A Method for Decomposing the Energy Consumption of CNC Machine Tool Components Based on Kalman Filter

Abstract

The present invention provides a method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering. The acquisition and analysis of the machine tool energy consumption are carried out through the hardware part including a signal acquisition module with current sensors and voltage sensors, a signal conditioning module, a data transfer card, a computer, and a power supply module, and the software part based on the LabView software platform, including the following steps: Step 1, collect the current and voltage of the machine tool through the current and voltage sensors and transmit them to the computer through the data transfer card, and calculate the total energy consumption of the machine tool; Step 2, collect the PLC data of the machine tool to obtain the power consumption of the machine tool drive components and the switch states of the machine tool components; Step 3, use the empirical formula to calculate the sum of the powers of each machine tool component in the on state and the calculation error, and compare it with the difference between the total power of the machine tool minus the power of the drive components. Combine the Kalman equation for continuous update and iteration, and at the same time continuously update the covariance matrix, and finally decompose the energy consumption of each machine tool component.

Description

Technical Field

[0001] The present invention relates to a method for decomposing the energy consumption of a numerical control machine tool component, and more particularly to a method for decomposing the energy consumption of a numerical control machine tool component based on Kalman filtering. Background Art

[0002] The continuous growth of global energy demand has made issues related to energy efficiency improvement more urgent. According to statistics, the energy consumption of the manufacturing industry accounts for more than 90% of the total energy consumption, and the energy consumption of machine tools or processing systems accounts for more than 75% of the energy consumption of the manufacturing industry. In China, the energy utilization rate of machine tools is relatively low. Therefore, improving the energy efficiency of machine tools is of great significance for reducing energy consumption. Understanding and decomposing the energy consumption of machine tool components plays a key role in improving the overall energy efficiency of machine tools.

[0003] In recent years, researchers at home and abroad have also conducted a large number of studies on machine tool energy consumption and detection. The published invention patent "Method for Obtaining the Energy Efficiency of the Electromechanical Main Transmission System during the Machining Process of a Numerical Control Machine Tool" (201210127826.5) discloses a new method for obtaining the energy efficiency of the electromechanical main transmission system during the machining process of a numerical control machine tool. This method is based on the mathematical model of the energy efficiency of the electromechanical main transmission system during the machining process of the machine tool, considering the relationship function between the no-load power and the rotational speed of the machine tool, as well as the tabular function of the additional load loss coefficient for a single-interval rotational speed machine tool or the additional load loss coefficient for a multi-interval rotational speed machine tool. Then, by recording the process data of the input power of the main transmission system motor during the machining process, the energy efficiency of the electromechanical main transmission system during the machining process of the machine tool is calculated from the mathematical model. The published invention patent "Configurable On-line Monitoring Method and System for the Energy Consumption of Multiple Energy Sources of a Machine Tool" (201410200886.4) installs power sensors for monitoring multiple energy sources of the machine tool respectively. By processing the electric power data of each power sensor, the real-time electric power values of multiple energy sources are obtained, and then the corresponding energy consumption information is obtained by analyzing the machining process, so as to conduct real-time on-line monitoring of the energy consumption status of multiple energy sources of the machine tool.

[0004] For the measurement of the moving components of a machine tool based on sensors, only the current energy status of the machine tool can be evaluated, and this measurement method requires a large amount of cost. In order to conduct a more long-term comprehensive analysis of the energy of the machine tool, it is more suitable to use fixed, continuous, and permanent detection. Summary of the Invention

[0005] The present invention is made to solve the above problems, and the purpose is to provide a method for decomposing the energy consumption of a numerical control machine tool component based on Kalman filtering.

[0006] The present invention provides a method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering, which has the following characteristics. The energy consumption of the machine tool is collected and analyzed through the hardware part including a signal acquisition module with a current sensor and a voltage sensor, a signal conditioning module, a data transmission card, a computer, and a power supply module, and the software part based on the LabView software platform, including the following steps:

[0007] Step 1: Collect the current and voltage of the machine tool through the current sensor and voltage sensor, and transmit them to the computer through the data transmission card to calculate the total energy consumption of the machine tool;

[0008] Step 2: Collect the PLC data of the machine tool through wireless communication to obtain the power consumption of the machine tool drive components and the switch states of the machine tool components;

[0009] Step 3: Decompose the energy consumption of the machine tool components. Use the empirical formula to calculate the sum of the powers of each machine tool component in the on state and the calculation error, and compare it with the difference between the total power of the machine tool minus the power of the drive components. Combine the Kalman equation for continuous update and iteration. At the same time, continuously update the covariance matrix. Finally, decompose the energy consumption of each machine tool component. Among them, the signal acquisition module collects the corresponding physical signals, which include current and voltage, and converts the physical signals into analog or digital signals and then transmits them to the signal conditioning module. After being processed by the signal conditioning module, a clear and complete signal is obtained and directly transmitted to the data acquisition card, and then the data acquisition card converts it into a USB interface signal, which is transmitted to the computer for storage and used for subsequent analysis. The computer performs parameter settings for collecting physical signals, displays the waveform diagrams of the data, stores and processes the data through the software part based on the LabView software platform. The parameter settings include the selection of physical channels, sampling mode, sampling rate, number of samples per channel, configuration of input mode, and setting of sampling maximum and minimum values. The waveform diagram display of the data includes three waveform diagram display interfaces for power, voltage, and current, a sampling parameter setting display interface, and a sampling signal value display interface. The machine tool components are decomposed into drive components, electrical control components, auxiliary components, and other energy consumption components.

[0010] In the method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering provided by the present invention, it may also have the following characteristics: Among them, the drive part includes the main shaft and the feed drive system, the electrical control part includes the electrical control cabinet, the auxiliary part includes the hydraulic and pneumatic system, the lighting system, the chip removal system, and the cooling and lubrication system. The hydraulic and pneumatic system includes a hydraulic pump and a pneumatic pump. The chip removal system includes a chip conveyor. The lighting system includes lighting lamps. The cooling and lubrication system includes a suction machine and a cooling and lubrication pump.

[0011] In the method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering provided by the present invention, it may further have the following characteristics: Among them, when collecting the machine tool PLC data in step 2, it is carried out through an OPC-UA-based acquisition system. The machine tool OPC-UA model is constructed through mapping for compilation and actual OPC-UA services. When in use, the machine tool historical status data is accessed by querying the database, so as to obtain the machine tool PLC data, and the power consumption of the machine tool drive components and the switch states of the machine tool components are obtained.

[0012] In the method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering provided by the present invention, it may further have the following characteristics: Among them, during the process of decomposing the energy consumption of machine tool components in step 3, the total energy consumption of the machine tool is composed of the corresponding energy consumptions of n machine tool components. The energy consumption of each component in the on state of the machine tool is used as the state quantity x i (k) (i = 1, 2, 3... n). The current state quantity is used as the optimal quantity optimized at the previous moment. The energy consumption of the machine tool is the predicted quantity given by the empirical formula and is represented by a linear differential equation. P(k) is the covariance matrix of the system, and the formula is as follows:

[0013] x(k) = Ax(k - 1) + Bu(k) + w(k) (1)

[0014] P(k) = AP(k - 1)A T + Q (2)

[0015] The energy consumption measurement value z i (k) (i = 1, 2, 3... n) is measured through the signal acquisition module, and the power consumption of the machine tool drive components and the switch states of the machine tool components are obtained by combining the machine tool PLC data. It is obtained by subtracting the power of the drive components from the total power of the machine tool. The energy consumption measurement value is expressed as

[0016] z(k) = Hx(k) + v(k) (3)

[0017] Through the Kalman filtering algorithm, according to the characteristic that the fusion of two Gaussian distributions is still a Gaussian distribution, iteration is carried out, and by combining the predicted part Gaussian distribution (μ0, Σ0) = (Hx(k), HP(k)H T ) and the measured part Gaussian distribution (μ1, Σ1) = (z(k), R), the updated Kalman coefficient Kg'(k), the optimized energy consumption estimation value x'(k), and the updated covariance matrix P'(k) are obtained.

[0018] x'(k) = x(k) + Kg’(k)[z(k) - Hx(k)] (4)

[0019]

[0020] P'(k) = P(k) - Kg'(k)HP(k) (6)

[0021] During the energy consumption decomposition process of the machine tool components, keep the drive components turned on, turn on only one auxiliary component each time, and keep the other components turned off. Through continuous update and iteration of formulas (1)-(6), the optimized energy consumption of different components of the machine tool at different times is decomposed.

[0022] When the energy consumption decomposition of the auxiliary components is completed, the root mean square error and relative error are calculated:

[0023]

[0024]

[0025] In formulas (1)-(3), x(k) is the system state at time k, u(k) is the control quantity of the system at time k, which is 0 when there is no control quantity for the system, A and B are system parameters, and z(k) is the measured value at time k, H is the parameter of the signal acquisition module, w(k) and v(k) respectively represent the noises in the prediction process and the measurement process, both are Gaussian white noises, and their covariances are Q and R respectively. In formulas (7) and (8), P measure is the power measurement value, and P disaggregated is the power prediction value.

[0026] Functions and effects of the invention

[0027] According to the method for decomposing the energy consumption of machine tool components based on Kalman filtering involved in the present invention, since only the total energy consumption of the machine tool needs to be collected through current sensors and voltage sensors, and based on the machine tool PLC data, the energy consumption of each component of the machine tool can be decomposed after calculation by the Kalman filtering algorithm, realizing continuous energy monitoring of the machine tool components. It not only has low cost and high efficiency, but also helps to better monitor the energy characteristics of the machine tool. Description of the drawings

[0028] Figure 1 is the flow chart of machine tool energy consumption acquisition in the embodiment of the present invention;

[0029] Figure 2 is the schematic diagram of the acquisition and sharing of machine tool PLC data in the embodiment of the present invention;

[0030] Figure 3 is the flow chart of the method for decomposing the energy consumption of machine tool components based on Kalman filtering in the embodiment of the present invention. Detailed implementation manners

[0031] In order to make the technical means and effects achieved by the present invention easy to understand, the present invention will be specifically described below in conjunction with the embodiments and the drawings.

[0032] <Embodiment>

[0033] A method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering in this embodiment collects and analyzes the energy consumption of the machine tool through the hardware part including a signal acquisition module with current sensors and voltage sensors, a signal conditioning module, a data transfer card, a computer, and a power supply module, and the software part based on the LabView software platform, including the following steps:

[0034] In this embodiment, the hardware part selects 1 NI cDAQ-9174, 1 NI-9201 voltage module, 3 power sensors RS-2131-44, 3 voltage sensors RS-1331-44D1, 3 current sensors JLB-10VD2Y2, 1 terminal block, 1 240V 10A power cord, 1 power filter, and several wires, etc.

[0035] The computer performs parameter settings for collecting physical signals, waveform display of data, and data storage and processing through the software part based on the LabView software platform.

[0036] The parameter settings include the selection of physical channels, configuration of sampling mode, sampling rate, number of samples per channel, input mode, and setting of sampling maximum and minimum values.

[0037] The waveform display of data includes three waveform display interfaces for power, voltage, and current, a sampling parameter setting display interface, and a sampling signal value display interface.

[0038] In this embodiment, the data format conversion program of the LabView software platform is designed as follows: Since the TDMS file writing speed is fast and it is conducive to the preservation of large-capacity data, the VI module is written through the TDMS file recorded in binary mode, and the TDMS file can be directly opened by Excel and then saved as Excel format.

[0039] Step 1, collect the current and voltage of the machine tool through current sensors and voltage sensors and transmit them to the computer through the data transfer card, and calculate the total energy consumption of the machine tool.

[0040] Figure 1 It is the flowchart of machine tool energy consumption acquisition in the embodiment of the present invention.

[0041] As Figure 1 shown, the signal acquisition module collects the corresponding physical signals, which include current and voltage, converts the physical signals into analog or digital signals and then transmits them to the signal conditioning module. After being processed by the signal conditioning module, clear and complete signals are obtained and directly transmitted to the data acquisition card, and then the data acquisition card converts them into USB interface signals, and the USB interface signals are transmitted to the computer for storage and used for subsequent analysis.

[0042] Step 2: Collect the data of the machine tool PLC through wireless communication to obtain the power consumption of the machine tool drive components and the switch states of the machine tool components.

[0043] Figure 2 It is a schematic diagram of the collection and sharing of the machine tool PLC data in the embodiment of the present invention.

[0044] As Figure 2 shown, when collecting the machine tool PLC data in Step 2, it is carried out through an OPC-UA-based collection system. Compile and actual OPC-UA services are carried out by constructing a machine tool OPC-UA model through mapping. When in use, access the machine tool historical status data by querying the database, so as to obtain the machine tool PLC data, and get the power consumption of the machine tool drive components and the switch states of the machine tool components.

[0045] In this embodiment, the OPC-UA-based collection system consists of an equipment layer, a data collection layer, and an application layer. The data collection layer distributes the collection work to each collection plugin by loading and configuring plugin templates, and uniformly receives the returned data. And the collection parameters are flexibly configured by configuring an XML document. The machine tool information model is constructed by the machine tool through three basic elements: attributes, methods, and objects.

[0046] Step 3: Decompose the energy consumption of the machine tool components. Use an empirical formula to calculate the sum of the powers of each machine tool component in the on state and the calculation error, and compare it with the difference between the total power of the machine tool minus the power of the drive components. Continuously update and iterate in combination with the Kalman equation. At the same time, continuously update the covariance matrix, and finally decompose the energy consumption of each machine tool component.

[0047] The machine tool components are decomposed into drive components, electrical control components, auxiliary components, and other energy consumption components.

[0048] The drive part includes the main shaft and the feed drive system. The electrical control part includes the electrical control cabinet. The auxiliary part includes the hydraulic and pneumatic systems, the lighting system, the chip removal system, and the cooling and lubrication system. The hydraulic and pneumatic systems include hydraulic pumps and pneumatic pumps. The chip removal system includes a chip conveyor. The lighting system includes lighting lamps. The cooling and lubrication system includes a suction machine and a cooling and lubrication pump.

[0049] Figure 3 It is a flowchart of the energy consumption decomposition method of the CNC machine tool components based on Kalman filtering in the embodiment of the present invention.

[0050] As Figure 3 shown, during the process of decomposing the energy consumption of the machine tool components in Step 3, the total energy consumption of the machine tool consists of the corresponding energy consumptions of n machine tool components. The energy consumptions of the components of the machine tool in the on state are used as the state quantity x i(k) (i = 1, 2, 3... n), taking the current state quantity as the optimal quantity optimized at the previous moment, the energy consumption of the machine tool is the predicted quantity given by the empirical formula, represented by a linear differential equation, P(k) is the covariance matrix of the system, and the formula is as follows:

[0051] x(k) = Ax(k - 1) + Bu(k) + w(k) (1)

[0052] P(k) = AP(k - 1)A T + Q (2)

[0053] The measured value of energy consumption z i (k) (i = 1, 2, 3... n), measured by the signal acquisition module, combined with the machine tool PLC data to obtain the power consumption of the machine tool drive components and the switch states of the machine tool components, obtained by subtracting the drive component power from the total machine tool power, and the measured value of energy consumption is expressed as

[0054] z(k) = Hx(k) + v(k) (3)

[0055] Through the Kalman filter algorithm, iterating according to the characteristic that the fusion of two Gaussian distributions is still a Gaussian distribution, combining the predicted part Gaussian distribution (μ0, Σ0) = (Hx(k), HP(k)H T ) and the measured part Gaussian distribution (μ1, Σ1) = (z(k), R), to obtain the updated Kalman coefficient Kg'(k), the optimized energy consumption estimation value x'(k), and the updated covariance matrix P'(k),

[0056] x'(k) = x(k) + Kg’(k)[z(k) - Hx(k)] (4)

[0057]

[0058] P'(k) = P(k) - Kg'(k)HP(k) (6)

[0059] During the process of decomposing the energy consumption of machine tool components, keep the drive components turned on, turn on only one auxiliary component each time, and keep other components turned off. Through continuous update and iteration of formulas (1) - (6), decompose to obtain the optimized energy consumption of different machine tool components at different times,

[0060] When the decomposition of the energy consumption of the auxiliary components is completed, calculate the root mean square error and relative error:

[0061]

[0062]

[0063] In formulas (1)-(3), x(k) is the system state at time k, u(k) is the control quantity of the system at time k, which is 0 when there is no control quantity for the system, A and B are system parameters, and z(k) is the measured value at time k, H is the parameter of the signal acquisition module, w(k) and v(k) respectively represent the noises in the prediction process and the measurement process, both of which are Gaussian white noises, and their covariances are Q and R respectively. In formulas (7) and (8), P measure is the power measurement value, and P disaggregated is the power prediction value.

[0064] Functions and effects of the embodiment

[0065] According to the method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering involved in this embodiment, since only the total energy consumption of the machine tool needs to be collected through current sensors and voltage sensors, and the energy consumption of each component of the machine tool can be decomposed through calculation by the Kalman filtering algorithm according to the machine tool PLC data, realizing continuous energy monitoring of the machine tool components, it not only has low cost and high efficiency, but also helps to better monitor the energy characteristics of the machine tool.

[0066] The above implementation manners are preferred cases of the present invention and are not used to limit the protection scope of the present invention.

Claims

1. A method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering, characterized in that, The acquisition and analysis of the energy consumption of the machine tool are carried out through the hardware part including a signal acquisition module with a current sensor and a voltage sensor, a signal conditioning module, a data transmission card, a computer, and a power supply module, and the software part based on the LabView software platform, including the following steps: Step 1, collect the current and voltage of the machine tool through the current sensor and the voltage sensor and transmit them to the computer through the data transmission card, and calculate the total energy consumption of the machine tool; Step 2, collect the PLC data of the machine tool through wireless communication to obtain the power consumption of the driving components of the machine tool and the switch states of the machine tool components; Step 3, perform energy consumption decomposition of the machine tool components. During the energy consumption decomposition of the machine tool components, keep the driving components turned on, and only turn on one auxiliary component each time, and keep the other components turned off. Use the empirical formula to calculate the sum of the powers of each machine tool component in the on state and the calculation error, and compare it with the difference between the total power of the machine tool minus the power of the driving components. Combine the Kalman equation for continuous update and iteration. At the same time, continuously update the covariance matrix, and finally decompose to obtain the optimized energy consumption of each machine tool component at different times. Among them, the signal acquisition module collects the corresponding physical signals, which include current and voltage, and converts the physical signals into analog or digital signals and then transmits them to the signal conditioning module. After being processed by the signal conditioning module, clear and complete signals are obtained and directly transmitted to the data transmission card, and then the data transmission card converts them into USB interface signals, and the USB interface signals are transmitted to the computer for storage for subsequent analysis. The computer performs parameter setting of the physical signals, waveform display of data, data storage and processing through the software part based on the LabView software platform. The parameter setting includes the selection of physical channels, sampling mode, sampling rate, number of samples per channel, configuration of input mode, and setting of sampling maximum and minimum values. The waveform display of the data includes three waveform display interfaces for power, voltage, and current, a sampling parameter setting display interface, and a sampling signal value display interface. The machine tool components are decomposed into the driving components, electrical control components, auxiliary components, and other energy consumption components.

2. The method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering according to claim 1, characterized in that: Among them, The driving components include a spindle and a feed drive system, the electrical control components include an electrical control cabinet, and the auxiliary components include a hydraulic and pneumatic system, a lighting system, a chip removal system, and a cooling and lubrication system. The hydraulic and pneumatic system includes a hydraulic pump and a pneumatic pump, the chip removal system includes a chip conveyor, the lighting system includes a lighting lamp, and the cooling and lubrication system includes a suction machine and a cooling and lubrication pump.

3. The method for decomposing the energy consumption of CNC machine tool components based on Kalman filtering according to claim 1, characterized in that: Among them, When collecting the machine tool PLC data in step 2, it is carried out through an OPC-UA-based acquisition system. The machine tool OPC-UA model is constructed through mapping for compilation and actual OPC-UA services. When in use, the machine tool historical status data is accessed by querying the database, so as to obtain the machine tool PLC data, and the power consumption of the machine tool drive components and the switch states of the machine tool components are obtained.

4. The method for decomposing the energy consumption of components of a numerically controlled machine tool based on Kalman filtering according to claim 1, wherein: Among them, In the process of decomposing the energy consumption of machine tool components in step 3, the total energy consumption of the machine tool is composed of the energy consumption corresponding to n machine tool components of the machine tool. The energy consumption of each component in the on state of the machine tool is used as the state quantity x i (k), i = 1, 2, 3... n. The current state quantity is taken as the optimal quantity optimized at the previous moment. The energy consumption of the machine tool is the predicted quantity given by the empirical formula and is represented by a linear differential equation. P(k) is the covariance matrix of the system. The formula is as follows: x(k) = Ax(k - 1) + Bu(k) + w(k) (1) P(k) = AP(k - 1)A T + Q(2) Energy consumption measurement value z i (k), i = 1, 2, 3... n, measured by the signal acquisition module, combined with the machine tool PLC data to obtain the power consumption of the machine tool drive components and the switch states of the machine tool components, obtained by subtracting the drive component power from the total machine tool power, and the energy consumption measurement value is expressed as z(k) = Hx(k) + v(k) (3) Through the Kalman filtering algorithm, iteration is carried out according to the characteristic that the fusion of two Gaussian distributions is still a Gaussian distribution, and combined with the predicted partial Gaussian distribution (μ0, Σ0) = (Hx(k), HP(k)H T ) and the measured partial Gaussian distribution (μ1, Σ1) = (z(k), R), the updated Kalman coefficient Kg'(k), the optimized energy consumption estimation value x'(k), and the updated covariance matrix P'(k) are obtained. x'(k) = x(k) + Kg’(k)[z(k) - Hx(k)] (4) P'(k) = P(k) - Kg'(k)HP(k) (6) During the process of decomposing the energy consumption of the machine tool components, keep the drive components turned on, and only turn on one auxiliary component each time, and keep other components turned off. Through continuous update and iteration of formulas (1)-(6), the optimized energy consumption of different components of the machine tool at different times is decomposed. When the energy consumption decomposition of the auxiliary components is completed, the root mean square error and relative error are calculated: In formulas (1)-(3), x(k) is the system state at time k, u(k) is the control quantity of the system at time k, which is 0 when there is no control quantity in the system, A and B are system parameters, and z(k) is the measured value at time k, H is the parameter of the signal acquisition module, w(k) and v(k) respectively represent the noises in the prediction process and the measurement process, both of which are Gaussian white noises, and their covariances are Q and R respectively. In formulas (7) and (8), P measure is the power measurement value, P disaggregated is the power prediction value, N is the total number of machine tool components, and i is the i-th machine tool component in the on state.

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