54results about How to "Implement diagnostics" patented technology

The invention discloses a cloud computer end-to-end fault root cause analysis method and system, and relates to the technical field of operation and maintenance data analysis, and the method comprises the steps: deploying data collectors on a virtual resource pool, a multi-stage cloud management platform and a cloud computer terminal, and monitoring and collecting various operation and maintenance data in real time; a unified operation and maintenance management center is introduced to complete the following work: providing management and configuration services of the data collector; receiving and summarizing operation and maintenance data; various AI models are accessed, operation and maintenance data from different sources are distributed based on the types of the AI models, the AI models perform deep analysis and diagnosis on the operation and maintenance data, and potential fault modes or abnormal behaviors are identified; customizing an alarm strategy, an alarm processing flow and an alarm notification mode; and introducing an AIOps service to generate an alarm and fault analysis report according to the analysis and diagnosis results of the AI model. According to the method, the service quality of the end-to-end full link of the cloud computer is monitored in real time, so that faults can be quickly positioned, early warning can be performed in advance, and the operation and maintenance cost and the fault risk are reduced.

The invention discloses a GIS partial discharge fault detection device and method based on D-S evidence multi-source information fusion, and relates to the technical field of power system control, and the device comprises a signal collection module which is used for collecting an electromagnetic wave signal, a mechanical vibration signal, an ultrasonic signal and a partial discharge optical radiation signal generated by the partial discharge in GIS equipment; the GIS partial discharge fault detection module is used for processing, storing and sending the signals acquired by the signal acquisition module; the coordinator is used for realizing data transmission between the GIS partial discharge fault detection module and the background terminal; and the background terminal is used for receiving the processing information of the GIS partial discharge fault detection module and judging the fault type of the GIS equipment. According to the GIS partial discharge fault diagnosis system, various signals generated by partial discharge in the GIS equipment are detected through the information acquisition module, and the relationship between the characteristic data and the GIS partial discharge fault is established, so that diagnosis of the GIS partial discharge fault is realized, and the fault judgment efficiency of the background terminal is improved.

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention relates to the technical field of bearing fault diagnosis, and discloses a fault self-adaptive diagnosis method for a low-speed heavy-duty bearing, which comprises the following steps: acquiring bearing vibration time domain data under a low-speed heavy-duty working condition, and carrying out data cleaning on the bearing vibration time domain data to obtain cleaned bearing vibration time domain data; performing spectrum analysis on the vibration time domain data of the cleaned bearing to obtain a spectrum diagnosis result; acquiring a noise reduction envelope spectrum of the cleaned bearing vibration time domain data by adopting a clustering weighted envelope spectrum method; performing envelope spectrum analysis on the noise reduction envelope spectrum to obtain a diagnosis result of the noise reduction envelope spectrum; if the frequency spectrum diagnosis result and the noise reduction envelope spectrum diagnosis result are normal, judging that the bearing is normal, otherwise, judging that a bearing fault exists; the method improves the efficiency and accuracy of bearing fault diagnosis.

The invention relates to a turn-to-turn short circuit fault diagnosis method, device and equipment for a dry-type air-core reactor, a readable storage medium and a program product, and relates to the technical field of electric power safety. By adopting the method, the initial turn-to-turn short circuit condition of the reactor can be detected in time, and the accuracy of fault diagnosis is improved. The method comprises the following steps: obtaining a reconstruction signal according to historical voltage and current data of the dry-type air-core reactor; on the basis of the reconstruction signal, constructing a Bowdrill curve, and calculating a characteristic parameter of the Bowdrill curve; calculating the change rate of the current state of the characteristic parameters relative to the health state, and obtaining the weight corresponding to the change rate through an entropy weight method; determining a standard value for judging the fault according to the change rate and the weight, and taking a target standard value corresponding to the target fault degree as a fault threshold value; and obtaining a current standard value according to the current voltage and current data of the dry-type air-core reactor, and comparing the current standard value with a fault threshold to obtain a turn-to-turn short circuit fault diagnosis result of the dry-type air-core reactor.

The invention discloses a beta-lactoglobulin modified near-infrared fluorescent targeting probe as well as a preparation method and application thereof. The probe comprises indocyanine green (ICG) and beta-lactoglobulin (beta-LG) which are covalently bound. Compared with the prior art, the probe disclosed by the invention has the advantages that (1) the probe disclosed by the invention improves the stability of the near-infrared fluorescent dye in a body and prolongs the retention time of the near-infrared fluorescent dye, so that the observation window time is prolonged, imaging information is fully acquired in real time, and the effective circulation time in the body is prolonged; (2) the probe has the advantages of good biocompatibility, low toxicity and the like; and (3) the probe can specifically enrich in renal tubules and perform targeted imaging in kidneys, so that diagnosis and treatment of kidney injury, especially acute injury, are realized.

The invention relates to a thermal power plant integrated energy system operation and maintenance regulation and control method based on a BP neural network, and the method comprises the steps: collecting the real-time data and load demands of at least one device in a thermal power plant integrated energy system; according to the real-time data, generating at least one piece of prediction data of a target steam quantity, a target fuel quantity and a target electric load; comparing the at least one piece of prediction data with the real-time data; according to the fault information, the at least one piece of prediction data, the real-time data and the load demand, regulating and controlling the operation of the integrated energy system; and regulating and controlling the integrated energy system according to the load fluctuation condition, the real-time data and the at least one piece of prediction data so as to generate a regulation and control scheme of the integrated energy system. Therefore, the problems that the prior art does not consider the problems of power generation hour reduction, insufficient profitability and the like mainly faced by a power plant in combination with an integrated energy system, prediction is carried out only by using a neural network model, the prediction accuracy is insufficient, and design is not made from the angle of operation and maintenance of the power plant are solved.

The embodiment of the invention provides a current magnetic source inversion positioning method and device based on a unidirectional magnetic field gradient, and the method comprises the steps: determining a fluxgate probe, and determining an auxiliary testing tool based on the fluxgate probe; wherein the auxiliary test tool comprises a two-dimensional grid; acquiring magnetic field data through a fluxgate probe based on the two-dimensional grid; determining magnetic field gradient data based on the magnetic field data, and determining a current density distribution based on the magnetic field gradient data; and determining current magnetic source position information based on the current density distribution. The method comprises the following steps: acquiring magnetic field data through a fluxgate probe based on a two-dimensional grid; determining magnetic field gradient data based on the magnetic field data, and determining a current density distribution based on the magnetic field gradient data; the current magnetic source position information is determined based on the current density distribution, and the diagnosis of the current state in the equipment and the judgment of the abnormal current value can be realized.

The invention provides a method for diagnosing and screening fault characteristics of a measurement and control antenna motor, which belongs to the technical field of motor diagnosis, and comprises the following steps: modeling an antenna, a motor and a transmission system between the antenna and the motor, and determining the working efficiency of the transmission system; and adding the modeled models into an upper computer, and simulating the external environment at the antenna in the upper computer in real time. After an instruction for driving the attitude of the antenna to change is issued, the instruction is transmitted to the upper computer, standard simulation of the model attitude is carried out in the upper computer, and the actual action of the antenna is compared with the standard simulation to analyze the difference between the actual action and the standard simulation in the movement process. And inferring the possible fault type of the motor in combination with the big data and a preset fault type library. The measurement and control antenna motor fault feature diagnosis and screening method provided by the invention can realize motor fault diagnosis under the condition that the motor is not disassembled, is relatively high in applicability, and has relatively great technical advantages.

The invention relates to the technical field of biological medicine, in particular to application of cerebrospinal fluid CTSC protein as a marker in preparation of neurosyphilis or nerve injury diagnosis products. The CTSC protein is also called Dipetidyl petidease 1, and according to the research conclusion of the invention, the CTSC protein can be used as a cerebrospinal fluid marker for early diagnosis, screening and typing detection of neurosyphilis patients. By measuring the concentration of CTSC protein in cerebrospinal fluid, the concentration is used as an index for early diagnosis of neurosyphilis, typing detection of symptomatic neurosyphilis, asymptomatic neurosyphilis, neurosyphilis and recessive syphilis, and nerve injury degree evaluation. The index can be independently used or used as a main reference index to be combined with other indexes for completing diagnosis screening, typing and nerve injury evaluation of the neurosyphilis, and the index has the advantages of being good in sensitivity, high in specificity and the like.

The invention discloses a time-space resolution high-temperature large-energy super-thermal electron diagnosis method and device, a super-thermal electron source bombards a metal target to generate tough-induced radiation, the tough-induced radiation passes through a front filter disc and micro pinholes in a pinhole array plate and then reaches a fluorescent sheet, most X-rays pass through the fluorescent sheet after being filtered and attenuated, and the X-rays are transmitted continuously; part of the X-rays interact with the fluorescent sheet to generate fluorescent light emitted in all directions, a detector with time response is installed to collect and record the fluorescent light and filter the soft fluorescent light through the rear filter disc, and an imaging filter disc and an imaging recording surface are installed in the transmission direction to record spatial distribution of the transmitted X-rays. And fitting the temperature and energy of the superthermal electrons by combining the total fluorescence intensity and the total intensity channel response function of the transmitted X-ray, and reversely deducing the time-space behavior of the superthermal electrons according to the time behavior of the fluorescence signal and the space distribution behavior of the transmitted X-ray. According to the invention, the temperature, energy, time behavior and spatial distribution of the superthermal electrons can be diagnosed at the same time.

The invention belongs to the technical field of time-frequency analysis, and particularly relates to a rolling bearing fault method and system based on Riesz atlas sparse decomposition, which utilizes a new time-frequency analysis method-Riesz four-parameter spectrum time-frequency analysis method to obtain four-parameter information of amplitude, frequency, phase and direction of a vibration signal in a time-frequency domain. The time-frequency aggregation and the anti-noise performance of an analysis result are improved; in order to screen out an optimal parameter spectrum and expand the symbol dynamics entropy to a two-dimensional scale, a new entropy calculation method, namely cosine symbol dynamics entropy, is provided; and reconstructing components of the parameter spectrum obtained by entropy screening, and constructing a sparse filter parameter optimization component amplitude, so that a decomposition result has physical significance. The bearing vibration signal under the variable speed working condition and the bearing vibration signal under the constant speed are analyzed, the RSSD method is provided on the basis, and feature extraction and diagnosis of rolling bearing faults under the constant rotating speed working condition and the variable rotating speed working condition are achieved.

This application relates to the field of pipeline network detection technology, and discloses a visual detection method and system for pipeline network node defects based on a detection robot. The method includes: acquiring node images of a sewer network using a detection robot, and performing circle detection and sector segmentation on the node images to obtain multiple sector region blocks; extracting the block feature vector of each sector region block; calculating angular attenuation weights and radial attenuation weights, and constructing a node connection relationship matrix based on the angular attenuation weights and radial attenuation weights; combining the block feature vectors and the node connection relationship matrix to perform feature aggregation and illumination compensation to obtain a global feature vector; calculating angular deviation index, radial mutation rate index, and leakage index, and generating defect visual detection results, thereby realizing comprehensive diagnosis and spatial positioning of pipeline network node connection defects, and improving the defect detection accuracy of sewer detection robots in complex node environments.

The invention relates to the technical field of biological information, in particular to application of a biological factor developed based on MAMMOT in preparation of a kit for sepsis identification. The biological factors comprise a genome and a microbiome; the genome comprises a plurality of genes such as PMS2P4 and the like, and the microbiome comprises a plurality of bacteria such as Citrobacter werkmanii, Streptococcus oranis and the like, and a plurality of bacteriophages such as Escherichia virus Lambda, Campylobacter phage A11a and the like. The invention further discloses a preparation method of the microbiome. A plurality of sepsis-related cfDNA host methylation sites and a plurality of sepsis-related bacteria and bacteriophages are excavated for the first time, and the biological factors can be applied to sepsis identification and have important values in the fields of sepsis-related detection and patient death rate prediction.

The application discloses a strategy for extracting fault features of electronic circuit systems, named as SSEST strategy, which is used for perceiving global information and paying attention to notable local information, and mining important local information means realizing expression of intermittent fault features of electronic circuit systems, specifically, first, S transformation is performed on a circuit output time sequence signal to acquire time-frequency domain features, then a squeeze and excitation network attention module is used to distribute channel weights, subsequently, input into a Swin Transformer framework, and pay attention to local information related to faults from global signals, and deep mining is performed on fault features, and two electronic circuits are taken as experimental circuits, the proposed diagnostic strategy realizes rapid and high-precision diagnosis, and shows that the proposed multiple attention mechanism is efficient for feature mining of intermittent faults of electronic circuit systems.

The invention discloses a gear fault diagnosis method based on linear discriminant analysis and random forest, and the method comprises the steps: building a fault simulation test bench, collecting a vibration signal, and constructing a fault sample data set; carrying out CEEMDAN-combined improved wavelet threshold noise reduction on the obtained vibration signal, and carrying out signal preprocessing; based on a random forest classifier, in combination with a linear discriminant analysis method, establishing an LDA-RFA gear fault diagnosis model; performing parameter optimization on the constructed gear fault diagnosis model to obtain optimal parameters of the model; and performing model training by using the gear fault public data set to complete gear fault diagnosis work. The fault information can be accurately extracted, and the accuracy of gear fault diagnosis is improved.

The invention provides a bearing fault diagnosis method of a self-optimization deep mixed kernel extreme learning machine, and belongs to the technical field of bearing fault diagnosis. The technical problems that the bearing fault diagnosis accuracy is low, and fault classification is prone to errors are solved. According to the technical scheme, the method comprises the following steps of S1, dividing a data set after fault information extraction into a training set and a test set; s2, setting the number of hidden layers and an activation function, and constructing a mixed kernel extreme learning machine model; s3, obtaining various parameters of the model by using an improved hawk algorithm; and S4, judging whether the bearing has a fault or not in the operation process and determining the fault type. The method has the advantages that the advantages of the improved optimization algorithm and the deep mixed kernel extreme learning machine are fused, the fault type of the bearing can be accurately judged, and the fault features of various faults can be effectively obtained for subsequent state judgment and fault type classification.

This invention discloses the application of MYDGF in the prevention and treatment of memory impairment and related diseases. This invention discloses that MYDGF shows significant differences in individuals with memory impairment. After administration of MYDGF, memory impairment can be prevented and memory impairment can be prevented and the death of newly generated neurons can be stopped.

The application relates to a vehicle wheel speed signal processing device, method, system and vehicle, comprising a vehicle wheel speed signal processing device, which comprises a wheel speed sensor, a collection decomposition module and a control module; the wheel speed sensor is used for outputting a wheel speed signal; the collection decomposition module is used for collecting the wheel speed signal output by the wheel speed sensor and decomposing the wheel speed signal to obtain a decomposition processing signal; and the control module is used for analyzing the decomposition processing signal to obtain an analysis result and controlling the vehicle based on the analysis result. According to the technical scheme of the application, the collection decomposition module is built by replacing an integrated chip with discrete devices, the diagnosis, collection and processing of the wheel speed signal are realized, the use cost is reduced, and the selectivity is improved.

The invention belongs to a battery parameter estimation method, and provides a lithium battery SOC estimation method and a related device for solving the technical problems that when a model-based method is adopted to estimate the SOC of a lithium battery, in an environment with high nonlinear intensity, the linearization error is easy to increase, and the estimation precision is obviously reduced or even diverged. And performing equivalent circuit modeling on the to-be-estimated lithium battery to obtain an equivalent circuit model. And establishing a voltage state equation and a transfer function based on the electrical characteristic parameters of the elements in the equivalent circuit model. And carrying out HPPC test on the lithium battery to be estimated, carrying out parameter identification by adopting a recursive least square method with a forgetting factor, and determining electrical characteristic parameter values of elements in the equivalent circuit model. And in combination with electrical characteristic parameter values of elements in the equivalent circuit model, estimating the SOC of the lithium battery by adopting an EKPF algorithm.

The present invention relates to the technical field of fault diagnosis of power grid equipment, and in particular to an adaptive learning power grid equipment abnormality detection and diagnosis method and system, comprising the following steps: fusing the collected data of different modes, firstly encoding and decoding the data based on the informer network structure, then introducing a score-guided network into the structure, taking the encoded data as input, taking the data and the cluster center distance ratio of normal and abnormal data as labels, training to obtain scores, and then designing a loss function based on the scores, input data and decoded data, to achieve automatic updating and optimization of the network structure, and adapt to the changing equipment status and environmental conditions. The present invention adopts an unsupervised adaptive learning method, utilizes multimodal data, solves the difficult problem in the detection of abnormal working status of power equipment, and provides a new technical solution for power grid equipment status detection and diagnosis.

This invention belongs to the field of image processing technology, specifically relating to an online monitoring method and system for the drying morphology of instant goat milk powder. The method includes: acquiring images of the fluidized bed interior to construct a spatiotemporal fluidized image tensor; obtaining fluidization confidence using eigenvalue decomposition of the local spatiotemporal structure tensor matrix to determine the effective fluidized region; performing morphological opening operations on the effective fluidized region using a sequence of structuring elements, statistically summing gray values ​​and constructing a morphological pattern spectrum to obtain the morphological pattern spectrum entropy; obtaining the pulsating velocity vector using an optical flow algorithm, thereby obtaining the average particle pseudo-temperature and coefficient of variation; and obtaining the fluidization steady-state potential energy index based on the morphological pattern spectrum entropy, average particle pseudo-temperature, and coefficient of variation, and generating control commands. This invention achieves monitoring of complex and abnormal conditions such as the initial stage of dead bed formation and channeling through the fluidization steady-state potential energy index.

The application relates to the technical field of detection of electrified railway contact network systems, and discloses a contact network system fluorescent fingerprint intelligent analysis system, which comprises a visual unit, an emitting unit, a deflection unit, a receiving unit, a detection unit and a main control unit. The visual unit calculates relative motion parameters of insulators, drives the deflection unit to perform reverse compensation motion, so that a laser landing point remains stationary relative to a target surface within an integration time. The detection unit respectively collects parallel and vertical polarization components of Rayleigh scattering light and Stokes luminescence after spectral separation. The main control unit calculates a surface roughness factor by using the Rayleigh scattering component, carries out numerical correction on a polarization anisotropy parameter of the Stokes luminescence, eliminates the influence of geometric depolarization, and extracts fluorescent lifetime and rotation-related time characteristics. The application solves the fluorescent detection distortion problem caused by rough surfaces in a dynamic environment, and realizes multi-dimensional diagnosis of the contamination type and the substrate aging degree of the insulator surface.

The invention belongs to the field of computer aided decision making, and particularly relates to a clinical aided decision making system and device based on a decision-making tree and a storable medium. The system comprises an acquisition unit used for acquiring pain information of a patient, wherein the pain information comprises whether a pain part can be positioned or not and the pain part; the decision-making unit is used for inputting the pain information into a decision-making tree model and then outputting pain causes of the patient; the decision-making unit comprises two sub-modules. The first sub-module is used for obtaining a first-level judgment result based on whether the pain part can be positioned or not. The second submodule is used for obtaining a second-level judgment result based on the first-level judgment result and the pain part; and the decision-making unit outputs the pain cause of the patient based on the second-stage judgment result. According to the method, efficient judgment of clinical pain reasons is realized, and doctors are effectively assisted in decision making.

The invention relates to the technical field of subway monitoring, in particular to an automatic subway monitoring and early warning system based on artificial intelligence, which comprises a hygrothermograph, an ultrasonic radar, a thermal imager and a central processing module, the central processing module corrects the tunnel ultrasonic point cloud according to the temperature in the tunnel and the relative humidity in the tunnel and generates a temperature position point cloud after registering the tunnel infrared image with the ultrasonic point cloud, and the difference between the temperature position point cloud and a laser point cloud in the prior art is that each point in the temperature position point cloud has a corresponding temperature. The temperature position point cloud represents the tunnel structure and also represents the temperature distribution in the tunnel, and the technical feature of the temperature position point cloud helps a subway monitoring neural network model to more easily diagnose tunnel diseases caused by temperature changes. According to the invention, the multi-modal fusion technology, the ultrasonic point cloud scanning technology and the infrared imaging technology are combined to realize multi-modal, moisture-resistant and high-reliability metro automatic monitoring and early warning.

The invention discloses application of serum alpha-non-red blood cell ghost protein 1 in preparation of drugs for diagnosing heart light chain amyloidosis diseases, and belongs to the technical field of biological medicines. The serum SPTAN1 protein is used for preparing the medicine for diagnosing the heart light-chain amyloidosis disease, the medicine is limited to be an enzyme linked immunosorbent assay kit, and the kit comprises an antibody of the SPTAN1 protein and further comprises a pre-coated plate, a standard substance, a coating buffer solution, a sample diluent, a confining liquid, horse radish peroxidase, an eluent and a color developing agent. The concentration of the SPTAN1 protein in the serum is measured through an enzyme linked immunosorbent assay kit, and early diagnosis of the heart AL amyloidosis disease is carried out. The invention provides a novel, efficient and sensitive biomarker, namely the serum SPTAN1 protein, early diagnosis of heart AL amyloidosis is realized by detecting the concentration of the SPTAN1 protein in serum, and the serum SPTAN1 protein has an important clinical application prospect.

The invention relates to the technical field of intelligent manufacturing and fault diagnosis, and discloses a laser cutting machine rack fault diagnosis method based on deep learning. Through constructing a rack image diagnosis model and a rack operation diagnosis model, automatic, rapid and accurate diagnosis of rack faults is realized. Firstly, a model is constructed according to historical data; secondly, rack picture data are collected and preprocessed when the laser cutting machine is started, and faults are judged through the image diagnosis model; if no fault exists, rack operation data are collected and preprocessed, and the fault is judged through an operation diagnosis model; when any model judges that a fault exists, a corresponding maintenance strategy is triggered; and finally, recording related data to a database, and sending a replacement prompt when the rack reaches the service life. According to the method, the diagnosis efficiency and accuracy are improved, the manual intervention cost is reduced, and powerful support is provided for intelligent maintenance of the laser cutting machine.

The invention relates to the field of electromagnetic sensitivity diagnosis, in particular to an electromagnetic sensitivity diagnosis method and a computer device, and realizes targeted diagnosis of the sensitivity of electromagnetic equipment in a complex environment. The method comprises the steps that a pulse signal with an adjustable rising edge is selected as an excitation signal, the rising edge of the excitation signal is adjusted, a tested object is tested, and if a sensitive threshold value of the tested object is directly related to the rising edge of the pulse signal, it is judged that the tested object is sensitive due to the fact that a differentiating circuit part is disturbed; the method comprises the following steps: selecting a pulse signal with an adjustable time duty ratio as an excitation signal, adjusting the time duty ratio, testing a tested object, if a sensitive threshold value of the tested object is related to the time duty ratio and the sensitive energy of the tested object is a fixed value in a fixed time period, judging that an integral circuit part of the tested object is disturbed, and otherwise, judging that the integral circuit part of the tested object is disturbed. Otherwise, whether the tested object is sensitive due to the fact that the digital logic circuit part is disturbed or not is diagnosed. The method is suitable for electromagnetic sensitivity diagnosis.

The application discloses a single-exposure compressed ultrafast coherent modulation imaging device. By combining coherent modulation imaging and compressed ultrafast imaging technology, a sample to be measured is illuminated by coherent light, after the sample diffracts the light wave, the phase modulation of a random phase modulation plate is carried out, the integral intensity distribution of the modulated wave front is recorded by deflection on a detection plane by using a slit fully opened stripe camera, a single compressed diffraction pattern is obtained, a spatial two-dimensional to space-time three-dimensional image reconstruction algorithm based on a plug-and-play framework and a depth image priori is developed, and data recovery is carried out on the collected image by using the algorithm. Compared with traditional ultrafast optical imaging and phase imaging technology, the application can quantitatively obtain complete intensity and phase information of a transient scene in a single measurement process, has the characteristics of picosecond time resolution and real-time quantitative intensity-phase imaging capacity in single-exposure phase imaging, and can be widely applied to various ultrafast phenomena requiring phase observation.

The invention belongs to the technical field of isolation switch fault detection, and particularly relates to an isolation switch fault diagnosis method and system, computer equipment and a medium. The method comprises the following steps: S1, acquiring a visible light image and an infrared image of a to-be-detected isolating switch; s2, preprocessing the obtained visible light image and infrared image; the preprocessing comprises a visible light image and an infrared image; converting the visible light image into an IHS image through IHS conversion; s3, converting the pre-processed visible light image into an IHS image through IHS conversion; taking the temperature data of the preprocessed infrared image as a brightness component of the infrared image; replacing the brightness component in the IHS image with the brightness component of the infrared image to obtain a fused image; and S4, performing fault diagnosis on the disconnecting switch according to the fused image. According to the invention, the technical problem of low fault diagnosis accuracy of the disconnecting switch in the prior art is solved.