56 results about "Evidence reasoning" patented technology

The invention relates to a method for processing the information of float cars under the structure of a parallel road network, which is mainly applied to the service field of the intelligent dynamic traffic information. The method comprises the steps: step 1, an SVM (Support Vector Machine) sorter is adopted to judge the matching relationship between sampling points and roads, and map match is carried out; step 2, according to map match information, a heuristic route deduction algorithm is adopted to deduce possible running routes of a car, and according to the properties of road chains contained in the routes, whether the car runs in the structure of a parallel road network or not is judged; the shortest running route or a main running route and a subsidiary running route are selectively output in different circumstances, and the reliability of average running speed information provided by the car is estimated; step 3, the D-S (Dempster-Shafer) evidence reasoning theory based on classification is adopted to merge road condition information provided by all the float cars passing by a certain road chain in a current period and consider the reliability of the road condition information in a merging process so as to obtain the mathematical expectations of the average speed and the travel time of the float cars passing by the road chain in the current period. On the basis that the data quality of the prior float cars is kept unchanged, the invention realizes the acquisition of the real-time dynamic traffic information under the structure of a parallel road network.

The invention, which belongs to the technical field of the video image, discloses an infrared-panorama-pick-up-head-based abnormal behavior identification method of elderly people living alone. The method comprises: step one, an infrared panorama pick-up head is used for carrying out infrared shooting, thereby obtaining a video image signal; step two, an infrared panoramic picture is unfolded by using a fast approximate unfolding method; step three, an indoor grid semantic map in a home environment is constructed; step four, on the basis of an improved hybrid Gaussian model algorithm, modeling of a human body target is carried out, a target block mass is identifier, block mass information is obtained, the target block mass is tracked in real time, and a human body track feature is obtained; step five, a human body moving track is extracted by using a hybrid Gaussian clustering method; step six, the human body track is segmented; and step seven, a convolutional neural network is trained by using the track after human body motion segmentation in the home environment as a training sample, feature extraction is carried out on a human body behavior track in the home environment, and classification is carried out by using evidence reasoning; and if an abnormal situation occurs, alarming is carried out.

The invention discloses a coupling certainty and uncertainty analysis fire risk evaluation method. The method comprises steps that S1, risk factors are identified, and a fire risk evaluation system isestablished; S2, a fuzzy analytic hierarchy method is utilized to determine the index weight of each risk factor; S3, the fuzzy theory is utilized to combine with the confidence to calculate the firerisk of each single factor; S4, the evidence reasoning operator is utilized to synthesize the fire risk of each single factor; S5, an expected utility value is utilized to acquire the clear evaluation result; and S6, the sensitivity analysis method is utilized to verify feasibility of a model.

The invention discloses a trustworthiness dynamic evaluation method facing software evolution. Based on the existing evaluation requirements of the trusted software, and by comprehensively considering the problems that a trustworthiness evaluation index system (TEIS) fails, trustworthiness evidence reasoning can not be developed normally and the like, a self-adaptive reconfiguration unit is designed to implement the TEIS based on associated matrix.. The research and application of the invention provide a convenient and effective solution for the problem of the trustworthiness dynamic evaluation under the background of software evolution, overcome the disadvantages of the conventional models in the aspects of dynamic nature, universality and uncertainty, and ensure the significant value in practical use.

The invention relates to a flight path and air line related algorithm based on evidence reasoning, in particular to a flight path and air line classifying method based on evidence reasoning. A construction method of distance and heading basic belief assignment based on a probability distribution function and of chord length basic belief assignment based on a linear function is given out. The problem of high conflict and uncertainty of evidence constructed at a low signal-to-noise ratio is taken into consideration, and multi-evidence combining is performed by adopting an evidence combining method based on evidence distances and conflicting factors, so that the objective of classifying and judging flight paths is achieved. Correlation of the flight paths and air lines under a low signal-to-noise ratio tracking system can be performed to realize flight path classifying, so that the flight path and air line classifying method based on evidence reasoning is of great significance to situation evaluation under the low signal-to-noise ratio tracking system.

The invention provides a Chinese comment emotion classification method and a Chinese comment emotion classification device based on evidence reasoning rules. The method comprises the following steps of: preprocessing the obtained Chinese comment text; Extracting features of the pre-processed Chinese comment text to obtain a vectorized Chinese comment text; Dividing the Chinese comment text into training data and test data, and training a plurality of preset base classifiers using the Chinese comment text; Predicting the emotion tendency of the test data by using the plurality of trained base classifiers to obtain weights of each base classifier; Based on the weight and reliability of each base classifier, the output probabilities of each base classifier are fused by using evidence reasoning rules to obtain the emotion classification results of Chinese comment texts. Thus, the invention uses evidential reasoning rules to fuse the output results of different base classifiers, and considers the weight values and reliability of different classifiers, so that the final fusion result is more reliable and accurate.

The invention discloses an evidence fusion method for mechanical fault diagnosis of an electric propulsion ship shafting propulsion system. The method comprises the following steps: frequency domain characteristic signals of the positions of a pedestal and a support bracket and the like in different failure modes are obtained; reference value of input characteristics is determined via a K-means algorithm, and similarity distribution of samples is calculated; a casting point statistic table reflecting a relationship between input and failure modes is established, and an input evidence matrix table is obtained via conversion; classification ability and overall uncertainty of an input information source is calculated based on rough set theory and information entropy; reliability and evidenceweight of the input information source are determined; evidence reasoning rules are used to fuse evidence of input sample vector activation, and a failure mode is determined based on fusion results. The method can be used for effectively estimating mechanical failure modes of the shafting propulsion system through vibration signals obtained via a sensor installed on a ship; the method is low in cost and high in precision, and real-time detection and accurate diagnosis of mechanical failure of the electric propulsion ship shafting propulsion system can be realized.

The invention discloses a transformer fuzzy prudent reasoning fault diagnosis method based on cost sensitive learning, and belongs to the field of transformer state evaluation and fault diagnosis. According to the invention, on the basis of acquiring a transformer state evaluation initial sample set and setting a cost sensitive initial matrix, firstly, by combining a Sigmoid multiattribute softening decision, a transformer fault diagnosis multi-class support vector matrix model is constructed; then, a support vector matrix is subjected to normalized ordered weighted averaging, and a fuzzy prudent membership degree weight is calculated; and finally, complementary confidence allocation and information fusion based on a PCR5 method are carried out on a weighed fuzzy prudent membership degree,and based on a confidence allocation fusion final value, transformer fault diagnosis decision determination is carried out. In such process, minimization of an error diagnosis sample number is used as an optimal object, a cost punishment element corresponding the cost sensitive matrix is iteratively corrected on the basis of the fuzzy prudent evidence reasoning process, and an online learning function of the fault diagnosis model is achieved.

The invention discloses a composite fault diagnosis method based on combination of the artificial immunity and the evidence theory. The composite fault diagnosis method based on the combination of the artificial immunity and the evidence theory comprises the steps that on-line computation of non-dimensional indexes is conducted through vibration monitoring data, and the value range corresponding to the non-dimensional indexes is established during a composite fault; fault diagnosis results obtained through the non-dimensional indexes are converted into credibility distribution by means of the artificial immunity method, diagnosis results of all artificial immunity fault detectors based on the non-dimensional indexes serve as input data through the evidence reasoning algorithm, a final fault diagnosis result serves as output data, a fusion diagnostic model is established, and then composite fault diagnosis is achieved after new input data and new output data are obtained. The composite fault diagnosis method based on the combination of the artificial immunity and the evidence theory fills the blank of a composite fault diagnosis method achieved by means of the advantages of the combination of the artificial immunity and the evidence theory, and real-time and accurate diagnosis of composite faults of rotating machinery is achieved.

The invention discloses a fuzzy logic and evidence reasoning-based transformer insulation stress calculation and evaluation method. The method comprises the following steps of firstly normalizing field data, namely, converting input data for describing a normal value, a limit value and the like of a transformer into dimensionless variables of a [0,1] region; secondly fuzzifying the input data, namely, mapping the input data to a fuzzy membership function, wherein a trapezoidal membership function, a generalized bell-like membership function or a Gaussian membership function can be used; thirdly building a transformer stress evaluation tree model, namely, establishing nodes from the input data of the transformer to an insulation stress of the transformer, and a connection mode of the nodes, and correlating the nodes of upper and lower layers by using a fuzzy correlation matrix; fourthly determining a weight value between the nodes, namely, obtaining a synthesized weight value by using a subjective weight value of an analytic hierarchy process and an objective weight value of eigenvector analysis; and finally obtaining the insulation stress of the transformer, namely, determining a total stress level of the transformer by using a reasoning mechanism and an evidence synthesis rule. The method has the advantages in accuracy, flexibility and capability of processing measurement uncertainty.

The invention discloses a network security situation assessment method based on an evidence reasoning rule. The method comprises the following steps: determining a basic structure of network securitysituation assessment; acquiring and preprocessing network data; determining an assessment rule of the network situation; extracting characteristics of the network data and calculating the trust of a basic attribute; calculating the final trust of a generalized attribute; and calculating the network security situation. According to the network security situation assessment method based on the evidence reasoning rule provided by the invention, a network behavior is divided into a normal behavior and an aggressive behavior; and the security situation of a network is analyzed through the changes of network resources such as traffic, a memory, a CPU and a disk, and the security level of the network is assessed through the above security factors and the evidence reasoning rule. In addition, quantitative analysis is performed on the obtained security situation; and through the change of a network security situation value, the change of the network security situation can be reflected. An experimental result shows that the method is effective and feasible, and has a good situation assessment effect.

The invention provides a method and a device for processing the manufacturing cloud service combination. The method comprises the following steps: screening manufacturing cloud service by utilizing an evidence reasoning method after a manufacturing task is received; selecting manufacturing cloud services fulfilling a designated target from the screened manufacturing cloud service for combining. According to the method, candidate manufacturing cloud services are screened by applying the evidence reasoning method based on screening indexes, the manufacturing cloud service with minimum satisfaction degree not in a preset range can be deleted, and the number of cloud services can be reduced, so that the feasible space of the cloud service combination can be shortened.

The invention discloses a deep foundation pit safety assessment method based on utility theory and D-S evidence reasoning. The method depends on deep foundation pit project detection data, predicts the future state of a deep foundation pit based on the third-order Volterra series, and improves the prediction capability of a scheme on project potential risks. The fusion weight of the evaluation index is determined by adopting the variation coefficient and the information entropy, the representation effect of the index variation amplitude and the contained information amount on safety evaluation is considered, and the evaluation accuracy is improved; utilization of utility theories and D S evidence reasoning is used for safety assessment, and qualitative and quantitative analysis of project safety is achieved. According to the method, the finite length unit impulse response filter is used for simplifying the prediction model, model coefficients are reduced, and the state prediction speed is increased. According to the method, a complex evaluation model does not need to be established, a large amount of project risk data is not needed for model training, good scheme realizability and evaluation accuracy are achieved, intervention of too many expert personnel is not needed, and effective use and popularization of the scheme in the later period are facilitated.

The invention discloses an abnormal wear diagnosis method for a marine diesel engine based on union reliability rule reasoning. Firstly, element contents in a working medium lubricating oil of a wearcomponent and a host of the marine diesel engine are used as input, and marine diesel engine abnormal wear component fault conditions are used as output; then, a reliability rule system is establishedand a corresponding attribute weight group is determined according to the correlation between fault features and fault modes; a evidence reasoning algorithm is used to fuse a reliability structure ina reliability rule consequent activated by the input to obtain the reliability value of a fault mode under a specific attribute weight group; and finally, the reliability value is balanced and analyzed to obtain an abnormal wear component diagnosis result. Various elements for marine diesel engine abnormal wear component diagnosis can be handled, a many-to-many complex relationship between the fault features and the fault modes can be characterized, the correlation between the fault features and the fault modes can be characterized, and a complicated condition of concurrence of multiple faultmodes can be diagnosed.

The embodiment of the invention discloses a multi-target optimized dispatch decision evaluation method for a power system. The method solves the technical problem in the prior art that during the current machine group joint optimization scheduling application process, no research is done on the above scientific decision-making method. According to the embodiments of the invention, the method comprises the steps of for a plurality of target functions associated with the optimized dispatch decision of a power system, solving out a Pareto solution set for multi-target optimized dispatch through the multi-target optimization algorithm so as to obtain all candidate solutions; subjecting the attributes of candidate solution sets containing the above candidate solutions to confidence evaluation calculation; according to confidence evaluation, obtaining the overall confidence of the candidate solutions through a multi-hierarchical structure and the evidence reasoning algorithm; and subjecting the total confidence to utility analysis and determining a final scheduling solution.

The invention relates to a marine diesel engine abrasive particle type identification method based on information fusion. The method comprises the following steps: obtaining an abrasive particle sample from an on-line oil monitoring system of the marine diesel engine, correcting and denoising an acquired abrasive particle image by utilizing an image processing technology, and extracting morphological characteristics of abrasive particles; determining a reference value set of the input characteristics, and calculating the comprehensive similarity distribution of the input characteristics with respect to the reference values; constructing an input point statistical table reflecting the relationship between the input signal and the five abrasive particle types by utilizing the comprehensive similarity distribution of the sample set; An evidence matrix table of the input characteristic signals is obtained through conversion of the point-throwing statistical table; and fusing the evidence activated by the input sample vector by using confidence rule reasoning and evidence reasoning rules, and reasoning the abrasive particle type corresponding to the abrasive particle sample from a fusion result. According to the invention, intelligent identification of the marine diesel engine abrasive particle type can be realized, the identification complexity is reduced, and the identification precision is improved.

The invention provides an integrated clustering method based on evidence reasoning for user behavior analysis. The method can fully consider the time characteristics of user data and the credibility of base clusters, and solve the problems of weak robustness and stability of single clusters and poor adaptability of existing integrated clustering methods by using evidential reasoning method, so asto improve the clustering effect of user behavior data. The invention has the advantages that: the problem that the traditional clustering algorithm fails due to the high dimension of the user behavior data can be overcome; the invention can solve the problems of weak robustness and stability of single clustering machines and poor adaptability of existing integrated clustering methods, so as to improve the clustering effect of user behavior data. The invention can be used for clustering of user behavior data, in particular for clustering of user behavior data with high-dimensional characteristics, and can also be used for clustering of stream data and the like, and has wide application range.

The invention relates to an evidence reasoning rule-based voltage source-type inverter DC bus capacitor fault diagnosis method. The method obtains a voltage characteristic value from the voltage source-type inverter and sets three levels of capacitor failure fault. The input of the built evidence reasoning fault diagnosis model is two-dimensional voltage characteristics and the output is reliability distribution of a failure fault level. According to the given voltage characteristic reference value, a fault reliability distribution matrix is built, the matrix is used to convert the input to adiagnosis evidence, the reliability factor of the diagnosis evidence is calculated, and then, two-dimensional input activation evidences are fused, and the level of the capacitor failure fault is estimated from the fusion result. Parameter optimization is carried out on the built diagnosis model, the fault level of a sample capacitor can be estimated and tested based on the optimized diagnosis model, and fault diagnosis on the capacitor can be realized.

The invention provides a lake long-time-sequence continuous water area change reconstruction method based on a remote sensing big data platform. Based on a multi-year month-by-month global water bodydistribution data set obtained by a water body extraction scheme integrating an expert system, visual analysis and evidence reasoning in the prior art, through percentage cutting of a water logging frequency image histogram of a researched lake object, an incomplete part in a lake water area inundated area obtained through an existing method is interpolated, and reconstruction of large lake long-time-sequence continuous water area changes is effectively achieved. According to the method, medium-high spatial resolution images (such as land resource satellite Landsat or environmental satellite environmental data) and auxiliary data which are obtained freely can be adopted, the application range of the method is expanded, and an important method support is provided for hydrological analysis and environmental change research of lakes and drainage basins of the lakes.

The invention discloses an evidence reasoning analysis algorithm and entropy weight based air conditioner starting temperature limit value simulation method. The method is implemented by the following steps of: 1) determining an air conditioner starting temperature limit value range and dividing the air conditioner starting temperature limit value range into a finite number of intervals; 2) identifying factors influencing an air conditioner starting temperature limit value; 3) constructing an air conditioner starting temperature limit value interval credibility matrix; 4) calculating weights of the factors influencing the air conditioner starting temperature limit value according to an entropy weight principle; 5) performing air conditioner starting temperature limit value interval comprehensive credibility synthesis with an evidence reasoning analysis algorithm; 6) determining an air conditioner starting temperature limit value interval at a simulation moment and taking a median of the interval as the air conditioner starting temperature limit value at the simulation moment; and 7) repeating the steps 3) to 6) to perform simulation calculation on simulation time periods hour by hour. According to the method, objective factors, subjective factors, quantitative factors and qualitative factors in the air conditioner starting temperature limit value simulation can be processed and incomplete information in the simulation also can be quantitatively processed.

The invention discloses a method for selecting a moving target defense optimal strategy based on DS evidence reasoning. The method comprises the following steps: establishing an attack and defense game model; enabling an attacker to select an attack strategy; enabling a defender to observe the attack strategy, and enabling the attacker and the defender to calculate benefits; refining Bayesian equilibrium solution to obtain an optimal defense strategy; and enabling the defender to correct the type posterior probability of the attacker through DS evidence reasoning. The establishment of the attack and defense game model comprises the steps of defining the attack and defense game model; defining attack and defense revenue quantification. The income calculation step of the attacker and the defender comprises the steps that the defender calculates a posterior probability according to an observed attack strategy; the attacking and defending parties calculate respective revenues; an attack type reasoning decision table is formed according to the characteristic attributes of the previous attack records; and a basic probability assignment of the attack evidence is determined according to the decision table. According to the method, the defect of the prior probability in the existing model is overcome, the profit of the defense strategy is more reasonable, and the defense success rate ofthe early stage of the system is improved.

The invention discloses a railway transportation risk probability calculation method based on a hybrid heuristic rule system. The method comprises the steps that firstly, the environment monitoring quantity, the equipment monitoring quantity and the carrying monitoring quantity of a certain railway transportation interval are collected, and the railway transportation risk probability is output; secondly, reducing the intersection heuristic confidence rule through a reduction rule to obtain a hybrid heuristic rule system comprising the intersection and the union; and finally, an evidence reasoning (ER) algorithm is utilized to fuse the confidence structure in the item after the activated confidence rule is input to obtain the risk probability. According to the invention, various elements ofrailway transportation risk calculation, including environment monitoring quantity, equipment monitoring quantity, carrying monitoring quantity and the like, can be processed; the method comprises intersection rules and union rules, so that the total number of the rules can be reduced, the rule combination explosion problem is avoided, and the calculation efficiency is improved.

The invention relates to a fault diagnosis method for a rotating mechanical ball bearing based on K-means clustering and evidence reasoning. According to the method, a likelihood reliability table andK reference center vectors are obtained by K-means clustering and K reference evidences are obtained by the likelihood reliability table; distances between fault feature data and the K reference center vectors are calculated, the reference evidences are corrected, and K diagnostic evidences are generated; and after on-line acquisition of values of multiple kinds of fault characteristics, activated diagnostic evidences are calculated respectively, the activated diagnostic evidences are fused, a fault decision is made by using the fused evidences, and a fault type corresponding to the online fault characteristic data is obtained. According to the invention, the fusion reasoning of fault diagnosis evidences is performed based on K-means clustering; and with the multi-source diagnostic information, the fault diagnosis accuracy of the rotating mechanical ball bearing can be improved effectively.

The invention relates to a town comprehensive disaster-bearing capability assessment method, apparatus and system. The town comprehensive disaster-bearing capability assessment method includes the steps: acquiring multiple levels and the index of each level of the index system influencing the town comprehensive disaster-bearing capability, wherein the multiple levels at least include the lowest level, at least one intermediate level and the highest level; utilizing an analytic hierarchy process to respectively calculate the weight value of the index of the lowest level and the weight value of the index of the intermediate level; acquiring the index data of the lowest level in the preset period of the region; and aiming at the index data, utilizing an evidence reasoning method and the weight values to calculate the comprehensive disaster-bearing capability index of the town.

The invention discloses a method for fusing measurement data of multi-platform sensors based on D-S reasoning, comprising the following steps: acquiring measurement data of K types of sensors; estimating a system track by adopting an optimal distributed fusion algorithm to obtain accurate measurement data of the K types of sensors; respectively synthesizing feature information of multi-platform same-type sensors for the measurement data of the K types of sensors obtained in the step 2 by utilizing a Dempster-Shafer evidence reasoning method; updating and optimizing the feature information of the multi-platform same-type sensors by adopting evidences of a Jeffrey rule based on a Bayes method, wherein strong condition mass function update evidences and weak condition mass function update evidences are defined in the rule; performing fusion of feature information on multi-platform different-type sensors according to a method which is the same as the step 3 and the step 4; and performing decision output to obtain total fusion data. The method of the invention solves the problem that large calculation amount and low accuracy can be caused if a conventional collaborative conflict resolution method is directly applied to a multi-platform multi-source heterogeneous sensor system.

The invention relates to a ship propeller air entrapment identification method based on evidence reasoning rules and adaptive lifting, and the method comprises the steps: obtaining a three-phase current root-mean-square value and a torque characteristic value from a ship electric propulsion system frequency converter in real time, determining the degree of an air entrapment effect caused by a severe sea condition according to a propeller torque loss coefficient, and setting three levels; firstly, an input feature reference value of each evidence reasoning rule weak learner is given through a K-means clustering method; establishing a fault reliability distribution matrix, converting the input into diagnosis evidence by using the matrix, calculating a reliability factor of the diagnosis evidence, fusing the evidence according to the reliability factor, and estimating the propeller air entrapment effect grade from a fusion result; counting the precision of the current weak learner, and calculating the learning coefficient of the weak learner; and repeating the process to finally obtain a self-adaptive lifting strong classifier composed of a plurality of weak learners, and obtaining anestimated value of the strong classifier for the air entrainment effect level, thereby realizing identification for the propeller air entrainment effect.

The invention relates to a DCS reliability evaluation method based on a dynamic evidence network. The method comprises the following steps of 1) obtaining text data for evaluating the reliability of acomponent, and calculating a probability reliability function of the basic component according to the obtained text data; 2) analyzing a system structure, and establishing the dynamic evidence network; 3) performing evidence reasoning according to the dynamic evidence network, thereby obtaining system reliability; and 4), evaluating the system reliability and the uncertainty. Compared with the prior art, the method has the advantages of simple reasoning method, capability of effectively expressing cognitive uncertainty information, small calculation amount and the like.

The invention discloses a ship equipment environment adaptability assessment method based on evidence reasoning, and belongs to the technical field of ship equipment environment adaptability assessment. The method comprises the following steps: firstly, carrying out importance sorting on ship equipment environment adaptability to-be-selected evaluation parameters by utilizing an analytic hierarchyprocess, and establishing a ship equipment environment adaptability evaluation index set; Secondly, constructing a data fusion model based on evidence reasoning, establishing an identification framework and basic credibility, giving an evidence conflict judgment basis, and synthesizing ship equipment environment adaptability evaluation indexes into a credibility function; And finally, based on the credibility function, evaluating the environmental adaptability of the ship equipment. According to the method, the influence factors of the natural environment and the mechanical environment can beaccurately cut, the evaluation index of the ship equipment environment adaptability is correctly selected, the evaluation information of an expert group is effectively processed, and the ship equipment environment adaptability is quickly and accurately evaluated.

The invention discloses an electronic device fault diagnosis method based on grey correlation analysis and improved DS reasoning, and relates to the technical field of fault diagnosis. The invention aims to solve the problem that fault diagnosis and fault prediction capabilities of node test equipment cannot be accurately realized by adopting a simple mode in the prior art. The method comprises: extracting fault characteristics of the tested electronic device to construct a parameter subspace of each fault characteristic; calculating the gray correlation degree of each fault characteristic parameter in the parameter subspace of each fault characteristic and the characteristic parameter in the fault type sample by adopting a gray correlation analysis method; selecting a fault correspondingto the characteristic parameter with the maximum correlation degree from the plurality of grey correlation degrees as a preliminary fault type of the tested electronic device; and performing probability assignment on each gray correlation degree to obtain a basic probability assignment of each fault type, and fusing the basic probability assignments by adopting an improved DS evidence reasoning fusion method to obtain a final fault type of the tested electronic device. The method is used for diagnosing electronic device faults.

The invention discloses a bearing fault diagnosis method for an oil transfer pump, which is characterized by comprising the steps of (1) performing data acquisition on bearing vibration signals by adopting acceleration sensors, carrying out EMD (Empirical Mode Decomposition) and intrinsic mode function (IMF) envelope spectrum analysis, and obtaining sample data characteristic physical quantities;(2) processing the sample data characteristic physical quantities based on a bearing fault characteristic matrix to obtain BPA (Basic Probability Assignment) values to serve as BPA evidences; and (3)improving D-S evidence reasoning rules to fuse the BPA evidences so as to obtain a bearing fault diagnosis result. The bearing fault diagnosis method provided by the invention for the oil transfer pump can process a circumstance that partial data loses efficacy or is abnormal because of the existence of damaged sensors in the multiple bearing acceleration sensors.