850 results about "Mechanical system" patented technology

The invention discloses an unmanned platform electromechanical system fault early warning method based on a Transform-BiGRU cross attention mechanism, and belongs to the technical field of fault early warning. The method comprises the following steps: acquiring parameters, and performing time sequence alignment and key feature screening; secondly, designing a parallelization spatio-temporal feature extraction framework: on one hand, capturing a long time sequence dependency relationship through a multi-layer Transform encoder, on the other hand, realizing bidirectional context modeling of a sequence by adopting a bidirectional gating circulation unit, and introducing an interpretable global attention mechanism to perform feature weight dynamic allocation on a BiGRU hidden layer state; and a cross attention feature fusion module is constructed for a Transform and BIGRU fusion problem, and cooperative characterization of a time sequence dynamic feature and a space correlation feature is realized through interactive attention calculation of a spatial-temporal feature tensor. By means of the method, advanced early warning from diesel engine lubricating oil pressure abnormity to whole ship electric power system cascade faults is achieved.

The present invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory. The method of the present invention comprises: first, collecting multi-source information of a mechanical device, wherein the multi-source information includes vibration data, image data and sound data; respectively calculating corresponding feature indicators; then, using a PCA algorithm to perform dimensionality reduction processing on the indicators; using a Bayesian fault diagnosis model to perform fault diagnosis, so as to obtain a diagnosis result from each sensor; and then, using the DS evidence theory to perform fault fusion diagnosis. The present invention can comprehensively consider the characteristics of various types of data, extract more effective fault indicators, achieve more comprehensive fault identification, and fuse diagnosis results from a plurality of sensors, thereby ensuring the accuracy of final diagnosis. The present invention is suitable for fault diagnosis of various complex mechanical systems.

The invention discloses a micro-cantilever geometric structure parameter inversion system, method and device, and belongs to the technical field of micro-electro-mechanical systems. According to the system, a dynamic model containing geometry, material and electromechanical coupling nonlinearity is constructed, a PINNs network fused with physical constraints is designed, a staged training optimization strategy is adopted, and efficient inversion of micro-cantilever geometric parameters is achieved. The core of the method is that a nonlinear kinetic equation is used as a constraint to be embedded into a neural network for training, experimental data and physical residual errors are combined to construct a composite loss function, and the problems that a traditional method neglects a nonlinear effect, depends on finite element simulation and is low in inversion precision are solved. The average relative error of inversion of the system is lower than 1%, which is obviously superior to that of a traditional method, and the method can be widely applied to MEMS device design, online detection and closed-loop control scenes.

The invention relates to the technical field of mechanical vibration and fluid excitation coupling effect analysis, in particular to a runner crown modification pressure pulsation amplitude measurement method, which comprises the following steps of: extracting pressure gradient distribution and cavitation phase change characteristic parameters of a runner surface through fluid dynamics simulation; targeted arrangement of a high-frequency pressure sensor array in a vortex core area, a boundary layer separation critical position and a modification structure mutation area is guided, and the signal acquisition precision is improved by adopting an annular encryption and axial equal-interval arrangement strategy. And establishing a dynamic error feedback mechanism of actual measurement and simulation. And verifying an abnormal frequency band proportion through frequency domain kurtosis and analyzing envelope spectrum harmonic waves, and screening a modification parameter combination with the pressure pulsation amplitude decreasing to reach the standard and stable frequency band distribution. According to the method, the problem of measurement errors caused by multi-source high-frequency noise and effective signal frequency domain aliasing is solved, a high-precision quantification basis is provided for runner modification optimization, and the method is suitable for the field of mechanical system dynamic characteristic testing.

A state signal generation unit that detects the state of a mechanical apparatus as a state signal, a condition signal generation unit that generates a condition signal by detecting the operating status of the mechanical apparatus as an operating condition, a state feature generation unit that generates state features from the state signal, a condition feature generation unit that generates condition features from the condition signal, an initial state learning unit that executes learning based on initial learning state features and outputs initial state learning results, an initial condition learning unit that executes learning based on initial learning condition features and outputs initial condition learning results, an anomaly degree calculation unit that calculates the degree of anomaly based on state learning results and detection state features, and an unknownness degree calculation unit that calculates the degree of unknownness based on condition learning results and detection condition features are included.

A diagnostic method localizes technical faults in a motion system that includes a base adapted to receive a motion stage for equipment, a machine frame resting on the floor, dampers adapted to support the base, and an active isolation system arranged between the base and the machine frame. The active isolation system and the base form a mechanical system. The active isolation system includes actuators, adapted to impart six degree-of-freedom (DOF) motion to the base in a reference frame, and inertial sensors adapted to provide a six DOF measurement of the base's motion. The method includes: i) applying a control signal for actuating or contributing to the actuation of the actuators of the active isolation system to impart a motion to the base; ii) obtaining, with the inertial sensors, a six DOF measurement of the base's motion relative to a reference point; iii) creating a measured process sensitivity matrix of the mechanical system using the six DOF measurement; and iv) determining, based on the measured process sensitivity matrix, whether all the actuators and sensors of the active isolation system are working as expected and / or whether there is a pivot point impeding the movement of the base.

Embodiments of the present disclosure may include a method of training an electro-mechanical system that includes local trainable AI, the method including training a local AI for a controller of the electro-mechanical surgical device or system with simulated data. Embodiments may also include performing a task or set of tasks or operations with the device or system as controlled, instructed, influenced, or the like by the controller having the local AI system. Embodiments may also include collecting real-life data from the step of performing. Embodiments may also include further training the local AI system and improving the AI simulated data generator with real-life data. Another embodiment relates to a system for providing a user with suggestions during social interactions. Embodiments of the invention may extend across many types of devices and systems, control systems, sensors, types of AI, and so forth, and combinations of the foregoing.

The invention provides a rotating mechanical system performance evaluation and optimization method and system based on system negentropy conversion rate, and the method comprises the following steps: carrying out the numerical simulation of a flow field of a rotating mechanical system through a computational fluid mechanics method, and obtaining the parameters of the flow field; calculating a viscous dissipation entropy yield, a turbulent dissipation entropy yield and a wall entropy yield in the fluid domain based on the flow field parameters; the total entropy yield of the system is obtained through the volume fraction and the wall area fraction of the whole fluid domain; converting the input power of the rotating machinery into an equivalent negentropy flow, and calculating the negentropy conversion rate of the system for evaluating the effective utilization degree of the input power; and evaluating the performance of the rotating machinery system based on the negentropy conversion rate, and identifying a key area of energy loss. According to the method, the negentropy conversion rate result is applied to structural design optimization, running state diagnosis and energy efficiency adjustment of the rotating machinery, and online calculation and dynamic visualization can be realized in combination with real-time monitoring data.

The invention discloses a bearing fault detection method based on an auto-encoder reconstruction error. The method belongs to the technical field of mechanical system fault detection, and comprises the following operation steps: selecting a sensor type, determining an installation position and setting related parameters; acquiring one-dimensional synchronous vibration data of k groups of bearings under normal working conditions, and respectively preprocessing the one-dimensional synchronous vibration data; training and testing an auto-encoder model by using the preprocessed data; obtaining one-dimensional synchronous vibration data of the real-time working condition of the bearing, and performing preprocessing; reconstructing the preprocessed real-time working condition data by using the trained auto-encoder model, and calculating a reconstruction error E; judging whether a fault occurs or not; according to the bearing fault detection method based on the self-encoder reconstruction error, the bearing fault can be effectively and reliably detected; the invention belongs to an artificial intelligence algorithm, and needs less manual expert knowledge; when the self-encoder model is trained, only the data in the healthy state of the bearing is used, and the problem of insufficient data volume during model training is solved.

The invention provides a multi-pump zero-displacement hydraulic agile control system and a control method thereof. The system is composed of a hydraulic system, a mechanical system and an electric control system. According to the control method, a multi-pump zero-displacement switching mechanism and a deep reinforcement learning strategy based on an Actor-Critic architecture are fused, the effective oil supply amount is adjusted by dynamically starting and stopping part of hydraulic pumps, a servo motor rotating speed instruction is output in real time in combination with a neural network, and the strong coupling dynamic relation between sliding block displacement and the angle of a swing rod is coordinated. The system switches part of the pumps to a bypass state when approaching balance so as to reduce energy consumption, and starts all the pumps to guarantee recovery capacity when greatly deviating. According to the technical scheme, on the premise of not depending on an accurate mathematical model, agile, stable and energy-saving control over the inverted pendulum type under-actuated system can be achieved.

The invention relates to the related technical field of fault prediction and health management, in particular to a cutting machine fault monitoring method and system fusing vibration and current signals, and the method comprises the steps: obtaining equipment feature parameters, determining a first fault sensitive feature set and a second fault sensitive feature set, and carrying out the correlation verification through combining historical fault data with a tool replacement log, and correcting the deviation of the fault monitoring evaluation function to obtain a fault monitoring evaluation result, and generating a fault monitoring report. The technical problems that the coupling operation state of a mechanical system and an electrical system cannot be completely represented only by depending on a single vibration or current signal, a dominant fault mode is difficult to accurately recognize when a cutting machine has multi-source degradation factors, and fault false alarm and fault missing alarm are caused are solved, first and second fault sensitive feature sets are determined, the fault type recognition precision is enhanced, and the working efficiency is improved. The evaluation result is verified and corrected through historical data and a cutter log, the deviation from the real state of the equipment is reduced, and the technical effects of timeliness and accuracy of operation state sensing and fault early warning of the cutting machine are guaranteed.

The invention relates to the technical field of optical-mechanical system glue application, and discloses a system and a method for testing glue curing shrinkage stability of an optical-mechanical system of a laser machine. The system comprises a pressing and fixing device which comprises a spring, a pull rod, a needle gauge, a pressing piece and a fixing block, and position switching of the needle gauge between the inner surface and the outer surface of the fixing block is achieved by rotating the pull rod head corresponding to the pull rod; the glue curing unit comprises a UV curing device, a precise temperature control heating platform and a moisture curing environment generator; the light source unit comprises a laser light source and a stable support structure with a clearance fit hoop; the optical machine gluing unit comprises a mirror base and a reflecting mirror; the pressing and fixing device is rigidly connected with the optical platform through the aluminum profile portal frame; the data acquisition unit comprises a CCD (charge coupled device) sensor, a three-dimensional adjustable fixing device and an analysis module, and the analysis module generates a multi-dimensional stability index by resolving the time sequence data of the coordinates of the reflected light spots acquired by the CCD sensor in real time so as to complete the glue curing shrinkage stability test.

The invention discloses a mechanical system structure health monitoring method and device based on hierarchical symbol transfer entropy, and relates to the technical field of data processing, and the method comprises the steps: collecting the vibration data of a plurality of channels of an electromechanical system in different health states; calculating a hierarchical symbol transfer entropy of each sub-channel vibration signal in the plurality of channel vibration data; performing superposition calculation on the level symbol transfer entropy of each level of each sub-channel vibration signal to obtain a second-order tensor feature corresponding to each sub-channel vibration signal; superposing the second-order tensor features corresponding to the vibration signals of each sub-channel to obtain second-order tensor feature matrixes corresponding to the vibration data of the multiple channels; training the initial two-dimensional extreme learning machine based on the second-order tensor feature matrix belonging to the training data set to obtain a trained two-dimensional extreme learning machine; the health state of the electromechanical system is monitored based on the two-dimensional extreme learning machine, and the accuracy of monitoring the health state of the electromechanical system is improved.

The present invention provides a low-cost, non-invasive device (100) for measuring flow and leakage detection of fluids within closed conduits. The invention utilizes affordable a Micro-Electro Mechanical System (MEMS) based Inertial Measurement Units (IMUs) sensor (102) in the device to non-invasively measure fluid flow and leakage of fluids which exhibit turbulence or flow-induced-vibrations within a conduit. This device fin directly estimates flow rate or velocity based on the concept of Reynolds number in fluid dynamics. Further, this device is easy to install on pipes of all dimensions, requires minimal calibration, has no moving parts in contact with the fluid, can be integrated with Geographic Information System (GIS) software, and possesses advanced metering infrastructure to support remote reading and automated bill generation.

The invention relates to a nonlinear damping element based on a Modelica language, and relates to the field of mechanical system modeling and simulation. According to the technical scheme, a novel damping element is designed based on the Modelica language, corresponding damping coefficients can be defined according to different movement speeds of the element, and the limitation of the prior art is effectively broken through; specifically, according to the technical scheme, the element types of the Modelica mechanical library are enriched, the blank of nonlinear damping elements in the library is filled up, and the application range of the library is expanded; moreover, simulation requirements of a nonlinear damping system can be accurately matched, so that the modeling process better fits nonlinear characteristics of a damping coefficient in an actual working condition; besides, free switching between non-linear damping and linear damping can be flexibly realized depending on a speed-damping force two-dimensional number table, the adaptability of the element to different modeling scenes is greatly enhanced, and powerful support is provided for precise modeling of a mechanical system based on Modelica.

The invention discloses a fault analysis method, device and equipment for a rotary drilling rig and a medium, and belongs to the technical field of engineering machinery technologies. The fault analysis method for the rotary drilling rig comprises the following steps: acquiring equipment parameters of the rotary drilling rig, and respectively establishing corresponding mathematical models for a plurality of functional systems of the rotary drilling rig according to the equipment parameters; wherein the functional system comprises a mechanical system, a hydraulic system, a control system and a power system; establishing a model library by utilizing the mathematical model; the simulation models corresponding to all the function systems are connected based on the model library, and a digital prototype of the rotary drilling rig is obtained; the digital prototype is controlled to operate according to multiple fault working conditions, and the component energy loss of the digital prototype under each fault working condition is obtained; and establishing a mapping relationship between the energy loss of the component and the fault type of the fault working condition, and performing fault analysis on the rotary drilling rig according to the mapping relationship. The fault analysis precision and efficiency of the rotary drilling rig can be improved.

The invention discloses a boat hanging frame mechanical system fault diagnosis method and system, a program product and a computer readable storage medium. The method comprises the following steps: acquiring vibration data of a boat hanging frame mechanical system in a normal-temperature environment and a low-temperature environment; performing noise reduction processing on mechanical vibration data in a normal-temperature environment and a low-temperature environment by a multi-domain fusion self-adaptive fault feature enhancement noise reduction method; dividing the denoised data into a training set, a verification set and a test set; training set data is transmitted to a fault diagnosis model, and normal temperature environment mechanical vibration data features after noise reduction and low temperature environment mechanical vibration data features after noise reduction are extracted; respectively aligning the extracted normal-temperature environment mechanical vibration data characteristics and the extracted low-temperature environment mechanical vibration data characteristics on a plurality of scales by using a multi-layer category perception multi-core adaptive maximum mean difference method; data features are transmitted into a classifier for fault classification, model parameters with the best performance on a verification set are taken, and the effect and performance of the model are checked on a test set.

A torsional micro-electro-mechanical systems (MEMS) mirror module provides for thermally-stable ancillary modes of mirror oscillation (e.g., vertical, horizontal, and rocking) by utilizing MEMS die packaging techniques that implement a cantilevered (i.e., fixed-free) die package in which one end of the die is partially fixedly-attached to a die carrier substrate while the non-attached end of the die is free from the die carrier substrate and unsupported. The cantilevered die package with fixed-free architecture effectively decouples the effects of coefficient of thermal expansion (CTE) mismatch of the MEMS die, die-bond adhesive, and die carrier substrate on temperature-dependent MEMS flexure stress. Ancillary mode oscillation frequencies changes with temperatures are thus limited to a smaller range relative to those experienced with conventional package designs.

The invention relates to a sensor, a measuring device and a measuring method for in-situ dynamic measurement of mechanical properties of a miniature bionic flapping-wing air vehicle, and belongs to the field of MEMS (micro electro mechanical system) mechanical sensors. Based on a micro electro mechanical system (MEMS) processing technology, a six-axis mechanical sensor is designed and prepared, and a measuring device is designed and prepared by integrating the six-axis mechanical sensor, so that integrated assembly of the mechanical sensor and the miniature flexible bionic flapping wing is realized, and in-situ dynamic measurement of the mechanical property of the miniature bionic flapping wing aircraft is realized. The problem that in-situ dynamic measurement of the mechanical property of the bionic flapping-wing aircraft is difficult to achieve through an existing measurement method is solved, and test data support is provided for further optimizing the flapping-wing structure and improving the flight efficiency.

The invention relates to a flexible hinge micro-displacement amplification mechanism, and relates to the field of intelligent material micro-driving. The device specifically comprises an input rod, a lever amplification structure, a flexible hinge and an output rod. By means of the lever amplification principle, amplification of infinitesimal displacement is achieved through elastic deformation of the flexible hinge and a two-stage lever amplification mechanism. The micro-displacement amplification mechanism not only eliminates parasitic displacement, but also has a high amplification coefficient and high amplification efficiency, and is suitable for the field of micro-driving of intelligent materials such as micro-electromechanical systems (MEMS), intelligent robots and medical instruments.

A control method for controlling an electro-mechanical system according to a task estimates the state of the system using an adaptive surrogate model of the system to produce an estimation of the state of the system. The adaptive surrogate model includes a neural network employing a weighted combination of neural ODEs of dynamics of the system in latent space, such that weights of the weighted combination of neural ODEs represent the uncertainty. The method controls the system according to the task based on the estimation of the state of the system and tunes the weights of the weighted combination of neural ODEs based on the controlling.

The present invention belongs to the technical field of designs of mechanical parameters or variables in electrical digital data processing. Disclosed is a method for optimizing the reliability of an ultra-deepwater pile hammer system, which method is used for performing reliability analysis on a pile hammer system. The method comprises: performing functional analysis and functional module division on a mechanical system, a power system, a hydraulic system, a pneumatic system and an electronic control system of an ultra-deepwater pile hammer system, viewing the five systems as five first-level subsystems, clarifying important parameters of the first-level subsystems, and further dividing the first-level subsystems into second-level subsystems; and performing criticality analysis on the second-level subsystems, and allocating reliability indexes of the first-level subsystems and the second-level subsystems of the ultra-deepwater pile hammer system and reliability indexes of failure modes of the second-level subsystems, so as to obtain an optimal reliability allocation scheme for the ultra-deepwater pile hammer system. In the present invention, various parameters of first-level systems and second-level systems are combined, and failure modes are taken into consideration, so as to obtain an optimal reliability allocation scheme.

The invention discloses an engineering machinery fault prediction and prevention method based on machine learning, and the method comprises the steps: collecting the multi-source operation data of equipment, and constructing a structured multi-dimensional time sequence; constructing a heterogeneous graph structure based on the connection relation of the equipment components, the control path and the energy transmission path; an improved expansion time convolutional network is adopted to extract node time sequence dynamic features, and node embedding representation is generated through channel attention mechanism weighting; inputting the embedded representation into an edge attention enhanced heterogeneous graph neural network, and fusing an edge type and time offset information to calculate a fault risk score; and constructing a retrieval index mechanism in combination with historical fault cases, generating a candidate set according to similarity matching, and outputting structured prevention suggestions. According to the method, fault risk prediction with higher accuracy can be realized under a complex structure working condition, the early warning response speed and the processing suggestion generation efficiency are remarkably improved, and the operation reliability and the maintenance intelligence level of an engineering mechanical system are effectively enhanced.

A piezoelectric Micro-Electro-Mechanical Systems (MEMS) resonator employs a thin-film piezoelectric layer as an anchor, which eliminates a dominant loss source, anchor loss, which stems from the irreversible mechanical energy radiation through the anchors. By implementing fundamental or higher-order thickness Lame modes (TLMs) in the thickness direction, the piezoelectric resonator exhibits a substantial reduction of thermoelastic damping (TED) and increase of anchor quality factor. The piezoelectric resonator also provides temperature stability by utilizing a substrate with a turnover temperature, minimizing deviation on resonance frequency. This approach enables the use of thin-film piezoelectric materials as anchors, which can be precisely controlled to minimize losses. The piezoelectric resonator's compact design and CMOS-compatibility make it suitable for batch production at a minimal cost per unit. Furthermore, the highly frequency-stable temperature point of the piezoelectric resonator can be used for implementing oven-controlled oscillators for ultra-stable clock generation in various applications.

Provided herein are processes for recycling hydrocarbons in a used high performance lubricant such as wind turbine gear oil. In the present process and methodologies, the used high performance lubricant is not mixed with another type of used oil. The used high performance oil is contacted with a solvent in an extraction process. Contaminates are extracted and circular hydrocarbons are formed. Solvent can be recovered, and the circular hydrocarbon combined with a high performance base stock for use as a high performance lubricant in a mechanical system.

The invention belongs to the technical field of system life prediction, and discloses a mechanical system residual life prediction method considering dynamic environment influence, and the specific technical scheme is as follows: step 1, system degradation modeling: firstly, according to received monitoring data representing an operation state and a degradation state, carrying out correlation analysis and processing on two types of obtained states, constructing a system dynamics evolution model and a part degradation model; step 2, according to the established system degradation model, estimating an observable degradation state and an observable operation state by using a dual-time scale Kalman filtering algorithm and a Rauch Tung Striebel filtering algorithm, and estimating model parameters by using a two-step expectation condition maximization algorithm; 3, defining the residual life of the system according to the first arrival time on the basis of considering the dual-time scale influence, the measurement error and the bidirectional correlation of the two states; the system life prediction precision is high, the effectiveness is good, and the system life prediction method has better general adaptability.

The invention belongs to the field of deep foundation pit intelligent supporting systems, and particularly relates to a deep foundation pit intelligent supporting system based on oblique beam grid and digital twinning and a collaborative construction method.The deep foundation pit intelligent supporting system based on oblique beam grid and digital twinning comprises an oblique beam grid supporting structure composed of two-way crossed steel oblique beams, and the outer layer of each steel oblique beam is a Q355B steel pipe with the thickness not smaller than 12 mm; the inclined beam is filled with ultra-high performance concrete with the compressive strength not smaller than 150 MPa, the inclination angle of the inclined beam ranges from 25 degrees to 60 degrees, and an MEMS micro-electro-mechanical system sensor is pre-buried at a node; and the digital twin platform integrates the geological BIM model and the LSTM neural network prediction model. The bending rigidity is improved through the oblique beam grid supporting structure and the concrete filled steel tube composite beam, the inclination angle ranges from 25 degrees to 60 degrees, force flow transmission is optimized, and micro-strain-level stress monitoring is achieved through the MEMS sensors embedded in the nodes. A digital twin platform and an LSTM model are fused with 12-dimensional parameters, the 72-hour deformation prediction error is smaller than or equal to 1.5 mm, and compared with a traditional experience algorithm, the precision is improved.

The invention relates to the technical field of micro-electro-mechanical system sensors, in particular to an MEMS pressure sensor which comprises a shell, an induction diaphragm and an ASIC signal processing unit, an induction mounting cavity and a signal processing cavity are formed in the shell, an air inlet is formed in the lower end face of the shell, a detection channel and a buffer cavity are formed in the shell, and the detection channel is communicated with the signal processing cavity. The buffer cavity is designed to be gradually expanded from bottom to top; pulse airflow firstly enters the detection channel through the air inlet, and direct disordered impact of the airflow is avoided; then the airflow enters the buffer cavity, the airflow speed and the pressure peak value are gradually reduced and the pulse energy is dispersed by utilizing the characteristic that the cross sectional area of the cavity is gradually increased, and the originally concentrated strong impact force is converted into gentle pressure transmission, so that the abrasion and damage risks caused by the fact that the sensing diaphragm frequently bears the violent pressure are reduced; the structure stability and the sensing sensitivity of the sensing diaphragm can be maintained, and the accuracy of pressure detection and the reliability of long-term work of the MEMS pressure sensor can be guaranteed.

In some examples, a rotary mechanical system, includes a motor, a housing, and a plurality of cooling channels. The housing at least partially encloses one or more components of the motor. The plurality of cooling channels are defined by a plurality of spaces between an external surface of the motor and an inner surface of the housing, and are configured to receive a refrigerant to cool the motor.

The invention provides an explosion-proof motor bearing fault detection method based on frequency domain sparse variational mode extraction, and the method comprises the following steps: S1, collecting a vibration signal of a detected device through a vibration sensor, then intercepting the vibration signal in unit time, and carrying out the mean value removal; s2, constructing a frequency domain sparse variational mode extraction model, deducing an iterative solution algorithm of the model, and extracting fault features from the vibration signals through the iterative solution algorithm; s3, analyzing the time domain waveform and the envelope spectrum of the fault features, calculating the theoretical fault feature frequency of the bearing according to the structural parameters and the rotating speed of the bearing, comparing each spectral line with the theoretical fault feature frequency of the bearing, and determining the fault type of the bearing according to the matching result; weak fault features can still be accurately extracted under multiple interference components, accurate detection of the bearing fault of the explosion-proof motor is achieved, and operation safety of a whole mechanical system is ensured.