701results about How to "Implement diagnostics" patented technology

The invention provides a layered exchange and control method for real-time monitoring system data by power dispatching. A CIM / XML (common information module) / (extensible markup language) interface, a CIM / E interface and other models of the electric power subsystems are processed and combined to establish a device model of the large power grid, therefore, perfect interaction of various application data including SCADA (supervisory control and data acquisition) real-time data, WAMS (wide area measurement system) data, EMS (enhanced message service) advanced application data and dynamic security analysis warning information can be realized, and power system control commands among the systems can be smoothly transmitted and executed. A data acquisition and exchange function can support multiple communication protocols, multi-application, multi-type and large amounts of data, satisfy the real-time requirements of all transmission environments, and provide comprehensive multi-scale real-time operation information of the power grid for other application analysis systems. A close integration mode is used to realize application integration and each subsystem and application can be monitored and managed, so a uniform access interface and a man-machine interface can be ensured to provide for each application system.

The invention relates to the field of specific molecular identification and diagnosis reagent and in particular discloses a near infrared fluorescent dye with structural formulas I and II; and the invention also discloses a near infrared molecular probe which is obtained through covalent bonding between the near infrared fluorescent dye with the structural formulas I and II and a ligand of specific molecules. The near infrared molecular probe can be used for early diagnosis of turmour diseases.

The invention discloses a fault diagnosis system and a method for a train based on a fault tree. The system comprises a remote detection and diagnosis subsystem and a data processing subsystem; the remote detection and diagnosis subsystem is used for collecting the fault data and event record environment data of train equipment as well as sending the data to the data processing subsystem;the remote detection and diagnosis subsystem comprises a storage module, a fault intelligent analysis module and an expert diagnosis knowledge base module; the storage module is used for storing the fault data and event record environment data; the fault intelligent analysis module is used for reconstructing the fault data and event record environment data and configuring a fault structure;the diagnosis is carried out by the expert diagnosis knowledge base module; the diagnosis result is received and output; the expert diagnosis knowledge base module is used for diagnosis according to the input fault structure and fault tree to generate the diagnosis result. The system and method provided in the invention can diagnose the train fault completely, analyze the fault automatically;and the system and method have the advantages of high diagnosis reliability, simplification and high efficiency.

The invention relates to a battery management system. The battery management system comprises a monitoring device, a mobile terminal and a cloud processor, wherein the monitoring device is used for detecting and transmitting working parameters of each single battery in real time when the battery is charged or discharged, comparing the working parameters of the single battery with the standard parameters, automatically adjusting the working state of the single battery when the working parameter exceeds the standard parameters and transmitting alarming information; the mobile terminal receives and displays the alarming information; the cloud processor receives and stores the working parameters. The invention aims at providing the battery management system, the voltage and temperature of the single battery are monitored and recorded in real time, the working state of a battery pack and the charging-discharging times are detected, and the integral temperature of the battery pack is detected, so that the working state of a charging-discharging device and a heating device is controlled, the functions such as battery quantity management, temperature management, fault pre-judgment and diagnosis and the like can be realized, and the safety and reliability of the battery can be guaranteed; a great amount of data is stored in a cloud server and can be pushed to enterprises for producing the battery or he battery management system users.

The invention discloses an asynchronous motor fault monitoring and diagnosing method based on a model. The method comprises the following steps: firstly, acquiring a three-phase input voltage signal and a three-phase output current signal of an asynchronous motor which can be normally operated, establishing a mathematical model to serve as a fault-free model; carrying out parallel running on the fault-free model under driving of a same input voltage u to obtain a residual signal d; then carrying out time domain analysis on the residual signal d, determining a threshold value eta of the residual signal of the asynchronous motor according to the 3 sigma principle, and judging whether a fault occurs or not by monitoring whether a residual effective value dRMS exceeds the threshold value eta or not when the asynchronous motor is stably operated; carrying out frequency domain analysis on the residual signal d again, and determining the fault type according to a fault feature frequency component fF appeared in a residual spectrum. The monitoring and diagnosing method disclosed by the invention can effectively weaken adverse effects on motor fault monitoring and diagnosis by an input voltage and improve the signal-to-noise ratio of a fault signal, thereby improving the sensitivity of the motor fault monitoring and the reliability of the fault diagnosis.

The invention discloses a distributed remote vibration monitoring and fault diagnosis system of a large steam turbine-generator, comprising a database subsystem in which steam turbine-generator vibration data is stored, a workstation subsystem capable of providing real-time status monitoring, historical data analysis and fault symptom analysis, and a Web server subsystem provided with remote monitoring and fault diagnosing software, wherein, the subsystems are connected with one another by the Ethernet, and are integrated by a Web services technique. A method combining a traditional fault diagnosing technique based on Fourier analysis, a novel independent component analysis feature extraction technique, a Hilbert-yellow time frequency analysis technique, an empirical decomposition algorithm trend analysis technique and a self-organizing map neural network is adopted for monitoring and diagnosing in the system, thus improving the monitoring real time and the accuracy of diagnosis. The system has strong universality and reliability; and the system configuration can be adjusted, thus conveniently realizing distributed remote monitoring and diagnosis based on the internet as a medium.

The invention discloses a detection method and system of water content in a plant leaf based on a multispectral image. The detection method comprises the following steps: a) getting monochrome images of a green light wave band, a red light wave band and a near-infrared wave band of the plant leaf of a sample; b) getting grayscale information of the monochrome images and getting grayscale texture characteristic quantity of the plant leaf of the sample; c) transforming the grayscale information to reflectivity information of the plant leaf of the sample and getting leaf vegetation index value through the reflectivity information; d) taking the grayscale texture characteristic quantity and the leaf vegetation index value as input vectors, taking actually measured water content value of the plant leaf as an output vector, and establishing a model; and e) getting the grayscale texture characteristic quantity and the leaf vegetation index value of the plant leaf to be detected by operating according to the steps a)-c) and putting into the model in the step d) to get the water content value of the plant leaf to be detected. The method disclosed by the invention can realize accurate, fast, non-destructive and real-time detection of the water content in the plant leaf.

The invention discloses a contact network failure detection and diagnosis method based on an unmanned aerial vehicle, which comprises the following steps: (1) image acquisition: carrying a video camera to shoot along a contact network by an unmanned aerial vehicle so as to respectively acquire contact network images under visible light and infrared light; (2) image graying; (3) image enhancement; (4) image segmentation; (5) image dissection; (6) image fusion: fusing Laplacian pyramid layers under visible light with corresponding Laplacian pyramid layers under infrared light, and carrying out image reconstruction on the fused Laplacian pyramid to obtain a contact network component image after the visible light image and the infrared light image are fused; and (7) carrying out image identification and failure judgment by a BP (back-propagation) neural network. The method can be used for effectively acquiring a contact network image in the operation process of a locomotive in a multidirectional multiangular real-time mode, automatically identifying the contact network component in the image, and judging whether the contact network fails and the type of the failure; and the judgment result is more accurate and reliable, and can better ensure the safety of railway transportation.

The invention discloses a rail transit vehicle door system fault diagnosis and early warning method based on multiple conditions. Operation data aiming at a door system is divided into initialized standard data, daily standard data and real-time operation data. Diagnosis and early warning can be accurately realized. Motor monitoring is used to monitor a parameter and a door controller IO signal in real time so as to diagnose a door-system typical fault, and a fault type of the door system, which can be diagnosed, is enriched. Through refining a fault characteristic, diagnosis precision is increased. A real-time health degree model is established, through a real-time parameter and a residual error characteristic of a model, a sub-health phenomenon is diagnosed, and a new method is provided for early warning of an early-stage fault. For a long-term slow degeneration sub-health problem of the door system, by using a traditional diagnosis method, a door system parameter can not be monitored; but, through using the method of the invention, by establishing a degeneration threshold model and comparing and analyzing a long-term change trend of a state parameter characteristic value, a long-term degeneration sub-health state can be identified, early stage early warning is realized and a good application prospect is possessed.

The invention relates to a rapid diagnosis and scoring method for a full-scale pathological section based on deep learning. Preprocessing is carried out on a full-scale pathological section staining map; the node number of a full-connection layer and an output layer of a traditional AlexNet neural network is changed to meet the needs of practical problems, a marked training sample set is selectedto train two AlexNet neural network models for diagnosis and scoring, and high-dimensional feature information of a lesion area is extracted; with the two improved AlexNet neural network models aftertraining, the full-scale pathological section staining map is diagnosed and scored; and according to a diagnosis predicted probability, a probability heat map is drawn and the lesion area is identified visually, statistics of proportions of small sampling block numbers with different lesion degrees is carried out, and the lesion degree of a tissue is scored. Therefore, the diagnosis and Gleason scoring of the full-scale pprostate tissue pathological section are realized automatically; and the accuracy rate and the calculation rate exceed the average level of the artificial diagnosis substantially.

The invention provides a greenhouse information automatic monitoring method based on a suspension-type slide rail platform. The method realizes online patrol monitoring of greenhouse crop comprehensive information of image and infrared temperature characteristics of plant nutrition, water, diseases and diseases and pests as well as plant crown breadth, plant height and fruit growth characteristicsand the like through multi-sensor information fusion by adopting a multi-sensor system, utilizing a binocular vision multifunctional camera and combining with a laser ranging sensor and an infrared temperature sensor. By mounting the multi-sensor system on the suspension-type slide platform and combining an elevating system and an electric control rotation pan-tilt, not only accurate positioningand stationary point detection can be realized in the detection heading direction, but also multi-sensor information patrol detection at different detection distance, different pitching view fields and different detection angles is realized; and by adjusting the detection range and pan-tilt azimuth angle, the method not only can detect big plants, such as tomatoes and cucumbers, but also can meetdetection requirements of lettuce and medium and small plants in different growth periods.

The invention discloses an expert system based numerically-controlled machine tool fault diagnosis system and belongs to the technical field of fault diagnosis. Temperature sensors are connected with a temperature measuring unit which sends multi-channel temperature signals acquired by the multi-channel temperature sensors to a 32-bit processor main control panel; multi-channel vibrating sensors and multi-channel noise sensors are all connected with a data acquisition card which sends multi-channel vibration signals and multi-channel noise signals which are acquired through a bus interface technology to the 32-bit processor main control panel. The fault diagnosis system comprises a human-computer interaction interface and monitoring main program modules. A fault detection system is connected with the human-computer interaction interface, and fault maintenance personnel and technicians perform human-computer interaction through the human-computer interaction interface. By means of the system, acquired data are analyzed and processed to be sent to the fault diagnosis system, diagnosis results are analyzed, human-computer interaction is performed, and accordingly, the numerically-controlled system operation reliability is improved.

The invention discloses a torsional vibration error monitoring method for a shaft system of a steam turbine generator unit, a monitor and a system. The method includes the steps of S1, collecting output signals of rotation speed sensors, key phase sensors and bearing base vibration acceleration sensors on the steam turbine generator unit; S2, conducting Fourier transform on the collected output signal of each bearing base vibration acceleration sensor to obtain vibration spectrum signals; S3, extracting an amplitude value in the vibration spectrum signal of each bearing base vibration acceleration sensor under the corresponding rotation frequency; S4, obtaining the types of to-be-detected frequencies and separately extracting the corresponding amplitude value and phase under each type of to-be-detected frequency from the vibration spectrum signal of each bearing base vibration acceleration sensor to serve as feature parameters; S5, on the basis of a judgment principle and the extractedfeature parameters, judging whether or not torsional vibration errors of the shaft system occur in the steam turbine generator unit. By means of the method, real-time online monitoring of the torsional vibration errors of the shaft system is achieved.

The invention discloses a method for diagnosing a heat radiation system, comprising the steps of obtaining a temperature value related to a heat resistance value during the normal working of the heat radiation system, figuring out a temperature value related to a designed ideal heat resistance value according to a mathematical model of the system by using the power loss of the system; comparing the obtained value with the calculated value; and determining that the heat radiation system works abnormally if the difference between the obtained value and the calculated value exceeds a preset threshold. Therefore, by using the method disclosed by the invention, the working status of the heat radiation system can be determined in advance so that maintenance and repairing are arranged for the heat radiation system according to working.

The invention discloses an analog circuit fault diagnosis method and an implementation method of the analog circuit fault diagnosis method. The content includes the first part of analog circuit fault feature information extraction, the second part of fault classifier construction, and the third part of implementation of algorithm software. The analog circuit fault diagnosis method includes the following steps of constructing a fault feature information base, selecting an optimal mother wavelet through an information entropy maximizing principle, conducting wavelet decomposition on response nodes of a measured circuit, extracting the optimal feature of the measured circuit, conducting dimensionality reduction on the fault features through principal component analysis, conducting fault classification and intelligent diagnosis, constructing a fault diagnosis device according to the obtained fault feature information and through a multi-classifier cascade connection model and the classifier integration technology so as to recognize existing faults and causes of the faults, and conducting specific implementation on the algorithm through a C#.NET platform and through combination with the Weka software. The diagnosis method and the implementation method have the advantages of being high in fault diagnosis performance, wider in diagnosis range, higher in algorithm robustness and higher in interpretability.

The present invention provides a MIPS-architecture-based CPU outlier location diagnosis method which including the following procedures: machine codes of a present function is scanned; a migration in instructions on constructing a function stack is analyzed and a function stack pointer of an invoking function is computed; the migration in the storage function of a return address is analyzed, the storage location of the return address in the function stack of the present function is computed and the return address is received. The function stack pointer of the call function and the return address obtained according to the present invention can output a call relation of the present function so as to locate and diagnose failure.

This invention relates to a suspension chip technology for the gene test to human papillomavirus used in testing and typing 26 kinds of papillomaviruses, in which, specific probes of which are crosslinked on 26 kinds of fluorescent microspheres to be reacted with the tested specimens then reacted with the report molecules labeled by the fluorescein to test the type specific nucleic acid of the virus by a fluorescent test device, which can test 26 kinds of ordinary HPV once and increases the test efficiency greatly and overcomes the shortcoming of missing testing the potential infections in the serology and immunity test method to realize early diagnosis to HPA diseases.

The invention discloses an intelligent multifunctional stethoscope, which comprises traditional and electroacoustic stethoscope components. The electroacoustic stethoscope component comprises two sound pickups, a pair of auscultating heads provided with micro speakers and a hardware box, wherein a circuit in the hardware box comprises a singlechip, a power amplifying circuit and a wireless transmitting module; and the singlechip comprises a microprocessor, an analog-digital and digital-analog conversion circuit, a memory chip and a USB interface circuit. The intelligent multifunctional stethoscope is characterized in that a microphone channel comprises a cavity of the hardware box, a rubber tube which is arranged in the center of the lower bottom surface of the cavity and is provided with a sound head at the outer end thereof, and two sections of rubber tubes arranged on two sides of the upper top surface of the cavity, wherein the outer ends of the two sections of the rubber tubes are inserted with an L-shaped metal bent pipe respectively and the end heads thereof are provided with the auscultating heads; the microphone channel also comprises a U-shaped elastic metal connecting sheet of which two ends are sleeved on the lower part of the bent pipe respectively; and one sound pickup is arranged in a cavity of the sound head, while the other sound pickup is arranged on the top surface of the hardware box. The electroacoustic stethoscope component can also be provided with a corresponding wireless transmitting and receiving device so as to achieve distant auscultations and diagnoses.

The invention discloses a rolling bearing fault diagnosis method using particle filtering and spectral kurtosis, and relates to particle filtering denoising processing and spectral kurtosis calculation. According to the method, on the basis of quick spectral kurtosis, by use of particle filtering denoising processing, the signal-to-noise ratio is increased, and the problem that the quick spectral kurtosis is low in feasibility under the condition of low signal-to-noise ratio is solved. The method comprises the following steps: constructing a state equation of a vibration signal; then extracting background noise, and taking a sum of the background noise and the state equation as an observation equation; constructing a state space model according to the state equation and the observation equation; reestimating the signal by a particle filtering algorithm to obtain a new sequence which is a denoised signal; finally obtaining an optimal analysis frequency band by a quick spectral kurtosis method so as to obtain a fault frequency. According to the rolling bearing fault diagnosis method, the noise interference in a fault signal is reduced, the signal-to-noise ratio is increased, and diagnosis of early weak fault of a rolling bearing is realized.

The invention discloses an online self-adaptive working condition monitoring and fault diagnosis method for a process industrial course and belongs to the technical field of fault monitoring and diagnosis of complex industrial courses. The method comprises the following steps of firstly, analyzing historical observation data under a normal working condition, introducing an elastic regression network combining Lasso constraints with Ridge constraints to establish an industrial course fault monitoring model on the basis of sparse principal component analysis, and then obtaining a course controllimit to industrial course fault monitoring statistics; during online monitoring of industrial course faults, adopting an order-1 matrix correcting algorithm for resolving a covariance matrix of the online monitoring data, conducting recursion updating on a load matrix of the sparse monitoring model to obtain the course control limit to the course fault monitoring statistics matched with the working condition, and achieving self-adaptive fault detection in the process industry course; finally, according to the detected faults, adopting a contribution plot method for obtaining specific causes of the faults. By means of the method, the faults of the process industry course with complex and changeable working conditions can be self-adaptively monitored for a long time; the method has the advantages of low calculation complexity, high precision, a low report missing rate and the like.

The invention provides a mineral processing equipment operating state monitoring system and a mineral processing equipment operating state monitoring method. The mineral processing equipment operating state monitoring system comprises a local server, a plurality of data acquisition sensors and a video acquisition module, wherein input ends of the plurality of data acquisition sensors are connected with monitored equipment in a mining plant, output ends of the plurality of data acquisition sensors are connected with the local server, and an output end of the video acquisition module is connected with the local server. Operating state data and equipment index data of the monitored equipment are acquired by means of the data acquisition sensors in real time, operation videos of the equipment are acquired by means of the video acquisition module in real time, the operating state data and the equipment index data of the equipment are monitored by means of the local server, the local server sends out early warning when the equipment index data exceeds a threshold value, calculates an equipment failure rate of the corresponding equipment and OEE analytical value of the equipment, and performs online diagnosis on the operating state data acquired in real time by utilizing a KPCA model. The mineral processing equipment operating state monitoring system and the mineral processing equipment operating state monitoring method can realize the real-time monitoring of the equipment operating state by the mineral processing production-manufacturing execution layer.

The invention provides a fault diagnosis method for electromechanical equipment based on a gray model, which comprises the steps of first, collecting operation parameters of the current electromechanical equipment; second, enabling the collected operation parameters to serve as an original modeling sequence, building a gray model, and predicting relevant data in the system by using a prediction model; third, obtaining sample data of the operation parameters of the electromechanical equipment in different fault states, building a reference model of faults of the electromechanical equipment, finding out a fault source of the system and determining a fault parameter of the system; fourth, magnifying the variation trend of the data, performing fault diagnosis, performing consistency checking on a predicted value and a solution of an analytical model, substituting the predicted result into a parameter estimation model to solve, obtaining a predicted value of the fault parameter at a futuremoment, predicting the fault parameter according to the prediction model and the parameter estimation model, and judging a fault cause according to a mechanical relationship between the variables. Thefault diagnosis method can realize diagnosis for the fault type of the electromechanical equipment during the operation.

The invention discloses a compound fault diagnosing method for a planetary gearbox by using matrix wavelet transformation. The compound fault diagnosing method comprises the following steps of: firstly, transforming an acquired one-dimensional vibration signal of a planetary gearbox into a multi-dimensional input signal by using a repeated sampling pre-processing mode in matrix wavelet transformation; secondly, constructing an optimal matrix wavelet function by a lifting method, adaptively decomposing the multi-dimensional signals by using the constructed optimal matrix wavelet function, and decomposing a plurality of fault features into signals in different branches; and finally, extracting and identifying represented planetary gearbox faults in various frequency band signals by a spectrum enveloping method, and separating and diagnosing features of composite faults of the planetary gearbox according to failure mechanism analysis. The result of the method is reliable, and the real-time capability of the method is high. Moreover, the method is simple and practicable, and is applicable to diagnosing compound faults of planetary gearboxes of equipment transmission mechanisms such as a satellite communication antenna, a wind driven generator and a heavy-load crane.

The invention discloses a composite nano-drug for integrated tumor diagnosis and treatment and a preparation method thereof. A chemotherapeutic drug, a photodynamic drug and shRNA plasmids capable of reversing multidrug-resistant MDR1 genes of tumor cells are located through a nano-carrier, a magnetic nano-sphere with precious metal nano-particles arranged on the surface serves as a kernel of the nano-carrier, mesoporous silica with the surface modified with amino groups serves as a shell of the nano-carrier, and a polyelectrolyte thin film layer structure with pH response is formed on the surface of the kernel-shell structure through a layer-by-layer self-assembly technology. The composite nano-drug integrates targeted drug delivery, multimode imaging, chemotherapy, photodynamic therapy and gene therapy, can be applied to early diagnosis of tumors, image mediation and real-time monitoring treatment at the same time and overcomes the defect that tumor diagnosis and treatment are separate in the prior art; besides, the composite nano-drug is simple in preparation process, high in operability and suitable for industrial production.

The invention provides a pressure monitoring system marking instrument of an automobile tyre and a marking method thereof. The marking instrument includes two parts; one is a tyre pressure sensor and the other is a diagnosing marking device; the tyre sensor includes a central control module 2, a sensor 1, a RF transmitting circuit and a lower frequency receiving circuit; the tyre sensor is connected with the lower frequency receiving circuit and the central control module 2; the central control module 2 is connected with the RF transmitting circuit and the sensor 1; the marking instrument consists of an SCM, the lower frequency transmitting circuit, the RF receiving circuit, a human-computer interface and a communication interface; the SCM is connected with the lower frequency transmitting circuit; the tyre sensor is connected with the RF receiving circuit which is connected with the SCM; the SCM is connected with the human-computer interface and the communication interface. The invention can simply realize the diagnosing and the marking of the pressure monitoring system sensor of the tyre, is suitable for the automobile types of any collocation, is not interfered by the tyre pressure monitoring system on other automobiles and can not affect other automobiles.

The invention discloses a steam turbine rotor fault diagnosis method based on LSTM, and belongs to the technical field of mechanical fault diagnosis. Firstly, multi-point acquisition sensors are deployed and controlled, and vibration signals of various typical turbine rotor faults are collected as a training set and a verification set. Secondly, the steam turbine rotor vibration signals are extracted from a power plant DCS system to serve as a testing set. Thirdly, the training set, the testing set and the verification set realize fusion of multi-point signal data and data enhancement throughsignal division, stacking and other operations. Fourthly, a neural network based on the LSTM is constructed, the training set and the verification set are used for completing training of the network,and finally, maintenance of a diagnostic model is achieved in cooperation with an actual diagnostic task, and finally the steam turbine rotor fault diagnosis is realized on the testing set.

The invention relates to a novel mobile hospital system which is characterized in that: the system is composed of six mobile medical treatment units including an on-site first aid group (1), an outpatient service group (2), a surgical treatment group (3), an on-site ward group (4), a sorting inspection group (5) and medical technologies guarantee group (6); each mobile medical treatment unit is respectively composed of one to more than two cars provided with medical treatment function, each car is composed of a car chassis, a carriage and medical treatment facilities and medical drugs arranged in the carriage; at least three carriages of the mobile medical treatment units are expandable carriages which are provided with one-side expansion structure or two-side expansion structure; adjacent carriages are connected to form a multi-functional diagnosis and treatment platform which is easy to assemble and disassemble. The invention can finish out-patient reception, realize mobile inspection and diagnosis, perform basic surgical operation and carry out ward and rehabilitation on the patients in postoperative period, provide medicaments and medical facilities, and provide food, water and electricity for patients and hospital staff, etc.

The invention discloses a general fault detecting and maintenance method for equipment. The general fault detecting and maintenance method for the equipment comprises the following steps of carrying out modeling on maintenance knowledge of the equipment faults, automatically detecting and diagnosing the faults and carrying out auxiliary maintenance based on a model, simulating maintenance training based on the model, optimizing an experience knowledge model in real time in the maintenance training process, and synchronizing maintenance information and experience knowledge model information in the maintenance training process. Compared with an existing equipment maintenance method, the general fault detecting and maintenance method for the equipment enables the accumulation and the sharing of equipment maintenance experience knowledge and the automatic diagnosis of equipment faults to be achieved, and assistance can be provided for related maintenance strategies through the analysis and the exploitation of the maintenance record information. In addition, effective transmission of the experience of experts to primary maintainers is achieved through the step of simulation training. The general fault detecting and maintenance method for the equipment is especially suitable for chemical defense troops and other occasions where a large amount of complex equipment needs to be maintained frequently and where the turnover rate of maintenance personnel is high. Practice proves that the general fault detecting and maintenance method for the equipment can obvious improve equipment maintenance efficiency, promote maintenance levels and improve maintenance quality.

A worm wheel type driving pipeline robot system based on a high-strength spring wall press apparatus is characterized by comprising a body mechanical structure and a control system; the body mechanical structure comprises a spiral head, a body and a holding frame; the body is a main body of the robot; the working method is that the robot system is a fully-closed loop servo control system taking wheel linear speed as a feedback quantity through a fuzzy adaptive PID control algorithm. The advantages of the robot system are: (1) the robot system can steadily and flexibly move within a tilted and vertical pipeline with a minimal variable diameter; (2) the robot system exhibits good dynamic responsiveness, robustness and stability; (3) signal transmission is convenient; and (4) the robot system is highly controllable and is high in accuracy.

The invention provides a method and a device for online diagnosis of a battery short circuit fault, and relates to the technical field of batteries. With the adoption of the diagnostic method providedby the embodiment of the invention, a current state of a battery cell can be determined, and the short circuit fault or a normal state of the battery cell can be judged. The diagnostic method requires a small amount of data and low calculation cost, and can quickly diagnose whether the short circuit fault occurs in any battery cell or one of the cells in a battery pack. The charging current and the discharging current used in the diagnosis process are small; charging and discharging are carried out in one direction, thereby realizing the diagnosis of the short circuit fault in the battery cell under a constant current condition.