50results about How to "Avoid unexpected accidents" patented technology

The invention discloses a tunnel construction informatization dynamic monitoring system and a monitoring method thereof. The system comprises an industrial personal computer, a PLC (programmable logic controller) wireless controller, a high-configuration digital camera, a deformation monitoring sensitization part, digital display acquisition equipment, wireless transmission equipment and a dynamic design feedback device. The method includes: 1), controlling commands to be transmitted to the PLC wireless controller by a computer control main station and controlling the digital camera and the digital display acquisition equipment; 2), acquiring internal force monitoring data by the aid of the digital display acquisition equipment; and acquiring a deformation monitoring image via the deformation monitoring sensitization part by the digital camera; 3), transmitting a monitored value to the computer control main station by the aid of the wireless transmission equipment; 4), analyzing results by the computer control main station via a real-time analysis module; and 5), transmitting the analyzed results to the dynamic design feedback device, analyzing relative difference of the really monitored value and a standard value, and early warming in real time by an early warning module. The tunnel construction informatization dynamic monitoring system is scientific, safe and practical, the method is convenient, instant, fast and effective, and safety problems in construction monitoring of tunnels are avoided.

The invention discloses a method and system for diagnosing bearing faults of a large-size wind turbine bearing. The method comprises the following steps of: firstly, measuring a vibration signal of a wind turbine main shaft bearing; secondly, automatically extracting a resonance frequency band of the bearing faults by adopting a self-adaptive spectrum kurtosis technology and obtaining a fault feature frequency by adopting a narrow-band filtering and envelope demodulation technology; and finally, comparing the fault feature frequency with a theoretical calculating value, positioning and recognizing to obtain a bearing fault type. The method and system disclosed by the invention are suitable for actual operation conditions of an actual wind turbine; and according to the method and system, the influence from non-stable work conditions and work condition noises of the wind turbine is overcome, the resonance frequency band caused by the bearing faults can be automatically recognized without manual operation and bearing fault types can be recognized automatically and quickly.

The invention relates to an online monitoring and condition maintenance management system, which is a layered and distributed data management and computer system, faces to a plurality of substation high-voltage equipment and adopts a multi-server level-to-level management system. The online monitoring and condition maintenance management system comprises an online monitoring equipment layer, a substation data network layer and a remote data center. By constructing and applying the management system, the online monitoring system is promoted to be transitioned to an all-round, networked, intelligent and comprehensive condition monitoring, diagnosis and service management system from the traditional isolated and static experimental system. The system provides a powerful technical supporting platform for overcoming periodic maintenance blindness, reducing maintenance times, reducing maintenance expenses, delaying maintenance cycle to the utmost extent, truly realizing condition maintenance for power system operation and management departments and further improving the operation rate of the equipment and the safety of power grids.

The invention discloses a clustering analysis-based intelligent fault diagnosis method for an antifriction bearing of a mechanical system. A diagnosis model is trained firstly, comprising the following steps: collecting standard vibration signal samples of five fault and normal bearing states of an outer ring, an inner ring, a rolling body and a holding frame; decomposing signals, extracting original vibration signals as well as time domain and frequency domain characteristics of decomposed components to obtain an original characteristic set; removing redundancy by means of a self-weight algorithm and an AP (Affinity Propagation) clustering algorithm to obtain Z optimal characteristics; classifying sample statuses by means of the AP clustering algorithm to obtain a well-trained diagnosis model. A fault diagnosis is performed by the following steps: collecting real-time vibration information of a bearing, decomposing the signals, extracting the optimal characteristics determined by the model, importing the AP clustering algorithm to cluster parameters based on the diagnosis model, comparing with the Z characteristics known in the model to obtain a category of a current unknown signal, so as to complete the fault diagnosis. According to the clustering analysis-based intelligent fault diagnosis method disclosed by the invention, both EEMD (Ensemble Empirical Mode Decomposition) and WPT are utilized to decompose the vibration signals, more refined bearing status information can be acquired, the self-weight algorithm and the AP clustering algorithm increase intelligence of the diagnosis, and therefore accurate diagnosis is ensured.

The invention discloses a time-frequency diagram processing method and system for mechanical equipment monitoring vibration signals. The time-frequency diagram processing method comprises the steps that firstly, linear or bilinear time-frequency transformation is carried out on collected mechanical equipment monitoring vibration signals to obtain a time-frequency diagram TFRs, and average threshold processing is carried out on the time-frequency diagram TFRs to obtain a sparse time-frequency diagram STFRs; secondly, time-frequency compression sampling is carried out on the STFRs in a random sampling mode to obtain a matrix M<STFR > with dimensionality being k times the dimensionality of an original matrix; thirdly, the parallel class FISTA proximal decomposition method is used for reconstructing an STFRs* through iterative computation. The time-frequency diagram processing system comprises a monitoring front-end machine and a server connected with the monitoring front-end machine. The monitoring front-end machine comprises an A / D data collection module, a time-frequency transformation module, an average thresholding module and a time-frequency compression sampling module and is connected with a vibration sensor. The server comprises an on-line data storage module and a reconstruction algorithm module. According to the time-frequency diagram processing method and system, the dimensionality of the reconstructed time-frequency diagram is greatly reduced, data storage and transmission are facilitated, and time-frequency analysis technology can be widely applied for mechanical equipment fault real-time quantitative analysis and diagnosis.

The invention discloses a composite fault diagnosis method and system of a gear case. The composite fault diagnosis method comprises firstly measuring and storing vibration signals of the gear case; then adopting 1 / 4 sampling translation dual tree complex wavelet transform for resolving; and finally extracting a plurality of fault characteristics by adopting an energy operator demodulation method in a plurality of sub-band signals obtained by resolving, and further identifying a composite fault mode. The composite fault diagnosis method is fused with a complementary characteristic using dual tree complex wavelets and energy operator demodulation, and the obtained gear case is high in recognition capability of composite fault characteristic extraction. By means of speed capability of parallel realization of the dual tree complex wavelet transform and the energy operator demodulation method, partial polling and on-line monitoring of the gear case in a working condition can be completely applied, sudden accidents can be avoided, and the composite fault diagnosis method and system can be applicable to gear case portions in different models.

The invention discloses an embedded data acquisition system-based mechanical fault diagnosis device of a mining fan. The embedded data acquisition system-based mechanical fault diagnosis device comprises an intrinsically safe sensor, an embedded data acquisition system and a fault judgment system, wherein the intrinsically safe sensor comprises an intrinsically safe acceleration sensor, an intrinsically safe temperature sensor and an intrinsically safe rotating speed sensor; the fault judgment system comprises an online monitoring module, a fault diagnosis module, an alarming and maintaining module and a data storage module; and the intrinsically safe acceleration sensor, the intrinsically safe bearing temperature sensor and the intrinsically safe magnetoelectric rotating speed sensor are connected to the online monitoring module, the fault diagnosis module, the alarming and maintaining module and the data storage module by an input interface of the embedded data acquisition system and an output interface of the embedded data acquisition system through circuits. The embedded data acquisition system-based mechanical fault diagnosis device disclosed by the invention is used for monitoring the parameters, such as vibration quantity, temperature, rotating speed and displacement on line when the fan works, diagnosing the mechanical faults of the fan and providing corresponding solutions and is suitable for the online monitoring and mechanical fault diagnosis of various fans.

The invention provides an urban tunnel construction monitoring method including a half-covered excavation and sequential construction method and a monitoring system, and belongs to the field of engineering monitoring. The method comprises a tunnel construction method including the half-covered excavation and sequential construction method, a manual monitoring method and an automatic monitoring method. The half-covered excavation and sequential construction method comprises steps of constructing one half of a road during the deep foundation pit construction; after excavating the road for a certain depth, enclosing the top; carrying out the rest engineering under an enclosed top cover; carrying out open-air excavation and construction o the other half of opened part; and after the road is excavated to a standard height, constructing a main body structure and carrying out water-proof operation from the bottom to the top from the lowest layer structure until carrying out backfilling and restoring tube circuits to the basic surface. The manual monitoring method comprises steps of presetting a test piece and a test point in a fixed position; and using an instrument to carry out manual measurement to acquire related data. The automatic monitoring method comprises the step of achieving construction monitoring on deformation and force bearing of a tunnel support system through the data automatic acquisition technology, the network wireless communication technology and the database application technology so that a data guidance dynamic construction scheme can be dynamically fed back and monitored at any time.

The invention provides a big data sensitive characteristic optimization selecting based equipment failure early warning method and system. The method includes the following steps: collecting vibrationdata of equipment under a normal working condition, and performing time and frequency domain index feature extraction on the vibration data to form vibration-like feature vectors; applying a compensation distance assessment technique to perform optimization selecting on the vibration-like feature vectors, and commonly forming a sensitive vector set by the optimally selected vibration-like featurevectors and the process data of the equipment under the normal working condition to be a training sample which supports a vector data description model so that a SVDD hypersphere model of the equipment under the normal working condition can be formed through training; and processing testing vibration data by adopting the above same steps, and forming a testing sensitive vector set with the obtained optimized selection feature vectors and the process data under a testing working condition to input to the SVDD hypersphere model under the normal working condition, and performing early warning analysis on output results when the equipment is abnormal or is about to be abnormal. Thus, the intelligent maintenance of the equipment can be realized.

The invention discloses an intelligent ship engine room monitoring alarm system based on a model. The system comprises a control system, a power system and a load system. A main propulsion system of aship shows a work state of the control system according to a change rule of parameters of a rotation speed fluctuation ratio, a rotation direction and a rotation speed. A thermal work process parameter of a host is used to represent a work state of the power system. A memory, one or more processors, and one or more programs are included. One or more programs are stored in the memory and are configured to be a process that one or more processors execute so as to complete fault type determination and an engine room system is integrally assessed. In the invention, through combining analysis of aconventional thermal parameter, mechanical states of motion parts are inspected and monitored according to a vibration signal so as to avoid a major sudden accident and realize integration assessmentof an engine room environment, marine engineer quality and a ship management level.

The invention relates to a cantilever type piling supporting structure and a combination stiffening filling pile in a branch anchor type piling supporting structure. The combination stiffening filling pile comprises a plain concrete pile shaft, a reinforcing cage, a profile steel skeleton which is coaxial with the plain concrete pile shaft wrapping up the reinforcing cage and the profile steel skeleton; the profile steel skeleton comprises more than one channel steels or I-beams; the reinforcing cage comprises longitudinal bars uniformly distributed on the circumference, ring-shaped reinforcing ribs and tie hoops encircling the longitudinal bars; the diameter of the plain concrete pile shaft is 400 to 1200 mm; and the pile length is 5-25 meters; more than one channel steels or the I-beams and the longitudinal bars of the reinforcing cage are positioned inside a tensile region of the same circumference or in the interior of the reinforcing cage; the channel steels or the I-beam ventral shields are uniformly distributed with shear keys from top to bottom.

The invention provides a method for predicting the residual life of complex rotating mechanical rolling bearings based on sparse coding. The method includes the steps: firstly, acquiring mechanical equipment monitoring vibration signals; secondly, performing time domain feature extraction, and building an over-complete dictionary serving as a base vector with time domain features as original signals according to a signal sparsity theory to guarantee validity of prediction information; thirdly, processing training data and testing data by regularization; fourthly, finding an optimal sparse weight; fifthly, performing failure trend prediction to omit training time of a traditional method, greatly reduce time burden and improve prediction accuracy and robustness of a system due to less parameter tuning; sixthly, performing residual life prediction to ensure residual life prediction accuracy of the bearings by high-precision prediction values.

The invention discloses an adaptive electromechanical equipment weak fault characteristic extraction method. The method comprises steps that firstly, vibration signals of electromechanical equipment are acquired; secondly, two-scale similarity transformation of selected initial multi-wavelets is carried out to improve the approximation order of a multi-wavelet scale function; thirdly, a multi-wavelet lifting matrix satisfying symmetry and translation constraint conditions is designed, and a multi-wavelet integration construction method is realized; and lastly, based on the vibration signals, agenetic algorithm is utilized to optimize free parameters, an adaptive multi-wavelet base function matching with fault characteristics is acquired, and an advantageous method is provided for weak fault characteristic extraction. The method is advantaged in that the dynamic matching bottleneck in the weak fault characteristic extraction process in the actual monitoring and diagnostic application of the electromechanical equipment is solved.

The invention provides a transformer state monitoring and diagnosis system. The transformer state monitoring and diagnosis system comprises a MDD host. The transformer state monitoring and diagnosis system is characterized in that the MDD host is connected with a transformer oil chromatogram on-line monitoring subsystem, a transformer partial discharge on-line monitoring subsystem, a transformer oil temperature on-line monitoring subsystem, a transformer winding deformation on-line monitoring subsystem, an iron core grounding current on-line monitoring subsystem and a transformer casing on-line monitoring subsystem. According to the MDD host, an integrated design ideas is adopted; with the transformer adopted as an objective, a hierarchical distributed network structure is adopted; remote, on-line, network centralized monitoring and fault diagnosis on the transformer can be achieved; and defects that the online monitoring systems of different equipment in one substation work independently in an isolated manner can be eliminated, and comprehensive fault diagnosis on the transformer can be realized.

The present invention discloses a fault diagnosis method for a reducer based on VMD (empirical mode decomposition) and PCT (polynomial frequency-modulated wavelet transform). The method comprises: selecting the modal component with most obvious features obtained by using the VMD algorithm as input of PCT to obtain a gear time-frequency diagram and a bearing time-frequency diagram respectively; according to the fault frequency and the cycle period on the gear time-frequency diagram, determining the position of a faulty gear for positioning a gear fault source; based on the number of obviously periodic time-frequency bands, giving out a determination result of the severity of the gear fault; and determining the type of the bearing fault according to the cycle period on the bearing time-frequency diagram. According to the method provided by the present invention, through the combination of gear diagnosis and bearing diagnosis, the diagnostic efficiency and the recognition accuracy are improved, and the complete diagnosis of the reducer is realized.

The invention relates to a health diagnosis method for a diesel generating set; and the method comprises the steps: combining a set health scoring system with a set fault recognition model, carrying the set health scoring system and the set fault recognition model on an intelligent IO controller to obtain a set of complete set health diagnosis system, and obtaining a current state operation healthindex of the set through employing set parameters as input; a parameters change is reflected by a health score in real time when the parameters change, and early warning is carried out in advance when a certain parameter is seriously deteriorated, so that fault prediction and fault diagnosis are realized. In the early stage of a fault or even when the state of the set is slightly abnormal, judgment and warning can be conducted in time, the operation health state of the set is mastered in real time and prevented in advance, sudden accidents are prevented, and the stability of the set and the system safety are improved. And a set of universal fault prediction algorithm solution is established and is gradually developed to provide a whole set of intelligent function development solution forfault diagnosis and fault prediction for various ship mechanical auxiliary equipment.

The invention relates to the technical field of power system detection, and discloses a portable GIS partial discharge detection device. The portable GIS partial discharge detection device comprises a main box body and an auxiliary box body at one side of the main box body. A detector body is arranged inside the main box body. A first UHF sensor and a second UHF sensor are arranged inside the auxiliary box body and are electrically connected with the detector body. The detector body comprises microprocessor, a control circuit board in electric connection with the microprocessor, and a display unit and an input unit which are electrically connected with the microprocessor. The control circuit board comprises a UHF sensor acquisition circuit, a UHF signal processing circuit and a power processing circuit. The invention has the beneficial effects that good anti-interference performance is realized by arranging the two UHF sensors so that the detection sensitivity is improved; in addition, the structure is simple, the cost is low and the installation is convenient.

The invention discloses a mechanical equipment fault feature extraction method based on adaptive integrated multi-wavelets. The method combines the advantages of two-scale similarity transformation and a lifting frame, so that the approximation order of the multi-wavelet multi-scale function is improved, the regularity is enhanced, and the energy of signals in a frequency domain is more concentrated; at the same time, the vanishing moment of the multi-wavelet function is improved, so that the localization ability and the smoothness are also improved, and higher-order complex signals can be described and expressed more accurately. An adaptive multi-wavelet basis function for signals to be analyzed is constructed by designing free parameters in the construction process, selecting specific basis function evaluation and optimization criteria and combining free parameters in the optional selection construction process of an optimization method such as a genetic algorithm, to realize the extraction of weak fault features.

The invention provides a device using a reflective high energy electron diffraction technology to detect high voltage cable partial discharge, which belongs to the field of nondestructive detection of charged high voltage cable partial discharge. The invention particularly relates to the device using the reflective high energy electron diffraction technology to detect high voltage cable partial discharge. The device which uses the reflective high energy electron diffraction technology to detect high voltage cable partial discharge is convenient for use. The device provided by the invention comprises a host casing, a high frequency current transformer, a calibration pulse generator, a 40KHz ultrasonic sensor, a 150KHz ultrasonic sensor, a special high frequency sensor, a transient ground voltage sensor, an upper computer and an electroacoustic integrated sensor. The device is technically characterized in that the host casing is provided with a channel signal input port, a power supply switch, a battery charging port, a host casing ground knob and a power supply output port; and the channel signal input port comprises a first electrical signal port, a second electrical signal port, a third electrical signal port, a fourth electrical signal port, a first optical signal port and a second optical signal port.

The present invention provides a containment circulating cooling unit fan vibration diagnosis apparatus and method. The containment circulating cooling unit fan vibration diagnosis apparatus comprises the a vibration sensor, a vibration characteristic processing unit, a diagnostic management unit, a wireless data interface unit, a standard signal interface unit, an in-containment repeater, an out-containment repeater, and a wireless data receiving and storage server, wherein the vibration sensor, the vibration characteristic processing unit, the diagnostic management unit, the wireless data interface unit, the standard signal interface unit and the in-containment repeater are located in the interior of the containment of the nuclear power plant, and the out-containment repeater and the wireless data receiving and storage server are located outside the containment of the nuclear power plant, the vibration sensor is arranged on a nuclear power plant containment circulating cooling unit fan casing, the vibration sensor vibration is in connection with the vibration characteristic processing unit, the vibration characteristic processing unit is in connection with the diagnostic management unit, and the diagnostic management unit is in connection with the wireless data interface unit and the standard signal interface unit. The containment circulating cooling unit fan vibration diagnosis apparatus reduces the amount of use of cables in the containment, and enhances the flexibility of the diagnostic framework.

The invention discloses a mining fan fault diagnosis system which comprises a sensor, an embedded data collecting system and a fault diagnosis system. The sensor comprises an acceleration sensor, a temperature sensor and a rotating speed sensor, and the fault diagnosis system comprises an online monitoring portion, a data storage portion, a fault diagnosis portion and an alarming repairing portion. The mining fan fault diagnosis system overcomes the defect that in the prior art, the mechanical fault of a coal mine fan cannot be monitored accurately and effectively under the conditions of gas, coal dust and other dangerous environments and severe work environments, the vibration amount, temperature, rotating speed, displacement and other parameters of the fan in the work process are monitored online, the mechanical fault of the fan is diagnosed, the corresponding solving method is provided, and the mining fan fault diagnosis system is suitable for the online monitoring and mechanical fault diagnosis of various coal mine fans.

The invention discloses a tunnel construction informatization dynamic monitoring system and a monitoring method thereof. The system comprises an industrial personal computer, a PLC (programmable logic controller) wireless controller, a high-configuration digital camera, a deformation monitoring sensitization part, digital display acquisition equipment, wireless transmission equipment and a dynamic design feedback device. The method includes: 1), controlling commands to be transmitted to the PLC wireless controller by a computer control main station and controlling the digital camera and the digital display acquisition equipment; 2), acquiring internal force monitoring data by the aid of the digital display acquisition equipment; and acquiring a deformation monitoring image via the deformation monitoring sensitization part by the digital camera; 3), transmitting a monitored value to the computer control main station by the aid of the wireless transmission equipment; 4), analyzing results by the computer control main station via a real-time analysis module; and 5), transmitting the analyzed results to the dynamic design feedback device, analyzing relative difference of the really monitored value and a standard value, and early warming in real time by an early warning module. The tunnel construction informatization dynamic monitoring system is scientific, safe and practical, the method is convenient, instant, fast and effective, and safety problems in construction monitoring of tunnels are avoided.

The invention discloses a gearbox gear fault diagnosis method, and belongs to the related technical field of gear fault diagnosis methods. The invention provides the gearbox gear fault diagnosis method based on the combination of EEMD (ensemble empirical mode decomposition), sample entropy and an extreme learning machine by considering other factors such as non-stationarity, fault type and fault positioning diagnosis of a gearbox gear vibration signal. The method comprises the following steps of: decomposing an original vibration signal into a plurality of intrinsic mode function parts by adopting an EEMD decomposition method; and selecting a main part according to a principle that a correlation coefficient between the part and an original vibration signal is greater than 0.1, calculating sample entropies of the main part to form a feature vector, and importing the obtained feature vector into an extreme learning machine to carry out fault type classification recognition. Meanwhile, through a frequency domain graph obtained by carrying out fast Fourier transform on an original signal, observing and recording a fault frequency and a side frequency thereof to obtain fault gear positioning. And finally, combining the gear type with fault positioning to obtain a gear fault diagnosis comprehensive conclusion.

The invention provides a high-voltage cable outdoor terminal silicone oil degradation and combustion test platform and a degradation and combustion test method. The high-voltage cable outdoor terminalsilicone oil degradation and combustion test platform comprises a gas source regulation and control system, a reactor, an exhaust system and a data analysis system, wherein the reactor comprises a reaction cavity and a degradation device; silicone oil is placed in the reaction cavity, and the degradation device degrades the silicone oil in the reaction cavity; the reactor comprises a gas inlet and a gas outlet; the gas source regulation and control system introduces gas into the reaction cavity through the gas inlet, and the reaction cavity exhausts gas to the exhaust system through the gas outlet; the data analysis system is in communication connection with the gas source regulation and control system and the exhaust system. The purposes of simulating the degradation and combustion process of the terminal silicone oil under the environmental conditions of electricity, heat, dampness, oxygen and the like are achieved, the degradation development rule of the cable silicone oil and combustion characteristic data is grasped, a detection strategy is correspondingly formulated, and the occurrence of sudden accidents caused by the degradation problem of the silicone oil is avoided.

The invention discloses a pressure-bearing sealing device for a pumping well. The pressure-bearing sealing device comprises a three-way connector, a second emptying valve, a second double-male pup joint, a sleeve pup joint and a sealing assembly. A first connector of the three-way connector is in sealed connection with one end of the casing pipe pup joint through threads. The other end of the casing pup joint is hermetically connected with a casing at the wellhead of the producing well through threads; a second interface of the three-way joint is hermetically connected with one end of a second double-male short joint through threads; the other end of the second double-male pup joint is hermetically connected with the second emptying valve through threads; the sealing assembly comprises a sealing flange, an oil pipe hanger, a first emptying valve, a first pressure gauge and a first stop valve, and a second stop valve is further arranged on the three-way connector; and the second stop valve is connected with a second pressure gauge. According to the pressure-bearing sealing device, the connecting reliability of all the connecting positions is high, meanwhile, the pressure change of the oil pipe can be checked through the first pressure gauge when water is injected into the oil producing well, and safety and sealing performance are improved.

The invention discloses a high-voltage cable terminal silicone oil sampling device, sampling equipment and a sampling method. The high-voltage cable terminal silicone oil sampling device comprises anouter sheath, a sampling box, a movable baffle, a first driving mechanism, a second driving mechanism and a control module, wherein the outer sheath is of a barrel-shaped structure with one end sealedand the other end opened, the upper end and the lower end are an upper connecting section and a lower connecting section respectively, the upper connecting section and the lower connecting section are male and female connecting assemblies, and the sampling box and the control module are both located in the outer sheath; a sampling opening is formed in the wall of the outer sheath, and the first driving mechanism drives the sampling box to do linear reciprocating motion, so that a sampling nozzle of the sampling box extends out or retracts from the sampling opening; and the second driving mechanism drives the movable baffle to block and release the sampling port. According to the high-voltage cable terminal silicone oil sampling device designed by the invention, the sampling device realizes end-to-end connection of a plurality of sampling devices through the upper and lower connecting sections of the outer sheath, so that one-time sampling is realized, and a plurality of samples with different heights can be obtained.

The invention provides a track full life cycle management system, which comprises a data acquisition terminal, a server, a database and a client, and is characterized in that the data acquisition terminal acquires data and uploads the data to the server through an operator network, the server screens and processes the uploaded data and stores the data into the database; the server receives a request of the client, and makes responses of data storage or data extraction and processing correspondingly; and the database is used for storing the data of the data acquisition terminal, and the clientsends an application request to the server and presents the requested data to the user after receiving the feedback data. Preventive and repairable monitoring and maintenance are carried out on the full life cycle of the steel rail, and parameter changes of the steel rail can be effectively monitored in time in each stage of the life cycle of the steel rail, so that maintainers are prompted to correct unfavorable steel rail problems in time, and sudden accidents caused by problem accumulation are prevented. And finally, the effects of prolonging the service life of the steel rail and improvingthe comfort, economy and safety of the train are achieved.

The invention relates to a cantilever type piling supporting structure and a combination stiffening filling pile in a branch anchor type piling supporting structure. The combination stiffening fillingpile comprises a plain concrete pile shaft, a reinforcing cage, a profile steel skeleton which is coaxial with the plain concrete pile shaft wrapping up the reinforcing cage and the profile steel skeleton; the profile steel skeleton comprises more than one channel steels or I-beams; the reinforcing cage comprises longitudinal bars uniformly distributed on the circumference, ring-shaped reinforcing ribs and tie hoops encircling the longitudinal bars; the diameter of the plain concrete pile shaft is 400 to 1200 mm; and the pile length is 5-25 meters; more than one channel steels or the I-beamsand the longitudinal bars of the reinforcing cage are positioned inside a tensile region of the same circumference or in the interior of the reinforcing cage; the channel steels or the I-beam ventralshields are uniformly distributed with shear keys from top to bottom.