2691 results about "Accelerometer" patented technology

Locator systems are disclosed for locating buried utilities such as conduits, cables, pipes. or wires. The locator system may include image-capturing and position and orientation measuring devices such as magnetic compass, accelerometers, gyros, GPS, and DGPS. The system may associate images captured during the locate process with buried utility position data and the other sensor data. These data and images may be further associated with terrain images from satellites or aerial photography to provide a highly precision map and database of buried utility locations and visual images of the burial site. Information so obtained may be transferred to a hand-held personal communication device, such as a smart phone to show the location of buried utilities in combination with photo-images and / or terrain maps.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention relates to the technical field of aviation communication, and discloses a true north calibration system and method for an aviation airborne laser communication equipment alignment system, and the method comprises the steps: synchronously collecting flight data through an IMU sensor and a double-antenna GPS at a preset sampling frequency, and outputting the real-time attitude angle information of an aircraft through an IMU; filtering and de-noising the synchronously acquired IMU and double-antenna GPS data, and carrying out noise suppression and deviation compensation on accelerometer and gyroscope data output by the IMU by adopting a Kalman filtering algorithm; based on the preprocessed sensor data, calculating an initial course angle by measuring relative position change and phase difference between antennas by using a double-antenna GPS (Global Positioning System); the calibration controller transmits the calculated true north direction information to an alignment system of the laser communication equipment in real time through a communication interface; and the attitude change and external environment disturbance of the aircraft are continuously monitored in the whole flight process. The method has the advantage that the alignment performance and the communication stability of the laser communication equipment are improved.

The invention relates to the technical field of geological landslide monitoring, in particular to a tower footing geological landslide monitoring and early warning system and method. The method comprises the following steps: arranging an inclination angle sensor, a Beidou-GNSS module, a soil moisture content probe and a micro-seismic accelerometer on a tower footing and the periphery of the tower footing, and collecting the inclination amount, three-dimensional displacement, soil moisture content and vibration response information of the tower footing to form a monitoring original data set; timestamp unification, WGS-84 projection coordinate system conversion and multi-frequency vibration elimination are carried out on the monitored original data set, and an air-ground integrated displacement profile is constructed; the whole process realizes high integration and automation in the aspects of data acquisition, processing, fusion and transmission, so that the timeliness, accuracy and data integrity of landslide risk monitoring are ensured, and scientific decision support is provided for disaster prevention and reduction.

The invention discloses a modular collaborative layout method for gyroscopes and accelerometers, relates to the technical field of inertial sensing, and solves the problem of accumulative errors of gyroscope drift and accelerometer zero offset drift in a complex geological environment under a mine, and by calculating a drift change rate Cpj and a zero offset change rate Azp in real time and executing error compensation, the gyroscope drift rate Cpj and the accelerometer zero offset change rate Azp are calculated in real time, and the accelerometer drift rate Cpj and the accelerometer zero offset change rate Azp are calculated in real time. The positioning and path tracking precision of the equipment under the condition of no GPS is improved; meanwhile, aiming at external interference factors such as vibration, noise and air flow in the mine, a data fusion strategy is adjusted in real time by calculating a dynamic interference adaptation index Xw and comparing the dynamic interference adaptation index Xw with a threshold value, and the influence of interference on attitude estimation is effectively reduced; besides, according to the scheme, sensor data are dynamically adjusted through a temperature compensation and humidity correction mechanism, the influence of temperature and humidity changes on the gyroscope and the accelerometer is compensated, the stability and precision of the system in the extreme environment are ensured, and the adaptability and reliability of the mine environment are enhanced.

The invention relates to a grid steel structure node failure diffusion prediction method, which comprises the following steps of: forming a failure inducement and evolution path model according to a connection form of a bolt-sphere node and a welding node, a stress transfer path, node local yield and fatigue accumulation factors; a diffusion logic of node failure-force flow redistribution-adjacent node stress concentration-chain reaction is constructed; nodes are regarded as middle points of the graph, rod pieces are regarded as edges, and propagation weights between the nodes are defined; the weight parameters are dynamically assigned based on material attributes, node connection types, spatial geometric positions and historical stress states; multi-source monitoring sensors including strain gauges, displacement meters, thermometers and accelerometers are arranged at key nodes of the grid structure, so that node states are sensed; in combination with real-time monitoring data, extracting strain rate change, vibration characteristics, frequency drift and residual deformation key characteristics of each node; a node health state feature vector set is constructed by using time sequence analysis, and the gradual degradation trend of nodes is reflected.

The invention discloses a ship inclination measurement method and system based on Kalman filtering fusion, and relates to the technical field of ship stability measurement, and the method comprises the steps: multi-sensor synchronous data collection, state space modeling and Kalman filtering fusion, adaptive dynamic adjustment of process noise, drift compensation, and fault tolerance and output. When the method and the system provided by the invention are used for measuring inclination, inclination angle error accumulation caused by gyroscope zero offset integral error, accelerometer dynamic interference, sensor noise and temperature drift, GNSS dependency and the like can be effectively avoided, available precision can still be maintained through multi-sensor depth coupling and an intelligent filtering algorithm, and the accuracy of inclination measurement is improved. The method is simple in operation, independent of satellite data, higher in stability, capable of greatly reducing hardware cost compared with GNSS and IMU schemes, and especially has the advantages of being simple in cost and installation configuration in ship refitting; and the method is also suitable for GNSS rejection or cost sensitive scenes such as inland ships, unmanned ships, offshore buoys and the like.

The invention discloses a lineation robot control system based on a satellite positioning and orientation technology, which relates to the technical field of robot motion control and comprises a satellite positioning and orientation module, an inertial navigation module, a fusion positioning and orientation software module, a motor control software module, a motion control software module and a pattern planning software module. The satellite positioning and orientation module is used for acquiring latitude and longitude coordinates of the current position of the robot by receiving multi-frequency satellite signals; the inertial navigation module is used for collecting posture and motion data of the robot in real time through a gyroscope and an accelerometer. According to the lineation robot control system provided by the invention, through collaborative design of the multi-frequency-point GNSS receiver and the anti-multipath interference antenna array, centimeter-level continuous positioning capability in a complex urban environment is realized, and satellite signal reflection and shielding interference in scenes such as viaducts and avenues are effectively overcome; and satellite positioning and inertial navigation data are deeply fused through a tight coupling Kalman filtering algorithm.

The invention relates to the technical field of robots, and discloses a multi-source data fusion method of a tracked robot, which realizes dynamic environment adaptation and autonomous decision optimization through a multi-source sensing cooperation mechanism, and comprises the following steps: dynamically adjusting exposure parameters of a visual module based on environment illumination intensity, and avoiding overexposure or underexposure of an image through a feedback adjustment mechanism; fusing data of the accelerometer and the gyroscope to generate attitude angle compensation parameters; the heat dissipation system is intelligently regulated and controlled by comparing infrared thermal imaging and temperature sensor data; dynamic task priority distribution is realized; optimizing an advancing route to reduce complex terrain navigation deviation, in addition, establishing a multi-source sensing coordination mechanism, monitoring health indexes such as motor temperature and vibration frequency in real time, and triggering abnormal state judgment; track optimization is implemented in combination with pavement mechanical characteristics and a kinematic model; and executing the staged energy management strategy. According to the method, the environment sensing precision and the motion control reliability of the tracked robot in a complex environment are remarkably improved.

The invention discloses a high-precision attitude sensor dynamic calibration system and a method thereof, and belongs to the technical field of attitude sensors. The invention discloses a dynamic calibration system and method for a high-precision attitude sensor. The dynamic calibration system comprises a data acquisition unit, a dynamic calibration unit and a calibration execution unit. The problem that the calibration effect is limited in the prior art is solved, the state of the sensor can be known more comprehensively by collecting the output data of the high-precision attitude sensor, the environmental data and the multi-source data of the accelerometer, the gyroscope and the magnetometer in real time, so that more accurate error analysis and calibration are carried out, and the calibration accuracy is improved. Modeling and error analysis are carried out by utilizing a machine learning algorithm, parameters are automatically adjusted through a model adaptive updating mechanism, error analysis can be carried out on data collected in real time so as to provide reference for subsequent dynamic calibration, and dynamic calibration operation of the high-precision attitude sensor is accurately executed according to an analysis result of the model. And high precision and high stability of attitude measurement are ensured.

An ultrasound imaging system configured to generate a three-dimensional (3D) ultrasound image of a target area. The ultrasound imaging system includes a console including one or more processors and non-transitory computer readable medium having stored thereon one or more logic modules, and an ultrasound probe configured to acquire a plurality of ultrasound images of a target area. The ultrasound probe can be coupled to the console by an ultrasound probe connector having optical fiber including one or more core fibers. The console is configured to generate the 3D ultrasound image by stitching together the plurality of ultrasound images, starting from a point of reference. The point of reference can be the ultrasound probe, one or more anatomical targets, an elongate medical device, a reference magnet, or one or more accelerometers.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.

Disclosed in the present invention are a UWB-based positioning apparatus and positioning method for a point target of a pointing apparatus. The positioning apparatus comprises a pointing apparatus and a receiving apparatus, wherein UWB antennas are respectively arranged on the pointing apparatus and the receiving apparatus, and an accelerometer and a gyroscope are further arranged on the pointing apparatus. By means of the pointing apparatus and the receiving apparatus, UWB-based positioning is performed to obtain coordinates of the pointing apparatus in a coordinate system in which the receiving apparatus is located; then by means of the accelerometer, a motion vector of the pointing apparatus is obtained, such that a linear equation for a corresponding pointing direction of the pointing apparatus is obtained; and by means of solving an intersection point of the linear equation and a pointing area, coordinates of a pointing point are determined, thereby achieving a design purpose.

The invention discloses a full-automatic cable vibration high-precision measurement method based on a machine vision technology, and relates to the technical field of structural vibration measurement. The method comprises the following steps: installing and calibrating equipment, and ensuring that a cable plane coincides with an imaging plane; high-frame-rate video acquisition is carried out, and comparison verification is carried out in combination with an accelerometer; screening effective pixels based on the pixel intensity time sequence variance, and constructing a pixel intensity space-time matrix by using the time sequence of the pixels; sVD decomposition is adopted to extract an accumulated energy ratio gt; 90% of the main modes are reconstructed, and real signals are revealed; a plurality of inhaul cables are automatically positioned and coded through gradient features and Hough transform; sub-pixel-level vertical displacement is calculated by applying an optical flow method, and median filtering is combined to resist noise; fourier transform is carried out to obtain a spectrogram, and a fundamental frequency is identified; and calculating cable force based on a string pulling theorem. According to the method, remote, full-field and multi-target synchronous monitoring is realized, the displacement measurement precision reaches a sub-pixel level, and a high-precision and full-automatic solution is provided for health monitoring of structures such as bridges.

The invention relates to the technical field of information processing, in particular to a mine microseism positioning control system and method.The mine microseism positioning control method comprises the steps that microseism signals are synchronously collected through a geophone, an accelerometer, an acoustic emission sensor and an optical fiber sensor, and data timestamps are aligned based on a GPS / Beidou clock; blind source separation is carried out on the signals after wavelet filtering, independent seismic source components are extracted, multi-sensor features are fused through a graph neural network, and a space-time joint feature matrix is constructed; classifying micro-seismic event types by using a ResNet-LSTM hybrid model, generating a seismic source coordinate initial solution based on deep reinforcement learning, and iteratively converging to an optimal solution through a particle swarm optimization algorithm; and mapping a positioning result to a three-dimensional geological grid model, simulating an energy diffusion path, triggering a three-level alarm according to the event energy density, and automatically pushing an emergency instruction to a mine safety management system. Therefore, the problem of low precision of the existing mine microseism positioning control system is solved.

The invention belongs to the technical field of underground engineering, and discloses an omnibearing intelligent monitoring system, method and device for an underground anchoring pull-out test. According to the method, electromagnetic signals generated in the stress process of the anchor rod are captured in real time through an electromagnetic sensor, and internal stress changes and energy distribution are reflected; the deformation and stress states of the anchor rod in the dynamic environment are evaluated through a strain gauge and accelerometer combined data combination method, the deformation condition of the anchor rod is monitored through the strain gauge, and the stress-strain relation is recorded; an accelerometer is used for detecting the dynamic response of surrounding rock mass so as to evaluate the overall stability of the rock mass; a coupling medium is injected, the computer terminal stores the collected signals locally or in a cloud end and analyzes the signals, stress and deformation parameters in an anchor rod pull-out test are extracted, and an anchor rod stress cloud picture is generated in real time. According to the method, the safety and reliability of underground engineering are remarkably improved, and a new technical support is provided for evaluation of anchoring quality.

The invention provides an MEMS acceleration sensor null drift determination method and device and a terminal, and relates to the technical field of MEMS acceleration sensors. According to the invention, the first accelerometer and the second accelerometer which are relatively fixed in position and different in measuring range are arranged and synchronously move in the use process, and the real-time zero-offset drift distance of the wide-range accelerometer is determined based on the difference between the monitoring values of the small-range accelerometer and the wide-range accelerometer by utilizing the accuracy of the small-range accelerometer. Therefore, the wide-range accelerometer can be corrected in real time by using the zero-offset drift amount, and the accuracy of the measured value of the acceleration sensor is improved in a relatively wide range. In addition, the two accelerometers move synchronously in the using process, the two accelerometers are the same in environment and inclination angle, the zero offset drift distance of the large-range accelerometer is determined based on the monitoring value of the small-range accelerometer, the influence of environment vibration can be avoided, the influence of gravity components of the inclination angles of the accelerometers can also be avoided, and the accuracy of determining the zero offset drift distance is improved.

The invention discloses a GNSS (Global Navigation Satellite System) / inertial navigation tightly integrated navigation method and system based on robust auto-velocity constraint factor graph optimization, and the method specifically comprises the following steps: extracting pseudo-range and Doppler observation data from original observation information of a GNSS satellite, and obtaining accelerometer and gyroscope data from an INS (Inertial Navigation System); constructing a pseudo-range residual block, a Doppler residual block, an inertial navigation pre-integration residual block and a speed constraint factor, and integrating into a sliding window estimator based on factor graph optimization; according to the size of a set sliding window, adding all residual blocks in the window to form an optimized objective function; performing a first round of factor graph optimization to obtain a preliminary state estimation result, and endowing the observed quantity containing gross error with a relatively low weight; and updating the prior weight matrix, and carrying out second round of optimization to obtain a joint optimal estimation result of a plurality of moment states in the current time window. According to the method, the influence of gross error-containing observation information on weight estimation and state estimation is suppressed, the positioning precision is high, the robustness is strong, and the reliability is high.

The invention discloses a gas extraction pipeline tiny leakage positioning method based on multi-sensor fusion, which belongs to the technical field of positioning, is used for gas extraction pipeline leakage positioning, and comprises the following steps: establishing a state equation and an observation equation of a motion process of an inspection robot, and realizing inspection robot positioning under a navigation system based on Kalman filtering. And realizing coal mine gas extraction pipeline leakage source positioning under a microphone array coordinate system based on a beam forming algorithm, and obtaining coal mine gas extraction pipeline leakage source positioning under a navigation system through coordinate conversion. According to the method, the leakage source position of the gas extraction pipeline under the navigation system is directly given, maintainers can conveniently and quickly reach the leakage source position, and the first-aid repair time is greatly shortened; the positioning error of the inspection robot can be effectively reduced, and the phenomenon that the positioning error is accumulated along with time when the inspection robot only uses an accelerometer and a gyroscope for positioning is avoided; the influence of background noise on a positioning result can be greatly reduced, and the spatial resolution of a leakage positioning result is improved.

The invention provides a bearing fault diagnosis method based on pyramid fusion of local details and residual KAN, relates to the technical field of deep learning and intelligent fault diagnosis, and combines a multi-scale feature fusion technology and a feature extraction network to improve the accuracy of fault diagnosis. Firstly, bearing vibration signals are collected through a preset sampling frequency and an accelerometer, and the bearing vibration signals are divided into a training set, a verification set and a test set. Then, a one-dimensional vibration signal is adopted as an input feature, and random Gaussian noise is added on the basis to improve the generalization performance of data; according to the constructed deep residual Kolmogorov-Arnod network, through a pyramid fusion strategy with local details reserved and a global attention mechanism, features are effectively extracted. Finally, the trained and optimized fault diagnosis model can quickly and accurately identify the reason of the bearing fault, and on this basis, the fault diagnosis requirements under different working conditions can be met.

A system for opening or closing a closure member of a vehicle and an accelerometer calibration method are provided. The system includes an actuator assembly with an electric motor operably coupled to an extensible member for opening or closing the closure member. The system also includes an accelerometer configured to sense movement of the closure member and output an accelerometer signal. An actuator controller is coupled to electric motor and to the accelerometer and is configured to determine an adjusted accelerometer signal as the accelerometer signal adjusted using one of a plurality of predetermined compensation factors determined through an accelerometer calibration process temporally before installation of the accelerometer in the vehicle. The actuator controller then controls the opening or closing of the closure member using the electric motor based on the movement of the closure member as represented by the adjusted accelerometer signal.

A wearable device includes an accelerometer, at least three electrodes to detect electromyography signal, a communication circuitry, and a processor circuitry. A user wears a primary and a secondary wearable device on two limbs. When the user performs a two-hand gesture, the wearable devices detect a primary and a secondary movement pattern. The primary device recognizes the two-hand gesture from the primary and secondary movement pattern.

The invention relates to a method for controlling the stability of a transport vehicle. Comprising the following steps that dynamic parameters of the speed, the acceleration, the steering angle and the sideslip angle of a vehicle are collected through a vehicle sensor comprising an accelerometer, a gyroscope and a wheel speed sensor; in combination with various sensors such as a vehicle-mounted radar, a laser radar and a camera, conditions of wet, slippery, rugged and uneven road surfaces are monitored in real time, and road surface friction coefficient parameters are generated through a data fusion algorithm; according to the dynamic state of the vehicle and the real-time road surface state, the stability of the vehicle is evaluated through a mathematical model, and the potential risk of current instability is calculated; a plurality of stability control modes are preset according to different driving scenes including expressways, mountain roads and wet and slippery roads, and different control parameters and algorithms are adopted in each mode; a sensor including a driver state monitoring system inside a vehicle and external environment data including meteorological data and traffic information are utilized to predict operation behaviors including sudden acceleration and sudden braking of a driver.

The invention discloses a seismic geological disaster monitoring and early warning device, and the device comprises an intelligent sensing system which carries out the real-time collection of geological parameters, facility states and environmental factors through a multi-mode sensor array, and comprises the steps: capturing the strain of a pipeline through a distributed optical fiber sensor, monitoring the vibration through an MEMS accelerometer, and providing deformation data through an InSAR satellite; the analysis system constructs a complex network modeling engine, fuses the historical transition probability matrix and the real-time deformation rate parameter by using a directed weighted network model, and combines a two-channel causal inference engine which comprises a physical causal channel and a data causal channel; the physical causal channel is embedded into a coulomb fracture criterion to calculate a fault stress accumulation rate, the Darcy law simulates pore pressure propagation, and the data causal channel generates an anti-fact sample through a CaualGAN and extracts a real causal chain in combination with a time causal convolutional network; and the execution and feedback system is used for triggering graded early warning based on an analysis result of the analysis system.

The invention discloses a fusion filtering attitude estimation method based on EKF and Mahony, and the method comprises the steps: firstly obtaining the original data information of an IMU sensor at the current moment, carrying out the normalization of the attitude information of an accelerometer and a magnetometer, carrying out the error calculation, and compensating the error to the angular velocity of a gyroscope through a PI controller, the method comprises the following steps: performing Mahony filtering correction on an angular velocity and a corrected attitude quaternion to obtain an angular velocity subjected to Mahony filtering correction and a corrected attitude quaternion, constructing a state equation of an EKF by using the corrected attitude quaternion and a gyroscope zero offset value, and constructing an observation equation by using data of an accelerometer and a magnetometer so as to obtain an optimally estimated attitude quaternion and a gyroscope zero offset value; and finally, compensating the optimally estimated zero offset value of the gyroscope to the angular velocity correction process in the Mahony filtering process.

The invention relates to the technical field of equipment fault diagnosis, and discloses a steam turbine vibration fault risk evaluation method and system.The method comprises the following steps that real-time data such as vibration, temperature, pressure and rotating speed of a steam turbine are collected through multiple sensors; the sensors comprise an accelerometer, a displacement sensor, a temperature sensor, a pressure sensor and a rotating speed sensor; the collected multi-source data is preprocessed, including de-noising, time synchronization and data standardization, to eliminate environmental noise and time difference between the data. The technical scheme based on multi-dimensional data acquisition and fusion is adopted, vibration sensors, temperature sensors, pressure sensors, rotating speed sensors and the like are arranged at key parts of a steam turbine, the running state of equipment is obtained in real time, multi-dimensional fusion and feature extraction are carried out on data, and the effects of comprehensive and real-time monitoring and early prediction of vibration faults of the steam turbine are achieved.

Provided herein are methods for Inertial Measurement Unit (IMU) sensor compensation utilizing deep neural network (DNN) technology. In some embodiments, the methods involve receiving sensor values from gyroscopes, accelerometers, and non-motion sensors, then loading these values into a deep learning algorithm alongside true sensor values. Unlike traditional calibration techniques that rely on complex parametric models, this approach may utilize a neural network to directly process raw sensor data and enhance output signal accuracy. In some embodiments, the training process employs ground truth data for approximately 70% of inputs, with the remaining 30% dedicated to sensor compensation. The technique may be implemented in standalone IMUs, augmented IMUs, and navigation systems, offering improved accuracy and reduced computational overhead across various technological applications.

The invention discloses a transformer fault ultrasonic detection system, which comprises an ultrasonic sensor module, a signal conditioning module, a data acquisition and processing module, a data fusion module, a learning module, a diagnosis decision module and a human-computer interaction interface, according to the ultrasonic sensor module, 6-8 ultrasonic sensors are arranged in a transformer shell in an array mode, piezoelectric ceramic sensors with the resonant frequency of 40 kHZ are adopted as the ultrasonic sensors, the sensitivity of the ultrasonic sensors is-65 dBV / uBar, and effective capture is weak. According to the ultrasonic detection system for the transformer fault, single ultrasonic detection is easily influenced by accidental interference, the system is synchronously connected into equipment such as an infrared thermal imager and a vibration accelerometer, and a multi-physical-quantity correlation analysis model is established. When high-frequency ultrasonic pulses are detected, the system automatically calls temperature data of the corresponding position, and if the temperature gradient exceeds 2 DEG C / cm, it is confirmed that discharging heating is conducted; and if the temperature rise does not exist and the low-frequency vibration of 10-200Hz is accompanied, the mechanical looseness is judged.

Aspects of the disclosure describe an implantable system for monitoring intracranial pressure (ICP) in a subject, the system comprising: a conduit for the flow of cerebrospinal fluid (CSF); a hermetically sealed compartment; a first pressure sensor configured to measure an absolute ICP and output a measurement, wherein the first pressure sensor is hermetically sealed in the compartment, and wherein the first pressure sensor comprises a surface exposed to the CSF in the conduit; a microcontroller disposed in the hermetically sealed compartment that receives multiple inputs including at least the output of the first pressure sensor; and an accelerometer disposed in the hermetically sealed compartment for monitoring a subject's positional status, and wherein the accelerometer communicates an output to the microcontroller.

The invention belongs to the field of accelerometers, and particularly discloses an accelerometer and a vibration rectification error compensation method thereof, and the design of the accelerometer needs to meet the condition that the quality factor of the accelerometer is smaller than or equal to the ratio of the accelerometer range to the maximum vibration acceleration in an application environment; the square of the resonance angular frequency of the accelerometer is greater than or equal to the ratio of the accelerometer range to the check mass displacement upper limit; the ratio of the resonance angular frequency to the quality factor is smaller than or equal to a preset value related to a preset mechanical thermal noise upper limit; the quality factor is the gain at the resonant angular frequency of the accelerometer, and the gain is the ratio of the accelerometer response corresponding to the acceleration input at different frequency points to the accelerometer response corresponding to the constant acceleration input with the same amplitude. According to the invention, the sensitivity of the mechanical structure of the accelerometer can be improved, output signal saturation is avoided, the sensitivity of the accelerometer is ensured, accurate vibration rectification error compensation can be carried out on the accelerometer, and the overall performance of the accelerometer is improved.