocessing the data; establishing a fault identification model based on a progressive transfer learning network architecture, and obtaining an optimal network parameter by combining a loss function in a propagation process and by taking a classification error of minimizing a source domain sample and a target domain sample and an inter-domain difference of minimizing sample distribution as targets; and collecting real-time signal data in a real industrial environment, inputting the real-time signal data into the optimized fault identification model based on the progressive transfer learning network architecture, and outputting a fault type by the fault identification model. According to the method, the sample migration success rate and the fault identification accuracy are improved by gradually constraining feature mapping in the sample migration learning process.

According to the scheme, the hardware fitting groove pressing die comprises a fixing plate, a control block is connected to the fixing plate, a bottom die is connected to the control block, a top plate is arranged above the fixing plate, a top die is connected to the top plate, a pair of supporting strips is installed on the fixing plate, and racks are installed on the supporting strips. A gear meshed with the rack is mounted on the rotating shaft, a pressure valve assembly is connected to the lower mounting block, and a gas cylinder is connected to one end of the connecting pipe; the top plate moves downwards to drive the control block to rotate, so that the bottom die faces upwards horizontally, the situation that the top die hurts hands due to mechanical faults is effectively avoided, a pressing rod is pressed, a sheet metal part is conveniently ejected out, when the pressing rod is pressed, compressed air is sprayed out from the tail end of the top cylinder, and therefore when the sheet metal part is ejected out, the sheet metal part can be conveniently ejected out. The compressed air can play a role in assisting in pushing the sheet metal part to be separated, and meanwhile, formed air flow can blow the surface of the bottom die, so that dust on the surface of the bottom die is removed, and dust particles are prevented from affecting the quality of the sheet metal part.

A steer-by-wire system is provided. The steer-by-wire system includes: a first powerpack having a first motor a first controller configured to determine a first mechanical fault of the first powerpack; a second powerpack having a second motor, and a second controller configured to determine a second mechanical fault of the second powerpack; and a steering rack operably coupled to the first motor of the first powerpack and the second motor of the second powerpack, the steering rack being configured to be linearly movable in response to rotation of at least one of the first motor and the second motor.

The invention relates to the field of intelligent diagnosis of mechanical faults, in particular to an intelligent diagnosis method and system for few-sample mechanical faults based on pseudo tags and a medium. The diagnosis method comprises the following steps: S1, data acquisition and division; real data without labels and real data with labels are formed; s2, constructing and training a semi-supervised adversarial learning model; the semi-supervised adversarial learning model comprises a generator and a discriminator; the generator is used for generating false data with a false label from the random noise z and a given false label y; the first output of the discriminator is used for discriminating whether the input sample data is real data or false data; and the second path of output is used for judging the label type of the input sample data. And S3, fault diagnosis testing. According to the method, the semi-supervised adversarial learning model is constructed based on the generator and the discriminator, and the false data with the false label is generated by using the generator to assist training, so that the dependence on real data can be reduced, and the fault diagnosis performance cannot be reduced due to the reduction of the real data.