78results about How to "Improve the accuracy of fault identification" patented technology

The invention discloses a machine room server remote monitoring method and system. The system comprises a plurality of IP cameras, a switch and a machine room monitoring center, wherein the IP cameras are used for receiving a shooting data acquisition command, and performing shooting on equipment state indicator lights in a machine room to generate a group of modeling image data; and the machine room monitoring center is used for acquiring and saving the group of modeling image data in advance, controlling each IP camera to perform shooting on server cabinets monitored by each IP camera according to a set shooting data sampling period, acquiring a group of real-time image data, comparing the chromatic value of each equipment state indicator light in the group of real-time image data with the chromatic values of corresponding equipment state indicator lights in the saved modeling image data one by one, checking abnormal servers in the machine room according to comparison results, and executing alarming. Through adoption of the machine room server remote monitoring method and system, accurate history image data which can be inquired is provided in order to perform server fault analysis and processing, and the server fault checking efficiency can be increased greatly.

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

The invention discloses a universal circuit breaker accessory fault diagnosis method based on deep learning. The method is used for fault diagnosis of a low-voltage universal circuit breaker switch-on-off accessory. By considering the feature of the switch-on-off coil current signal, a self-adaptive one-dimensional deep convolution neural network is adopted, and a receptive field region is enlarged by setting a convolution kernel of the first layer of convolution layer of the model as the wide convolution; and then the self-adaptive feature extraction is performed on the current signal by utilizing a feature extraction layer; and finally, a Softmax classifier is used for outputting a fault diagnosis result. The fault diagnosis of the switch-on-off accessory shows that the same fault is effectively identified under different switch-on phase angles, and high fault recognition rate still can be maintained in a generalization experiment, and the influence on the fault diagnosis result by the switch-on phase angle change can be effectively overcome.

The invention relates to a rolling bearing fault diagnosis method based on a vibration signal. A CEEMDAN algorithm is adopted to decompose the vibration signal, de-trending fluctuation analysis is carried out on an obtained intrinsic mode function, a scale function value of each IMF component is calculated, and a noise dominant IMF component is selected to carry out de-noising processing; the noise can be better removed, and the distortion degree of the signal is reduced; calculating correlation coefficients and kurtosis values of all orders of IMF components, selecting IMF components with relatively large correlation coefficients and kurtosis values to perform signal reconstruction, performing Hilbert envelope spectrum analysis on reconstructed signals, extracting fault feature frequency, introducing a grey wolf algorithm to optimize initial parameters of multi-scale permutation entropy, performing MPE value calculation on the reconstructed signals, selecting a proper MPE value to construct a rolling bearing fault feature set, and inputting a fault feature vector into the trained support vector machine to carry out rolling bearing fault recognition, so that the entropy discrimination degree is high, the constructed fault feature vector is better, and the recognition rate is higher.

The invention discloses a fault identification method of an analog circuit based on an improved extreme learning machine, wherein the method comprises steps: the voltage eigenvectors of analog circuits at different corner frequencies are used as inputs, and then input vectors corresponding to each hidden layer neuron are selected based on each entropy rate, and weights and offsets that reach the highest correlation degree are generated through the multi-dimensional particle swarm optimization algorithm, then the appropriate parameters are found by PSO algorithm and iterative operation, so as to construct an efficient hidden layer model; finally, the hidden layer model is trained to identify the fault of analog circuit, and the fault of analog circuit can be identified. The model has the characteristics of high fault identification accuracy and high speed.

The invention relates to the technical field of information processing, and provides an equipment failure identification method, an equipment failure identification device and terminal equipment. Themethod comprises the following steps: collecting working voice information when target equipment works, and inputting the working voice information into a first neural network model for carrying out identification to obtain a first identification result, wherein the first identification result comprises a first failure position and a first failure type; collecting image information when the targetequipment works, and inputting the image information into a second neural network model for carrying out identification to obtain a second identification result, wherein the second identification result comprises a second failure position and a second failure type; matching the first identification result and the second identification result, and if the first failure position is the same as the second failure position and the first failure type is the same as the second failure type, generating equipment failure information, wherein the equipment failure information comprises failure positionand failure type. The method is combined with voice and image for carrying out identification on the equipment failure, so that the failure identification accuracy can be improved accurately.

The invention relates to the technical field of fault identification and fault-tolerant control of an autonomous underwater robot, in particular to an autonomous underwater robot fault identification method based on a wavelet approximate entropy. The autonomous underwater robot fault identification method includes the steps that sensor and controller data of the autonomous underwater robot are decomposed through a multi-layer wavelet decomposition method; fault characteristics of a wavelet detail coefficient and a wavelet approximation coefficient obtained in the step (1.2) are extracted through an approximate entropy extraction method; fault identification is carried out on to-be-detected fault signals of the autonomous underwater robot through a correlation coefficient method. The autonomous underwater robot fault identification method based on the wavelet approximate entropy effectively resolves the problems that an AUV sensor and a controller are affected by external disturbance and are low in fault identification accuracy, obtains the redundant description related to the faults of an AUV propeller through the multi-band frequency characteristic of multi-layer wavelet decomposition, extracts fault characteristics of the multi-band frequency fault information to form a fault characteristic matrix, improves the fault identification accuracy of the AUV, and provides accurate fault information for the fault-tolerant controller.

The invention discloses an abnormality detection method and a device for a beam position monitor based on an auto-encoder. The method comprises: obtaining turn-by-turn beam position data of the beam position monitor; inputting the turn-by-turn beam position data as raw input data into a pre-trained auto-encoder model after preprocessing, processing and outputting reconstructed input data by the auto-encoder model; calculating a reconstruction error between the raw input data and the reconstructed input data; and determining whether the corresponding beam position monitor is abnormal by comparing the reconstruction error with an abnormality threshold, wherein the beam position monitor whose reconstruction error exceeds the threshold is determined to be in an abnormal or fault state. The abnormality detecting method and the device for the beam position monitor based on the auto-encoder solves the problems of abnormality of beam position monitors of storage rings and automatic identification of faults, improves the efficiency and reduces the research and development cost, and the method has high accuracy, high reliability and high practicability.

The invention discloses a wind turbine generator fault diagnosis method, which comprises the following steps: according to the vibration signal characteristics of a wind turbine generator gearbox, carrying out variational mode decomposition on signals under different working conditions to obtain a series of intrinsic mode function components, and respectively solving multi-scale permutation entropies of the intrinsic mode function components; combining the multi-scale permutation entropy and the original signal time domain feature into a feature vector, and inputting the feature vector into atransfer learning algorithm; the covariance of a source domain and a target domain being minimized through a linear transformation matrix, the distribution difference of signal data of the source domain and the target domain being reduced through second-order statistics alignment, and then inputting the feature vectors of the aligned signal data of the source domain and the target domain into a support vector machine for fault classification. According to the method, the problem of poor classification effect caused by different distribution of the vibration signal data under different workingconditions can be solved, and the method has higher accuracy in wind turbine generator fault diagnosis under variable working conditions.

The invention discloses a fault diagnosis method for a mine fan. The fault diagnosis method comprises the following steps: utilizing SCADA to detect a real-time state of fan operation; adopting a waycombining machine learning and deep learning to train data monitored under various working states of the fan; and adopting a GBDT-CNN model to perform fault diagnosis on fan equipment. After determining and judging the fault position of the integral equipment, the diagnosis method determines specific fault degree including light fault degree, medium fault degree and heavy fault degree, of the device. A worker is reminded to select a proper equipment maintenance way while cost is saved as much as possible. Fault diagnosis of equipment is performed in step, and specific fault judgment is not performed while faults of the equipment are judged at the first step, so that diagnosis efficiency is improved while calculation ability of a computer is calculated. Relatively easy-to-capture relation exists between attributes of monitored data and judged fault types. A GBDT integrated learning algorithm is adopted, so that an accuracy rate is relatively high.

The embodiments of the invention disclose a sparse automatic encoder optimized neural network-based power transmission line fault identification method. The method includes the following steps that: S1, data acquisition is performed: fault current traveling waves generated on a power transmission line after the failure of the power transmission line are acquired; S2, a fault waveform feature quantity is extracted: the feature quantity of the fault current traveling wave data of the power transmission line which are acquired in the step 1 is extracted; S3, a sparse automatic encoder is trainedwith a random gradient descent algorithm, so that the sparseness expression of intrinsic feature information in the feature quantity in the step S2, the weight W and offset b of the sparse automatic encoder which are obtained after the training is finished are saved; S4, the input layer and hidden layer of a neural network are initialized with the weight W and the offset b of the sparse automaticencoder in the step S3, a forward iterative algorithm and a reverse iterative algorithm are adopted to train the neural network; and S5, the optimized neural network and a BP neural network are adopted to identify the fault type of the transmission line under the condition of different quantities of training samples.

The invention relates to a fault diagnosis method for an energy storage mechanism of a high-voltage circuit breaker. The method comprises the following steps: firstly, removing background noise from acollected acoustic signal by adopting morphology, putting forward a kurtosis and envelope similarity-based time scale alignment method to ensure the synchronism of an acoustic vibration signal, thenconstructing a two-dimensional image feature matrix for the acoustic vibration signal subjected to data expansion by utilizing a Pearson correlation coefficient, and finally training the feature matrix by utilizing a CNN (Convolutional Neural Network). A CNN model structure is improved by adopting local mean normalization and kernel function decorrelation, so that the influence of large data change in the energy storage process on the diagnosis accuracy of the circuit breaker energy storage mechanism is reduced. According to the method, the overall diagnosis accuracy reaches 98.1%, the generalization performance is good, and the method has obvious advantages compared with a traditional method.

The invention provides a freight train fault detection method and device and electronic equipment, and relates to the technical field of fault detection. The freight train fault detection method comprises the steps of obtaining a train body image of a freight train, and performing region segmentation on the train body image to obtain region images of all regions of a train body; acquiring a targetdetection model corresponding to the regional image, and performing target identification on the regional image based on the target detection model to obtain an identification result; and determininga fault detection result of the regional image based on the identification result. According to the invention, the fault detection result of each area image can be rapidly determined, and the fault detection efficiency of the freight train is improved.

The invention discloses a rolling bearing fault detection method based on an actual measurement signal. The rolling bearing fault detection method comprises the steps of: firstly, converting a rollingbearing fault time domain vibration signal to an angle domain through employing an order tracking technology; secondly, carrying out parameter optimization on variational mode decomposition through adpopting a longicorn beard search algorithm, and decomposing all state vibration signals of a rolling bearing to obtain a series of intrinsic mode functions, wherein frequency band energy in differentintrinsic mode functions can change when different faults happen to the bearing; thirdly, extracting Renyi entropy features from modal components containing main fault information, and constructing afeature subset; and finally, using normal state vibration signals easy to obtain for training, extracting fault characteristic quantities, establishing fault data samples and incremental learning data samples, acquiring a fault recognition model through training by adopting a single-class support vector machine incremental learning algorithm, judging whether the rolling bearing breaks down or notaccurately, and achieving fault early warning.

The invention relates to an on-load tap-changer spring energy storage insufficiency fault identification method based on a neural network response surface. The method is characterized by comprising the following steps: based on a finite element method, establishing an on-load tap-changer fault simulation model by using an entity finite element grid; generating a training sample of a neural networkresponse surface model by adopting a uniform design method, and obtaining output fault characteristics through simulation of a tap switch spring energy storage insufficiency fault; constructing a neural network response surface model through regression analysis of input parameters and output characteristics of a tap switch spring energy storage insufficiency fault; and with the output characteristics of the spring energy storage insufficiency fault adopted as a reference, a willingness function-based multi-target recognition algorithm is adopted to recognize the output fault characteristics of the spring energy storage insufficiency fault. The method is different from a traditional fault identification model based on test data, and has the advantages of high modeling efficiency, high fault identification precision and the like.

The invention discloses a spindle bearing fault detection method and system, equipment and a readable storage medium. The method comprises the following steps that: based on a deep learning wide residual network and a signal processing technology, ensemble empirical mode decomposition is performed on vibration signals, mode components are screened based on a kurtosis value, singular value decomposition optimization is performed on screened-out components; signals are reconstructed; signal features in an image form are outputted through short-time Fourier transform; a wide residual network us built;the image feature data are inputted into the wide residual network to train the network;and finally the wide residual network with a fault diagnosis function is obtained. According to the method, signal processing technology and deep leaning technology are combined, so that the influence of bearing vibration signal noise is reduced, meanwhile; the wide residual network also relieves the requirement of a general artificial neural network model for the high performance of a computer; the problem of performance degradation frequently occurring along with the increase of the number of network layers is solved; and the accuracy and efficiency of fault diagnosis are improved.

The invention provides a gas pressure regulator intelligent RBF fault diagnosis method and system based on PCA data processing, a terminal and a computer storage medium. The method comprises the stepsof acquiring original fault data of a gas pressure regulator with a known fault type; carrying out dimension reduction processing on the original fault data by adopting a principal component analysismethod to generate low-dimension irrelevant sample data; and performing fault classification on the sample data subjected to dimension reduction processing by the principal component analysis methodby using a radial basis function neural network. According to the invention, the original fault sample data of the gas pressure regulator is preprocessed by using the principal component analysis method, and the new sample data after dimension reduction processing is taken as the input of the radial basis function neural network, so that the dependence on experience is effectively reduced, the uncertainty caused by artificial participation is reduced, the fault early warning and intelligent fault diagnosis of main vulnerable parts of the pressure regulator can be realized, and an important role is played in the stable, safe and reliable operation of gas equipment.

The invention relates to a ship power equipment fault identification method. The method is characterized by comprising the following specific steps: S1, collecting and classifying monitoring data samples; s2, establishing and training a classifier; s3, applying a model. According to the method, a fault of ship power equipment is recognized through a support vector machine, so that a fault recognition result under small sample data is more accurate; random behaviors in an AFSA algorithm are improved, blindness generated by artificial fish through a traditional random behavior optimization process is avoided, and the phenomenon of invalid optimization in the optimization process is reduced; a GWO-AFSA algorithm is optimized, so that the convergence rate and the recognition precision are improved, and the defect that traditional AFSA is easy to fall into local optimum is overcome; and SVM parameters are optimized by using the improved GWO-AFSA, so that the fault identification precision of the ship power equipment is improved.

The invention discloses a transformer fault identification method based on a hybrid intelligent algorithm, comprising the following steps: establishing a sample set, determining the structural scale of the identification model, and presetting parameters; generating a particle swarm according to preset parameters of the recognition model, wherein each particle represents a set of recognition modelparameters to be optimized; the fitness of each particle in the particle swarm is calculated, the evolution mode of the particle is selected, the position and velocity of the particle are updated, theoptimization result is output when the set iteration times are reached, the optimization result is taken as the parameter of the identification model, the identification model is established, and theidentification model is used for the identification of transformer faults. This method uses algorithm to mine and analyze a large amount of data, and then make judgments and predictions. It is not limited by expert experience and subjective cognition, and has strong scalability. It realizes intelligence through self-learning and reasoning ability based on data.

The invention relates to a method for optimizing a multi-kernel multi-feature fusion support vector machine and identifying a bearing fault. The method comprises a step of selecting bearing vibrationsignals collected under a single sensor, a step of decomposing bearing vibration signals at different rotational speeds by EMD to obtain IMF energy entropy and IMF permutation entropy, a step of extracting IMF energy entropy and IMF permutation entropy at different rotation speeds and fusing the IMF energy entropy and IMF permutation entropy to obtain fusion features including different rotationalspeed information for support vector machine training samples so as to obtain the multi-kernel multi-feature fusion support vector machine which is adapted to fault identification at different rotation speeds, a step of integrating Gaussian radial basis function kernel and polynomial function kernel performance, allowing the training samples to be in linear regression from a nonlinear function space to high-dimensional space mapping such that the training samples are classified according to different characteristics, forming a multi-kernel least square support vector machine, and enabling thesupport vector machine to identify a fault feature under a variable load, and a step of carrying out parameter optimization on the training samples with a self-adjusting particle swarm algorithm withstrong convergence, comparing the training samples and a test sample, and identifying the bearing fault.

The invention discloses a mechanical fault monitoring and diagnosis system establishment method based on SDAE-RCmvMSE. The method comprises the following steps of: firstly, acquiring vibration signalsof equipment through n vibration sensors, training an SDAE model in a diagnosis model through acquired digital signals under different working conditions, and obtaining optimal parameters of the SDAEmodel; extracting an RCmvMSE value of the acquired digital signal to train an SVM classifier, and obtaining an optimal parameter of the SVM; and deploying the SDAE, the RCmvMSE and the SVM into an embedded industrial control all-in-one machine to complete fault diagnosis model deployment, and putting the fault diagnosis model into use on site. The diagnosis model established through the method ishigh in fault recognition accuracy and good in fault tolerance performance.

The invention discloses an initial detection method and system for a turn-to-turn short circuit fault of a permanent magnet synchronous motor, a medium and equipment, and belongs to the technical field of motor fault detection. The method comprises the steps: 1), obtaining a three-phase stator current signal and a rotor rotating speed signal of a permanent magnet synchronous motor; 2), calculatinga frequency band range where a fault characteristic harmonic component is located according to the permanent magnet synchronous motor rotor speed signal in the diagnosis period; 3), carrying out continuous wavelet transform processing on a stator winding three-phase current signal of the permanent magnet synchronous motor, setting a wavelet coefficient outside a frequency band range where the fault characteristic harmonic component is located in a transform result to be zero, and carrying out reconstruction to obtain a reconstruction result; 4), performing synchronous compression wavelet transform on a reconstruction result, reconstructing time-frequency distribution, and obtaining a time-frequency structure, and 5), performing fault detection based on the time-frequency structure. The method has the advantages of small calculation amount, high detection precision, wide application range and the like.

The invention discloses a depth feature and statistical feature fused inverter fault diagnosis method, which comprises the following steps: firstly, extracting depth features of three-phase output current signals of an inverter by using an SE-DenseNet method, extracting statistical features of current signal samples by using a Hilbert-Huang transform (HHT) method, and combining the depth features and the statistical features; secondly, performing dimension reduction on the combined high-dimensional features by using a local Fisher discriminant analysis algorithm LFDA to obtain low-dimensional features capable of expressing inverter fault features, and realizing fusion of deep features and statistical features; and finally, adopting an extreme learning machine (ELM) classifier, and taking the low-dimensional features as input to realize fault state identification of the three-level inverter. Compared with a traditional diagnosis method, the diagnosis method provided by the invention is higher in fault recognition accuracy, can obtain ideal performance in fault diagnosis under different working conditions, and has higher adaptive capacity and generalization capacity for actual industrial scenes.

The invention provides a combine harvester threshing cylinder harvesting state fault diagnosis method. The method comprises the following steps of sample collection and preprocessing, training of a network pre-model under a source domain data sample, supervised fine adjustment of a prediction model of a few marked samples in a target domain, and output of a diagnosis result. According to the established prediction model, the weight and the offset value can be updated layer by layer to perform hierarchical expression on the input signal, and correct diagnosis can be more effectively made for the roller fault. According to the method, the weight and the offset value are migrated from the source domain to the target domain to adapt to new target sample recognition, and finally the effect of improving the target domain sample fault recognition accuracy is achieved. By combining the advantages of the transfer learning method in the aspect of solving the samples in different fields, the problem that the sample fault cannot be accurately recognized when the working state samples of the threshing cylinder are insufficient due to many factors is solved, and the target domain sample fault recognition accuracy is improved.

The invention relates to the technical field of power data analysis. The invention discloses a non-invasive household electric equipment online monitoring system and a fault identification method. A non-invasive electrical signal acquisition device and a real-time power utilization information multivariate feature extraction system complete acquisition of waveform signals generated by household electric equipment and extraction of multivariate power utilization features. An autoregressive moving average model ARMA, a multi-objective optimization model and an LSTM classification system analyzeand process the multivariate power utilization features to obtain an abnormal probability and a normal probability of each currently running electric equipment or a line where the electric equipment is located under each multivariate time sequence power utilization feature vector, and finally, whether a fault occurs or not is judged by a joint judgment model according to a joint probability: whenthe joint abnormal probability is greater than the joint normal probability, the current running electric equipment or the line where the current running electric equipment is located has a fault. According to the invention, the technical problem that fault identification is difficult to carry out on household electric equipment according to signals containing various electric appliance componentsis solved, the fault identification cost is reduced, and the identification accuracy is improved.

The invention discloses a method for judging the magnetizing inrush current of a no-load transformer during the switching phase reclosing process of a circuit breaker, which comprises the following steps: when over current is generated in a switching phase reclosing process of a circuit breaker, an equivalent circuit and a corresponding voltage equation are established based on the switching-on phase number and the switching-on phase current characteristic; calculating the equivalent inductance value of each sampling time according to the voltage and the current sampling values and taking theaverage value; comparing the average equivalent inductance value with a threshold value. When the average equivalent inductance value is greater than or equal to the threshold value, the condition ismagnetizing inrush current. When the average equivalent inductance value is smaller than the threshold value, the condition is short circuit failure. If the average equivalent inductance value is notonly one, when all of the average equivalent inductance values are larger than or equal to the threshold value, the condition is magnetizing inrush current, otherwise, the condition is short circuit failure. The method can accurately identify the magnetizing inrush current of the no-load transformer during the switching phase reclosing process of the circuit breaker, and effectively improve the fault identification accuracy rate and the reclosing success rate.

The invention discloses a transformer fault diagnosis method based on hard voting ensemble learning. The method comprises the following steps of: step 1, establishing a hard voting ensemble learning classification model for performing fault diagnosis on a transformer; and step 2, carrying out fault type identification on unknown transformer faults. According to the method, the fault diagnosis model of the transformer is built by adopting the machine learning, the fault identification accuracy is high, the fault identification is simple and efficient, effective support is provided for initial transformer operation state identification, the working intensity is greatly reduced, and the safety and reliability of transformer fault type identification are guaranteed.

The invention provides an artificial intelligence fault identification system and method of a power transmission line transient waveform, and the system comprises a signal preprocessing module, a manual marking module, a waveform identification module, a training and tuning module, and a performance test and optimization module. The method for realizing fault identification based on the system mainly comprises the following steps: performing fault type manual labeling on original waveform signals to generate data labels, and establishing a power transmission line transient waveform fault sample library; preprocessing the transient waveform signal of the power transmission line based on a sliding window method to obtain corresponding sequence image data; building a deep learning model, and achieving the transient waveform recognition of the power transmission line; performing parameter training and tuning on the deep learning model; and performing performance testing on the deep learning model, and completing targeted optimization to improve the performance. Through testing, the method can quickly realize the transient waveform identification of the power transmission line, the identification accuracy reaches 92.67%, and the method can replace human experts to carry out the work in the aspect.

The invention relates to a machine learning sample generation method for aircraft thrust fault on-line identification, and the method is suitable for the field of typical power system fault on-line identification in the aircraft flight process. Data fusion generation is carried out on flight motion information (such as flight position, speed, acceleration, attitude angle and angular speed) of a control system, and corresponding data is intercepted as a machine learning training and testing sample according to the design method provided by the invention. Factors such as mass center motion, disturbance center motion, structural disturbance, aerodynamic force and moment of force of the aircraft are considered, deviation combination circulation is introduced into the simulation model to generate data, the data is more real and credible, and improvement of actual fault identification precision is facilitated. According to the method, the fault mode is refined, related data with relatively fine granularity of the fault mode is generated, and the identification precision is improved.

The invention discloses a fault diagnosis and test method based on a proportional valve shaft controller. The method comprises the following steps: collecting test data of different types of proportional valves in different states; extracting slow features of the test data of the proportional valve; generating a plurality of training sample subsets according to the newly calibrated slow features of the test data of the proportional valve; training different random forest models for different types of proportional valves; fault diagnosis of a proportional valve shaft controller: after the model of the proportional valve is determined, inputting the slow data characteristics of the proportional valve to be detected into the random forest model of the corresponding model, and outputting a fault type label of the proportional valve; and optimizing the random forest model on line. The fault of the proportional valve shaft controller is judged after the proportional valve flow characteristic experiment, the proportional valve threshold characteristic experiment and the proportional valve pressure gain experiment are combined and subjected to characteristic fusion, and the fault recognition rate is higher than the fault recognition precision of the proportional valve shaft controller of a single experiment.