69 results about "Fault detection rate" patented technology

The invention relates to the technical field of industrial data, discloses a mining equipment state monitoring method and system based on the Internet of Things, and effectively solves the problem of insufficient model generalization ability caused by scarcity of fault samples of mining equipment. The virtual sample generation technology expands the available training data volume by 3-5 times, so that the early fault detection rate is improved to 85% or above. The self-adaptive feature selection mechanism reduces the consumption of computing resources by more than 30%, and maintains the integrity of key fault features at the same time. The multi-stage early warning system realizes accurate grading of fault severity, so that the maintenance resource distribution efficiency is improved by about 40%. The closed-loop optimization mechanism enables the model to continuously evolve in the operation process, and the annual false alarm rate is reduced by about 15%. The explainable diagnosis report provides a clear technical basis for field maintenance, and the average troubleshooting time is shortened by about 50%.

The invention discloses a multi-dimensional intelligent analysis and fault traceability system for ACU final test data, and relates to the technical field of data analysis. The multi-source heterogeneous data acquisition module is used for acquiring operation parameters, function test data and assembly data through a CAN bus, test equipment and a production system, and is provided with a high-precision timestamp; the data cleaning and preprocessing module is used for standardizing data, filtering and denoising, synchronizing time and complementing missing values; the multi-dimensional feature extraction module is used for extracting features from a time domain, a frequency domain, a time sequence and a space; the intelligent fault diagnosis module is used for classifying faults by using a random forest and a D-S evidence theory and evaluating severity; the fault traceability reasoning module is used for constructing a fault propagation graph and positioning root causes through a Bayesian network; and the visual display and early warning module is used for generating a three-dimensional fault tree and a thermodynamic diagram and triggering graded early warning. According to the invention, the fault detection rate and traceability efficiency are improved, the false alarm rate is reduced, early warning is realized, the ACU test process is optimized, and the product quality and safety are guaranteed.

The invention discloses a fault diagnosis method and a fault diagnosis system for an electric valve actuating mechanism. The method comprises the following steps: synchronously acquiring signals through an anti-EMI (Electro-Magnetic Interference) multi-source sensor; adopting complex Morlet wavelet packet decomposition to extract a 1.2-2.4 kHz energy entropy minimum frequency band, and calculating a kurtosis index; separating the third harmonic of the current through variational mode decomposition, and calculating the total distortion rate of the third harmonic; a graph attention network with 12-dimensional features is constructed, and weighted fusion is carried out through a multi-head attention mechanism; the lightweight CNN outputs a fault type, and when the confidence coefficient is less than 0.9, a knowledge graph rule engine is triggered; and updating a threshold value based on a historical diagnosis clustering result, and aggregating edge model parameters by federal learning. The system comprises a wafer-level micro-strain sensing layer, an FPGA accelerated edge computing layer, a cloud platform supporting federated learning, and an AR maintenance guidance and block chain evidence storage module. The early fault detection rate is improved, the false alarm rate under strong EMI is reduced, and the average repair time is shortened.

The invention relates to an airplane airborne BIT system testability verification method based on a correlation model, and the method comprises the steps: carrying out the testability modeling of an object system, and obtaining a dependency relation model which describes the relation between a fault mode of the object system and a test method of the object system; based on the dependency relationship model, obtaining a BIT theoretical fault report set of each fault mode under all test methods by utilizing reachability analysis, and deducing an optimal diagnosis fault fuzzy group; constructing a fault sample library, verifying the BIT theoretical fault report set by using samples in the library, and adjusting the dependency relationship model according to a verification result; and performing fault simulation based on the verified dependency matrix, and calculating a total fault detection rate and a fault isolation rate of the object system according to a generated fault report. According to the method, the isolation rate index of the fault can be effectively verified in the incomplete and unformed stage of the fault diagnosis strategy.

The invention discloses a vibration signal processing method based on self-adaptive wavelet packet and deep learning fusion, and belongs to the field of sewage plant equipment fault diagnosis. The method aims at solving the problems that traditional signal processing is poor in flexibility, the non-stationary signal processing capacity is weak, the deep learning data requirement is large, and the high-frequency weak feature capturing capacity is limited. According to the method, the high-frequency acceleration sensor is adopted, the vibration signals of the sewage plant equipment are accurately collected, the self-adaptive wavelet packet decomposition technology is applied, the primary function is dynamically selected, the number of decomposition layers is optimized, self-adaptive threshold noise reduction is achieved, and the signal processing quality is improved. Meanwhile, in combination with a one-dimensional convolutional neural network and a bidirectional LSTM model, local and global features of the signal are extracted respectively, and pre-processed data are formed through gating weighted fusion. According to the method, the signal-to-noise ratio and the weak fault detection rate are remarkably improved, feature redundancy and data requirements are reduced, the calculation efficiency and diagnosis accuracy are improved, the method is suitable for sewage plant equipment fault diagnosis, and the industrial applicability is enhanced.

The invention relates to the technical field of port equipment evaluation, in particular to an optimization method for port large-scale machine equipment state evaluation, which comprises the following steps: S1, multi-source heterogeneous data collaborative acquisition; s2, data preprocessing and feature enhancement; s3, carrying out multi-modal feature fusion modeling; s4, state evaluation of transfer learning driving; s5, dynamic threshold early warning and residual life prediction; and S6, model optimization under a federated learning framework. According to the method, the early fault detection rate is changed from 82% to 95% through multi-modal fusion, the false alarm rate is reduced by 60%, the diagnosis precision is improved, a dynamic threshold value adapts to working condition fluctuation, the maintenance cost is optimized, the prediction error for the residual life is smaller than or equal to 15%, excessive maintenance is avoided, the replacement period of key components is prolonged by 30%, and accurate state evaluation can be carried out.

The invention discloses a health monitoring method and device for industrial equipment. The method comprises the following steps: acquiring a target multi-modal feature vector set; performing time sequence alignment and feature fusion processing on the target multi-modal feature vector set to obtain a target fusion feature vector sequence; inputting the target fusion feature vector sequence into a target equipment health state prediction model for processing to obtain a target equipment collaborative analysis result; obtaining health state prediction data of the target equipment according to the collaborative analysis result of the target equipment; generating a target equipment fault root cause diagnosis report, a target equipment maintenance suggestion and a target risk assessment report; and performing optimization processing on the target equipment health state prediction model according to the target equipment actual operation data, the target equipment fault root cause diagnosis report, the target equipment maintenance suggestion and the target risk assessment report. According to the invention, the fault detection rate of industrial equipment can be improved, the false alarm rate can be reduced, and organic combination of millisecond-level abnormal response and deep historical analysis is realized.

According to the fault early warning system for the wind power booster station provided by the invention, the limitation of traditional single-point monitoring is broken through by fusing the infrared ultrasonic technology and the ultrahigh frequency technology through the non-contact sensor, so that the early fault detection rate is greatly improved; the composite heat dissipation system suppresses the temperature rise of a transformer hot spot based on a dynamic heat management mechanism of a phase change material and a heat pipe, and the energy-saving efficiency is improved; the edge calculation early warning center converts temperature, partial discharge, strain and other multi-source heterogeneous data into a unified health degree index through a weighted fusion algorithm, and in combination with an equipment topology correlation analysis model, the positioning accuracy of a fault source is greatly improved, and the early warning response time delay is greatly reduced.

The application provides an ECC attack-resistant method, device, equipment and medium suitable for a car security chip. The application adjusts the points accumulated in the scalar average decomposition method, so that the operation operations in each cycle are the same, that is, each cycle performs point multiplication and point addition. In this way, the branch in the operation process can be eliminated, the power consumption attack cannot identify the leakage of sensitive information, and the problem that the false operation mode cannot meet the high fault detection rate required by the car chip ISO 26262 is avoided. In the case of increasing a small amount of calculation and pre-storing points, the application can achieve the beneficial effects described above.

The application relates to the technical field of vehicles, in particular to a test method and device of an electric drive system, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining an electric drive test requirement of a to-be-tested electric drive system; controlling a load simulator, an environment simulator, a simulated charging pile and a battery simulator to provide at least one test state for the to-be-tested electric drive system according to the electric drive test requirement; obtaining test information output by the to-be-tested electric drive system in the corresponding test state, so as to generate a test result according to the test information. Thus, the problem of single test dimension and insufficient dynamic performance evaluation in the electric drive boost charging test method is solved, the real complex working condition is reproduced by synchronously applying electrical, thermal and control multi-dimensional stress, the dynamic response capability and control robustness of the system are quantitatively evaluated through step disturbance and transient mode switching, and the fault detection rate and the research and development efficiency are improved.

A system testability prediction method based on an aero-engine functional architecture comprises the following steps: determining system test architecture data according to the aero-engine functional architecture; based on the system test architecture data, obtaining a fault mode probability lambda Di capable of being correctly tested by the ith basic-level maintainable function unit and a fault rate lambda i of the ith basic-level maintainable function unit, and establishing a system fault detection rate rFD mathematical model; obtaining a function fault mode probability lambda Ii which can be isolated to an ith basic level maintainable function unit, and establishing a system fault isolation rate rFI mathematical model; obtaining a fault mode probability lambda Ai that the i-th basic-level maintainable function unit with the test function may generate a false alarm, and establishing a system inherent false alarm rate rFA mathematical model; and system testability prediction is completed. The method can be used for scheme design of aero-engines and other complex products, system testability prediction in the detailed design stage and evaluation of whether the scheme design of the aero-engines meets testability quantitative index requirements or not.

The invention discloses a fault early warning method for a chemical equipment explosion-proof inspection robot based on vibration analysis, and belongs to the field of robot fault early warning. Comprising the steps of multi-source vibration data acquisition and anti-explosion signal processing, vibration-environment multi-source data fusion diagnosis, statistical process control (SPC) vibration health assessment, anti-explosion environment self-adaptive early warning mechanism and graded early warning and maintenance decision. According to the chemical equipment anti-explosion inspection robot fault early warning method based on vibration analysis, the Db4 wavelet denoising technology is adopted, the fault detection rate is greatly improved compared with a traditional method, mechanical faults and electrical risks are accurately distinguished, the ATEX highest anti-explosion standard is achieved, the sensor self-healing capacity is achieved, the system reliability is improved, and the fault early warning effect is good. Through the daily increasing trend of the entropy value of the vibration envelope spectrum, faults are pre-judged 72 hours ahead of time, most of accidental shutdown losses are reduced, meanwhile, three-level response strategy synergy is achieved, similar vibration modes are combined through the K-means clustering algorithm in each quarter, and the annual diagnosis precision is improved.

The invention discloses a multi-target test selection method and system under a test uncertainty condition based on E domination guidance. The method comprises the following steps of: firstly, explicitly modeling uncertainty in a test process by using a detection probability matrix and a false alarm probability matrix; secondly, establishing a multi-objective optimization model which takes the minimum test cost, the minimum missing report rate and the minimum false alarm rate as objectives and takes the fault detection rate and the fault isolation rate as constraints; introducing an E domination relation fused with the preference of a decision maker to improve a traditional Pareto domination criterion; on the basis, designing an E-GPSO algorithm fusing a genetic algorithm and particle swarm optimization to perform iterative search, and dynamically maintaining a non-inferior solution set by adopting an external file based on an E domination criterion; and finally, outputting an optimal test scheme set meeting the constraint and the preference. According to the method, the test cost and the diagnosis reliability can be effectively coordinated and optimized, the decision space is remarkably reduced, and the efficiency and the quality of testability design are improved.

The invention relates to a carrier roller skin damage fault diagnosis method based on distributed sound and temperature fusion monitoring in the technical field of coal mine underground conveying equipment fault monitoring. The carrier roller skin damage fault diagnosis method sequentially comprises the following steps of S1 sound and temperature data acquisition, S2 data preprocessing, S3 wear feature extraction, S4 sound and temperature fusion diagnosis model training and S5 fault diagnosis and mathematical model verification. Aiming at a core pain point of an existing carrier roller wear fault diagnosis technology, through synchronous data acquisition, targeted preprocessing, fault sensitive feature extraction, attention weighted fusion and full-process optimization of a robust classification model, a technical breakthrough is realized, the problems of time alignment and quality of multi-source data are solved, the distinguishing capability of different wear types is improved, and the fault diagnosis accuracy is improved. The diagnosis accuracy and the anti-interference generalization ability are improved, the fault detection rate is guaranteed, and key fault omission is avoided.

The present application relates to the technical field of boiler heating surface fault diagnosis, and particularly relates to a heating surface fault diagnosis and prediction method and system based on boiler flow field analysis. The method comprises the following steps: collecting multi-dimensional data of the boiler; performing internal flow field analysis of the boiler; constructing a heating surface fault diagnosis model; coupling the flow field simulation results and the fault diagnosis model to construct a fault prediction model; extracting features and reducing dimensions; using a Gaussian mixture model to perform fault mode recognition; calculating a comprehensive fault score and performing risk classification. The present application significantly improves the accuracy of fault diagnosis and the early warning time by fusing flow field analysis, deep learning and multi-source data. Experimental results show that, compared with traditional methods, the fault detection rate of the present application is increased to 96.7%, the false positive rate is reduced to 3.2%, the early warning time is extended to 12.3 hours, and the diagnosis accuracy is 94.1%. The present application provides strong support for safe and efficient operation of the boiler, and has important engineering application value.

The application provides a process micro-fault detection method based on stationary subspace integrated probability slow features, and belongs to the technical field of industrial process fault detection. The technical scheme is as follows: the application is composed of an offline stage and an online stage. The application fuses the stationary information of SSA and the time sequence dynamics of SFA to form complementary feature description, finally realizes fine division of non-stationary multivariate data through integrated learning, realizes effective extraction of micro-fault probability information, and greatly improves the micro-fault detection rate.

This invention discloses a monitoring method and system for the preparation process of ternary cathode materials based on RVAE. It constructs a nonlinear dynamic system model of the sintering process based on a variational autoencoder; assigns different weights to samples at different times in the constructed nonlinear dynamic system model of the sintering process, derives the loss function of the nonlinear dynamic system model of the sintering process, and trains the model parameters through backpropagation; defines the statistics of the nonlinear dynamic system model of the sintering process based on the cyclic variational autoencoder, and obtains the control threshold of the nonlinear dynamic system model of the sintering process through kernel density estimation; collects online data as a test set for the nonlinear dynamic system model, calculates the monitoring statistics online and compares them with the control limits to determine whether a fault has occurred. This invention can significantly improve the fault detection rate and false alarm rate, providing a strong guarantee for the stable operation of the sintering process.

The invention discloses an intelligent fault diagnosis method and device for a complex aerospace system based on correlation modeling, and relates to the technical field of aerospace system fault diagnosis, and the method comprises the steps: constructing a testability model of the aerospace system; the distinguishing capability of different faults is simplified, and test points are optimized; automatically generating a fault diagnosis strategy based on the optimized testability model; performing simulation evaluation on the fault diagnosis strategy, and counting a fault detection rate and a fault isolation rate; and performing iterative optimization by judging whether the fault detection rate and the fault isolation rate meet preset requirements or not. According to the method, the technical problems of low diagnosis efficiency and insufficient fault detection and isolation accuracy caused by dependence on artificial experience and incomplete static fault tree coverage of complex spaceflight system fault diagnosis in the prior art are solved, intelligentization and precision of complex spaceflight system fault diagnosis are realized, and the fault diagnosis efficiency is improved. And the fault detection rate and the isolation rate are improved.

The application discloses an industrial boiler fault detection method based on dynamic weighted differential principal component analysis, which comprises the following steps: setting a fixed length time window to expand the boiler sample points to obtain an expanded boiler sample set; finding the first neighbor in space and the neighbor set of the first neighbor of the expanded boiler sample set, performing weighted differential processing to solve the multi-modal problem of the boiler sample; establishing a PCA model based on the processed boiler sample data, and collecting online data pairs of the boiler operation; judging whether the collected current data is abnormal based on the established PCA model, so that the adverse effects of dynamic characteristics and multi-modal characteristics on the boiler fault detection are eliminated; and performing fault detection on the boiler system by using a traditional PCA model and the dynamic weighted differential principal component analysis respectively, wherein experiments prove that the dynamic weighted differential principal component analysis method can effectively improve the fault detection rate, and has important significance in the production practice of the boiler system.

The invention belongs to the technical field of big data algorithms, and particularly relates to a transformer area intelligent health diagnosis method and system based on a big data algorithm, and the method comprises the steps: obtaining the current parameters of a transformer area, the current parameters of the transformer area comprise the electrical parameters of the electrical equipment of the current transformer area, the mechanical parameters of the mechanical equipment of the transformer area, and the environmental parameters of the transformer area; preprocessing the current parameters of the transformer area to obtain standardized actual measurement parameters; calculating the intelligent health index pd of the transformer area according to the standardized actual measurement parameters, wherein the calculation formula is shown in the specification; and performing grading evaluation according to the pd, and when the pd is smaller than a preset threshold value, triggering early warning and generating a diagnosis report. And a monitoring system covering full dimensions of equipment operation is constructed. Compared with traditional single electrical parameter monitoring, the fault detection rate is improved.

The invention discloses a fault detection rate allocation method considering multi-factor integration, belongs to the technical field of testability design, and is suitable for power systems of objects such as new energy automobiles and hybrid electric vehicles. The method comprises the following steps: considering the characteristics of a power system, taking the fault occurrence frequency, the electronization degree, the state monitoring coverage degree and the simple judgment fault proportion as fault detection rate distribution influence factors, and carrying out data preparation and normalization processing on the influence factors; determining the weight of each influence factor through multi-expert scoring; performing weighted summation on the influence factor data of each distribution object to obtain a comprehensive distribution coefficient of each distribution object; and completing fault detection rate index distribution according to the comprehensive distribution coefficient and the distribution model. According to the method, the problem of failure rate information loss in the system is effectively solved, the feasibility of engineering implementation is remarkably improved, the failure detection requirement of each distribution object in the system can be reflected more accurately, and good adaptability and stability are achieved.

The application discloses a strip steel hot rolling process multi-performance index abnormality monitoring method and system, and belongs to the technical field of industrial process control and fault diagnosis, and the method comprises the following steps: collecting process variable data and performance index data in a historical strip steel hot rolling process and performing standardization pretreatment; dividing the strip steel hot rolling process into multiple different process subblocks; for each process subblock, performing space-time feature extraction on the corresponding process variable data to obtain a space-time feature representation; constructing a local statistic quantity of each performance index; fusing the local statistic quantities to obtain a global statistic quantity and a global control limit; and based on this, realizing multi-performance index abnormality monitoring and alarm. The application effectively solves the problems that cross-process time lag dependence is difficult to capture, space-time feature extraction is not comprehensive, and single performance index monitoring leads to abnormality missed reports in the strip steel hot rolling process, significantly improves the fault detection rate and reduces the false alarm rate.

The invention relates to the field of wind power equipment monitoring, and discloses a fan tower drum fault diagnosis method, a storage medium and a program product. The method comprises the following steps: synchronously acquiring vibration signals through an acceleration sensor array, separating inherent frequencies and vibration mode vectors of first m-order modals, constructing modal flexibility matrixes of all orders based on a mass normalization condition, superposing the modal flexibility matrixes to form an integral flexibility matrix Fmodal, and calculating the deviation degree of Fmodal elements relative to a finite element reference flexibility matrix, so as to obtain the flexibility of the first m-order modals. And accurate positioning and classification of flange plate bolt pre-tightening force attenuation, crack propagation and cylinder instability faults are realized according to the grading threshold values. The problem of missed judgment caused by the fact that a traditional method depends on single-mode parameters is solved, and the early-stage fault detection rate is remarkably increased.

The application discloses a kind of lithium battery fault intelligent diagnosis method based on deep learning, comprising the following steps: collecting lithium battery operating data and carrying out quality correction, obtain operating data set;Input to operating condition perception model generates multi-scale feature vector representing different operating states;Initial fault diagnosis model is obtained by initial fault class label, initial fault probability value and diagnostic feature vector;Form uncertainty feature vector set by confidence evaluation;Perform bidirectional backtracking correlation analysis, extract associated time period operating data subset;Diagnosis is carried out again, generates review result and carries out consistency comparison with initial result;Conflict result generates conflict mode information and is stored to fault mode library, without conflict when directly output final diagnosis result;The diagnosis result of new data is corrected using fault mode library Pattern matching, output corrected final diagnosis result.The application can significantly improve fault detection rate, reduce misjudgment rate and shorten diagnosis duration.

According to the energy storage battery pack fault monitoring method and system provided by the invention, through multi-source sensing fusion and a CNN-LSTM-XGBoost hybrid model, accurate capture of early gradual change faults is realized, and the fault detection rate is obviously improved; a dynamic threshold mechanism is combined with working condition self-adaptive adjustment, and the false alarm rate is controlled to be extremely low; the fault tree analysis module provides a fault root positioning and grading treatment strategy, so that the thermal runaway response time is further shortened; the residual life prediction model guides accurate maintenance and prolongs the service life of the battery pack.

This invention discloses a method and apparatus for audio line status identification in communication equipment rooms. The method includes audio data acquisition, acoustic physical constraint modeling, multi-region acoustic feature importance classification, audio line status identification model design, constraint loss dynamic balance optimization, acoustic feature loss function construction, and audio line status identification. This invention belongs to the field of data processing technology, specifically referring to a method and apparatus for audio line status identification in communication equipment rooms. This scheme models the sound wave propagation in the equipment room based on a three-dimensional acoustic wave equation to suppress non-fault noise interference; it divides the equipment room into zones, quantitatively allocates the number of monitoring points and regional loss weights, focuses on core areas, and significantly improves the fault detection rate of critical lines; it introduces the measured background noise sound pressure level of the equipment room to construct a noise correction coefficient, significantly improving the robustness of fault identification; and it designs an exponential dynamic relaxation coefficient, combined with benchmark weight allocation, to further enhance the early fault detection capability in critical areas.

The application discloses an industrial process fault detection method and system based on double subspace division, which firstly performs normality evaluation on process variables, divides a data space into Gaussian subspace and non-Gaussian subspace; a fault detection model based on principal component analysis is established in the Gaussian subspace, and a fault detection model based on support vector data description is established in the non-Gaussian subspace; current test data variables are divided into the Gaussian subspace and the non-Gaussian subspace, and are respectively input into the trained models, so that fault detection statistics corresponding to each subspace are calculated; each subspace statistic is converted into a fault probability by using Bayesian inference and is weightedly fused to construct a comprehensive monitoring statistic, and fault detection is realized. The application effectively solves the problem that a traditional method has poor adaptability to Gaussian and non-Gaussian mixed distribution data, improves the fault detection rate while significantly reducing the false alarm rate, and is suitable for real-time monitoring requirements of complex industrial processes.

The application provides a device fault diagnosis method based on multi-scale graph convolution, relates to the technical field of intelligent fault diagnosis of industrial equipment, and comprises the following steps: extracting time domain, frequency domain and time-frequency characteristics of a vibration signal through a multi-scale input layer, and generating a 32-dimensional feature vector by fusion through a cross-scale feature coupling module. A device relationship perception graph convolution model is constructed, a dynamic adjacency matrix is generated in combination with a physical distance and a real-time working condition, time-space fusion features are extracted through time-space convolution, transient and periodic characteristics are enhanced through an occasional fault sensitive time sequence module, time sequence features are output in combination with double-channel fusion and a self-attention mechanism. Finally, fault identification and positioning are realized through a double-threshold detection and a device comparison enhancement strategy, and an explainable diagnosis evidence chain containing multi-scale feature contributions is generated, so that the weak fault detection rate and the diagnosis reliability are improved.

The invention belongs to the technical field of intelligent mobile phone hardware testing, and particularly relates to a testing method and system for an intelligent mobile phone mainboard, and the testing method comprises the steps: dividing a mainboard testing task into a plurality of independent sub-testing tasks, and constructing a composite testing scene; distributing each independent sub-test task to different threads for parallel execution based on a multi-core processing architecture, and establishing a real-time data interaction link between the test equipment and the mainboard; mainboard operation parameters and environment response data are collected in real time in the test, data abnormal characteristics are recognized through an intelligent analysis model and an abnormal confidence coefficient calculation formula, and a test strategy is dynamically adjusted according to an analysis result; compared with a traditional static test, the mobile phone mainboard quality evaluation method has the advantages that the fault detection rate is increased, compared with a serial test, the time consumption of the same test content is reduced, and the quality evaluation is quantifiable.

The invention discloses an industrial fault detection method based on joint sparse low-rank double-dictionary learning, relates to the technical field of engineering fault detection, and is technically characterized in that sparse representation and low-rank representation are combined, single-dictionary learning is expanded, and the industrial fault detection method based on double-dictionary learning is constructed; according to the method, the problems of nonlinearity, noise interference, high-dimensional data and the like in a complex industrial process are solved, and the fault detection rate of the system is remarkably improved; in a high-dimensional complex industrial process including noise pollution and a multivariable coupling relationship, the scheme can effectively detect system faults.