CN118050588B - Electric fault detection method for ultrasonic cutting knife - Google Patents
Electric fault detection method for ultrasonic cutting knife Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention provides an electric fault detection method for an ultrasonic cutting knife, which relates to the technical field of electric fault detection, and is used for collecting voltage signal and current signal data of each branch of the ultrasonic cutting knife and preprocessing the voltage signal and current signal data; obtaining the voltage signals and the current signals of all the branches after pretreatment, and calculating the voltage signals of the first branch s passing through the mean value point and the last branch e passing through the mean value point and the corresponding current signals thereof; selecting current signals and voltage signals of N branches between a tail branch e and a head branch s for signal processing to obtain discrete current signals and voltage signals, and extracting electric signal characteristics; detecting the branch intervals where the electric faults are located by using a Gaussian mixture model detection algorithm on the electric signal characteristics of the N branches; by inputting the electric detection sub-signals with equal energy, the position of the branch circuit with electric faults is determined, so that whether faults occur can be distinguished.
Description
Technical Field
The invention relates to the technical field of electric fault detection, in particular to an electric fault detection method for an ultrasonic cutting knife.
Background
The ultrasonic cutting knife is applied to laparoscopic minimally invasive surgery or open surgery in general surgery, ultrasonic electric energy is converted into mechanical energy by utilizing the electric or magnetostriction effect, the knife head is pushed to work on human tissues by the amplification and coupling effect of the amplitude transformer, and the soft tissue cutting and hemostasis functions are realized by the cavitation effect and the thermal effect.
In the use process of the ultrasonic knife, the energy converter and the knife head can be damaged in different degrees and types due to certain reasons, so that the energy converter and the knife head can not work normally and the operation efficiency is influenced, so that the system needs to detect the states of the energy converter and the knife head in real time, accurately report the fault type or prompt information of the energy converter and the knife head, indicate the fault problem to medical staff or after-sales staff, and improve the problem solving efficiency.
In the use process of the ultrasonic knife system, due to the reasons of vibration fatigue and the like caused by external force, temperature, long service time, the transducer or the ultrasonic knife in the ultrasonic knife system is easy to fail or even damage, and the ultrasonic knife system cannot work normally. Therefore, it is important and necessary to realize accurate and rapid detection of failure problems so as to prompt the user to perform component replacement and maintenance in time.
Meanwhile, as the faults of the transducer and the cutter head are more in types, the existing technical scheme can only report whether faults exist in a general way, but specific fault types cannot be pointed out in detail, and the existing fault detection scheme does not provide a solution to the scene of 'failure degree is not reached, but prompt' is needed. The fault alarm of the ultrasonic cutting hemostatic cutter system is frequently generated, and the false alarm condition exists.
Disclosure of Invention
In order to solve the technical problems, the invention provides an electric fault detection method for an ultrasonic cutting knife, which comprises the following steps:
s1, collecting voltage signal and current signal data of each branch of an ultrasonic cutting knife, and preprocessing the voltage signal and current signal data;
S2, obtaining voltage signals and current signals of all branches of the ultrasonic cutting knife after pretreatment, and calculating a first branch S of the first voltage signal passing through a mean value point, a last branch e of the last voltage signal passing through the mean value point and corresponding current signals;
S3, selecting current signals and voltage signals of N branches between the first branch S and the last branch e to perform signal discrete processing to obtain discrete current signals and voltage signals, and extracting electric signal characteristics of the N branches;
S4, detecting the branch intervals where the electric faults are located by using a Gaussian mixture model detection algorithm on the electric signal characteristics of the N branches;
s5, determining the position of the branch circuit with the electric fault by inputting electric detection sub-signals with equal energy to each branch circuit of the electric fault branch circuit section detected in the step S4.
Further, in step S3, N branch current signals between the first branch S and the last branch e are selected for signal processing, so as to obtain a discrete current signal i (N) and a discrete voltage signal p (N) as follows:
i(N)=i0+cA(M-N)+rf(N)；
p(N)=p0+bB(M-N)+RF(N)；
Wherein i 0 is the current matrix of N branches when the first branch passes through the mean point; a (M-N) is the current matrix of the rest M-N branches when the first branch passes through the mean point, f (N) is the current matrix of the N branches in the running state, and c and r are matrix coefficients. p 0 is the voltage matrix of N branches when the first branch passes through the mean point; b (M-N) is the voltage matrix of the rest M-N branches when the first branch passes through the mean point, F (N) is the voltage matrix of the N branches in the running state, and B and R are matrix coefficients.
Further, extracting the electrical signal characteristics in the discrete current signals and the discrete voltage signals, and expressing the electrical signal characteristics by using x (N):
。
Further, in step S4, the gaussian mixture model detection algorithm is applied to the electrical signal characteristics x (N) of different branches to determine the electrical fault branch, and the electrical signal characteristics of N branches are set as follows: x (1), … x (j), … x (N), distribution function The method comprises the following steps:
let j take values from [1 … … N ] in turn, calculate each value of j corresponds to When/>Greater than threshold/>And if so, the branch corresponding to the value of j and the first branch interval have electric faults.
Further, in step S5, the power divider divides one path of electric detection signal energy into multiple paths of electric detection sub-signals with equal energy, and the multiple paths of electric detection sub-signals with equal energy are respectively connected to the input end of each branch of the electric fault branch section detected in step S4;
Setting the amplitude value of the electric detection sub-signals input by the input end of each branch circuit to be in a multiple relationship in sequence, and judging that at least one branch circuit between two branch circuits has faults if the amplitude value relationship of the output signals between the two branch circuits received by the signal receiver is different from the multiple relationship of the amplitude values of the input electric detection sub-signals.
Further, in step S1, voltage signal and current signal data of each branch of the ultrasonic cutting blade are collected, the signal data are screened, the signal data exceeding the test limit value are set as abnormal data, and the abnormal data are deleted.
Further, fitting the screened signal data by using a least square method to obtain a polynomial curve, mapping the fitted polynomial curve and a standard data curve, and adjusting the current test limit value according to the mapping result.
Compared with the prior art, the invention has the following beneficial technical effects:
Collecting voltage signal and current signal data of each branch of the ultrasonic cutting knife, and preprocessing the voltage signal and current signal data; obtaining the voltage signals and the current signals of all the branches after pretreatment, and calculating the voltage signals of the branch e passing through the mean value point at the first time and the branch s passing through the mean value point at the last time and the corresponding current signals thereof; selecting current signals and voltage signals of N branches between the branch e and the branch s for signal processing to obtain discrete current signals and voltage signals, and extracting electric signal characteristics; detecting the branch intervals where the electric faults are located by using a Gaussian mixture model detection algorithm on the electric signal characteristics of the N branches; the position of the branch circuit with the electric fault is determined by inputting the electric signals distributed according to the power proportion, so that whether the fault is generated or not can be distinguished, the problem that the fault alarm of the ultrasonic cutting knife system frequently occurs in the prior art is solved, the situation of false alarm exists, and the safety is higher.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic flow chart of an electrical fault detection method for an ultrasonic cutting blade according to the present invention;
FIG. 2 is a schematic diagram of a 3-term polynomial curve according to the present invention;
fig. 3 is a schematic diagram of the electrical signal characteristics of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the drawings of the specific embodiments of the present invention, in order to better and more clearly describe the working principle of each element in the system, the connection relationship of each part in the device is represented, but only the relative positional relationship between each element is clearly distinguished, and the limitations on the signal transmission direction, connection sequence and the structure size, dimension and shape of each part in the element or structure cannot be constructed.
As shown in fig. 1, a flow chart of an electrical fault detection method for an ultrasonic cutting blade according to the present invention includes the following steps:
s1, collecting voltage signal and current signal data of each branch of the ultrasonic cutting knife, and preprocessing the voltage signal and current signal data.
The ultrasonic cutting knife equipment is input with the commercial electric signal, so that the equipment can be activated to enter various running states. The method comprises the steps of collecting voltage signal and current signal data of each branch of an ultrasonic cutting tool equipment circuit, screening the signal data to reduce adverse effects among the data, setting the signal data exceeding a test limit value as abnormal data, deleting the abnormal data, and achieving preliminary screening of the signal data.
In a preferred embodiment, a polynomial curve is obtained by fitting the filtered signal data by using a least square method, and a schematic diagram of the polynomial curve with 3 terms is shown in fig. 2. Mapping the fitted polynomial curve and the standard data curve, so as to realize adjustment of the test limit value of the data preprocessing of the current equipment.
S2, obtaining voltage signals and current signals of all branches of the ultrasonic cutting knife after pretreatment, and calculating the branches of the first voltage signal and the last voltage signal passing through the mean value point and the corresponding current signals.
After the working state of the ultrasonic cutting knife is stable, the voltage signals and the current signals of all branches of the ultrasonic cutting knife after pretreatment are obtained, the lowest point V low and the highest point V high of the voltage signals of all branches are found, and the average point V 0＝(Vlow+Vhigh)/2 of the voltage signals is obtained.
The voltage signal is judged according to the time sequence as follows: when the voltage of the d branch and the d-1 branchSatisfy/>And/>If the following relationships are satisfied at the same time:
The branch that first passes through the mean point is denoted as the first branch s, the voltage is denoted as V s, and the corresponding current signal is denoted as I s, where the size of m is the offset of the dithering process, preferably m=10, and m is defined as the total number of branches.
When the last branch passing through the mean value point is found, the branch is marked as a last branch e, the voltage is marked as V e, and the corresponding current signal is marked as I e.
S3, selecting N branch current signals and voltage signals between the first branch S and the last branch e to perform signal discrete processing to obtain discrete current signals and voltage signals, and extracting electrical signal characteristics in the discrete current signals and the voltage signals.
N branch current signals between the first branch s and the last branch e are selected for signal processing, and a discrete current signal i (N) and a discrete voltage signal p (N) are obtained as follows:
i(N)=i0+cA(M-N)+rf(N)；
p(N)=p0+bB(M-N)+RF(N)；
Wherein i 0 is the current matrix of N branches when the first branch passes through the mean point; a (M-N) is the current matrix of the rest M-N branches when the first branch passes through the mean point, f (N) is the current matrix of the N branches in the running state, and c and r are matrix coefficients. p 0 is the voltage matrix of N branches when the first branch passes through the mean point; b (M-N) is the voltage matrix of the rest M-N branches when the first branch passes through the mean point, F (N) is the voltage matrix of the N branches in the running state, and B and R are matrix coefficients.
Extracting electrical signal characteristics in discrete current signals and voltage signals, and expressing the electrical signal characteristics by using x (N):
s4, detecting the branch intervals where the electrical faults are located by using a Gaussian mixture model detection algorithm on the electrical signal characteristics of the N branches.
In general, an electrical fault signal of the ultrasonic cutting blade device can be interfered by drift interference of a tool bit baseline of the ultrasonic cutting blade, interference of electrode contact noise and electrode polarization noise, interference of noise of an amplifying circuit in the ultrasonic cutting blade device and the like. The ultrasonic cutting knife electric fault signal acquisition based on the Gaussian mixture model has practicability and generalization capability by carrying out non-parametric calculation on ultrasonic cutting knife electric fault signal sample data.
In the electric fault signal acquisition method based on the Gaussian mixture model, an electrocardiograph monitor electric fault mode library is required to be established according to various different ultrasonic cutter equipment electric fault types, different ultrasonic cutter equipment electric fault degrees, models under different ultrasonic cutter equipment working conditions and the like for identifying the ultrasonic cutter equipment electric fault signals.
The ultrasonic cutting knife equipment fault signal acquisition based on the Gaussian mixture model is to quantize current and voltage change signals and fit the distribution of electrocardiosignal data change through a distribution function.
In order to clearly show the dependence relationship between the Gaussian mixture model distribution and the corresponding parameters, the distribution condition of the Gaussian mixture model is constructed by using a distribution function.
Judging the electric fault branch by using a Gaussian mixture model detection algorithm for the electric signal characteristics x (N) of different branches; assume that the N branch electrical signals are characterized by: x (1), … x (j), … x (N), distribution functionThe method comprises the following steps:
Threshold value selected according to actual condition Let j take values from [1 … … N ] in turn, calculate each value of j corresponds toWhen/>Greater than threshold/>And if so, the branch corresponding to the value of j and the first branch interval have electric faults.
Preferably, for the above-mentioned electrical fault detection condition verification, two verification methods are sampled:
And (3) extracting electrical signal characteristics of discrete current signals and voltage signals of different ultrasonic cutting knife devices, and checking whether the detection process accords with a Gaussian mixture model detection algorithm.
And measuring a plurality of fault states of the same ultrasonic cutting knife equipment, checking the change association degree of the electric signal characteristics in the discrete current signals and the voltage signals and the fault event, and comparing the change association degree with the time of manual detection. Selecting ultrasonic cutting knife equipment to check whether the current and state data trend are met; the same fault rule is used among different types of ultrasonic cutting knife equipment for measuring the same type of ultrasonic cutting knife equipment, and whether the identification rate of more than 90% can be achieved through adjustment and optimization. As shown in fig. 3, the electrical signal is characterized by time on the abscissa and signal amplitude value on the ordinate.
S5, determining the position of the branch circuit with the electric fault by inputting electric detection sub-signals with equal energy to each branch circuit of the electric fault branch circuit section detected in the step S4.
The electric detection sub-signals distributed according to the energy proportion are input through a power distributor, and the power distributor is a device for dividing one path of input signal energy into two paths or multiple paths of output equal or unequal energy. In this embodiment, the power divider divides one path of electric detection signal energy into multiple paths of electric detection sub-signals (the electric detection signals may be current detection signals, voltage detection signals, etc.) with equal energy, and the multiple paths of electric detection sub-signals with equal energy are respectively connected to the input end of each branch of the electric fault branch section detected in step S4.
Setting the amplitude value of the electric detection sub-signals input by the input end of each branch circuit to be in a multiple relationship in sequence, and judging that at least one branch circuit between two branch circuits has faults if the amplitude value relationship of the output signals between the two branch circuits received by the signal receiver is different from the multiple relationship of the amplitude values of the input electric detection sub-signals.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (3)
1. An electrical fault detection method for an ultrasonic cutting knife is characterized by comprising the following steps:
s1, collecting voltage signal and current signal data of each branch of an ultrasonic cutting knife, and preprocessing the voltage signal and current signal data;
s2, obtaining voltage signals and current signals of all branches of the ultrasonic cutting knife after pretreatment, finding out the lowest point V low and the highest point V high of the voltage signals of all branches, and obtaining the average point V 0＝(Vlow+Vhigh)/2 of the voltage signals; calculating a first branch s of the first voltage signal passing through the mean value point and a last branch e of the last voltage signal passing through the mean value point and corresponding current signals;
S3, selecting current signals and voltage signals of N branches between the first branch S and the last branch e to perform signal discrete processing to obtain discrete current signals and voltage signals, and extracting electric signal characteristics of the N branches; n branch current signals between the first branch s and the last branch e are selected for signal processing, and a discrete current signal i (N) and a discrete voltage signal p (N) are obtained as follows:
i(N)=i0+cA(M-N)+rf(N)；
p(N)=p0+bB(M-N)+RF(N)；
Wherein i 0 is the current matrix of N branches when the first branch passes through the mean point; a (M-N) is a current matrix of the rest M-N branches when the first branch passes through the mean point, f (N) is a current matrix of the N branches in an operation state, c and r are matrix coefficients, and p 0 is a voltage matrix of the N branches when the first branch passes through the mean point; b (M-N) is the voltage matrix of the rest M-N branches when the first branch passes through the mean value point, F (N) is the voltage matrix of the N branches in the running state, and B and R are matrix coefficients;
Extracting electric signal characteristics in the discrete current signals and the discrete voltage signals, and expressing the electric signal characteristics by x (N):
；
S4, detecting the branch intervals where the electric faults are located by using a Gaussian mixture model detection algorithm on the electric signal characteristics of the N branches; and judging the electric fault branch by applying a Gaussian mixture model detection algorithm to the electric signal characteristics x (N) of different branches, wherein the electric signal characteristics of the N branches are as follows: x (1), … x (j), … x (N), distribution function The method comprises the following steps:
；
let j take values from [1 … … N ] in turn, calculate each value of j corresponds to When/>Greater than threshold/>If the value of j is equal to the value of j, the corresponding branch and the first branch section have electric faults;
S5, determining the position of a branch circuit with electric faults by inputting electric detection sub-signals with equal energy to each branch circuit of the electric fault branch circuit section detected in the step S4; the power divider divides the energy of one path of electric detection signal into a plurality of paths of electric detection sub-signals with equal energy, and the electric detection sub-signals with equal energy are respectively connected to the input end of each branch of the electric fault branch section detected in the step S4;
Setting the amplitude value of the electric detection sub-signals input by the input end of each branch circuit to be in a multiple relationship in sequence, and judging that at least one branch circuit between two branch circuits has faults if the amplitude value relationship of the output signals between the two branch circuits received by the signal receiver is different from the multiple relationship of the amplitude values of the input electric detection sub-signals.
2. The electrical fault detection method according to claim 1, wherein in step S1, voltage signal and current signal data of each branch of the ultrasonic cutting blade are collected, the signal data are screened, the signal data exceeding the test limit are set as abnormal data, and the abnormal data are deleted.
3. The electrical fault detection method according to claim 2, wherein the filtered signal data is fitted by using a least square method to obtain a polynomial curve, the fitted polynomial curve and the standard data curve are mapped, and the current test limit is adjusted according to the mapping result.
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| CN105676088A (en) * | 2016-02-15 | 2016-06-15 | 珠海派诺科技股份有限公司 | Device and method for testing fault arc detection apparatus |
| CN110568254A (en) * | 2019-09-27 | 2019-12-13 | 宁夏凯晨电气集团有限公司 | Method for accurately detecting attenuated direct-current component parameters in fault current |
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| CN115308534B (en) * | 2022-09-16 | 2023-07-21 | 西南石油大学 | T-junction transmission line fault branch diagnosis method |
| CN116430139B (en) * | 2023-03-29 | 2023-12-05 | 河南省驼人医疗科技有限公司 | Ultrasonic cutting hemostatic cutter fault detection system and detection method thereof |
| CN220481903U (en) * | 2023-08-03 | 2024-02-13 | 东莞市西喆电子有限公司 | Portable ultrasonic cutting tool |
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| CN105676088A (en) * | 2016-02-15 | 2016-06-15 | 珠海派诺科技股份有限公司 | Device and method for testing fault arc detection apparatus |
| CN110568254A (en) * | 2019-09-27 | 2019-12-13 | 宁夏凯晨电气集团有限公司 | Method for accurately detecting attenuated direct-current component parameters in fault current |
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