CN105955198B - Lathe work step energy-consumption monitoring method based on least-squares iteration algorithm - Google Patents
Lathe work step energy-consumption monitoring method based on least-squares iteration algorithm Download PDFInfo
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Abstract
The invention discloses a kind of lathe work step energy-consumption monitoring method based on least-squares iteration algorithm, includes the following steps：One：The main transmission input power of lathe is collected, and input power signal is filtered；Two：By the analysis of main transmission system of machine tool input power data, lathe on-line operation state is judged；Three：By measuring machine tool chief axis realtime power, cutting power is estimated in conjunction with the power balance equation and additional load loss characteristic of main transmission system of machine tool, rational cutting energy consumption model is established, reaches the On-line Estimation of machine cut power；Four：With the off-line identification algorithm of the lathe added losses function coefficients based on least-squares iteration algorithm, machine cut power parameter is sought.Invention not only avoids the high efficiency at low cost measured in the direct method of measurement in cutting energy consumption, while also improving the big problem of the error rate in the indirect method of measurement, and more accurately energy consumption data can be provided for the work step energy consumption monitoring of lathe.
Description
Technical Field
The invention relates to the field of energy consumption monitoring of industrial machine tool manufacturing systems, in particular to a machine tool step energy consumption monitoring method based on a least square iterative algorithm.
Background
With the current energy crisis and environmental problems becoming more serious, energy conservation and emission reduction have been regarded as national strategies in many countries. The manufacturing industry, as a supporting industry of national economy, consumes a large amount of manufacturing resources, particularly energy, and has serious influence on the environment while creating huge economic wealth. The energy consumption of the manufacturing industry is up to 30% -50% of the total global energy consumption, and particularly, the proportion of the energy consumption mainly based on machine tools is up to 17% -20% of the global energy consumption. By 2030, the industry will reach 100,000 million tons. Energy problems and environmental problems become intuitive factors restricting economic and social development, and from the strategy of sustainable development, the research on the problem of machine tool energy consumption is imperative.
Enhancing the energy efficiency evaluation of enterprises and improving the energy efficiency of manufacturing systems become urgent matters in the manufacturing industry, and improving the energy consumption efficiency of machine tools requires the support of energy consumption data of the machine tools, so that online real-time monitoring of the energy consumption of the machine tools is necessary, and the key of the energy consumption evaluation of the machine tools lies in measuring the energy consumption of machine tool machining in real time. There are two ways to conventionally obtain the machine tool processing energy consumption:
(1) direct measurement: the method is used for directly measuring the cutting torque and the rotating speed during machining, a torque sensor needs to be arranged on a machine tool, the price is high, the influence on the rigidity of the machine tool is easily caused, and the performance and the cost are unacceptable for enterprises.
(2) Indirect measurement method: the machining power is indirectly obtained by measuring the input power of the machine tool, a power sensor is required to be installed in the method, although the rigidity of the machine tool cannot be influenced, the method for estimating the cutting power (the input power-no-load power is equal to the cutting power) by utilizing the main shaft power of the main transmission system neglects the additional loss of the machine tool, the result is inaccurate, and the error is up to 30%.
In view of the exigency of online detection of machine tool energy consumption and the defects of the method, the invention provides that the cutting energy consumption model (mathematical formula) is adopted to accurately obtain the cutting power, thereby not only meeting the actual situation, but also meeting the economic benefits of enterprises. According to the method, a model relation (mathematical formula) between the cutting energy consumption of the machine tool and the step energy consumption of the machine tool is researched, the real-time power of a main shaft of the machine tool is measured, the cutting power is estimated by combining a power balance equation of a main transmission system of the machine tool and the additional load loss characteristic, and a reasonable cutting energy consumption model is established. And a certain optimization algorithm is adopted for solving, so that the method is a cutting energy consumption acquisition method which saves cost, improves precision, accords with reality and is easily accepted by enterprises.
Disclosure of Invention
The invention aims to provide a method for monitoring the energy consumption of the machine tool in the process step based on the least square iterative algorithm, which not only avoids the high cost and low efficiency in the process of measuring the cutting energy consumption in a direct measurement method, but also improves the problem of large error rate in an indirect measurement method, can provide more accurate energy consumption data for monitoring the energy consumption of the machine tool in the process step, saves the cost, improves the precision, accords with the reality and is an easily accepted method for obtaining the cutting energy consumption for enterprises.
In order to achieve the purpose, the method for monitoring the energy consumption of the machine tool in the process step based on the least square iterative algorithm is implemented according to the following steps:
the method comprises the following steps: collecting input power of a main transmission system of the machine tool, and filtering an input power signal;
step two: the online running state of the machine tool is judged through the analysis of the input power data of the main transmission system of the machine tool: starting, idling or processing;
step three: the cutting power is estimated by measuring the real-time power of a main shaft of the machine tool and combining a power balance equation of a main transmission system of the machine tool and the loss characteristic of an additional load, and a reasonable cutting energy consumption model is established to achieve the on-line estimation of the cutting power of the machine tool;
step four: and solving the cutting power parameter of the machine tool by using an off-line identification algorithm of the additional loss function coefficient of the machine tool based on a least square iterative algorithm.
Specifically, the filtering process of the power signal in the step one adopts a sliding filter to estimate the no-load power
Wherein, psp(k) The sampling value of the input power at the kth moment is obtained;input power p for the nth time instantsp(n) an estimated value; l is the selected length of the sliding filter, and is divided into the following two cases according to whether the real-time power is full of the filter:
(1.1) in the initial stage of the operation of the machine tool, the real-time power acquisition times are small, the filter is not filled, in this case, the weighted average is directly carried out according to the formula (1), and the filtering power value is lower than the actual power value;
(1.2) when the real-time power values fill the filter, adding the first M real-time power values collected in the filter, and then carrying out weighted average, wherein the result accords with the actual situation;
before the sliding filter is applied, whether the filter is filled or not needs to be checked, if the filter is not filled, the weighted average is carried out by using the number of samples according to the method of the condition (1.1); if the filling is full, carrying out filtering processing according to the method of the condition (1.2); and after the filtering result, inputting the new sampling power value into the filter, and withdrawing the old sampling value from the filter, and repeating the steps to finish filtering.
Specifically, the operation state is judged according to the real-time power value,
2.1, judging the starting of the machine tool: sending the real-time power value filtered in the step one into a computer background database array M [ n ], measuring whether the real-time power value of a Machine tool spindle is larger than a reference constant of the Machine tool, judging the State of the Machine tool as the spindle starting when more than two numerical values larger than the constant appear in the array M [ n ], and setting a Machine tool State parameter, namely Machine State, to be 01;
2.2, the judgment of the machine tool no-load state comprises three steps: 2.2.1, checking whether the machine tool state is starting, if soThe next step is carried out; 2.2.2, judging array M [ n ]]If the intermediate real-time power value is stable, turning to the step 2.2.3, otherwise returning to the step 2.2.1; 2.2.3, judging the Machine tool State as the spindle no-load, setting the Machine State parameter Machine State to 10, and taking the current power value as the no-load power value pu；
2.3, judging the machining state of the machine tool: according to the formulaDetermining the machine tool machining state, whereinFor the value of the input power of the machine tool spindle, PuC is a set constant representing the power fluctuation condition, and is generally about 5 percent; real-time power value of main shaft of interpretation machine toolWhether or not to conform to the formulaIf not, the real-time power of the main shaft is continuously measured through the first step and the second stepAnd PuUntil the formula is metJudging the Machine tool State as machining, and setting a Machine tool State parameter Machine State as 11;
specifically, the third step comprises:
3.1 converting the input power p of the main drive system of the machine toolspReduced to no-load power PuCutting power pcAnd parasitic load loss power paThe sum of the three parts, approximately representing the power loss of the actual machine tool,
Ρsp＝Ρu+Ρa+Ρc(2)
wherein the no-load power puThe method comprises the following steps: the machine tool main transmission system stably runs at a certain specified rotating speed and the state which is not processed is called an idle state, and the power consumed in the idle state is called idle power; the cutting power pcThe method comprises the following steps: the machine tool main transmission system is used for cutting consumed power when the workpiece requirement standard is completed; the parasitic load loss power paThe method comprises the following steps: the additional loss generated by the main transmission system of the machine tool in the cutting state exists only in the cutting state;
the load loss factor α is proportional to the cutting power, i.e.,
the compound is obtained by combining the formula (2) and the formula (3):
Ρsp＝Ρu+a1 2Ρc 2+(1+a0)Ρc(4)
wherein, 1+ a0、a1For the purpose of adding to the coefficients of the loss function,
as can be seen from equation (4), the input power Pp is measuredspZero load power puThe additional load loss power p can be estimatedaAnd cutting power pc；
3.2 on-line estimation of the cutting power Ppc：
The additional loss function coefficient matrix can be determined by equation (4) and the cutting power can be obtained by combining equation (2), that is
Wherein, puIs the no-load power value of the main shaft of the machine tool;and the input power value of the main shaft of the machine tool.
Specifically, the off-line identification method of the additional loss function coefficient of the machine tool based on the least square iterative algorithm in the step four is as follows:
as can be seen from equation (5), if the Machine State parameter Machine State is determined to be 11, the idle power p is determinedu，a0、a1The cutting power p can be estimatedc；
From equation (4), in the rotation speed determination, the no-load power p is obtaineduThen measuring the cutting power under the cutting parameter, and solving a through least square iterative algorithm function fitting0，a1：
From a multivariable system y (t) ═ Φ (t) θ + v (t), where y (t) ═ y1,y2,...ym]T∈RmFor m-dimensional system output vector, phi (t) belongs to Rm×nIs an information matrix formed by input and output data of the system, and theta is belonged to RnIs the vector of system parameters to be identified,is a zero mean white noise vector;
considering that the filter length is L in formula (1), a is solved by least square algorithm function fitting from the latest L group data of i-t-L +1 to i-L0、a1(ii) a Firstly, defining accumulation output vector Y (t), accumulation information matrix phi (t) and accumulation white noise vectorThe following were used:
byDefining a criterion function:
J(θ)＝||Y(t)-Φ(t)θ||2. (9)
minimizing the criterion function J (θ) with its derivative to θ being zero yields:
then a least squares estimate of the parameter vector θ given by the above equation can be obtained by a matrix operation:
the corresponding parameters of the additional loss function are substituted to solve the a0,a1，
Where Φ (t) ═ a, θ ═ 1+ a0,a1](12)
From Anθ＝Ynn∈{ni,i＝1,2,…m} (13)
Wherein,
the filtering length selected in the step one is L, so that the cutting experiment times are set to be L times, and the statistics and calculation of experiment data are facilitated; pcLCutting power measurement value of the first test; p is a radical ofn,uIs the measured value of the idle power of the machine tool spindle at the speed n.
The invention has the beneficial effects that: the method comprises the steps of establishing a relation between the cutting energy consumption of the machine tool and the step energy consumption of the machine tool, estimating the cutting power by measuring the real-time power of a main shaft of the machine tool and combining a power balance equation of a main transmission system of the machine tool and the loss characteristic of an additional load, and establishing a reasonable cutting energy consumption model. And an iterative optimization algorithm based on least square is adopted for solving, so that the current situations of low precision and high cost in the conventional cutting energy consumption measurement are well overcome. The method is scientific and reasonable, and accords with the national strategy of sustainable development and improvement of enterprise energy efficiency optimization.
Drawings
FIG. 1 is a graph of the power curve of the machine tool spindle and the machining process of the invention.
Fig. 2 is the spindle power flow for steady operation of the machine tool of the present invention.
Fig. 3 is a flow chart of the implementation of the power signal sliding filter of the present invention.
Fig. 4 is a flow chart of the machine tool state judgment algorithm of the present invention.
FIG. 5 is a flow chart of the least squares iterative algorithm of the present invention to calculate parameters.
Fig. 6 is a flow chart of the machine tool energy efficiency related data algorithm of the invention.
Detailed Description
The method establishes online monitoring of the energy consumption state of the machine tool by taking the monitoring and acquisition of the energy consumption of the machine tool in the process step as a target, and accurately obtains the cutting power parameter by a least square iterative algorithm. The method specifically comprises the following steps:
firstly, filtering a power signal:
the operating environment of factory workshops is severe, and the voltage and current of the power signal are easily interfered by fluctuation and noise under the environment.
The invention adopts a sliding filter to estimate the no-load power.
The meaning of the parameter variables in the formula:
Ρsp(k) the input power sampling value at the kth moment;input power p at time instant nsp(n) an estimated value; l, sliding filter length, and selecting parameters according to actual conditions.
The formula (1) is obtained by simplifying according to the filter analysis principle of the sliding filter, and the no-load power value at the n moment in the sliding filter is a weighted average value of L real-time power values before the n moment. Depending on whether real-time power fills the filter as shown in fig. 3, the following two cases can be distinguished:
1) in the initial stage of the operation of the machine tool, the real-time power acquisition times are small, the filter is not filled, in this case, the filtering power value is lower than the actual power value according to the formula (1) for direct weighted average.
2) When the real-time power value fills the filter, the first L real-time power values collected in the filter are added, and then weighted average is carried out, so that the result accords with the actual situation.
In view of the 2 situations that can occur when the sliding filter in the formula (1) is applied, before the sliding filter is applied, it is necessary to check whether the filter is full, and if the filter is not full, the weighted average is performed by using the number of samples according to the method 1); if the filling is full, the filtering processing is carried out according to the method 2). And after the filtering result, inputting the new sampling power value into the filter, and withdrawing the old sampling value from the filter, and repeating the steps to finish filtering.
Secondly, online judging of the machine tool running state:
the machine tool process contains three typical machine tool states: starting, no-load, machining (cutting). Under different operation states of the machine tool, the power curve of the main shaft of the machine tool changes along with the change of the operation state of the machine tool. Which includes several typical parts: starting stage, idle stage and processing stage. As shown in fig. 1, in the starting stage of the machine tool, the power of the main shaft of the machine tool is rapidly increased and then rapidly decreased; in the no-load stage of the machine tool, the power of a main shaft of the machine tool tends to be stable; in the machining stage of the machine tool, the power of the main shaft of the machine tool stably runs at an off-load power value higher than the no-load power. The accurate determination of the operating state from the real-time power value is the key to measuring the energy consumption of the machine tool, and is analyzed in detail below.
① judging the start of the Machine tool, sending the real-time power value filtered in step (1) into the background database array M [ n ] (the array M [ n ] is cleared when the Machine tool is stopped), measuring whether the real-time power value of the main shaft of the Machine tool is larger than the reference constant of the Machine tool (the constant should be larger than the zero drift value of the power sensor and can be set according to the specific situation of the Machine tool), when more than two values larger than the constant appear in the array M [ n ], judging the State of the Machine tool as the start of the main shaft, and setting the State of the Machine tool as Machine State 01 (00: main shaft stop; 01: main shaft start; 10: main shaft no-load; 11: processing).
② judging the no-load State of the Machine tool, which is a State of stable relative power before the machining is started after the main shaft is started, the no-load State of the Machine tool is judged by the following three steps of (a) checking whether the Machine tool State is started, if so, turning to the next step (b), (b) judging whether the real-time power value in the array M [ n ] is stable, if so, turning to (c), otherwise, returning to (a), (c) setting the Machine tool State as Machine State as 10, and taking the current value as the no-load power value.
③ judging the machining state of the machine tool according to the formulaDetermining the machine tool machining state, whereinFor the value of the input power of the machine tool spindle, PuC is a set constant representing the power fluctuation condition; judging real-time power value of machine tool spindleWhether or not to conform to the formulaIf not, continuing to measure the real-time power of the main shaftAnd PuUntil the formula is metJudging the Machine tool State as machining, and setting a Machine tool State parameter Machine State as 11;
the machine tool state judgment is a key step of machine tool cutting energy consumption estimation, and the accurate judgment of the machine tool state can provide accurate data reference for the machine tool starting time, the dead time and the machining time. Referring to fig. 4, the machine state algorithm is divided into the following key steps: 1) and (3) starting up and judging: according to whether the filtered main shaft power is larger than a null shift threshold value or not; 2) taking the stable value of a time period after starting up as the no-load power; 3) and judging whether the latest power value meets the requirement of a formula, if so, determining that the machine tool is in a machining state, and if not, determining that the machine tool is in an idle state for a long time and updating the idle value.
Thirdly, on-line estimation of cutting power of the machine tool:
the online monitoring of the cutting power of the machine tool is the core of the machine tool energy efficiency monitoring technology, and the step discusses the relevant technology and method for estimating the cutting power of the machine tool from the input power of the main transmission system of the machine tool.
① equation for power balance of main drive system of machine tool
The main transmission system of the machine tool generally comprises a motor drive, a motor and a mechanical transmission (including a main shaft), the energy consumption of each part relates to the influence of a plurality of structures, parameters and environments, and the measurement is complex. Therefore, a large number of tests and researches show that the power of the main transmission system is simplified into three parts, namely no-load power, cutting power and additional load loss power, and the power loss of the actual machine tool can be approximately represented. Wherein the definition of no-load power, cutting power and additional load loss power is as follows:
no load power pu: the state that the main transmission system of the machine tool stably runs at a certain specified rotating speed and is not machined is called an idle state, and the power consumed in the idle state is called idle power.
Cutting power pc: the machine tool main transmission system completes the power consumed for cutting when the workpiece requires standard.
Parasitic load loss power pa: the additional loss generated by the main transmission system of the machine tool in the cutting state exists only in the cutting state.
As can be seen from fig. 2, the input power of the main transmission system of the machine tool can be divided into idle power, cutting power and additional load loss power:
Ρsp＝Ρu+Ρa+Ρc(2)
the additional load power loss is the additional electric loss and mechanical loss generated by the motor and the mechanical transmission part in a cutting state, and the additional load power loss is also complex and can not be directly measured accurately. Recent studies have shown that: the parasitic load loss coefficient is proportional to the cutting power. That is to say that the first and second electrodes,
the compound is obtained by combining the formulas (2) and (3):
Ρsp＝Ρu+a1 2Ρc 2+(1+a0)Ρc(4)
as can be seen from equation (4), the input power Pp is measuredspZero load power puThe accessory load loss p can be estimatedaAnd cutting power pc。
② estimating the cutting power p onlinec
The additional loss function coefficient matrix can be determined by equation (4) and the cutting power can be obtained by combining equation (2), i.e.
Wherein, pu: the no-load power value of the machine tool spindle;and inputting the power value of the main shaft of the machine tool.
Fourthly, identifying the additional loss function coefficient of the machine tool off line based on the least square iterative algorithm:
as can be seen from equation (5), if the Machine State is determined to be 11, the idle power p is determinedu，a0、a1The cutting power p can be conveniently estimated as followscThus, the additional load function coefficient (1+ a) is determined0,a1) Is very important.
From equation (4), in the rotation speed determination, the no-load power p is obtaineduThen measuring the cutting power under the cutting parameter, and solving a through least square algorithm function fitting0，a1。
(1) Least squares iterative identification algorithm principle
From a multivariable system y (t) ═ Φ (t) θ + v (t), where y (t) ═ y1,y2,...ym]T∈RmFor m-dimensional system output vector, phi (t) belongs to Rm×nIs an information matrix formed by input and output data of the system, and theta is belonged to RnIs the system parameter vector to be identified, v (t) [ v ]1(t),v2(t),...vm(t)]∈RmIs a zero mean white noise vector.
Considering that the filter length is L (L is the data length) in the formula (1), as shown in fig. 5, a is solved from the latest L group data of i-t-L +1 to i-L by the least square algorithm function fitting0、a1. Firstly, defining accumulation output vector Y (t), accumulation information matrix phi (t) and accumulation white noise vectorThe following were used:
byDefining a criterion function:
J(θ)＝||Y(t)-Φ(t)θ||2. (9)
minimizing the criterion function J (θ) with its derivative to θ being zero yields:
then a least squares estimate of the parameter vector θ given by the above equation can be obtained by a matrix operation:
here we substitute the corresponding parameters of the additional loss function to solve for a0,a1。
Where Φ (t) ═ a, θ ═ 1+ a0,a1](12)
From Anθ＝Ynn∈{ni,i＝1,2,…m} (13)
Wherein,
l: the number of cutting tests is more than or equal to 2; pcLCutting power measurement value of the first test; p is a radical ofn,uIs the measured value of the idle power of the machine tool spindle at the speed n.
FIG. 6 is a machine tool energy efficiency correlation algorithm, which can realize the calculation related to the statistics of the machine tool service time, the statistics of the overall machine tool energy consumption, the estimation of the machine tool cutting energy consumption and the machine tool energy utilization rate. Mainly comprises the following steps; 1) counting the service time of the machine tool according to the working state of the machine tool; 2) under the cutting state of the machine tool, estimating the cutting energy consumption of the machine tool by combining the identification result of the additional loss function coefficient of the machine tool; 3) and (4) counting the related data of the total energy consumption, the energy efficiency, the energy utilization rate and the like of the machine tool by combining the machine tool load-independent energy consumption. And the data is written into a database, so that data support is provided for further energy-saving design and experimental use.
Fifth, experimental design proves
This test is carried out at C26136HK \1 of the numerically controlled machine tool for carrying out the relevant experiments. Main drive system input power is measured with an EDA9033A power difference sensor. In order to verify the effectiveness and accuracy of the current cutting power, a torque sensor is mounted on the machine tool. The data sampling period of the power sensor and the data sampling period of the torque sensor are both 50ms, and the filtering length L is 5.
The key point of the method is to monitor the cutting power in the process step energy consumption, so the estimation algorithm of the cutting power is the key point of the test. Below we will experiment on the validity and accuracy of the algorithm.
TABLE 1 Experimental cutting parameters
| Spindle speed (rpm) | Feed speed (mm/rev) | Eating quantity (mm) |
| 400 | 0.4 0.8 | 0.4 |
| 400 | 0.1 | 1.0 2.0 |
| 400 | 0.4 | 4.0 |
| 800 | 0.4 0.8 | 0.4 |
| 800 | 0.1 | 1.0 2.0 |
| 800 | 0.4 | 4.0 |
TABLE 2 cutting test results
TABLE 3 comparison of estimated cutting power
In the test, a 45# steel bar with the thickness of 50mm is used as a raw material, 10 groups of experiments are carried out according to the cutting parameters in table 1, the test result is shown in table 2, the estimation error of the cutting power is within 10% according to the test result, and the result is compared with the traditional method (α -0, α -0.15 and α -0.2), and the method has good precision and effectiveness according to table 3.
Claims (6)
1. The machine tool working step energy consumption monitoring method based on the least square iterative algorithm is characterized by comprising the following steps of:
the method comprises the following steps: collecting input power of a main transmission system of the machine tool, and filtering an input power signal;
step two: the online running state of the machine tool is judged through the analysis of the input power data of the main transmission system of the machine tool: starting, idling or processing; judging the running state according to the real-time power value,
2.1, judging the starting of the machine tool: sending the real-time power value filtered in the step one into a computer background database array M [ n ], measuring whether the real-time power value of a Machine tool spindle is larger than a reference constant of the Machine tool, judging the State of the Machine tool as the spindle starting when more than two numerical values larger than the constant appear in the array M [ n ], and setting a Machine tool State parameter, namely Machine State, to be 01;
2.2, the judgment of the machine tool no-load state comprises three steps: 2.2.1, checking whether the machine tool is started or not, and if so, turning to the next step; 2.2.2, judging array M [ n ]]If the intermediate real-time power value is stable, turning to the step 2.2.3, otherwise returning to the step 2.2.1; 2.2.3, judging the Machine tool State as the spindle no-load, setting the Machine State parameter Machine State to 10, and taking the current power value as the no-load power value pu；
2.3, judging the machining state of the machine tool: according to the formulaDetermining the machine tool machining state, whereinFor the value of the input power of the machine tool spindle, PuC is a set constant representing the power fluctuation condition; judging real-time power value of machine tool spindleWhether or not to conform to the formulaIf not, the real-time power of the main shaft is continuously measured through the first step and the second stepAnd PuUntil the formula is metJudging the Machine tool State as machining, and setting a Machine tool State parameter Machine State as 11;
step three: the cutting power is estimated by measuring the real-time power of a main shaft of the machine tool and combining a power balance equation and the additional load loss characteristic of a main transmission system of the machine tool, and a cutting energy consumption model is established to achieve the on-line estimation of the cutting power of the machine tool;
3.1 converting the input power P of the main drive system of the machine toolspReduced to no-load power puCutting power pcAnd parasitic load loss power paThe sum of the three parts, approximately representing the power loss of the actual machine tool,
Ρsp＝Ρu+Ρa+Ρc(2)
wherein the no-load power puThe method comprises the following steps: the machine tool main transmission system stably runs at a certain specified rotating speed and the state which is not processed is called an idle state, and the power consumed in the idle state is called idle power; the cutting power pcThe method comprises the following steps: the machine tool main transmission system is used for cutting consumed power when the workpiece requirement standard is completed; the parasitic load loss power paThe method comprises the following steps: the additional loss generated by the main transmission system of the machine tool in the cutting state exists only in the cutting state;
the load loss factor α is proportional to the cutting power, i.e.,
the compound is obtained by combining the formula (2) and the formula (3):
Ρsp＝Ρu+a1 2Ρc 2+(1+a0)Ρc(4)
wherein, 1+ a0、a1For the purpose of adding to the coefficients of the loss function,
as can be seen from equation (4), the input power Pp is measuredspZero load power puThe additional load loss power p is estimatedaAnd cutting power pc；
3.2 on-line estimation of the cutting power Ppc：
The additional loss function coefficient matrix is determined by equation (4) in combination with equation (2) to obtain the cutting power, i.e.
Wherein, puIs the no-load power value of the main shaft of the machine tool;is the input power value of the main shaft of the machine tool;
step four: and solving the cutting power parameter of the machine tool by using an off-line identification method of the additional loss function coefficient of the machine tool based on a least square iterative algorithm.
2. The method for monitoring energy consumption of machine tool working steps based on least square iterative algorithm as claimed in claim 1, wherein the filtering process of the power signal in the step one adopts a sliding filter to estimate the no-load power
Wherein, psp(k) The sampling value of the input power at the kth moment is obtained;input power p for the nth time instantsp(n) an estimated value; l is the selected length of the sliding filter, and is divided into the following two cases according to whether the real-time power is full of the filter:
(1.1) in the initial stage of the operation of the machine tool, the real-time power acquisition times are small, the filter is not filled, in this case, the weighted average is directly carried out according to the formula (1), and the filtering power value is lower than the actual power value;
(1.2) when the real-time power values fill the filter, adding the first M real-time power values collected in the filter, and then carrying out weighted average, wherein the result accords with the actual situation;
before the sliding filter is applied, whether the filter is filled or not needs to be checked, if the filter is not filled, the weighted average is carried out by using the number of samples according to the method of the condition (1.1); if the filling is full, carrying out filtering processing according to the method of the condition (1.2); and after the filtering result, inputting the new sampling power value into the filter, and withdrawing the old sampling value from the filter, and repeating the steps to finish filtering.
3. The method for monitoring the energy consumption of the machine tool in the process step based on the least square iterative algorithm as claimed in claim 2, wherein the off-line identification method of the additional loss function coefficient of the machine tool based on the least square iterative algorithm in the step four is as follows:
as can be seen from equation (5), if the Machine State parameter Machine State is determined to be 11, the idle power p is determineduCoefficient a0、a1The cutting power p is estimatedc；
From equation (4), in the rotation speed determination, the no-load power p is obtaineduThen measuring the cutting power under the cutting parameter, and solving a through least square iterative algorithm function fitting0，a1：
From a multivariable system y (t) ═ Φ (t) θ + v (t), where y (t) ═ y1,y2,...ym]T∈RmFor m-dimensional system output vector, phi (t) belongs to Rm×nIs an information matrix formed by input and output data of the system, and theta is belonged to RnIs the vector of system parameters to be identified,is a zero mean white noise vector;
considering that the filter length is L in formula (1), a is solved by least square algorithm function fitting from the latest L group data of i-t-L +1 to i-L0、a1(ii) a Firstly, defining accumulation output vector Y (t), accumulation information matrix phi (t) and accumulation white noise vectorThe following were used:
byDefining a criterion function:
J(θ)＝||Y(t)-Φ(t)θ||2. (9)
minimizing the criterion function J (θ) with its derivative to θ being zero yields:
then a least squares estimate of the parameter vector θ given by the above equation can be obtained by a matrix operation:
the corresponding parameters of the additional loss function are substituted to solve the a0,a1，
Where Φ (t) ═ a, θ ═ 1+ a0,a1](12)
From Anθ＝Ynn∈{ni,i＝1,2,···m} (13)
Wherein,L≥2；θ＝[1+a0a1]； (15)
the filtering length selected in the step one is L, so that the cutting experiment times are set to be L times, and the statistics and calculation of experiment data are facilitated; pcLCutting power measurement value of the first test; p is a radical ofn,uIs the measured value of the idle power of the machine tool spindle at the speed n.
4. The method for monitoring the energy consumption of the machine tool in the working step based on the least square iterative algorithm as claimed in claim 1, wherein in step 2.1, the array M [ n ] is cleared when the machine tool is stopped.
5. A method for monitoring the energy consumption of the process steps of a machine tool based on a least squares iterative algorithm as claimed in claim 1, wherein said reference constant in step 2.1 should be greater than the null shift value of the power sensor.
6. The method for monitoring the energy consumption of the Machine tool working step based on the least square iterative algorithm as claimed in claim 1, wherein in the second step, the Machine State parameter Machine State is 00, which indicates that the spindle is stopped.
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