14119results about "Engine components" patented technology

An online testing and diagnosis method for vibration characteristics of blades of wind turbine is disclosed. Steps of testing and diagnosing blade vibration comprises: S1: installing vibration sensors at key positions of a blade, designing an adaptive data acquisition strategy, and automatically adjusting a sampling rate according to a vibration amplitude and environmental changes monitored in a real time; S2: extracting key features reflecting health status of the blade from massive data, and evaluating an impact of wind speed, temperature, and environmental factors on vibration characteristics; S3: designing a customized deep learning model for damages of the blade of a wind turbine, extracting a time sequence data and a vibration signal, identifying a damage among different types of damages and evaluating a damage degree; and S4: automatically adjusting a warning threshold based on a real-time data stream and a historical trend, and drafting a preventive maintenance plan.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The shutdown of a compressor (10) installed in a refrigerant circuit (2) in air conditioning or refrigeration system is controlled so as to prevent unpowered reverse rotation of the compressor. Prior to terminating electric power to the compressor drive motor (24), the pressure within the system is relieved and substantially equalized across the compressor, thereby eliminating the possibility of unpowered reverse rotation of the compressor at shutdown. Pressure relief and equalization may be achieved by reducing the operating speed of the compressor to a low forward speed for a period of time prior to deenergizing the compressor drive motor. Pressure equalization may also be achieved by driving the compressor in reverse rotation prior to deenergizing the drive motor.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention relates to the technical field of fault detection, and discloses a diesel generating set fault detection method and system based on deep learning, and the method comprises the steps: obtaining first vibration signal data, and carrying out the time-frequency decomposition, and obtaining a dynamic change feature; de-noising processing is carried out on the dynamic change features to obtain a time-frequency feature sequence; extracting a peak energy distribution data set, and calculating each frequency band entropy value to obtain a frequency band entropy value sequence; classifying the frequency band entropy sequence, determining a random fluctuation reference mode, and separating to obtain an abnormal frequency component; calculating a spectral line spacing and amplitude ratio, obtaining a spectral line feature data set, classifying the spectral line feature data set, and determining a fault classification result; obtaining current second vibration signal data, performing similarity calculation on the current second vibration signal data and a pre-established normal mode library, and outputting a fault feature vector; and verifying the fault feature vector to obtain a final fault detection result. According to the method, closed-loop diagnosis from signal acquisition to fault classification can be realized, and the fault detection precision of the diesel generating set is improved.

The invention discloses a matrix yaw system of a wind power plant. The matrix yaw system comprises a data acquisition module, a wind plant modeling module, a parameter extraction module and a yaw control module. The data acquisition module acquires a wind field original data set containing three-dimensional space coordinates and timestamps by using a multi-source sensor; the wind field modeling module calculates correlation between points by combining a covariance function through a Gaussian process regression algorithm, and constructs a three-dimensional dynamic wind field model; the parameter extraction module is used for extracting wind regime parameter vectors in the model by adopting a nearest neighbor interpolation algorithm on the basis of actual space coordinates of a fan; and the yaw control module utilizes a depth deterministic strategy gradient reinforcement learning model to generate a yaw angle adjustment instruction in combination with the wind regime parameter vector, the current yaw state of the fan and a preset maximum cumulative reward function. Through multi-module cooperation and intelligent algorithm optimization, accurate matching of wind field dynamic modeling and yaw control is achieved, and the energy efficiency and operation stability of the wind generating set are improved.

The invention relates to the technical field of generator monitoring, in particular to an online state monitoring method and system for a water-turbine generator set, and the method comprises the steps: collecting data, and carrying out the preprocessing of the data through a lightweight TinyML reasoning model; dimension reduction processing is carried out on the preprocessed data through PCA, and dynamic normalization is carried out on the data after dimension reduction; performing convolution feature extraction on the normalized data through a feature extraction module, and extracting attention enhancement features from the convolution features through a sparse attention mechanism; calculating a posterior probability based on Bayesian network topology through a Bayesian network feature fusion module; a fault probability vector and an integrated feature vector are obtained through combination of a multi-modal fusion model and a posterior probability; and through the fault probability vector, predicting residual life and current working condition characteristics, and outputting an early warning level, a fault type and predicted fault time. According to the scheme, the diagnosis efficiency and reliability are improved through multi-modal fusion and Bayesian reasoning.

The invention relates to the technical field of fan blade deicing, and discloses an intelligent temperature control type fan blade electric heating deicing control system and method, and the system comprises a meteorological sensing module which is used for collecting the environment temperature, humidity, wind speed and rainfall type; the blade state monitoring module is used for acquiring blade surface temperature distribution and ice layer thickness data; the dynamic decision-making module is used for generating a partition heating instruction based on the data of the weather sensing module and the blade state monitoring module; the temperature control execution module is used for converting the instruction into a gradient signal; the heating array module is used for executing electric heating deicing operation in the blade surface subareas; and the energy coordination module is used for dynamically constraining the total power of the system. The meteorological sensing module and the blade state monitoring module are integrated through the system, real-time collection and fusion of multi-source data of blade surrounding environment parameters and the thickness of an ice layer are achieved through the design, comprehensiveness and accuracy of icing monitoring are improved, a reliable basis is provided for dynamic decision making, and therefore the response capacity and early warning precision of the system to icing risks are improved.

The invention discloses a semi-submersible wave energy-wind energy integrated power generation platform and a damping regulation and control method of a damping regulation and control system of the semi-submersible wave energy-wind energy integrated power generation platform. The platform comprises a semi-submersible wind driven generator platform, a wind driven generator set, a wave energy power generation device, an energy storage system, a sea area detection system and the damping regulation and control system. The wave energy power generation devices are divided into two groups on the inner side and the outer side of the platform, each wave energy power generation device comprises a platform inner side wave energy power generation device and a platform outer side wave energy power generation device, and each wave energy power generation device comprises a swing arm floater, a universal hinge, a connecting arm and a damping adjusting module. The semi-submersible wind power generation device and the wave power generation device are combined, and offshore renewable resources are comprehensively utilized; the six-buoy design is adopted, buoyancy is dispersed, stability and safety are improved, and meanwhile space is provided for a wave power generation device; the floaters are symmetrically arranged in a hexagonal shape and carry a damping regulation and control system, and the whole structure is more easily stabilized by adjusting the damping of the floaters in all directions.

The invention discloses a rotating wheel nondestructive testing robot and a testing method thereof, and belongs to the technical field of specialized robots and nondestructive testing. The rotating wheel nondestructive testing robot comprises a permanent magnet adsorption walking mechanism, a multi-degree-of-freedom mechanical arm, a module flaw detection unit, a multi-sensor positioning system and a central controller; the adsorption walking mechanism is used for realizing all-attitude stable crawling on the curved surface of the rotating wheel; the multi-degree-of-freedom mechanical arm is arranged above the middle part of the permanent magnet adsorption walking mechanism and is used for moving the module flaw detection unit to a specified position; the module flaw detection unit is used for performing high-resolution detection on the damage of the composite material on the rotating wheel; the multi-sensor positioning system is used for real-time pose feedback of the robot; the central controller is used for controlling all the components to work cooperatively. According to the nondestructive detection robot for the runner and the detection method thereof, the problems that in the prior art, detection of internal cracks of turbine runner blades depends on a manual frame erecting mode, the high-altitude operation risk is large, the number of detection blind areas is large, and efficiency is low can be effectively solved.

An insertion tool is provided. The tool includes a flexible section comprising a plurality of rigidizable links, an end effector actuator, and an end effector coupled to the flexible section. A flexible is shaft inserted through the flexible section, wherein torque is transferred from the end effector actuator to the distal end via the flexible shaft to cause a rotation of the end effector. A tool may include a tool-less disconnect interface between the end effector and the flexible shaft.

A wave energy power generation device includes a plurality of up-down moving mechanisms configured to move up and down along a spiral rod under the action of waves, a mass block arranged at a lower part of the power generation device; a buoyancy barrel arranged in the middle of the power generation device, a guide rail arranged on one side of the up-down moving mechanism, and a spiral rod. Through the interaction of the up-down moving mechanism and the spiral rod, the up-down movement of waves can be converted into rotational kinetic energy, a scroll spring is used for storing energy, then the rotating speed is stabilized through an escapement mechanism, a power generation assembly accelerates energy output, and finally a generator is driven to generate power.

The invention discloses a wind generating set fault prediction method and system based on edge computing, and the method comprises the steps: collecting the vibration, temperature, rotating speed and other parameters of a wind generating set in real time through a data collection module deployed by edge computing equipment, carrying out the preliminary screening and packaging through a built-in data processing unit, and synchronizing to a constructed digital twinborn model; establishing an edge calculation fault analysis model based on model data, identifying potential fault features, comparing a physical unit operation state with a digital twin model simulation state parameter by parameter, performing preliminary diagnosis in combination with the fault features, evaluating a unit health state according to a fault mechanism model, and outputting a fault prediction result and part information. The system is correspondingly provided with six units including a data acquisition interaction unit, a data processing unit and a model construction updating unit, and all the units work cooperatively, so that accurate prediction and health management of faults of the wind generating set are realized.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine bearing temperature and lubricating oil state monitoring and fault diagnosis method and system. According to the method, triple-redundancy temperature measurement values of a water turbine bearing are adopted, multiple parameters are fused, redundancy design is adopted, sensor failure diagnosis is executed, a temperature-vibration-oil pressure combined feature space is constructed according to the triple-redundancy temperature measurement values and online oil quality parameters, a fault propagation map is established, and the fault propagation map is analyzed. And finally, based on the multi-level diagnosis model and in combination with the fault propagation atlas, composite fault mode recognition is realized, a fault classification result is obtained, and the composite fault mode recognition is realized based on the multi-level diagnosis model, so that the problems of low reliability, single monitoring dimension and insufficient fault recognition capability of a sensor in the prior art are effectively solved. And the equipment reliability and the operation safety are obviously improved.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The present invention belongs to the field of tightening control methods and automatic tightening technology, and discloses a tightening control method and automatic tightening device for an aero-engine rotor based on bolt preload feedback. The automatic tightening device is based on a main body structure and is provided with a rotary table structure, a spherical guide structure and a skid platform structure to achieve the degree of freedom of movement of the automatic tightening device for an aero-engine rotor within an inner cavity space of the rotor, enabling the tightening structure to be fed and withdrawn; in addition, a preload detection structure is provided, which relies on an ultrasonic preload measurement system to measure a real-time preload of a bolt during a tightening process and transmit the value of the preload to a tightening gun to achieve a tightening method for preload feedback control.

The invention relates to the technical field of natural gas treatment, and particularly discloses a turbine and motor double-drive magnetic levitation natural gas expansion recompression unit, system and method, and the turbine and motor double-drive magnetic levitation natural gas expansion recompression unit comprises a natural gas expansion machine, a high-speed synchronous motor and a natural gas compressor which are coaxially and rigidly connected; the magnetic suspension supporting system comprises an expansion side magnetic suspension bearing arranged at the output end of the natural gas expansion machine and a compression side magnetic suspension bearing arranged at the input end of the compressor, and is used for realizing non-contact suspension and torque transmission of the rotor; the electric control unit comprises an electric appliance cabinet and a pressure sensor, a converter and a main controller are arranged in the electric appliance cabinet, the converter is connected with a power grid and the high-speed synchronous motor, and the main controller dynamically adjusts the operation mode and power of the high-speed synchronous motor by taking the outlet pressure of the natural gas compressor as a control variable. The natural gas compressor is driven by the natural gas expansion machine and the highly synchronous motor, the unit with the reverse power generation function is adopted, the operation flexibility of the unit is improved, and the comprehensive utilization rate of energy is remarkably increased.

Systems and methods described herein may include pumps and pump components including carbon-manganese-chromium (C—Mn—Cr)-based steel alloys for enhanced resistance to wear or corrosion. A pump component may include one or more components formed of a C—Mn—Cr-based ferrous alloy, which may result in enhanced wear or corrosion resistance. The C—Mn—Cr-based ferrous alloy may include a carbon content ranging from about 0.1 weight percent (wt. %) to about 2 wt. %, a manganese content ranging from about 10 wt. % to about 30 wt. %, and a chromium content ranging from about 0.1 wt. % to about 20 wt. %.

The invention belongs to the technical field of hydroelectric generating set fault diagnosis, and particularly discloses a hydroelectric generating set cavitation fault diagnosis method based on multi-channel acoustic emission signal fusion. Through the technical means of acquiring the acoustic emission signals at multiple positions, a more comprehensive cavitation phenomenon data basis is provided; the representation of time-frequency characteristics is optimized through the Mel time-frequency diagram, and the problem that the dynamic characteristics of acoustic emission signals cannot be effectively captured through a traditional method is solved; multi-channel feature extraction is performed on the Mel time-frequency graph through a preset deep convolutional neural network model, so that automatic learning of deep cavitation working conditions is realized; and multi-channel fusion and classification identification are carried out on a feature extraction result through the model, so that effective integration and fault classification of features are realized. Compared with the prior art, a more accurate and comprehensive cavitation fault diagnosis effect is realized, and the diagnosis precision is improved.

The invention discloses a wind turbine generator state monitoring method driven by network fusion of a knowledge graph and a space-time diagram. The method comprises the following steps: S1, collecting time sequence SCADA data and wind power text data of a wind turbine generator; s2, constructing the wind power text data into a wind power operation and maintenance knowledge graph; s3, generating time sequence diagram data by querying a wind power operation and maintenance knowledge graph and combining the preprocessed SCADA data; s4, carrying out normal behavior modeling on the time sequence diagram data by utilizing the time-space diagram neural network model, predicting each node in the diagram data in the healthy operation time period of the unit in the normal behavior modeling process, and calculating the maximum absolute value of the difference between the predicted value and the actual value of each node as the maximum absolute value of the difference between the predicted value and the actual value; the maximum absolute error is used as a health threshold value of each node to perform state monitoring; in the state monitoring stage, the matching condition of the mapping information of each node in the graph structure in the monitoring result of the real-time data and the fault transmission chain is compared, the health state of the unit is judged, and the fault early warning time is determined. According to the invention, the accuracy and robustness of the monitoring result are enhanced.

A gate linkage control method and device and a parallel water supply and power generation system are provided. The method comprises the following steps of: acquiring current gate opening degree information of the parallel water supply and power generation system and a current water level of a third trunk canal, the current gate opening degree information comprising current first gate opening degree information and current second gate opening degree information; determining a target gate linkage control strategy of the parallel water supply and power generation system according to the current gate opening degree information of the parallel water supply and power generation system and the current water level of the third trunk canal; and controlling a gate opening degree of a target gate according to the target gate linkage control strategy of the parallel water supply and power generation system.

The invention relates to the technical field of engines, in particular to a turbocharger and an engine system. The turbocharger comprises a rotating shaft, an integrated bearing and a middle shell, the rotating shaft is sleeved with the integrated bearing, the integrated bearing is sleeved with the middle shell, an oil supply pipeline is arranged in the middle shell and comprises an oil supply inlet, a connecting pipeline, an oil storage chamber, a bearing chamber, an oil outlet pipeline and an oil return cavity which are sequentially communicated, and the integrated bearing is located in the bearing chamber. The oil supply inlet is higher than the bearing chamber, the oil storage chamber is lower than the bearing chamber, and the oil outlet pipeline is higher than the bearing chamber. According to the turbocharger, under the transient working condition, the oil supply amount of the bearing chamber can meet the use requirement; and under the cold start working condition, the oil supply time is short, the rotating shaft is prevented from being abraded, the service life of the rotating shaft is prolonged, and therefore the turbocharger has the long service life.

A hub motor includes a housing rotatably disposed to a first shaft and a second shaft, and a motor installed in the housing and having a stator and a rotor. A reduction mechanism is installed in the housing, including a gear shaft connected with the rotor, a fixed bracket connected with the stator, a movable bracket disposed on the gear shaft and the second shaft, a first ring gear disposed to the fixed bracket, a second ring gear disposed to the housing, multiple first planet gears disposed to the movable bracket and meshed with the gear shaft and the first ring gear, and multiple second planet gears disposed to the second shaft and meshed with the movable bracket and the second ring gear. By 10 inserting a hollow tube into the first shaft, the gear shaft, the movable bracket, and the second shaft, the waterproof and dustproof performance can be enhanced.

A bearing system includes a housing having an inner surface defining a bore and a slot, and a foil bearing assembly positioned within the bore. The foil bearing assembly includes an outer foil assembly, a bump foil assembly, and an inner foil assembly. The outer foil assembly includes an outer foil extending from a first outer foil end to a second outer foil end, and a first anti-rotation tab at the first outer foil end that is received by the slot to limit circumferential movement of the outer foil in a first direction. The inner foil assembly is secured to the outer foil and includes an inner foil extending from a first inner foil end to a second inner foil end, and a second anti-rotation tab at the second inner foil end that is received by the slot to limit circumferential movement of the inner foil in a second direction.

The invention discloses an intelligent self-adaptive silt filtering system and method for a pumped storage power station, and belongs to the technical field of pumped storage power stations. The system comprises a multi-parameter water quality monitoring module, an intelligent self-adaptive filtering module, a hydraulic cyclone enhanced separation module, a self-cleaning and backwashing module and a central control and data processing module. According to the method, river sediment characteristics are monitored in real time, filtering parameters are dynamically adjusted through an artificial intelligence algorithm, efficient sediment interception is achieved by combining hydraulic cyclone separation and multi-stage filtering, the problem of filtering medium blockage is solved through a self-cleaning mechanism, and meanwhile the self-adaptive learning capacity is achieved so as to optimize system performance. The system can significantly improve the filtering efficiency, reduce energy consumption and operation and maintenance cost, adapt to complex water quality working conditions, and guarantee safe and stable operation of key equipment of the pumped storage power station.

The invention discloses a pumped storage unit fault diagnosis method based on multi-modal data fusion, and the method comprises the steps: collecting a voiceprint signal, an infrared thermal imaging image and historical operation data of a pumped storage unit, and carrying out the noise reduction of a voiceprint set through employing an improved unscented Kalman filtering algorithm, a COMRes + model is used to train the voiceprint signal after noise reduction and historical operation data, a future voiceprint signal is predicted, an improved Deeplabv3 + model is used to train an image set, and features of an infrared thermal imaging image are extracted; the voiceprint feature, the historical operation data text feature and the image feature are input into an improved CentraNet model for multi-modal data fusion, and a fault category is output through a classification recognition module, so that diagnosis of the pumped storage unit fault is completed; according to the method, through fusion of the multi-modal data and optimization of the deep learning model, the accuracy and efficiency of fault diagnosis can be effectively improved, and a powerful guarantee is provided for safe operation of the pumped storage unit.

Embodiments disclosed herein include buoyant wave energy converters. In an embodiment, the wave energy converter comprises an upper chamber having a first fluid reservoir and a first gas pocket, and a lower chamber having a second fluid reservoir and a second gas pocket. An injection tube is between and fluidly coupled to the upper chamber and the lower chamber, where the injection tube is to impel a fluid from the second fluid reservoir into the first fluid reservoir when the upper chamber, the lower chamber and the injection tube oscillate about a waterline with the upper chamber adjacent to the waterline and the lower chamber below the waterline and vertically beneath the upper chamber. In an embodiment, an effluent tube is fluidly coupled to the upper chamber and the lower chamber, where the effluent tube is to return the fluid from the first fluid reservoir to the second fluid reservoir.

The invention relates to the technical field of thermal management optimization, and discloses an active cooling and power optimization system of a plateau turbine generator, which comprises a multi-physics field modeling module, a reinforcement learning decision module, a strategy optimization training module and an edge execution control module, and is used for resolving a temperature field / flow field / stress field coupling state in real time, providing environment dynamic characterization, and optimizing the power of the plateau turbine generator. According to the method, multi-source sensing data is processed, a cooling liquid flow rate and cooling fin angle control instruction is generated, strategy iteration optimization is carried out in a digital twin environment, the physical rationality and energy efficiency optimality of a control algorithm are ensured, a lightweight strategy network is deployed, and sub-second-level real-time response and fault safety protection are achieved. Through intelligent control and multi-physical field cooperation, the temperature adjusting precision and the system response speed are improved, efficient power output of the turbine is kept under different altitudes and climate conditions through a self-adaptive strategy, and a reliable clean energy solution is provided for high-altitude areas in combination with multiple safety protection mechanisms.

The invention particularly discloses a visual monitoring system and an analysis method for the abrasion state of an impulse turbine set, and relates to the technical field of hydroelectric power generation equipment monitoring. The device comprises a visual monitoring module, a control module and an abrasion analysis early warning module, the visual monitoring module collects images through an industrial camera, an LED light source, a packaging protection structure and a water and mist removing device. The control module formulates an imaging mechanism according to working conditions such as strong water mist interference, idling no water, static no water and the like, and is linked with a unit PLC control system to realize imaging triggering, state adjustment and data interaction; and the abrasion analysis and early warning module identifies an abrasion area through a U-Net + + algorithm, calculates abrasion parameters based on a multi-view vision technology, grades the abrasion parameters, predicts a trend by adopting an ARIMA model, and recommends a maintenance scheme through rule reasoning in combination with threshold early warning. Automatic and intelligent monitoring and analysis of the abrasion state of the impact type unit are achieved, and guarantee is provided for safe and efficient operation of the unit.

The invention discloses a semi-submersible fan-oscillating water column wave energy device integrated device with heaving plates. The semi-submersible fan-oscillating water column wave energy device integrated device comprises floating fan equipment, a central platform, buoys, hydraulic heaving plate connecting rods, an upper-layer permeable heaving plate, a lower-layer non-permeable heaving plate, a control system, a wind speed monitoring sensor and a laser wave surface displacement sensor. The floating fan equipment is arranged at the upper part of the central platform; the buoy is formed by connecting a permeable protective cover, a hydraulic connecting rod, an OWC device, an air pressure enhancing device, an internal supporting rod, a telescopic supporting plate and a pneumatic turbine. The upper-layer permeable heaving plate and the lower-layer non-permeable heaving plate are connected to the bottom of the buoy through the hydraulic heaving plate connecting rod; the control system is positioned in the lower horizontal plate of the central platform; the wind speed monitoring sensor and the laser wave surface displacement sensor are arranged on a stand column of fan equipment. The fan operation efficiency and the system reliability are improved, the whole life cycle cost is effectively reduced, and good durability and maintainability are achieved.