Hydraulic pump output flow soft measurement method considering service performance degradation

Abstract

The invention provides a hydraulic pump output flow soft measurement method considering service performance degradation. The method comprises the steps that the rotating speed, the inlet and outlet pressure difference, the oil liquid temperature and the actual flow of a hydraulic pump under different working conditions are obtained; a hydraulic pump flow prediction model is constructed, the hydraulic pump flow prediction model comprises a neural network and a parameter correction module, input parameters, corresponding to an input layer, of the neural network are the obtained rotating speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, and an output layer is predicted flow; the parameter correction module corrects the optimal weight matrix and the optimal threshold matrix of the neural network based on the rotating speed, the inlet and outlet pressure difference, the oil temperature and the actual flow of the hydraulic pump obtained when the service performance of the hydraulic pump is degraded; and on the basis of the corrected hydraulic pump flow prediction model, the flow output after the service performance degradation of the hydraulic pump is predicted. According to the method, even if the service performance of the hydraulic pump is degraded, the output flow of the hydraulic pump can be accurately predicted.

Description

Technical Field

[0001] The invention belongs to the field of hydraulic pump flow measurement, and in particular relates to a hydraulic pump output flow soft measurement method taking into account service performance degradation. Background Art

[0002] As the core component of the hydraulic system, the precise control of the flow of the hydraulic pump is crucial to the performance of the entire system. In traditional hydraulic systems, the flow of the hydraulic pump is mainly measured by additional flow meters and sensors. However, configuring flow meters and sensors will not only increase the purchase cost and maintenance cost of the system, but also bring additional energy loss. At the same time, flow meters and sensors are not suitable for occasions with compact space.

[0003] Compared with the direct flow measurement method, indirect flow measurement methods such as flow maps and polynomial fitting can effectively overcome the above problems. However, due to external load changes, uncertainty in hydraulic oil parameters, and internal leakage in the pump body, there is a large deviation between the actual output flow of the hydraulic pump and the expected output flow, and the actual output flow is highly nonlinear. Therefore, the interaction of these factors makes it difficult to establish an accurate mathematical model of the hydraulic pump. At the same time, many parameters inside the pump body are difficult to measure directly and need to be estimated through complex identification algorithms, which further increases the difficulty of model establishment.

[0004] In addition, after long-term operation, the internal components of the hydraulic pump will wear and degrade, which will cause the flow characteristics to change. At this time, the indirect flow measurement model based on the flow map and polynomial fitting needs to be re-established. Summary of the invention

[0005] The present invention provides a hydraulic pump output flow soft measurement method considering service performance degradation, so as to solve the problem that when the service performance of the hydraulic pump is degraded, it is difficult to accurately predict the output flow of the hydraulic pump by using the existing hydraulic pump flow model.

[0006] According to a first aspect of an embodiment of the present invention, there is provided a soft measurement method for hydraulic pump output flow considering service performance degradation, comprising: step S100, obtaining a speed, an inlet and outlet pressure difference, an oil temperature and an actual flow of the hydraulic pump under different working conditions;

[0007] Step S200, constructing a hydraulic pump flow prediction model, the hydraulic pump flow prediction model includes a neural network and a parameter correction module, the input parameters of the neural network corresponding to the input layer are the obtained speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, and the output layer is the predicted flow;

[0008] Step S300, the parameter correction module corrects the optimal weight matrix and the optimal threshold matrix of the neural network based on the speed, inlet and outlet pressure difference, oil temperature and actual flow rate of the hydraulic pump obtained when the hydraulic pump has service performance degradation;

[0009] Step S400: Based on the modified hydraulic pump flow prediction model, predict the flow output by the hydraulic pump after service performance degradation occurs.

[0010] In an optional implementation, the nonlinear mapping from the input layer to the first hidden layer of the neural network in step S200 is:

[0011]

[0012] where u j is the jth neuron in the first hidden layer, j is an integer greater than 0 and less than or equal to the number of neurons in each hidden layer m, ω is the speed of the hydraulic pump, Δp is the inlet and outlet pressure difference, T is the oil temperature, w 1j is the weight of the hydraulic pump speed to the jth neuron in the first hidden layer, w 2j is the weight of the import and export pressure difference to the jth neuron in the first hidden layer, w 3j is the weight of the oil temperature to the jth neuron in the first hidden layer, is the threshold of the jth neuron from the input layer to the first hidden layer, and f(x) is the activation function corresponding to the input layer to the first hidden layer;

[0013] The nonlinear mapping between two adjacent hidden layers is:

[0014]

[0015] where u p is the pth neuron in the hidden layer of this layer, p is an integer, which is greater than 0 and less than or equal to the number of neurons m in each hidden layer, w 1p is the weight from the first neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u 1p is the first neuron in the previous adjacent hidden layer, w 2p is the weight from the second neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u 2p is the second neuron in the previous adjacent hidden layer, w mp is the weight from the mth neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u mp is the mth neuron in the previous adjacent hidden layer, is the threshold of the pth neuron from the previous adjacent hidden layer to the current hidden layer, and f(x) is the activation function corresponding to the previous adjacent hidden layer to the current hidden layer;

[0016] The nonlinear mapping from the last hidden layer to the output layer is:

[0017]

[0018] Where Q′ is the output layer predicted flow, w k is the weight of the kth neuron in the last hidden layer to predict the flow to the output layer, u k is the kth neuron in the last hidden layer, k is an integer greater than 0 and less than or equal to the number of neurons m in each hidden layer, b is the threshold of the predicted flow from the last hidden layer to the output layer, and g(x) is the activation function corresponding to the last hidden layer to the output layer.

[0019] In another optional implementation, the step S200 specifically includes:

[0020] Step S201, designing the number of hidden layers and the number of neurons in each hidden layer in the neural network: taking the number of hidden layers within a set range, taking the number of neurons within a set range, wherein each number of layers within the set range is respectively combined with each number of neurons within the set range;

[0021] Step S202: When the optimal weight matrix and the optimal threshold matrix in the neural network are constant, for each combination, the hydraulic pump speed, the inlet and outlet pressure difference and the oil temperature under each working condition obtained when the hydraulic pump has no service performance degradation are taken as a group of samples, and each group of samples is input into the neural network corresponding to the combination to obtain the predicted flow of each group of samples, and the root mean square error of the flow corresponding to the combination is calculated according to the actual flow and the predicted flow of each group of samples;

[0022] Step S203: determine the combination with the smallest flow root mean square error among all combinations, and use the number of layers and the number of neurons in the combination as the number of hidden layers and the number of neurons in each hidden layer in the neural network.

[0023] In another optional implementation, the step S200 further specifically includes:

[0024] Step S204: when the number of hidden layers of the neural network and the number of neurons in each hidden layer are constant, each group of samples obtained when the hydraulic pump has no service performance degradation is input into the neural network for training to obtain the predicted flow of each group of samples, and the neural network determines the network performance of the neural network according to the actual flow and predicted flow of each group of samples;

[0025] Step S205, determining whether the network performance of the neural network meets the standard, if so, obtaining the optimal weight matrix and the optimal threshold matrix of the neural network, otherwise, adjusting the weight matrix and the threshold matrix in the neural network, and returning to step S204;

[0026] The weight matrix includes the weight of each input parameter in the input layer to each neuron in the first hidden layer, the weight of each neuron in the hidden layer to each neuron in the next hidden layer, and the weight of each neuron in the last hidden layer to the predicted flow in the output layer; the threshold matrix includes the threshold of each neuron in the first hidden layer from the input layer, the threshold of each neuron in the next hidden layer from the hidden layer, and the threshold of the predicted flow from the last hidden layer to the output layer.

[0027] In another optional implementation, the method further includes: before constructing the neural network, normalizing the obtained rotation speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, and the output predicted flow rate.

[0028] In another optional implementation, the step S300 specifically includes:

[0029] Step S310, assuming that the correction coefficients of the optimal weight matrix and the optimal threshold matrix are α and β respectively, taking the hydraulic pump speed, inlet and outlet pressure difference and oil temperature under each working condition obtained when the hydraulic pump has service performance degradation as a group of samples, inputting each group of samples into the neural network respectively, and obtaining the predicted flow of each group of samples;

[0030] Step S320: determining a loss function according to the actual flow and predicted flow of each group of samples;

[0031] Step S330, based on the gradient descent method, according to the loss function and the set descent speed, iteratively update the correction coefficients α and β;

[0032] Step S340, determine whether the number of iterations is greater than the set number or whether the root mean square error is less than the set value. If so, the update of the correction coefficients α and β is completed; otherwise, return to execute step S310.

[0033] In another optional implementation, before step S320, the method further includes: obtaining a mathematical model related to the speed of the hydraulic pump, the inlet and outlet pressure difference and the oil temperature according to a mathematical model of the hydraulic pump output flow rate and a relationship between oil viscosity, oil temperature and inlet and outlet pressure difference;

[0034] Add samples different from the groups of samples obtained when the hydraulic pump has service performance degradation, input each group of added samples into the mathematical model, and obtain the theoretical calculated flow of each group of added samples; input each group of added samples into the neural network, and obtain the predicted flow of each group of added samples;

[0035] The step S320 includes: determining a loss function according to the actual flow and predicted flow of each group of samples obtained when the service performance of the hydraulic pump is degraded and the theoretical calculated flow and predicted flow of each group of additional samples.

[0036] In another optional implementation, the step S330 includes: iteratively optimizing the descent speed using an adaptive learning rate optimization algorithm; and iteratively updating the correction coefficients α and β according to the loss function and the optimized descent speed.

[0037] In another optional implementation, in step S320, the loss function J is determined according to the following formula based on the actual flow and predicted flow of each group of samples: t :

[0038]

[0039] where n t is the number of samples obtained when the hydraulic pump shows service performance degradation, These are samples obtained when the hydraulic pump has performance degradation in service. is the actual flow rate obtained when the hydraulic pump has service performance degradation, f t () is a neural network;

[0040] In step S330, the correction coefficients α and β are iteratively updated according to the loss function and the set descent speed according to the following formula:

[0041]

[0042] Where v is the gradient descent speed, e is the number of iterations, is the partial differential;

[0043] The corrected weight matrix W t and threshold matrix B t They are:

[0044] W t =α * W s

[0045] B t =β * B s

[0046] where α *and β * are the correction coefficients of the optimal weight matrix and the optimal threshold matrix when the number of iterations is greater than the set number or the root mean square error is less than the set value, W s and B s It is the optimal weight matrix and optimal threshold matrix of the neural network when the hydraulic pump has no service performance degradation.

[0047] In another optional implementation, the mathematical model Q related to the hydraulic pump speed, inlet and outlet pressure difference and oil temperature is pump for:

[0048]

[0049] Where D is the displacement of the hydraulic pump, ω is the speed of the hydraulic pump, μ is the oil viscosity, ρ is the oil density, Δp is the pressure difference between the inlet and outlet of the hydraulic pump, and C a , C b , C c , C d Some coefficients related to the internal structure of the hydraulic pump;

[0050] a, b, c are coefficients related to hydraulic oil, and T is the oil temperature;

[0051] In step S320, the loss function J is determined according to the following formula based on the actual flow rate and predicted flow rate of each group of samples obtained when the service performance of the hydraulic pump is degraded and the theoretical calculated flow rate and predicted flow rate of each group of additional samples:

[0052]

[0053] where n t is the number of samples obtained when the hydraulic pump shows service performance degradation, and They are samples obtained when the hydraulic pump has service performance degradation and additional samples. is the actual flow rate obtained when the hydraulic pump has service performance degradation, f t () is a neural network; After inputting the mathematical model into the corresponding group of additional samples, the theoretical calculated flow rate of the group of additional samples is obtained, where m is the number of additional samples;

[0054] In step S330, the correction coefficients α and β are iteratively updated according to the loss function and the optimized descent speed according to the following formula:

[0055]

[0056] Where υ′ is the gradient descent speed after this iteration optimization, e is the number of iterations, is the partial differential;

[0057] The corrected weight matrix W t and threshold matrix B t They are:

[0058] W t =α * W s

[0059] B t =β * B s

[0060] where α * and β * are the correction coefficients of the optimal weight matrix and the optimal threshold matrix when the number of iterations is greater than the set number or the root mean square error is less than the set value, W s and B s It is the optimal weight matrix and optimal threshold matrix of the neural network when the hydraulic pump has no service performance degradation.

[0061] The beneficial effects of the present invention are:

[0062] 1. The present invention sets a parameter correction module in the hydraulic pump flow prediction model, and corrects the optimal weight matrix and the optimal threshold matrix of the neural network based on the speed, inlet and outlet pressure difference, oil temperature and actual flow of the hydraulic pump obtained when the service performance of the hydraulic pump is degraded. In this way, even when the service performance of the hydraulic pump is degraded, the output flow of the hydraulic pump can be accurately predicted;

[0063] 2. The present invention designs the number of hidden layers and the number of neurons in the hidden layer of the neural network, so that it can better learn the features corresponding to different tasks; the design of the neural network structure based on the actual flow calibration data can make the obtained neural network model more realistically reflect the actual flow characteristics of the hydraulic pump, thereby improving the flow prediction accuracy and generalization ability of the model;

[0064] 3. When training the optimal weight matrix and the optimal threshold matrix of the neural network, the present invention is based on each group of samples obtained when the hydraulic pump has no service performance degradation. Each group of samples obtained when the hydraulic pump has service performance degradation is only used to correct the optimal weight matrix and the optimal threshold matrix, and does not involve the establishment of the optimal weight matrix and the optimal threshold matrix. In this way, the neural network can more truly reflect the actual flow characteristics of the hydraulic pump;

[0065] 4. The present invention normalizes the obtained speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, as well as the output predicted flow rate, so as to prevent different dimensions and data scales from affecting the model performance;

[0066] 5. The present invention sets correction coefficients α and β corresponding to the optimal weight matrix and the optimal threshold matrix of the neural network, inputs each group of samples obtained when the service performance of the hydraulic pump is degraded into the neural network, obtains the predicted flow of each group of samples, determines the loss function according to the actual flow and the predicted flow of each group of samples, and iteratively updates the correction coefficients α and β according to the loss function and the set descent rate, thereby realizing the correction of the optimal weight matrix and the optimal threshold matrix, so that the hydraulic pump flow prediction model adapts to the flow characteristic changes caused by the wear of the internal components after the hydraulic pump has been running for a long time, and the predicted flow takes into account the service performance degradation of the hydraulic pump, and the flow prediction accuracy is high when the service performance of the hydraulic pump is degraded;

[0067] 6. The present invention introduces a mathematical model of the hydraulic pump output flow rate related to the hydraulic pump speed, the inlet and outlet pressure difference and the oil temperature. When the number of samples obtained when the service performance of the hydraulic pump is degraded is small, samples different from the obtained samples are added, and each group of added samples is input into the mathematical model respectively to obtain the theoretical calculated flow rate of each group of samples, and each group of added samples is input into the neural network to obtain the predicted flow rate of each group of added samples. According to the actual flow rate and predicted flow rate of each group of samples obtained when the service performance of the hydraulic pump is degraded, as well as the theoretical calculated flow rate and predicted flow rate of each group of added samples, a loss function is determined. According to the loss function and the optimized descent rate, the correction coefficients α and β of the optimal weight matrix and the optimal threshold matrix are iteratively updated. In this way, with a small number of samples and a limited number of working conditions, the hydraulic pump output flow rate can also be accurately predicted after the service performance of the hydraulic pump is degraded. In addition, the present invention also uses an adaptive learning rate optimization algorithm to iteratively optimize the descent rate, thereby further improving the accuracy of flow prediction after the service performance of the hydraulic pump is degraded. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flow chart of an embodiment of a soft measurement method for hydraulic pump output flow rate considering service performance degradation of the present invention;

[0069] Figure 2 It is a structural diagram of an existing pump-controlled hydraulic system test bench;

[0070] Figure 3 It is a schematic diagram of the neural network structure of the present invention;

[0071] Figure 4 It is a design table of the number of hidden layers and the number of neurons in each hidden layer in the neural network of the present invention;

[0072] Figure 5 It is a principle diagram of an embodiment of the present invention for correcting the optimal weight matrix and the optimal threshold matrix of a neural network;

[0073] Figure 6 It is another principle diagram of an embodiment of the present invention for correcting the optimal weight matrix and the optimal threshold matrix of a neural network;

[0074] Figure 7 It is a comparison chart of the average flow prediction errors of the improved transfer neural network model and the initial neural network model corresponding to the method of the present invention on all data sets after service performance degradation. DETAILED DESCRIPTION

[0075] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention and to make the above-mentioned purposes, features and advantages of the embodiments of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings.

[0076] In the description of the present invention, unless otherwise specified and limited, it should be noted that the term "connection" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection between two elements. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0077] See also Figure 1 , is a flow chart of an embodiment of a hydraulic pump output flow soft measurement method considering service performance degradation of the present invention. The hydraulic pump output flow soft measurement method considering service performance degradation may include the following steps:

[0078] Step S100, obtaining the speed, inlet and outlet pressure difference, oil temperature and actual flow rate of the hydraulic pump under different working conditions;

[0079] Step S200, constructing a hydraulic pump flow prediction model, the hydraulic pump flow prediction model includes a neural network and a parameter correction module, the input parameters of the neural network corresponding to the input layer are the obtained speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, and the output layer is the predicted flow;

[0080] Step S300, the parameter correction module corrects the optimal weight matrix and the optimal threshold matrix of the neural network based on the speed, inlet and outlet pressure difference, oil temperature and actual flow rate of the hydraulic pump obtained when the hydraulic pump has service performance degradation;

[0081] Step S400: Based on the modified hydraulic pump flow prediction model, predict the flow output by the hydraulic pump after service performance degradation occurs.

[0082] It can be seen from the above embodiments that the present invention sets a parameter correction module in the hydraulic pump flow prediction model, and corrects the optimal weight matrix and optimal threshold matrix of the neural network based on the speed, inlet and outlet pressure difference, oil temperature and actual flow of the hydraulic pump obtained when the service performance of the hydraulic pump is degraded. In this way, even when the service performance of the hydraulic pump is degraded, the output flow of the hydraulic pump can be accurately predicted.

[0083] In the above step S100, Figure 2 As shown, the outlet of the oil tank is connected to the oil return port of the oil tank through the hydraulic pump 1, the one-way valve 3, the flow meter 8, the overflow valve 5 and the return oil filter 6 in sequence. The connection node of the one-way valve 3 and the flow meter 8 is connected to the connection node of the overflow valve 5 and the return oil filter 6 through the throttle valve 4, and the air cooler 7 is set next to the return oil filter 6; the servo motor 2 is connected to the hydraulic pump 1, and the speed of the hydraulic pump 1 can be obtained by detecting the speed of the servo motor 2; the outlet of the hydraulic pump 1 is connected to the pressure detection device, which is used to detect the outlet pressure of the hydraulic pump. Since the inlet of the hydraulic pump is atmospheric pressure, the inlet and outlet pressure difference of the hydraulic pump can be obtained according to the detected outlet pressure of the hydraulic pump and the atmospheric pressure; the temperature detector is set in the oil tank to detect the oil temperature; the flow meter 8 is used to detect the actual flow output by the hydraulic pump. The speed of the hydraulic pump is controlled by the host computer, and the outlet pressure is controlled by adjusting the overflow pressure of the overflow valve. The oil temperature is heated by the overflow valve for temperature increase control, and cooled by the air cooler.

[0084] In the above step S200, the neural network may include an input layer, multiple hidden layers and an output layer. Figure 3 As shown, the nonlinear mapping from the input layer to the first hidden layer of the neural network can be expressed as:

[0085]

[0086] where u j is the jth neuron in the first hidden layer, j is an integer greater than 0 and less than or equal to the number of neurons in each hidden layer m, ω is the speed of the hydraulic pump, Δp is the inlet and outlet pressure difference, T is the oil temperature, w 1j is the weight of the hydraulic pump speed to the jth neuron in the first hidden layer, w 2j is the weight of the import and export pressure difference to the jth neuron in the first hidden layer, w 3j is the weight of the oil temperature to the jth neuron in the first hidden layer, is the threshold of the jth neuron from the input layer to the first hidden layer, and f(x) is the activation function corresponding to the input layer to the first hidden layer;

[0087] The nonlinear mapping between two adjacent hidden layers can be expressed as:

[0088]

[0089] where u p is the pth neuron in the hidden layer of this layer, p is an integer, which is greater than 0 and less than or equal to the number of neurons m in each hidden layer, w 1p is the weight from the first neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u 1p is the first neuron in the previous adjacent hidden layer, w 2p is the weight from the second neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u 2p is the second neuron in the previous adjacent hidden layer, w mp is the weight from the mth neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u mp is the mth neuron in the previous adjacent hidden layer, is the threshold of the pth neuron from the previous adjacent hidden layer to the current hidden layer, and f(x) is the activation function corresponding to the previous adjacent hidden layer to the current hidden layer;

[0090] The nonlinear mapping from the last hidden layer to the output layer can be expressed as:

[0091]

[0092] Where Q′ is the output layer predicted flow, w k is the weight of the kth neuron in the last hidden layer to predict the flow to the output layer, u k is the kth neuron in the last hidden layer, k is an integer greater than 0 and less than or equal to the number of neurons m in each hidden layer, b is the threshold of the predicted flow from the last hidden layer to the output layer, and g(x) is the activation function corresponding to the last hidden layer to the output layer.

[0093] In the above step S200, the establishment of the neural network can be divided into designing the number of hidden layers and the number of neurons in each hidden layer in the neural network and determining the optimal weight matrix and the optimal threshold matrix of the neural network when the hydraulic pump does not experience service performance degradation, wherein the step S200 can specifically include:

[0094] Step S201, design the number of hidden layers and the number of neurons in each hidden layer in the neural network: the number of hidden layers is set within a set range, and the number of neurons is set within a set range, wherein each number of layers within the set range is combined with each number of neurons within the set range. Figure 4As shown, the number of hidden layers can be in the range of 1 to 5, the number of neurons in each hidden layer can be in the range of (2, 4, 6, 8, 10), and each number of layers in 1 to 5 is combined with the number of neurons respectively.

[0095] Step S202: When the optimal weight matrix and the optimal threshold matrix in the neural network are constant, for each combination, the hydraulic pump speed, inlet and outlet pressure difference and oil temperature under each working condition obtained when the hydraulic pump has no service performance degradation are used as a group of samples, and each group of samples is input into the neural network corresponding to the combination to obtain the predicted flow of each group of samples, and the root mean square error of the flow corresponding to the combination is calculated based on the actual flow and predicted flow of each group of samples. The root mean square error RMSE can be expressed as:

[0096]

[0097] Where n is the number of samples, Q i is the actual flow value of the i-th group of samples, Q i ′ is the predicted flow value of the i-th group of samples.

[0098] Step S203: determine the combination with the smallest flow root mean square error among all combinations, and use the number of layers and the number of neurons in the combination as the number of hidden layers and the number of neurons in each hidden layer in the neural network. Figure 4 As shown in the figure, it can be seen that when the number of hidden layers is 3 and the number of neurons is 8, the RMSE of the network is the smallest, and the structural performance of the corresponding neural network is the best.

[0099] The present invention designs the number of hidden layers and the number of neurons in the hidden layers of the neural network, so that it can better learn the features corresponding to different tasks; the design of the neural network structure based on actual flow calibration data can make the obtained neural network model more truly reflect the actual flow characteristics of the hydraulic pump, thereby improving the flow prediction accuracy and generalization ability of the model.

[0100] In addition, the step S200 may further specifically include:

[0101] Step S204: When the number of hidden layers of the neural network and the number of neurons in each hidden layer are constant, each group of samples obtained when the hydraulic pump has no service performance degradation is input into the neural network for training to obtain the predicted flow of each group of samples. The neural network determines the network performance of the neural network based on the actual flow and predicted flow of each group of samples.

[0102] Step S205: determine whether the network performance of the neural network meets the standard. If so, obtain the optimal weight matrix and optimal threshold matrix of the neural network. Otherwise, adjust the optimal weight matrix and optimal threshold matrix in the neural network and return to step S204. -6 ), it can be determined that the network performance of the neural network meets the standard. The weight matrix may include the weight of each input parameter in the input layer to each neuron in the first hidden layer, the weight of each neuron in the hidden layer to each neuron in the next hidden layer, and the weight of each neuron in the last hidden layer to the predicted flow in the output layer; the threshold matrix may include the threshold of each neuron in the first hidden layer from the input layer, the threshold of each neuron in the next hidden layer from the hidden layer, and the threshold of the predicted flow from the last hidden layer to the output layer. Each group of samples in step S200 may be samples obtained when the hydraulic pump has no service performance degradation.

[0103] In this embodiment, the data obtained when the hydraulic pump has no service performance degradation is used as the source domain data, and the data obtained when the hydraulic pump has service performance degradation is used as the target domain data; the source domain data D s and target domain data D t Can be expressed as:

[0104]

[0105] n s is the number of samples in the source domain, n t is the number of samples in the target domain, and n t <<n s , / is the separator.

[0106] The initial weight matrix W of the neural network s and the initial threshold matrix B s It can be expressed as:

[0107]

[0108] in and are the weight matrix and threshold matrix from the input layer to the first hidden layer, respectively. and They are the weight matrix and threshold matrix corresponding to the last hidden layer to the output layer respectively.

[0109] When training the optimal weight matrix and the optimal threshold matrix of the neural network, the present invention is based on the groups of samples obtained when the hydraulic pump has no service performance degradation. The groups of samples obtained when the hydraulic pump has service performance degradation are only used to correct the optimal weight matrix and the optimal threshold matrix, and do not involve the establishment of the optimal weight matrix and the optimal threshold matrix. In this way, the neural network can more truly reflect the actual flow characteristics of the hydraulic pump.

[0110] In order to prevent different dimensions and data scales from affecting the model performance, the method may further include: before constructing the neural network, normalizing the obtained speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, and the output predicted flow rate, the expression of which is:

[0111]

[0112] is the expression for normalizing each input parameter separately; An expression for normalizing the output layer predicted flow.

[0113] Since the wear of the internal components of the hydraulic pump after long-term operation may cause changes in its flow characteristics, the present invention designs a parameter correction module to correct the optimal weight matrix and optimal threshold matrix of the neural network based on the speed, inlet and outlet pressure difference, oil temperature and actual flow rate of the hydraulic pump obtained when the hydraulic pump has service performance degradation. Figure 5 As shown, the step S300 may specifically include:

[0114] Step S310, assuming that the correction coefficients of the optimal weight matrix and the optimal threshold matrix are α and β respectively, the hydraulic pump speed, inlet and outlet pressure difference and oil temperature under each working condition obtained when the service performance of the hydraulic pump is degraded are taken as a group of samples, and each group of samples is input into the neural network to obtain the predicted flow of each group of samples. In this embodiment, the initial values ​​of the correction coefficients α and β can be any values. Since the input parameters and the output layer predicted flow are normalized in advance, the initial values ​​of α and β are also limited to between (0,1].

[0115] Step S320: Determine the loss function based on the actual flow and predicted flow of each group of samples. In this step, the loss function J can be determined according to the following formula: t :

[0116]

[0117] where n t is the number of samples obtained when the hydraulic pump shows service performance degradation, These are samples obtained when the hydraulic pump has performance degradation in service. is the actual flow rate obtained when the hydraulic pump has service performance degradation, f t () is a neural network.

[0118] Step S330: Based on the gradient descent method, the correction coefficients α and β are iteratively updated according to the loss function and the set descent speed. In this step, the correction coefficients α and β are iteratively updated according to the following formula:

[0119]

[0120] Where υ is the gradient descent rate (which can be the same as the iterative descent rate during neural network training, set to 0.01), e is the number of iterations, is the partial differential symbol.

[0121] Step S340, determine whether the number of iterations is greater than the set number or whether the root mean square error is less than the set value. If so, the update of the correction coefficients α and β is completed; otherwise, return to execute step S310.

[0122] The corrected weight matrix W t and threshold matrix B t They are:

[0123] W t =α * W s

[0124] B t =β * B s

[0125] where α * and β * are the correction coefficients of the optimal weight matrix and the optimal threshold matrix when the number of iterations is greater than the set number or the root mean square error is less than the set value, W s and B s It is the optimal weight matrix and optimal threshold matrix of the neural network when the hydraulic pump has no service performance degradation.

[0126] The present invention sets correction coefficients α and β corresponding to the optimal weight matrix and the optimal threshold matrix of the neural network, inputs each group of samples obtained when the service performance of the hydraulic pump is degraded into the neural network, obtains the predicted flow of each group of samples, determines the loss function according to the actual flow and the predicted flow of each group of samples, and iteratively updates the correction coefficients α and β according to the loss function and the set descent rate, thereby realizing the correction of the optimal weight matrix and the optimal threshold matrix, so that the hydraulic pump flow prediction model adapts to the changes in flow characteristics caused by wear of internal components after long-term operation of the hydraulic pump, and the predicted flow takes into account the service performance degradation of the hydraulic pump, and the flow prediction accuracy is high when the service performance of the hydraulic pump is degraded.

[0127] Although the embodiment of step S300 can accurately predict the flow rate when the hydraulic pump has service performance degradation, the accuracy of flow prediction needs to be further improved due to the small number of samples obtained when the hydraulic pump has service performance degradation and the limited coverage of working conditions. Figure 6 As shown, step S300 in this embodiment may specifically include:

[0128] Step S310, assuming that the correction coefficients of the optimal weight matrix and the optimal threshold matrix are α and β respectively, and the hydraulic pump speed, inlet and outlet pressure difference and oil temperature under each working condition obtained when the service performance of the hydraulic pump is degraded are taken as a group of samples, and each group of samples is input into the neural network respectively to obtain the predicted flow of each group of samples; according to the mathematical model of the hydraulic pump output flow and the relationship between the oil viscosity and the oil temperature and the inlet and outlet pressure difference, a mathematical model related to the hydraulic pump speed, inlet and outlet pressure difference and oil temperature is obtained; samples different from the groups of samples obtained when the service performance of the hydraulic pump is degraded are added, and each group of added samples is input into the mathematical model to obtain the theoretical calculated flow of each group of added samples; each group of added samples is input into the neural network to obtain the predicted flow of each group of added samples.

[0129] The mathematical model Q related to the hydraulic pump speed, inlet and outlet pressure difference and oil temperature pump for:

[0130]

[0131] Where D is the displacement of the hydraulic pump, ω is the speed of the hydraulic pump, μ is the oil viscosity, ρ is the oil density, Δp is the pressure difference between the inlet and outlet of the hydraulic pump, and C a , C b , C c , C d Some coefficients related to the internal structure of the hydraulic pump;

[0132] Since the input of the neural network in the hydraulic pump flow prediction model has three variables, ω, Δp and T, and there is no independent variable T in the mathematical model, it is necessary to combine it with the loss function of the jointly corrected neural network model and introduce the temperature independent variable. The temperature and pressure difference variables mainly affect the viscosity of the oil, causing the leakage of the hydraulic pump to change, thereby affecting its output flow. Therefore, the oil viscosity μ in the mathematical model is further improved based on the viscosity-temperature equation and the viscosity-pressure equation.

[0133] Since the viscosity-temperature equation is:

[0134]

[0135] Where: μ0 is the dynamic viscosity of the oil at a given temperature under one atmosphere, a, b, c are coefficients related to the hydraulic oil, and t is the oil temperature.

[0136] (2) Viscosity-pressure equation:

[0137] μ1=μ0e λΔp

[0138] In the formula: μ1 is the dynamic viscosity of the oil, λ is the viscosity-pressure coefficient, and Δp is the pressure difference between the inlet and outlet of the hydraulic pump.

[0139] Therefore, the oil viscosity μ in the mathematical model of the hydraulic pump output flow can be:

[0140] a, b, c are coefficients related to hydraulic oil, and T is the oil temperature.

[0141] Step S320: Determine a loss function based on the actual flow and predicted flow of each group of samples obtained when the hydraulic pump has service performance degradation, as well as the theoretical calculated flow and predicted flow of each group of additional samples. This step can determine the loss function J according to the following formula:

[0142]

[0143] where n t is the number of samples obtained when the hydraulic pump shows service performance degradation, and They are samples obtained when the hydraulic pump has service performance degradation and additional samples. is the actual flow rate obtained when the hydraulic pump has service performance degradation, f t () is a neural network; After inputting the mathematical model into the corresponding group of additional samples, the theoretical calculated flow rate of the group of additional samples is obtained, where m is the number of additional samples.

[0144] Step S330: Based on the gradient descent method, the correction coefficients α and β are iteratively updated according to the loss function and the set descent speed. In this step, the correction coefficients α and β can be iteratively updated according to the following formula:

[0145]

[0146] Where υ′ is the gradient descent speed after this iteration optimization, e is the number of iterations, is a partial differential.

[0147] Step S340, determine whether the number of iterations is greater than the set number or whether the root mean square error is less than the set value. If so, the update of the correction coefficients α and β is completed; otherwise, return to execute step S310.

[0148] The corrected weight matrix W t and threshold matrix B t They can be:

[0149] W t =α * W S

[0150] B t =β * B s

[0151] where α * and β * are the correction coefficients of the optimal weight matrix and the optimal threshold matrix when the number of iterations is greater than the set number or the root mean square error is less than the set value, W s and B s It is the optimal weight matrix and optimal threshold matrix of the neural network when the hydraulic pump has no service performance degradation.

[0152] The present invention introduces a mathematical model of the hydraulic pump output flow rate related to the hydraulic pump speed, the inlet and outlet pressure difference and the oil temperature. When the number of samples obtained when the service performance of the hydraulic pump is degraded is small, samples different from the obtained samples are added, and each group of added samples is input into the mathematical model respectively to obtain the theoretical calculated flow rate of each group of samples, and each group of added samples is input into the neural network to obtain the predicted flow rate of each group of added samples. According to the actual flow rate and predicted flow rate of each group of samples obtained when the service performance of the hydraulic pump is degraded, and the theoretical calculated flow rate and predicted flow rate of each group of added samples, a loss function is determined. According to the loss function and the optimized descent rate, the correction coefficients α and β of the optimal weight matrix and the optimal threshold matrix are iteratively updated. In this way, with a small number of samples and a limited number of working conditions, the output flow rate of the hydraulic pump can be accurately predicted after the service performance of the hydraulic pump is degraded. In addition, the present invention also adopts an adaptive learning rate optimization algorithm to iteratively optimize the descent rate, thereby further improving the accuracy of flow prediction after the service performance of the hydraulic pump is degraded.

[0153] Figure 7 This is a comparison chart of the average traffic prediction errors of the improved migration neural network model and the initial neural network model corresponding to the method of the present invention on all target domain data sets. It can be seen from the figure that the prediction error of the improved neural network has been greatly reduced.

[0154] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0155] It will be appreciated that the invention is not limited to the precise construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from its scope. The scope of the invention is governed solely by the appended claims.

Claims

1. A soft measurement method for hydraulic pump output flow considering service performance degradation, characterized in that: include: Step S100, obtaining the speed, inlet and outlet pressure difference, oil temperature and actual flow rate of the hydraulic pump under different working conditions; Step S200, constructing a hydraulic pump flow prediction model and a parameter correction module, wherein the hydraulic pump flow prediction model includes a neural network, the input parameters of the neural network corresponding to the input layer are the obtained speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, and the output layer is the predicted flow; Step S300, the parameter correction module corrects the optimal weight matrix and the optimal threshold matrix of the neural network based on the speed, inlet and outlet pressure difference, oil temperature and actual flow rate of the hydraulic pump obtained when the service performance of the hydraulic pump is degraded; Step S400: Based on the modified hydraulic pump flow prediction model, predict the flow output by the hydraulic pump after service performance degradation occurs.

2. The soft measurement method for hydraulic pump output flow considering service performance degradation according to claim 1 is characterized in that: The nonlinear mapping from the input layer to the first hidden layer of the neural network in step S200 is: where u j is the jth neuron in the first hidden layer, j is an integer greater than 0 and less than or equal to the number of neurons in each hidden layer m, ω is the speed of the hydraulic pump, Δp is the inlet and outlet pressure difference, T is the oil temperature, w 1j is the weight of the hydraulic pump speed to the jth neuron in the first hidden layer, w 2j is the weight of the import and export pressure difference to the jth neuron in the first hidden layer, w 3j is the weight of the oil temperature to the jth neuron in the first hidden layer, is the threshold of the jth neuron from the input layer to the first hidden layer, and f(x) is the activation function corresponding to the input layer to the first hidden layer; The nonlinear mapping between two adjacent hidden layers is: where u p is the pth neuron in the hidden layer of this layer, p is an integer, which is greater than 0 and less than or equal to the number of neurons m in each hidden layer, w 1p is the weight from the first neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u 1p is the first neuron in the previous adjacent hidden layer, w 2p is the weight from the second neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u 2p is the second neuron in the previous adjacent hidden layer, w mp is the weight from the mth neuron in the previous adjacent hidden layer to the pth neuron in the current hidden layer, u mp is the mth neuron in the previous adjacent hidden layer, is the threshold of the pth neuron from the previous adjacent hidden layer to the current hidden layer, and f(x) is the activation function corresponding to the previous adjacent hidden layer to the current hidden layer; The nonlinear mapping from the last hidden layer to the output layer is: Where Q′ is the output layer predicted flow, w k is the weight of the kth neuron in the last hidden layer to predict the flow to the output layer, u k is the kth neuron in the last hidden layer, k is an integer greater than 0 and less than or equal to the number of neurons m in each hidden layer, b is the threshold of the predicted flow from the last hidden layer to the output layer, and g(x) is the activation function corresponding to the last hidden layer to the output layer.

3. The soft measurement method for hydraulic pump output flow considering service performance degradation according to claim 1 or 2, characterized in that: The step S200 specifically includes: Step S201, designing the number of hidden layers and the number of neurons in each hidden layer in the neural network: taking the number of hidden layers within a set range, taking the number of neurons within a set range, wherein each number of layers within the set range is respectively combined with each number of neurons within the set range; Step S202: When the optimal weight matrix and the optimal threshold matrix in the neural network are constant, for each combination, the hydraulic pump speed, the inlet and outlet pressure difference and the oil temperature under each working condition obtained when the hydraulic pump has no service performance degradation are taken as a group of samples, and each group of samples is input into the neural network corresponding to the combination to obtain the predicted flow of each group of samples, and the root mean square error of the flow corresponding to the combination is calculated according to the actual flow and the predicted flow of each group of samples; Step S203: determine the combination with the smallest flow root mean square error among all combinations, and use the number of layers and the number of neurons in the combination as the number of hidden layers and the number of neurons in each hidden layer in the neural network.

4. The soft measurement method for hydraulic pump output flow considering service performance degradation according to claim 3 is characterized in that: The step S200 further specifically includes: Step S204: when the number of hidden layers of the neural network and the number of neurons in each hidden layer are constant, each group of samples obtained when the hydraulic pump has no service performance degradation is input into the neural network for training to obtain the predicted flow of each group of samples, and the neural network determines the network performance of the neural network according to the actual flow and predicted flow of each group of samples; Step S205, determining whether the network performance of the neural network meets the standard, if so, obtaining the optimal weight matrix and the optimal threshold matrix of the neural network, otherwise, adjusting the weight matrix and the threshold matrix in the neural network, and returning to step S204; The weight matrix includes the weight of each input parameter in the input layer to each neuron in the first hidden layer, the weight of each neuron in the hidden layer to each neuron in the next hidden layer, and the weight of each neuron in the last hidden layer to the predicted flow in the output layer; the threshold matrix includes the threshold of each neuron in the first hidden layer from the input layer, the threshold of each neuron in the next hidden layer from the hidden layer, and the threshold of the predicted flow from the last hidden layer to the output layer.

5. The soft-sensing method for hydraulic pump output flow considering service performance degradation according to claim 1, characterized in that: The method further comprises: before constructing the neural network, normalizing the obtained rotation speed, inlet and outlet pressure difference and oil temperature of the hydraulic pump, and the output predicted flow.

6. The soft-sensing method for hydraulic pump output flow considering service performance degradation according to claim 1, characterized in that: The step S300 specifically includes: Step S310, assuming that the correction coefficients of the optimal weight matrix and the optimal threshold matrix are α and β respectively, taking the hydraulic pump speed, inlet and outlet pressure difference and oil temperature under each working condition obtained when the hydraulic pump has service performance degradation as a group of samples, inputting each group of samples into the neural network respectively, and obtaining the predicted flow of each group of samples; Step S320: determining a loss function according to the actual flow and predicted flow of each group of samples; Step S330, based on the gradient descent method, according to the loss function and the set descent speed, iteratively update the correction coefficients α and β; Step S340, determine whether the number of iterations is greater than the set number or whether the root mean square error is less than the set value. If so, the update of the correction coefficients α and β is completed; otherwise, return to execute step S310.

7. The soft-sensing method for hydraulic pump output flow considering service performance degradation according to claim 6, characterized in that: Before step S320, the method further includes: obtaining a mathematical model related to the speed of the hydraulic pump, the inlet and outlet pressure difference and the oil temperature according to a mathematical model of the hydraulic pump output flow rate and a relationship between oil viscosity, oil temperature and inlet and outlet pressure difference; Add samples different from the groups of samples obtained when the hydraulic pump has service performance degradation, input each group of added samples into the mathematical model, and obtain the theoretical calculated flow of each group of added samples; input each group of added samples into the neural network, and obtain the predicted flow of each group of added samples; The step S320 includes: determining a loss function according to the actual flow and predicted flow of each group of samples obtained when the service performance of the hydraulic pump is degraded and the theoretical calculated flow and predicted flow of each group of additional samples.

8. The soft-sensing method for hydraulic pump output flow considering service performance degradation according to claim 7, characterized in that: The step S330 includes: iteratively optimizing the descent speed by using an adaptive learning rate optimization algorithm; iteratively updating the correction coefficients α and β according to the loss function and the optimized descent speed.

9. The soft-sensing method for hydraulic pump output flow considering service performance degradation according to claim 6, characterized in that: In step S320, the loss function J is determined according to the actual flow and predicted flow of each group of samples according to the following formula: t : Where n t is the number of samples obtained when the hydraulic pump shows service performance degradation, These are samples obtained when the hydraulic pump has performance degradation in service. is the actual flow rate obtained when the hydraulic pump has service performance degradation, f t () is a neural network; In step S330, the correction coefficients α and β are iteratively updated according to the loss function and the set descent speed according to the following formula: Where υ is the gradient descent speed, e is the number of iterations, is the partial differential; The corrected weight matrix W t and threshold matrix B t They are: W t =a * W s B t =b * B s where α * and β * are the correction coefficients of the optimal weight matrix and the optimal threshold matrix when the number of iterations is greater than the set number or the root mean square error is less than the set value, W s and B s It is the optimal weight matrix and optimal threshold matrix of the neural network when the hydraulic pump has no service performance degradation.

10. The soft-sensing method for hydraulic pump output flow considering service performance degradation according to claim 8, characterized in that: The mathematical model Q related to the hydraulic pump speed, inlet and outlet pressure difference and oil temperature pump for: Where D is the displacement of the hydraulic pump, ω is the speed of the hydraulic pump, μ is the oil viscosity, ρ is the oil density, Δp is the pressure difference between the inlet and outlet of the hydraulic pump, and C a , C b , C c , C d Some coefficients related to the internal structure of the hydraulic pump; a, b, c are coefficients related to hydraulic oil, and T is the oil temperature; In step S320, the loss function J is determined according to the following formula based on the actual flow rate and predicted flow rate of each group of samples obtained when the service performance of the hydraulic pump is degraded and the theoretical calculated flow rate and predicted flow rate of each group of additional samples: Where n t is the number of samples obtained when the hydraulic pump shows service performance degradation, and They are samples obtained when the hydraulic pump has service performance degradation and additional samples. is the actual flow rate obtained when the hydraulic pump has service performance degradation, f t () is a neural network; After inputting the mathematical model into the corresponding group of additional samples, the theoretical calculated flow rate of the group of additional samples is obtained, where m is the number of additional samples; In step S330, the correction coefficients α and β are iteratively updated according to the loss function and the optimized descent speed according to the following formula: Where v′ is the gradient descent speed after this iteration optimization, e is the number of iterations, is the partial differential; The corrected weight matrix W t and threshold matrix B t They are: W t =a * W s B t =b * B s where α * and β * are the correction coefficients of the optimal weight matrix and the optimal threshold matrix when the number of iterations is greater than the set number or the root mean square error is less than the set value, W s and B s It is the optimal weight matrix and optimal threshold matrix of the neural network when the hydraulic pump has no service performance degradation.

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