CN115824636A - Automobile gearbox state monitoring method for self-adaptive energy growth sparsity measurement - Google Patents
Automobile gearbox state monitoring method for self-adaptive energy growth sparsity measurement Download PDFInfo
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Abstract
A state monitoring method of an automobile gearbox for self-adaptive energy growth sparsity measurement comprises the steps of constructing state monitoring indexes under all gears, constructing overall health state monitoring indexes of the gearbox, monitoring the state of the gearbox and positioning faults; firstly, acquiring a vibration signal of a gearbox, and realizing self-adaptive energy increase sparsity evaluation by constructing state monitoring indexes under each gear; then carrying out weighted fusion to obtain a monitoring index capable of reflecting the overall health state of the gearbox; then, fault location is carried out by utilizing the monitoring indexes and the evaluation result of the importance of the segmentation amplitude and the growth rate in the calculation process; the invention realizes the automatic state monitoring and the auxiliary fault positioning of the automobile gearbox, and improves the accuracy of the state monitoring of the automobile gearbox.
Description
The technical field is as follows:
the invention belongs to the technical field of automobile transmission state monitoring, and particularly relates to an automobile transmission state monitoring method for self-adaptive energy growth sparsity measurement.
Background art:
automotive transmissions are indispensable components of automobiles, and the reliability of the automotive transmissions affects the overall performance of the automobiles. When the automobile gearbox works, gear conversion often occurs, and impact or load change is caused; and because of the complexity of the automobile gearbox structure and the variability of the operation conditions, the effective state monitoring of the automobile gearbox is very difficult.
The automobile gearbox takes a gear as a main transmission component, and common faults of the automobile gearbox comprise failure modes such as tooth surface abrasion, tooth surface pitting, gear breakage and the like. The existing commonly used method for monitoring the state of the gearbox (gold light, yuan Zhaodan, jiang Guanyi, and the like; transmission assembly endurance test early failure diagnosis [ J ]. Automobile technology, 2019 (06): 53-58.) is a component structure monitoring index for observing the meshing order of gears and the side band thereof in a vibration signal order spectrum, and has the defects of order spectrum difference of different devices, discontinuous monitoring index and difficult setting of failure threshold value when the state of the gearbox is monitored due to the complex transmission structure and the change of a transmission path of the gearbox. Therefore, a monitoring index capable of reflecting the degradation of the health state of the gearbox is constructed, the automatic state monitoring of the gearbox is realized, the position of a fault gear is further obtained, and the method has important significance for the maintenance of the automobile gearbox.
The invention content is as follows:
in order to overcome the defects in the prior art, the invention aims to provide the automobile gearbox state monitoring method for self-adaptive energy increase sparsity measurement, so that the automatic state monitoring and the auxiliary fault location of the automobile gearbox are realized, and the accuracy of the state monitoring of the automobile gearbox is improved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a state monitoring method of an automobile gearbox for self-adaptive energy growth sparsity measurement comprises the steps of firstly, obtaining vibration signals of the gearbox, and realizing self-adaptive energy growth sparsity evaluation by constructing state monitoring indexes under all gears; then carrying out weighted fusion to obtain a monitoring index capable of reflecting the overall health state of the gearbox; and then the monitoring index and the sectional amplitude in the calculation process are utilized and carrying out fault location on the growth rate importance evaluation result.
A state monitoring method of an automobile gearbox for self-adaptive energy growth sparsity measurement comprises the following steps:
1) And (3) establishing state monitoring indexes under each gear:
1.1 Obtain the basic data: collecting vibration signals of the whole service life of an automobile gearbox bench experiment in an equiangular sampling mode, and obtaining an original order spectrum signal y at the time t through fast Fourier transform t ＝[y 0 ,y 1 ,…,y N ]The corresponding order sequence is x = [0,x = δ ,2x δ ,…,N·x δ ]The order spectrum is a discrete order domain signal with (N + 1) points and the resolution is x δ (ii) a The value range of the time t is [ t ] start ,t end ]Mapping G (t) = { G, c } to obtain a gear G and a gear cycle number c at the moment t, namely the moment t is from the cycle of the c to the gear G; basic information of the gearbox structure is also needed, for example, a gear G belongs to G, and G is a set of gears G corresponding to all the time obtained by mapping G (t) = { G, c }; order of engagement of each gear g ＝[Order 1 ,Order 2 ,…,Order n ]Wherein Order i I belongs to 1, …, n represents the meshing order of the ith pair of gears under the gear g, and n meshing orders are total; in addition, a monitoring index alarm threshold value is also required to be set according to a historical monitoring result, and if no historical monitoring data exists or the monitoring data has large difference, the default alarm threshold value is 0.3;
1.2 De-mesh order component: removing each meshing order and higher harmonic components thereof under the current gear in the order spectrum, namely setting the value of the meshing order component corresponding to the gear participating in transmission in the order spectrum at each moment under the g gear to be 0;
1.3 Segment-wise sum of amplitudes: dividing the order spectrum into E order segments, the number of spectral lines in each order segment is
Each order range having a width of
When the time t is positioned below the gear g, the sum of the amplitude values of all the stages of the order spectrum segmentation is as follows:
1.4 Computing segment amplitude and growth rate: calculating the average value of the order spectrum segmented amplitude sum of the gearbox in the 1 st cycle of each gear as follows:
in the formula, N t The number of times to satisfy G (t) = { G,1 };
the vector is divided element by element into
In μ g,1 For the reference value calculated for the segment energy growth rate, the order spectrum segment amplitude and growth rate at time t are:
1.5 Segment amplitude and growth rate importance assessment: carrying out importance evaluation on the obtained segmented amplitude and the obtained growth rate by using an alpha-softmax (·) function, so that the order with larger growth rate is divided into larger weight values, and the order with small growth rate or even negative growth rate is divided into smaller weight values; the specific formula of the segment importance evaluation is as follows:
wherein alpha is 1 >0, a parameter for controlling ω sparsity;
1.6 Segment magnitude and growth sparsity measures: according to the energy increase of different stages in the order spectrum, screening out the first K stages with the largest change, and constructing a sectional amplitude value and an increase sparsity measurement index SCSI under a gear g to indicate the health state of the gearbox, wherein the formula is as follows:
wherein
sort () is the descending sort function, K is SCSI t,g The number of stages with a greater energy growth rate being used in the calculation, i.e. calculating SCSI t,g The first K stages after descending sorting are adopted; due to omega t The sum of all elements is 1, so the obtained SCSI t,g With a defined upper or lower bound, i.e. SCSI t,g ∈[0,1]；
2) The overall health state monitoring index of the gearbox is established as follows:
2.1 Extend the SCSI target at each gear to full time: SCSI in gear g t,g SCSI in extended gear G is calculated only when { t | G (t) = { G, c } } is calculated t,g When the indicator reaches other time, the SCSI is determined to be { t | G (t) ≠ { G, c } } time t,g Is taken as the closest SCSI value t,g I.e. SCSI t′,g T' = max ({ t | G (t) = { G, c } }), and for different gears G ∈ G, SCSI exists at any time t,g ；
2.2 Evaluate the importance of the SCSI indicators for different gears: for SCSI under each gear at the same time t,g Carrying out importance evaluation by using an alpha-softmax (·) function, so that the index data is higher and is divided into larger weight values; the specific formula for the evaluation is:
wherein alpha is 2 >0, is a control
A parameter of sparsity;
2.3 Weighted fusion of SCSI indicators for different gears: SCSI under different gears at the same time t,g Push button
The weight of the transmission is weighted and fused into an index WHI reflecting the overall health state of the transmission, and the specific formula is as follows:
3) Monitoring the state of the gearbox and positioning the fault: the obtained WHI is used for monitoring the overall health state of the gearbox, and the smaller the value of the WHI is, the closer the current state of the gearbox is to the normal state, namely the health state is better; the larger the value of WHI is, the larger the deviation of the current state of the gearbox from the normal state is, namely the health state is worse; setting a threshold value according to historical monitoring data, alarming when the WHI continuously exceeds the threshold value for multiple times, and preliminarily judging the meshing order of the fault gear on an order spectrum according to omega in the SCSI calculation process after alarming so as to judge the meshing pair of the fault gear; and finally, searching the side frequency band of the meshing pair of the fault gear by using the original order spectrum to determine the fault gear.
The invention has the beneficial effects that:
the invention provides a state monitoring method of an automobile gearbox for measuring self-adaptive energy increase sparsity, which comprises the steps of firstly constructing state monitoring indexes under each gear through self-adaptive energy increase; then carrying out weighted fusion to construct a monitoring index of the overall health state of the gearbox; finally, analyzing layer by using the obtained monitoring index, the obtained segmented amplitude and the obtained importance evaluation result of the growth rate and the original order spectrum signal to determine the position of the failed gear of the gearbox; the automatic state monitoring and auxiliary fault positioning of the automobile gearbox are realized, and the defects that the traditional method is discontinuous in monitoring index, inconvenient in setting of failure threshold values and the like are overcome.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 shows the monitoring result of the transmission state of the vehicle according to the embodiment of the invention.
Detailed Description
The invention is further elucidated with reference to the drawings and embodiments.
Referring to fig. 1, a method for monitoring the state of an automobile transmission by adaptive energy growth sparsity measurement comprises the following steps:
1) And (3) establishing state monitoring indexes under each gear: collecting monitoring data of the operation process of the automobile gearbox, and constructing a spectrum change sparsity measurement index SCSI for monitoring the gear state of the gearbox by acquiring a vibration signal order spectrum, removing meshing order components, calculating the amplitude sum in sections, calculating the section amplitude and growth rate and evaluating the importance of the section amplitude and the growth rate, wherein the specific steps are as follows:
1.1 Obtain the basic data: collecting vibration signals of the whole service life of an automobile gearbox bench experiment in an equiangular sampling mode, and obtaining an original order spectrum signal y at the time t through fast Fourier transform t ＝[y 0 ,y 1 ,…,y N ]The corresponding order sequence is x = [0,x = δ ,2x δ ,…,N·x δ ]The order spectrum is a discrete order domain signal with (N + 1) points and the resolution is x δ . The value range of the time t is [ t ] start ,t end ]Mapping G (t) = { G, c }, so that a gear G and a gear cycle number c at the moment t can be obtained, namely the moment t is from the cycle of the c to the gear G; basic information of the gearbox structure is also needed, such as a gear G belongs to G, and G is a set of gears G corresponding to all moments obtained by mapping G (t) = { G, c }; order of engagement of each gear g ＝[Order 1 ,Order 2 ,…,Order n ]Wherein Order i I belongs to 1, …, n represents the meshing order of the ith pair of gears under the gear g, and n meshing orders are total; in addition, the monitoring device also needs to be set according to historical monitoring resultsSetting a monitoring index alarm threshold, and if no historical monitoring data exists or the monitoring data has larger difference, taking the default alarm threshold as 0.3;
1.2 De-mesh order component: in the obtained order spectrum, the meshing order and the higher harmonics thereof are order components existing in normal operation and fault states of the gearbox, and the amplitude of the order components is generally far higher than that of other order components; in order to avoid that the meshing order component submerges each order component and highlights the order component change caused by gearbox faults, each meshing order and the higher harmonic component of the meshing order under the current gear are removed from the order spectrum, namely the value of the meshing order component corresponding to the gear participating in transmission in the order spectrum of each moment under the g gear is set to be 0;
1.3 Segment-wise sum of amplitudes: dividing the order spectrum into E order segments, the number of spectral lines in each order segment is
Each order range having a width of
And when the time t is positioned below the gear g, the sum of the amplitude values of all the sections of the order spectrum is as follows:
1.4 Computing segment amplitude and growth rate: calculating the average value of the order spectrum segmented amplitude sum of the gearbox in the 1 st cycle of each gear as follows:
in the formula, N t The number of times to satisfy G (t) = { G,1 };
the vector is divided element by element into
In μ g,1 Parameters calculated for fractional energy growth rateConsidering values, the amplitude and growth rate of the order spectrum segment at the time t are as follows:
1.5 Segment amplitude and growth rate importance assessment: carrying out importance evaluation on the obtained segmented amplitude and the obtained growth rate by using an alpha-softmax (·) function, so that the order segment with larger growth rate is divided into larger weight values, and the order segment with small growth rate and even negative growth rate is divided into smaller weight values; the specific formula of the segment importance evaluation is as follows:
wherein alpha is 1 >0, is a parameter for controlling ω sparsity;
1.6 Segment magnitude and growth sparsity measures: the segmented energy growth rate can become sparse gradually in the degradation process of the transmission gear, the energy growth rate of each stage tends to 0 in a normal state, and the energy growth of the stage of the fault related part is gradually developed to be remarkable, so that the first K stages with the largest change are screened out according to the energy growth of different stages in the stage spectrum, the segmented amplitude and the growth sparsity measurement index SCSI in the gear g are constructed to indicate the health state of the transmission gear, and the formula is as follows:
wherein
sort () is the descending sort function, K is SCSI t,g The number of stages with a large energy growth rate adopted in calculation; due to omega t The sum of all elements is 1, so the obtained SCSI t,g With a defined upper or lower bound, i.e. SCSI t,g ∈[0,1]；
2) Establishing overall health state monitoring indexes of the gearbox: in order to realize the overall state monitoring of the gearbox in different gears, expansion, evaluation and weighting fusion are carried out on the basis of SCSI, and a monitoring index WHI capable of reflecting the overall health state is provided, and the method specifically comprises the following steps:
2.1 Extend the SCSI target at each gear to full time: SCSI in gear g t,g SCSI in extended gear G is calculated only when { t | G (t) = { G, c } } and the calculation is performed in extended gear G t,g When the indicator reaches other time, the SCSI is determined to be { t | G (t) ≠ { G, c } } time t,g Is taken to be the closest SCSI t,g I.e. SCSI t′,g T' = max ({ t | G (t) = { G, c } }), and for different gears G ∈ G, SCSI exists at any time t,g ；
2.2 Evaluate the importance of the SCSI indicators for different gears: for SCSI under each gear at the same time t,g Carrying out importance evaluation by using an alpha-softmax (·) function, so that the index data is higher and is divided into larger weight values; the specific formula for the evaluation is:
wherein alpha is 2 >0, is a control
A parameter of sparsity;
2.3 ) weight fusion of SCSI indexes of different gears: SCSI under different gears at the same time t,g Push button
The weight of the transmission is weighted and fused into an index WHI reflecting the overall health state of the transmission, and the specific formula is as follows:
3) Monitoring the state of the gearbox and positioning faults: the obtained WHI can be used for monitoring the overall health state of the gearbox, and the smaller the value of the WHI is, the closer the current state of the gearbox is to the normal state, namely the health state is better; the larger the value of WHI is, the larger the deviation of the current state of the gearbox from the normal state is, namely the health state is worse; a threshold value can be set according to historical monitoring data, an alarm is given when the WHI continuously exceeds the threshold value for multiple times, and after the alarm is given, the meshing order of the failed gear can be preliminarily judged on an order spectrum according to omega in the SCSI calculation process, so that the meshing pair of the failed gear is judged; and finally, searching the side frequency band of the meshing pair of the fault gear by using the original order spectrum to determine the fault gear.
Example (b): based on the life-cycle experimental data of the automobile gearbox, the validity of the method is verified. The order spectrum used in this example has an order range of [0,256]The order spectrum data length is N +1=2048, and the resolution is 0.125 order; the gear cycle of the adopted gearbox experimental data is 3-4-5-6-7-8-1-2; selecting a parameter combination: the number of order spectral segments E =128, the number of order segments with a large rate of change K =16, α 1 ＝1，α 2 =10, set alarm threshold to 0.3; the method of the invention is used for monitoring the health state, and SCSI and WHI at each moment are calculated in sequence. As shown in fig. 2 (a), the WHI indicator alarms that the 4 th gear of the third cycle exceeds the preset threshold, and the change process of ω under the 4 th gear is further observed, as shown in fig. 2 (b), it can be seen that the order corresponding to the 4 th to 6 th orders changes significantly, and corresponds to the meshing order of the meshing gear pair of the intermediate shaft and the output shaft on the order spectrum of fig. 2 (c), so that the gear pair is judged to be faulty. And the box disassembly test shows that the intermediate shaft of the gearbox has broken teeth and pitting corrosion, thereby proving the effectiveness of the method.
The method is suitable for monitoring the health state of various gearboxes, and in practical application, an implementer can correspondingly adjust the parameters and the threshold value according to the actual situation, and then can monitor the health state of different gearboxes by using the method, thereby being beneficial to improving the real-time performance and the accuracy of the state monitoring of the gearboxes.
Claims (2)
1. A state monitoring method of an automobile gearbox for self-adaptive energy growth sparsity measurement is characterized by comprising the following steps: firstly, acquiring a vibration signal of a gearbox, and realizing self-adaptive energy increase sparsity evaluation by constructing state monitoring indexes under each gear; then carrying out weighted fusion to obtain a monitoring index capable of reflecting the overall health state of the gearbox; and then, fault positioning is carried out by utilizing the monitoring indexes and the evaluation result of the importance of the sectional amplitude and the growth rate in the calculation process.
2. A state monitoring method of an automobile gearbox for self-adaptive energy growth sparsity measurement is characterized by comprising the following steps:
1) And (3) establishing state monitoring indexes under each gear:
1.1 Obtain the basic data: collecting vibration signals of the whole service life of an automobile gearbox bench experiment in an equiangular sampling mode, and obtaining an original order spectrum signal y at the time t through fast Fourier transform t ＝[y 0 ,y 1 ,…,y N ]The corresponding order sequence is x = [0,x = δ ,2x δ ,…,N·x δ ]The order spectrum is a discrete order domain signal composed of (N + 1) points and having a resolution of x δ . The value range of the time t is [ t ] start ,t end ]Mapping G (t) = { G, c } to obtain a gear h and a gear cycle number c at the moment t, namely the moment t is from the cycle of the c to the gear G; basic information of a gearbox structure is also needed, and gears G belong to G, wherein G is a set of gears G corresponding to all moments obtained by mapping G (t) = { G, c }; order of engagement of each gear g ＝[Order 1 ,Order 2 ,…,Order n ]Wherein Order i I belongs to 1, …, n represents the meshing order of the ith pair of gears under the gear g, and n meshing orders are total; in addition, a monitoring index alarm threshold value is also required to be set according to a historical monitoring result, and if no historical monitoring data exists or the monitoring data has large difference, the default alarm threshold value is 0.3;
1.2 De-mesh order component: removing each meshing order and higher harmonic components thereof under the current gear in the order spectrum, namely setting the value of the meshing order component corresponding to the gear participating in transmission in the order spectrum at each moment under the g gear to be 0;
1.3 Segment-wise sum of amplitudes: dividing the order spectrum into E order segments, the number of spectral lines in each order segment is
Each order range having a width of
When the time t is positioned below the gear g, the sum of the amplitude values of all the stages of the order spectrum segmentation is as follows:
1.4 Computing segment amplitude and growth rate: calculating the average value of the order spectrum segmented amplitude sum of the gearbox in the 1 st cycle of each gear as follows:
in the formula, N t The number of times to satisfy G (t) = { G,1 };
the vector is divided element by element into
In μ g,1 For the reference value calculated for the segment energy growth rate, the order spectrum segment amplitude and growth rate at time t are:
1.5 Segment amplitude and growth rate importance assessment: carrying out importance evaluation on the obtained segmented amplitude and the obtained growth rate by using an alpha-softmax (·) function, so that the order segment with larger growth rate is divided into larger weight values, and the order segment with small growth rate and even negative growth rate is divided into smaller weight values; the specific formula of the segment importance evaluation is as follows:
wherein alpha is 1 >0, is a parameter for controlling ω sparsity;
1.6 Segment magnitude and growth sparsity measures: according to the energy increase of different stages in the order spectrum, screening out the first K stages with the largest change, and constructing a sectional amplitude value and an increase sparsity measurement index SCSI under a gear g to indicate the health state of the gearbox, wherein the formula is as follows:
wherein
sort (. Cndot.) is a descending sort function, K is SCSI t,g The number of stages with a greater energy growth rate being used in the calculation, i.e. calculating SCSI t,g The first K stages after descending sorting are adopted; due to omega t The sum of all elements is 1, so the obtained SCSI t,g With a defined upper or lower bound, i.e. SCSI t,g ∈[0,1]；
2) Establishing overall health state monitoring indexes of the gearbox:
2.1 Extend the SCSI target at each gear to full time: SCSI in gear g t,g SCSI in extended gear G is calculated only when { t | G (t) = { G, c } } is calculated t,g When the indicator reaches other time, the SCSI is determined to be { t | G (t) ≠ { G, c } } time t,g Is taken to be the closest SCSI t,g I.e. SCSI t′,g T' = max ({ t | G (t) = { G, c } }), and for different gears G ∈ G, SCSI exists at any time t,g ；
2.2 Evaluation ofImportance of different gear SCSI indicators: to SCSI under each gear at the same time t,g Using an alpha-softmax (·) function to evaluate the importance, so that the index data is higher in score and larger in weight value; the specific formula evaluated is:
wherein alpha is 2 >0, is a control
A parameter of sparsity;
2.3 Weighted fusion of SCSI indicators for different gears: SCSI under different gears at the same time t,g Push button
The weight of the transmission is weighted and fused into an index WHI reflecting the overall health state of the transmission, and the specific formula is as follows:
3) Monitoring the state of the gearbox and positioning the fault: the obtained WHI can be used for monitoring the overall health state of the gearbox, and the smaller the value of the WHI is, the closer the current state of the gearbox is to the normal state, namely the better the health state is; the larger the value of WHI is, the larger the deviation of the current state of the gearbox from the normal state is, namely the worse the health state is; setting a threshold value according to historical monitoring data, alarming when the WHI continuously exceeds the threshold value for multiple times, and preliminarily judging the meshing order of the fault gear on an order spectrum according to omega in the SCSI calculation process after alarming so as to judge the meshing pair of the fault gear; and finally, searching the side frequency band of the meshing pair of the fault gear by using the original order spectrum to determine the fault gear.
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| CN117268535B (en) * | 2023-11-22 | 2024-01-26 | 四川中测仪器科技有限公司 | Motor rotating shaft state monitoring method based on vibration data |
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