166 results about "Fault occurrence" patented technology

The invention provides a circuit board electrical performance test and diagnosis method, which comprises the following steps of: constructing a structured process-electrical data set by collecting process parameters such as etching time, pressing temperature, drilling precision, copper foil thickness and the like and electrical test indexes such as impedance deviation, leakage current, signal attenuation and the like; screening key process variables by adopting mutual information analysis and a maximum correlation-minimum redundancy criterion; through modeling of a three-layer causal diagram and a structural equation, quantitative influence evaluation of process parameters on electrical performance and fault types is realized; when a fault is detected, the system can automatically perform reverse reasoning, identify a main manufacturing deviation item and generate attribution diagnosis and process optimization suggestions, so that the accuracy of fault diagnosis and the pertinence of process adjustment are improved, and the fault occurrence rate is reduced and the product quality is improved.

The invention discloses an automatic fault repairing method for an operating system based on artificial intelligence, and relates to the technical field of automatic fault repairing, and the method comprises the steps: carrying out the feature analysis of system operation data through a pre-trained fault feature extraction model, generating a fault feature vector, inputting the fault feature vector into a fault classifier, and obtaining a fault feature vector; a current fault type is identified through a multi-classification algorithm, fault cause primary tracing is performed according to the fault type to obtain a fault generation factor, secondary tracing is performed on the fault generation factor to obtain a fault influence factor, positioning is performed based on the fault generation factor, and a corresponding repair strategy is matched from a knowledge base. The method comprises the following steps: acquiring historical system operation data with relevance on the basis of a fault influence factor, acquiring updated real-time system operation data after executing a repair operation to calculate a system optimization coefficient, judging a forward trend of a repair strategy according to a preset optimization threshold value, and updating the forward trend into a knowledge base to realize rapid and efficient automatic repair.

The invention discloses a power transmission cable ground fault adaptive diagnosis method and system, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing and extraction of the multi-modal data, and forming a statistical feature vector; the method comprises the following steps: adaptively decomposing sheath current data by adopting OVMD, calculating segmented TKEO energy operators, and realizing fault early warning through a ratio criterion of maximum values of TKEO derivatives of adjacent segments; inputting the real-time acquisition time sequence working condition class feature vector into a trained LSTM to obtain a fault probability, dynamically updating a fault probability threshold based on LSTM output, and judging that a fault occurs when the fault probability exceeds a continuous set period of the fault probability threshold; and inputting the statistical class feature vector into ANN, inputting the sheath current data into 1D-CNN for fault classification, and performing adaptive weighted voting integrated decision on the output of the two types of networks to identify the fault type. According to the invention, the problems of low fault detection sensitivity, poor positioning precision, lack of early warning capability and the like in the prior art are solved.

The invention relates to the field of shield tunneling machines, and discloses a fault diagnosis method for a quasi-rectangular shield tunneling machine based on artificial intelligence, and the method comprises the steps: collecting the operation state data of the shield tunneling machine through a sensor array, so as to obtain the operation data of the shield tunneling machine; performing component operation state difference analysis on the operation data of the shield tunneling machine to obtain shield component state difference data; performing abnormal feature distribution density analysis according to the shield part state difference data to obtain abnormal feature distribution density data; according to the method, sensor data collection, state difference analysis and abnormal feature analysis are combined, so that the operation state and the abrasion condition of each part of the shield tunneling machine can be accurately monitored, and the reliability of the shield tunneling machine is improved. A fault prediction model is established through a deep reinforcement learning method, potential faults can be recognized in advance, sudden faults are reduced, and the reliability and safety of equipment are improved.

The invention discloses a power distribution network small current grounding fault diagnosis positioning method and system based on edge calculation, and belongs to the field of power system automation, and the method comprises the steps: extracting and recognizing transient features, if a suspected grounding fault is detected, carrying out the multi-source feature fusion analysis through a regional main node, and constructing a transient response time sequence map; a weighted fuzzy clustering algorithm is combined with a multi-channel criterion to determine a fault channel, and a specific fault branch is further positioned through a topology tracking algorithm; introducing a multi-scale wavelet packet energy analysis method into the regional main node, performing energy inversion calculation on the transient current signal under multiple frequency bands, and identifying an energy concentration region near the grounding point through an energy distribution gradient; and constructing a robustness data redundancy model based on the historical data of the edge nodes and the topological relation, and identifying and eliminating abnormal feature data containing noise or distortion. According to the method, transient characteristics are rapidly extracted and preliminary judgment is made after a fault occurs, so that the real-time performance of fault response is greatly improved.

The invention relates to a computer-aided maintenance method (10) for correcting a fault condition (12) on a processing machine (14, 14a-c), comprising the method steps: a) Detecting (16) the fault condition (12) by identifying an error message (18) on the processing machine (14, 14a-c) and determining the time of the fault; b) Creating (22) a fault description (24) by determining fault data (26) at the time of the fault; c) Identifying (38) similar and / or identical fault conditions (42) by comparing (40) the fault description (24) with stored fault descriptions (42) by comparing the fault data (26) determined for the fault descriptions (24, 42); e) Issuing (50) a recommendation for action (52), including at least one suggestion for remedying the fault condition (12).

The invention discloses a fault diagnosis and self-healing control method, system, equipment and medium for a flexible interconnection device of a power distribution network, and belongs to the technical field of fault diagnosis and control of the power distribution network, and the method comprises the steps: building a system state vector, collecting operation parameters of the power distribution network, and carrying out the weighted fusion; performing quality inspection on the original data based on the acquired operation parameters, and dynamically adjusting model parameters through digital twin synchronization and model parameter updating; deep learning is carried out to carry out multi-level fault feature extraction, and multi-scale feature fusion is carried out; a fault mode identification module is started, and fault probability is calculated for fault classification; evaluating the severity of the fault according to a fault diagnosis result, selecting an optimal control strategy, and carrying out self-healing control; and fault early warning is started to estimate a future fault occurrence probability, and early warning information of different levels is issued to perform preventive maintenance. According to the invention, full-process automation and intelligentization from data perception and intelligent diagnosis to active control and prospective maintenance are realized.

The invention relates to an energy storage power supply fault detection method and system and computer equipment. Extracting fault correlation characteristics from the real-time operation data of the energy storage power supply; performing digital twinborn simulation processing based on the real-time operation data, establishing a digital twinborn model, and synchronously mapping the real-time operation data to the digital twinborn model; performing prediction based on the digital twin model to obtain a first fault prediction result; performing fault trend prediction based on a digital twinborn model; calling the current fault diagnosis model to predict and obtain a second fault prediction result based on the fault trend prediction data and the fault correlation characteristics; obtaining a target fault prediction result according to the first fault prediction result and the second fault prediction result; and simulating operation data of the energy storage power supply based on the target fault prediction result through the digital twin model when the multiple types of associated faults occur, obtaining a sample data set corresponding to the multiple types of associated faults, and updating and training the current fault diagnosis model based on the sample data set. The method can improve the fault detection accuracy.

The invention relates to the technical field of power equipment, in particular to a high-voltage cable fault diagnosis method and system. The method comprises the following steps: collecting discharge data in a discharge process of a to-be-detected high-voltage cable, and analyzing fault trend data of the high-voltage cable in a target interval to obtain a trend prediction waveform; the method comprises the following steps: constructing a reference fault-sample waveform signal mapping library through historical discharge data of a to-be-detected high-voltage cable, selecting at least one reference fault from the reference fault-sample waveform signal mapping library to form a fault combination, and superposing sample waveform signals corresponding to the reference faults in the fault combination to obtain a sample waveform signal; obtaining a sample comprehensive signal corresponding to the fault combination; and predicting a potential fault and a possible occurrence time point of the high-voltage cable based on the sample comprehensive signal and the corresponding fault combination. According to the invention, before the fault occurs, the possible fault can be checked in time, and targeted advanced processing is carried out.

The invention discloses an electrical equipment fault prediction method and system based on big data, and relates to the technical field of electrical equipment data management.The electrical equipment fault prediction system comprises an extraction module, a construction module and a prediction module; a hierarchical fault sensitive feature library is built, a prediction model is built in combination with the hierarchical fault sensitive feature library and the correlation degree, the input quantity of the prediction model is feature data, the output quantity of the prediction model is the fault occurrence probability and the fault type of the electrical equipment, and the hierarchical fault sensitive feature library is built by extracting the feature data and combining a health degree quantification result; a long-short-term memory network architecture with three or more hidden layers is adopted, feature priorities are dynamically adjusted according to the load rate and the health degree of the electrical equipment, the sample proportion is controlled, parameters are finely adjusted according to the fault frequency, the fault probability and the fault type are accurately output, reliable operation of the electrical equipment is supported, the cost is reduced, and stable energy supply is achieved.

The invention discloses a GIS latent defect diagnosis method and system based on multi-source signal fusion, and belongs to the field of power equipment fault diagnos.The method comprises the steps that a UWB sensor is adopted to monitor PD signals in a GIS, real-time filtering and spectral analysis are conducted on the signals in combination with an FPGA preprocessing chip, and the sampling sensitivity is dynamically adjusted according to the PD signal strength; gIS internal ultrasonic source three-dimensional imaging is realized by adopting a three-dimensional ultrasonic array; pD and ultrasonic signals are processed based on wavelet transform, PD and ultrasonic data are processed based on CNN, and a signal time sequence is processed by LSTM; constructing a GIS fault knowledge graph, setting expert rules, and calculating a GIS fault occurrence probability by adopting Bayesian reasoning; and predicting a GIS fault evolution path according to the Markov chain model, and performing dynamic adjustment in combination with reinforcement learning. According to the method, the fault state transition model of the GIS equipment is established based on the Markov chain, the evolution probability between different fault states is quantified, long-term steady-state fault risk assessment is provided, and the operation health condition of the GIS equipment can be prejudged.

The invention provides a power distribution network early fault section identification method and system based on multi-dimensional feature probability mapping, and belongs to the technical field of power system power distribution network fault detection. The method comprises the following steps: collecting zero-sequence voltage of a power distribution network bus and zero-sequence current signals of head ends of all feeder lines, and obtaining derivative signals of the zero-sequence voltage and the zero-sequence current signals; calculating the third harmonic energy of the zero-sequence current of each feeder line, and when the third harmonic energy exceeds a set threshold value, judging that the feeder line has an early fault; processing the zero-sequence current signal, and extracting an IMF1 component; calculating a correlation characteristic quantity and a dot product characteristic quantity for each section formed by the current difference values of the adjacent measuring devices based on the IMF1 component; and respectively carrying out standardized transformation on the correlation characteristic quantity and the dot product characteristic quantity, substituting the correlation characteristic quantity and the dot product characteristic quantity into a Laplacian cumulative distribution function, calculating a probability value of a fault in each section, and judging a fault section according to a set probability threshold value. The method can effectively and reliably identify the early fault section of the power distribution network.

The invention discloses a fault tracing method and system based on a double-knowledge graph, a medium and a product, and relates to the field of electric digital data processing, and the method comprises the steps: obtaining a multi-dimensional data source of a to-be-analyzed industrial product, and constructing the double-knowledge graph; receiving a tracing request, and determining a target component node based on the equipment identity identifier and the fault occurrence time window; calculating semantic similarity of entities in the fault description text and the fault knowledge graph, and determining a target fault phenomenon entity; searching a causal association path connecting the target component node and the target fault phenomenon entity, and extracting the entity on the path as a candidate fault root cause set; skipping to a standard knowledge graph through a public mapping center of the product structure tree, and determining compliance parameter thresholds corresponding to the candidate fault root causes; and acquiring actual operation state data, and generating a fault tracing report containing a comparison out-of-limit result. According to the invention, the method can improve the positioning accuracy of the fault root cause of the complex industrial product in the whole life cycle operation and maintenance.

The invention provides an information system fault early warning method and device, electronic equipment and a storage medium, and belongs to the technical field of network operation and maintaining.The method comprises the steps that feature extraction is conducted on target data in real time based on a convolutional neural network, a first feature is obtained, and the target data is obtained by conducting data preprocessing on original data; performing causal relationship reasoning on the target data in real time to obtain a second feature; fusing the first feature and the second feature to obtain a fused feature; the fusion features are input into a Transform encoder, and sequence features output by the Transform encoder are obtained; and determining a system state corresponding to the target data based on the sequence features. According to the method, the causal invariance principle is fused, the complex time sequence information of the data is subjected to causal reasoning analysis, feature expression is optimized, and the fault recognition accuracy is improved; the real-time fault prediction timeliness of the system is high, the system has perspectiveness, and the fault occurrence probability is reduced.

The invention discloses a phase deviation testing method based on a rotation test of a self-aligning rolling bearing, relates to the technical field of bearing detection, and solves the technical problems that potential risks in a normal state are lack of dynamic monitoring, a real-time operation state and a risk level are not combined, and the deviation between a prediction result and the reality is large. According to the method, by analyzing the stability of the periodic offset characteristic and the association rule of the periodic offset characteristic, the rotating speed and the load, an abnormal reason can be directly positioned, a basis is provided for targeted maintenance, blind shutdown and maintenance cost are reduced, and for a bearing in a normal state, the change rate of a key index is calculated in real time by establishing a phase-deviation-free reference database, so that the reliability of the bearing is improved. And comparing with a preset comparison value, so that a potential risk can be found before a fault occurs, precautions are taken in the bud, and a residual life calculation formula is dynamically corrected based on a risk early warning level in combination with a basic rated dynamic load, an actual equivalent dynamic load and a working condition correction coefficient, so that a prediction result is more fit with an actual operation state of equipment.

The invention discloses a three-level inverter open-circuit fault diagnosis method based on fault feature vector prediction, and the method comprises the steps: reconstructing a theoretical stator current through reference current based on a motor steady-state model, and introducing an amplitude per-unit algorithm to calculate a current residual error, thereby recognizing the fault, and locking a fault phase; collecting a system state at the moment of fault triggering, deducing a modulation switch sequence in a future detection period on line by using a reference voltage vector time sequence prediction model, and generating a dynamic reference value of theoretical action times of each switch state; performing real-time statistics on the actually-measured cutoff times of the fault phase in different switch states by using an edge triggering mechanism, and constructing an actually-measured feature vector; and determining a fault device by calculating the minimum distance between the actual measurement vector and each fault modal theoretical feature. The method can effectively eliminate the influence of the rotating speed and load change of the motor on the diagnosis threshold, solves the problem that the fault features of the inner tube and the clamping diode are overlapped and are difficult to distinguish, and improves the robustness and accuracy of fault diagnosis.

The invention discloses a neutral point ungrounded fault positioning device and method based on dynamic time window inversion, and belongs to the technical field of new energy power system fault detection. Aiming at the problems that most of the existing methods depend on single signal characteristics or fixed window length analysis and are difficult to adapt to time-varying characteristics and complexity of a fault transient process in a new energy current collection line, a sensor is used for sensing current generated between a transformer shell and a grounding point when a fault occurs, and dynamic time window selection and an integral inversion algorithm are combined, so that the time-varying characteristics of the fault transient process in the new energy current collection line are obtained. Processing and feature extraction are carried out on the voltage value, self-adaptive analysis of the fault transient waveform of the neutral ungrounded system is achieved, and fault features are accurately extracted. According to the method, the problems of fixed time window and insufficient feature extraction capability of a traditional method are solved, and the system fault positioning speed and precision are improved. In addition, the method can also realize real-time monitoring, fault positioning and data acquisition and storage of fault current, thereby improving the stability and operation and maintenance efficiency of the power system.

The application relates to the technical field of network disconnection fault positioning, and discloses an intelligent substation process layer network disconnection fault analysis device which comprises a dynamic monitoring module, a static analysis module and a prewarning display module; the dynamic monitoring module is used for detecting whether the process layer network port running state is abnormal at the device node interface end in real time; the static analysis module is used for receiving information at the receiving end and analyzing the process layer network fault causes and positioning fault point coordinates; and the prewarning display module is used for displaying the fault analysis results, predicting the probability of the next fault occurrence on the basis of the static analysis, and reminding and displaying the coordinate position of the next fault point occurrence. The process layer network interface running state is judged through the monitoring of the device node end, a "point-chain" network distribution graph model is constructed, historical fault data and online real-time data are compared and analyzed, the time for manually checking faults can be effectively shortened, and the fault processing efficiency can be directly and effectively improved.

The invention discloses a power safety monitoring and fault diagnosis method and system for an energy storage system, and relates to the technical field of safety control of electrochemical energy storage systems. According to the invention, all working modes of'shutdown ', 'standby', 'charging 'and'discharging' of the energy storage converter are covered, a monitoring blind area in the prior art is thoroughly eliminated, and the safety problems of standby micro-power risk, abnormal shutdown state and the like which are neglected in the past are effectively solved; according to the method, power numerical value abnormity can be found, deep logic faults such as reverse charging and discharging directions can be diagnosed, and the safety level of the system is improved essentially; according to the method, passive trust is changed into active verification, cross verification is carried out through multi-source data (PCS, ammeter and BMS), the accuracy is high, intervention can be carried out in the early stage of fault occurrence or the stage of potential risk accumulation, and the accuracy of fault recognition is remarkably improved.

The invention provides a firmware testing method, device and equipment for a memory and a medium, and the firmware testing method comprises the following steps: identifying a physical address of a global variable related to a function test in a memory space of the memory according to a preset address mapping table; performing function test on the memory, and detecting the running state of the memory; after the to-be-tested firmware of the memory enters an assertion processing flow due to triggering of the assertion code, sending a parameter reading command to the memory; and receiving a parameter value of a global variable fed back by the memory according to the parameter reading command, and determining fault information in the firmware to be tested according to the parameter value of the global variable. According to the firmware testing method and device for the memory, the equipment and the medium provided by the invention, the technical problem that the parameter value of the global variable is difficult to quickly obtain when a fault occurs is solved.

The application provides a method and device for analyzing faults of industrial power supply maintenance equipment. The method provided by the application comprises the following steps: determining historical faults of the industrial power supply maintenance equipment; screening root nodes of fault trees from the historical faults; determining associated components and external conditions of faults for each root node of the fault trees; taking each associated object in the associated components and the external conditions as a fault tree child node under the root node for each root node of the fault trees, and constructing a fault tree of the industrial power supply maintenance equipment, wherein a state of the associated object is taken as a state value of the fault tree child node; calculating an importance weight of each part according to a working principle of the industrial power supply maintenance equipment; calculating a fault probability of each root node of the fault tree based on the importance weight, and predicting a fault occurrence situation of the industrial power supply maintenance equipment. The method realizes comprehensive construction of the fault tree and accurate prediction of the fault occurrence.

The embodiment of the application discloses a device management method and system based on task priority evaluation. The method comprises the following steps: determining a first device and a scheduling scene according to a business label of a task; determining a task priority of the first device according to at least a business parameter and the scheduling scene; the task priority is used for at least resource scheduling and fault handling of the task; predicting a fault occurrence condition when the first device processes the task based on an artificial intelligence (AI) model to obtain fault prediction information; starting a fault handling plan according to the task priority when the fault prediction information indicates that a specified period meets a specified condition; and managing and handling a fault of the first device according to the fault handling plan when the fault occurs.

The invention discloses a power transmission line fault reason identification method based on fault recording and geographic meteorology, and the method comprises the steps: obtaining power transmission line fault data, including fault recording data, tower geographic position data and all meteorological data of a line crossing region; treating and analyzing the fault recording data; calculating a fault distance measurement result according to the fault recording data, determining a fault occurrence area in combination with the geographic position data of the tower, and obtaining meteorological data of the fault occurrence area in combination with all meteorological data of the line crossing area; carrying out fault reason calibration on the fault data to set a fault reason label, and carrying out fault reason label prediction on the fault data of which the fault reason is not calibrated; and an integrated learner model is established, model training and parameter tuning are completed, and the model takes meteorological features, waveform morphological features and electrical quantity features of fault data as input and takes fault cause labels as output. According to the invention, more intelligent power transmission line fault reason diagnosis can be realized.

The application discloses a kind of fault intelligent monitoring and fine diagnosis method, device and storage medium, the method includes: according to the uncertain generalized system of fault to be detected, the actuator fault augmented system is additional specific state of uncertain generalized system, constructs generalized system model;Based on neural network, the uncertain model caused by part of unmodeled dynamic and parameter disturbance is obtained with the boundary information of dynamic characteristics using boundary identification algorithm;According to generalized system model, the upper and lower bound dynamic system state and output are coupled using Min-Max function, and a dynamic-output multi-coupling Luenberger interval observer is constructed, and then an observer error variable system is constructed, to realize the fault diagnosis of machine in a large class;According to the fine diagnosis of large class fault.The method provided by the application can timely and effectively find faults, and finely judge fault types when faults occur, to realize intelligent monitoring and fault diagnosis of the system.

The present application relates to the technical field of wind power generator, and particularly relates to a wind power generator fault monitoring method and device based on operation data, comprising the following steps: S1, sensor arrangement and installation: arranging magnetostrictive sensors on rotating parts; S2, data acquisition: acquiring strain signals and deformation data in real time; S3, data preprocessing: preprocessing the strain signals and deformation data; S4, fault feature extraction: extracting fault feature parameters; S5, model construction and training: constructing a wind power generator fault pre-detection model to identify early fault signs and predict the probability of fault occurrence; S6, real-time monitoring and fault early warning: monitoring the strain signals and deformation data of the rotating parts in real time, and generating a fault early warning signal. The present application improves the real-time health state of the key components of the wind power generator, and effectively prevents the occurrence of potential faults.

The invention relates to a fault detection method of an energy storage system and the energy storage system. The energy storage system is applied to an energy storage side end and is composed of a plurality of equipment assemblies. The fault detection method of the energy storage system comprises the steps that basic operation data generated in the operation process of a plurality of equipment components are acquired, fault recognition is carried out on the basic operation data, fault event data are obtained, and the fault event data represent fault types of the equipment components; extracting target fault event data in a preset time window from a preset fault event time sequence database; according to the target fault event data, performing fault assessment on the plurality of equipment components in a preset time window to obtain fault assessment feature data, the fault assessment feature data representing fault occurrence conditions of the plurality of equipment components in the preset time window; and sending the target basic operation data and the fault evaluation feature data in the preset time window to the energy storage cloud. By adopting the method, the fault detection effect of the energy storage system can be improved.

This application relates to a power grid fault prediction method, apparatus, computer equipment, readable storage medium, and program product. The method includes: acquiring the location coordinates of historical fault occurrence points, fault types, equipment types of historical power equipment, and equipment maintenance data of historical power equipment; determining a spatial region based on the location coordinates of the fault occurrence points, and acquiring historical multi-source meteorological data for the spatial region; inputting the fault type, equipment type, equipment maintenance data, and historical multi-source meteorological data into a power grid fault prediction model to obtain a trained power grid fault prediction model; acquiring current multi-source meteorological data for the spatial region to be monitored, and inputting the current multi-source meteorological data into the trained power grid fault prediction model, outputting the probability of fault occurrence at any power grid point in the spatial region to be monitored, and the predicted fault type at the power grid point. The method provided in this application can improve fault prediction efficiency.

The invention relates to the technical field of fault analysis, in particular to an MES (Manufacturing Execution System) for automatically adjusting a server manufacturing process based on a current network fault, which comprises a fault data processing module, a fault trend prediction module, a risk component identification module, a propagation path tracking module and a parameter adjustment module. According to the method, continuous statistical process monitoring is carried out on multi-dimensional quality indexes such as the first-time power-on fault rate, the annual fault rate and the existing network batch problem of the server, the abnormal fluctuation trend of the quality indexes can be recognized in advance before an isolated fault event occurs, the system does not passively respond to the occurring fault any more, and the system reliability is improved. Through fusion analysis of environment and operation factors such as load and memory utilization rate during server operation and temperature and humidity of a data center where the server is located, complex combination conditions causing quality indexes to deviate from a normal range are deeply mined. And the specific probability and the time window of future fault occurrence are calculated in a quantization mode.

The invention discloses a medium and low voltage equipment predictive fault diagnosis system based on full life cycle management, and relates to the technical field of power equipment management. By arranging a fault diagnosis module, an installation fault diagnosis module and an operation fault diagnosis module, the whole process monitoring and prediction of the equipment from design to operation are covered. Historical fault data and design parameters are utilized, potential structural defects in the design stage are evaluated through a statistical regression algorithm, and the fault risk in the design stage is predicted. And based on historical wiring fault data, carrying out fault probability inference by adopting a Bayesian network, and predicting a fault risk in an installation stage. The current waveform of the medium and low voltage power transmission and distribution equipment is monitored, harmonic components are extracted by using a wavelet transform algorithm, harmonic fault prediction is performed in combination with historical data, and the fault occurrence probability is judged through the fault recognition unit. Fault prediction and diagnosis of the whole life cycle of equipment design, installation and operation are realized, and the safety and the operation reliability of the equipment are improved.

This invention relates to the field of engine testing technology. It discloses a dual-modal integrated engine performance testing system, comprising a hardware testing module, a data acquisition and preprocessing module, a central control and analysis module, a result display and storage module, a predictive analysis submodule, and a preprocessing execution submodule. The invention also discloses a dual-modal integrated engine performance testing method, comprising the following steps: Step 1: System initialization; Step 2: Engine start-up and modal identification; Step 3: Parameter acquisition and preprocessing; Step 4: Data processing, performance evaluation, and predictive analysis; Step 5: Preprocessing decision-making and execution; Step 6: Result display and storage; Step 7: Real-time control. Through the above settings, potential faults can be avoided in advance, reducing the probability of fault occurrence and improving the stability and safety of engine operation.