Online damage detection method and system based on acoustic emission index

Abstract

The invention discloses an online damage detection method and system based on an acoustic emission index, and belongs to the field of mechanical indexing table damage detection.The online damage detection method comprises the steps that acoustic emission waveforms of equipment to be detected are collected; extracting AE events based on the original waveform of the acoustic emission waveform, recording the starting point and the ending point of each AE event, and counting the duration of the AE events; calculating and counting an acoustic emission index of each AE event, and constructing a data set; carrying out absolute value and normalization processing on the constructed data set to obtain a new data set; calculating a damage evaluation index of each AE event in the new data set; and dividing the damage grade of the to-be-detected equipment according to the numerical value interval of the damage evaluation index. According to the invention, online dynamic detection can be carried out on the running equipment, a detailed damage grade evaluation report can be given, the time and the damage degree of the equipment damage can be monitored in time, and then fault prevention can be carried out in advance.

Description

Technical Field

[0001] The invention relates to the field of damage detection of mechanical indexing turntables, and in particular to an online damage detection method and system based on acoustic emission indicators. Background Art

[0002] The mechanical indexing turntable is a precise measuring tool, which consists of a turntable rotating on a horizontal plane and a indexing table rotating on a vertical plane. It can realize workpiece transfer, process switching and workpiece positioning. In the automated automobile production line, the mechanical indexing turntable is usually integrated with other systems and equipment, for example, it is integrated with a welding robot. The indexing turntable transfers the welding workpiece to the designated workstation, and then the welding robot performs welding operations on the transferred workpiece, which enables the production line to be highly automated, improve production efficiency and reduce labor costs. In actual applications, the mechanical indexing turntable is prone to damage under fatigue operation such as continuous operation at full load and uneven load distribution. However, since the indexing turntable is generally installed at the bottom of the entire production line, the installation space is relatively narrow and it is not easy to perform safe maintenance. Therefore, it is very important to monitor the health status of the mechanical indexing turntable online.

[0003] Traditional rotating machinery detection methods mainly include vibration detection and temperature detection. Among them, the vibration detection method is to analyze and process the vibration signal of the equipment to extract the normal and fault characteristics of the signal. However, vibration detection requires the sensor to be installed on the inspected component, which is suitable for detecting known fault types and is not sensitive enough to early material damage. The temperature-based detection method is to monitor the temperature changes of the equipment. Abnormal temperature changes suddenly occur when the equipment is running, which means that the equipment may have a fault. This detection method is only applicable to some fault types and has low sensitivity to certain mechanical problems. Both methods are insensitive to equipment failures and cannot effectively solve the problem of online detection. Therefore, a dynamic detection method that can perform online detection of mechanical indexing turntables is needed.

[0004] Acoustic emission (AE) provides an effective method for damage detection of indexing turntables with its high sensitivity. Among them, AE characteristic indicators are usually used to describe the damage information of the equipment, such as energy, amplitude and frequency. However, a single characteristic parameter cannot fully characterize the true response process of damage, especially the noise interference problem faced by the turntable in a complex working environment is prominent, making the identification of useful AE characteristic parameters more difficult. Summary of the invention

[0005] The technical solution of the present invention aims at the technical problem that the existing technical solutions are too single, and provides a solution that is significantly different from the existing technology. It mainly provides an online damage detection method and system based on acoustic emission indicators to solve the technical problem that the traditional rotating machinery detection method mentioned in the above background technology cannot effectively detect online.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] An online damage detection method based on acoustic emission indicators comprises the following steps:

[0008] S1. Collect the acoustic emission waveform of the equipment to be tested;

[0009] S2, extracting AE events based on the original waveform of the acoustic emission waveform, recording the starting point and end point of each AE event, and counting the duration of the AE event;

[0010] S3, calculate and count the acoustic emission indicators of each AE event and construct a data set;

[0011] S4, performing absolute value and normalization processing on the data set constructed in step S3 to obtain a new data set;

[0012] S5, calculating the damage evaluation index of each AE event in the new data set obtained in step S4;

[0013] S6. According to the numerical range of the damage evaluation index, the damage level of the equipment to be tested is divided.

[0014] Furthermore, in step S1, the acoustic emission sensor adopts a piezoelectric ceramic sensor.

[0015] Furthermore, in step S2, the AE event is identified based on a preset threshold voltage and duration.

[0016] Furthermore, in step S2, the data points in the original waveform that exceed the threshold voltage are first screened, and it is determined that the interval between two adjacent data points is greater than a preset duration; then the extracted AE events are analyzed, and when it is found that the waveform distribution is not ideal or non-standard AE events exist, the preset duration is set again for secondary screening.

[0017] Further, in step S3, the acoustic emission indicators include amplitude, energy, average power and skewness;

[0018] The data set is X i =[AM i ,E i ,P i ,SK i], i = 1, 2, …, m, m represents the total number of AE events extracted; AM represents amplitude, E represents energy, P represents average power, and SK represents skewness.

[0019] Further, in step S4, the new data set is

[0020]

[0021] Among them, x ij is the j-th dimension index of the i-th AE event; i = 1, 2, ..., m; j = 1, 2, 3, 4.

[0022] Furthermore, in step S5, the calculation formula of the damage evaluation index is:

[0023]

[0024] Furthermore, in step S6, the damage levels are divided into:

[0025] If the damage evaluation index is less than 0.3, it means that the equipment to be tested has slight damage;

[0026] The damage evaluation index is between 0.3 and 0.7, indicating that the equipment to be tested has moderate damage;

[0027] If the damage evaluation index is greater than 0.7, it means that the equipment to be tested has been seriously damaged.

[0028] The present invention also provides an online damage detection system based on acoustic emission indicators, which is used to implement the steps of the above-mentioned online damage detection method based on acoustic emission indicators. The online damage detection system based on acoustic emission indicators includes an acoustic emission acquisition module, an acoustic emission signal processing module, and a damage identification module;

[0029] The acoustic emission acquisition module includes an acoustic emission sensor, which is installed on the device to be detected to collect the AE signal released during its operation and convert the sound signal into an electrical signal;

[0030] The acoustic emission signal processing module includes a preamplifier and an acoustic emission instrument. The input end of the preamplifier is connected to the output end of the acoustic emission sensor through a signal cable; the acoustic emission instrument can filter and perform analog-to-digital conversion on the amplified signal, and generate a waveform file for transmission to the host;

[0031] The damage identification module includes a host, which is used to obtain the original acoustic emission waveform file and install an online damage detection method based on acoustic emission indicators for detection.

[0032] Furthermore, the damage identification module is provided with a signal light warning device, which is connected to the host and warns of different damage levels of the equipment to be detected through different states of the signal light.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] (1) The present invention calculates the time domain characteristic parameters of the AE event and performs weighted combination on each characteristic parameter to propose a damage evaluation index SCORE, which can characterize the actual damage behavior of the equipment to be detected during operation and obtain specific damage information. Compared with vibration detection and temperature detection methods, the present invention can perform online dynamic detection of the running equipment, reflect the release behavior of strain energy in the damage process of mechanical equipment through AE indicators, and count the index values ​​of AE events at different times, so as to provide a detailed damage level evaluation report, timely monitor the time and degree of equipment damage, and then prevent faults in advance.

[0035] (2) The detection system provided by the present invention is easy to deploy and has a wide range of applications. The acoustic emission sensor is easy to install and has high detection sensitivity. It can be installed in different detection locations according to different needs. It is suitable for a variety of occasions: new machine detection, operation detection, and shutdown detection. It can detect early damage with high accuracy.

[0036] (3) The present invention constructs a data set through multidimensional acoustic emission indicators such as amplitude, energy, average power and skewness to fully characterize the true response process of damage; and performs absolute value and normalization processing on the data set to solve the problems of inconsistent units of different acoustic emission indicators and negative numbers of SK indicators, so as to facilitate the subsequent calculation of damage evaluation indicators.

[0037] (4) The present invention collects the AE signals released during the operation of the equipment to be detected through an acoustic emission sensor and converts the sound signals into electrical signals; then the signal-to-noise ratio is improved through a preamplifier, and the amplified signal is filtered and analog-to-digital converted through an acoustic emission instrument to generate a waveform file, thereby achieving efficient identification of useful AE characteristic parameters, thereby solving the noise interference problem faced by the equipment to be detected in a complex working environment.

[0038] The present invention will be explained in detail below with reference to the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The overall flow chart of the online damage detection method based on acoustic emission indicators provided by the present invention;

[0040] Figure 2 A structural block diagram of an online damage detection system based on acoustic emission indicators provided by the present invention;

[0041] Figure 3 This is the AE event extraction result exemplified in the present invention. DETAILED DESCRIPTION

[0042] To facilitate understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings, but the present invention can be implemented in different forms and is not limited to the embodiments described in the text. On the contrary, these embodiments are provided to make the content disclosed in the present invention more thorough and comprehensive.

[0043] It should be noted that when an element is referred to as being "fixed on" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used in this article are for illustrative purposes only.

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly connected by technicians in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in this article includes any and all combinations of one or more related listed items.

[0045] Please refer to the attached Figure 1 , an online damage detection method based on acoustic emission indicators, comprising the following steps:

[0046] Step (1), collecting the acoustic emission waveform (electrical signal) of the mechanical indexing turntable to be tested.

[0047] At least one acoustic emission sensor is installed on the mechanical indexing turntable to be detected to collect AE signals released when the mechanical indexing turntable to be detected is in operation, and convert the sound signals into electrical signals.

[0048] Step (2), set the threshold voltage (h) and duration (T), and extract AE events based on the original waveform. The AE event extraction algorithm based on the original waveform is as follows: (1) Set the threshold voltage h and duration T of the AE event in the data analysis software; (2) Find the position of the data point in the original waveform that exceeds the threshold voltage, and determine the position of the data point where the interval between two adjacent impacts is greater than the duration T; (3) If all the intervals of the found data points are less than the set duration, the data points that exceed the threshold voltage and are within the preset duration are collectively regarded as one AE event; (4) Analyze the extracted AE events. If there are AE events with unsatisfactory and non-standard waveform distribution (for example: only the first half or the second half, or even AE events with noise distribution), the preset duration can be set again (the value is set according to the extracted standard AE event characteristics) for secondary screening. The final AE event extraction results are as follows: Figure 3shown.

[0049] Among them, the threshold voltage refers to a pre-set voltage value, and only when the amplitude of the AE signal exceeds this voltage value can it be detected; the duration refers to the duration of the AE event; the preset duration refers to the duration of the ideal AE event for secondary screening; the impact refers to any signal that exceeds the threshold voltage and causes the system channel to collect data.

[0050] Step (3), calculate and count the acoustic emission indicators of each AE event: amplitude (AM), energy (E), average power (P) and skewness (SK).

[0051] Among them, suppose a set of discrete data obtained by sampling a certain signal x(t) is x1, x2, ..., x i ,…x N , N is the signal length, is the mean, σ x is the standard deviation, then:

[0052] AM=max{|x i |};

[0053]

[0054] in,

[0055] Furthermore, for the i-th AE event, a dataset X consisting of these four indicators is constructed. i =[AM i ,E i ,P i ,SK i ], i = 1, 2, ..., m (m represents the total number of AE events extracted).

[0056] Step (4): Since the units of the four selected acoustic emission indicators are inconsistent and the SK indicator has negative numbers, the data set Xi is processed by absolute value and normalization to obtain a new data set:

[0057]

[0058] Among them, x ij is the j-th dimension index of the i-th AE event; i = 1, 2, ..., m; j = 1, 2, 3, 4.

[0059] Step (5), calculate the data set The injury evaluation index (SCORE) of the i-th AE event in ;

[0060]

[0061] Step (6), according to the numerical range of the damage evaluation index Score, divide the damage levels of the mechanical indexing turntable to be tested:

[0062] If SCORE < 0.3, it indicates that the mechanical indexing turntable to be tested has suffered minor damage, marked as I;

[0063] If 0.3 < SCORE < 0.7, it indicates that the mechanical indexing turntable to be tested has suffered moderate damage, marked as II;

[0064] If SCORE > 0.7, it indicates that the mechanical indexing turntable to be tested has suffered severe damage, marked as III.

[0065] An on-line detection system based on acoustic emission technology is used to implement the steps of the above on-line damage detection method based on acoustic emission indicators. Please refer to the appendix Figure 2 wherein, the system includes:

[0066] An acoustic emission acquisition module, which installs an acoustic emission sensor (such as a piezoelectric ceramic sensor) on the mechanical indexing turntable to be tested, is used to collect the AE signals released during its operation, and convert the sound signals into electrical signals;

[0067] An acoustic emission signal processing module, including a preamplifier and an acoustic emission instrument. The output end of the acoustic emission sensor is connected to the input end of the amplifier through a signal cable. Because after the acoustic emission signal is converted into an electrical signal, its output is often small. After such weak signals are transmitted over a long distance, it may be impossible to distinguish useful signals from noise signals. Therefore, a preamplifier is set to improve the signal-to-noise ratio. The acoustic emission instrument can filter, perform analog-to-digital conversion on the amplified signal, and generate a waveform file for transmission to the host;

[0068] A damage identification module, which transmits the acquired original acoustic emission waveform file to the data analysis software MATLAB on the host, and then performs detection according to the steps (2)-(6) of the above on-line damage detection method based on acoustic emission indicators. A signal lamp warning device can also be set and connected to the host; when the mechanical indexing turntable suffers from grade I damage, the signal lamp does not light; when the mechanical indexing turntable suffers from grade II damage, the signal lamp flashes; when the mechanical indexing turntable suffers from grade III damage, the signal lamp is always on.

[0069] Example 1: Apply the technical method proposed by the present invention to the mechanical indexing turntable of the engine compartment line of a certain automobile company, model EDX1370. The turntable to be tested consists of a turntable plane, bearings, a housing, a driving cam, a driven wheel, a motor, etc.; there are three working states in one working cycle: rotating 180° counterclockwise, stopping and waiting, rotating 180° clockwise; mainly realizing the transfer of workpieces on the automatic production line, transmitting the welded workpieces to the designated position, and then the welding robot above the turntable completes the welding operation.

[0070] In this embodiment, the acoustic emission sensor uses a RS-2A piezoelectric ceramic sensor with a resonant frequency of 150KHz; the gain of the preamplifier is adjusted to 40dB; the acoustic emission instrument is a Beijing Ruandao DS5-16C acoustic emission instrument, and the sampling rate is set to 2.5MHz. The acoustic emission sensor is installed on the main shaft of the turntable.

[0071] The AE signals were collected during the 180° reverse and 180° forward rotation of the mechanical indexing turntable. The threshold voltage h = 250mV and the duration T = 2000uS were set in the MATLAB analysis software to extract the AE events. Five AE events were extracted in the reverse working state, and 11 AE events were extracted in the forward working state. The amplitude, energy, average power and skewness of the AE events in the two working states were calculated respectively, and finally the corresponding evaluation index SCORE was obtained. The damage rating standard is shown in Table 1. The acoustic emission parameters and damage ratings of the equipment in the two working states are shown in Table 2.

[0072] Table 1 Damage rating standard

[0073]

[0074] Table 2 Acoustic emission parameters and damage rating results

[0075]

[0076] Note: Parameter Standardization means parameter standardization.

[0077] Example 2: The technical method proposed in the present invention is applied to a mechanical indexing turntable of a rear floor line of a certain automobile company, model EDX1370. The experimental acquisition equipment is the same as that of Example 1, and the acoustic emission sensor is installed at the main axis of the turntable.

[0078] The AE signals of the indexing turntable during the 180° reverse and 180° forward rotation were collected respectively. The threshold voltage h = 250mV and the duration T = 2000uS were set in MATLAB to extract the AE events; 5 AE events were extracted in the reverse working state and 6 AE events were extracted in the forward working state; the amplitude, energy, average power and skewness of the AE events in the two working states were calculated, and finally the corresponding evaluation index SCORE was obtained. The acoustic emission parameters and damage ratings of the equipment in the two working states are shown in Table 3.

[0079] Table 3 Acoustic emission parameters and damage rating results

[0080]

[0081] Note: Parameter Standardization means parameter standardization.

[0082] Compared with the traditional technology, the present invention proposes the damage evaluation index SCORE by calculating the time domain characteristic parameters of the AE event and weighting the characteristic parameters, which can characterize the real damage behavior of the indexing turntable during operation and obtain specific damage information.

[0083] The present invention obtains the evaluation index SCORE by weighting the AE acoustic emission index, establishes a damage database for the indexing turntable, and objectively evaluates the damage level. Compared with the original method, the present invention can meet the needs of early dynamic non-destructive testing of the indexing turntable. Combined with the time when the AE event occurs, the time when the turntable damage occurs and the degree of damage can be identified, and an alarm reminder can be issued through a warning light, providing a diagnostic basis for the health detection of the indexing turntable.

[0084] The above is an exemplary description of the present invention in combination with the accompanying drawings. It is obvious that the specific implementation of the present invention is not limited to the above-mentioned method. As long as such non-substantial improvements are made by adopting the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. An online damage detection method based on acoustic emission indicators, characterized in that: The steps include: S1. Collect the acoustic emission waveform of the equipment to be tested; S2, extracting AE events based on the original waveform of the acoustic emission waveform, recording the starting point and end point of each AE event, and counting the duration of the AE event; S3, calculate and count the acoustic emission indicators of each AE event and construct a data set; S4, performing absolute value and normalization processing on the data set constructed in step S3 to obtain a new data set; S5, calculating the damage evaluation index of each AE event in the new data set obtained in step S4; S6. According to the numerical range of the damage evaluation index, the damage level of the equipment to be tested is divided.

2. The online damage detection method based on acoustic emission index according to claim 1 is characterized in that: In step S1, the acoustic emission sensor adopts a piezoelectric ceramic sensor.

3. The online damage detection method based on acoustic emission index according to claim 1 is characterized in that: In step S2, an AE event is identified based on a preset threshold voltage and duration.

4. The online damage detection method based on acoustic emission index according to claim 3 is characterized in that: In step S2, firstly, the data points exceeding the threshold voltage in the original waveform are screened, and it is determined that the interval between two adjacent data points is greater than the preset duration; The extracted AE events are then analyzed, and when AE events with unsatisfactory waveform distribution and non-standard waveform distribution are found, the preset duration is set again for secondary screening.

5. The online damage detection method based on acoustic emission index according to claim 1 is characterized in that: In step S3, the acoustic emission indicators include amplitude, energy, average power and skewness; The data set is X i =[AM i ,E i ,P i ,SK i ], i = 1, 2, …, m, m represents the total number of AE events extracted; AM represents amplitude, E represents energy, P represents average power, and SK represents skewness.

6. The online damage detection method based on acoustic emission index according to claim 5 is characterized in that: In step S4, the new data set is Among them, x ij is the j-th dimension index of the i-th AE event; i = 1, 2, ..., m; j = 1, 2, 3, 4.

7. The online damage detection method based on acoustic emission index according to claim 6 is characterized in that: In step S5, the calculation formula of the damage evaluation index is:

8. The online damage detection method based on acoustic emission index according to claim 1 is characterized in that: In step S6, the damage levels are divided into: If the damage evaluation index is less than 0.3, it means that the equipment to be tested has slight damage; The damage evaluation index is between 0.3 and 0.7, indicating that the equipment to be tested has moderate damage; If the damage evaluation index is greater than 0.7, it means that the equipment to be tested has been seriously damaged.

9. An online damage detection system based on acoustic emission indicators, characterized in that: Steps for implementing an online damage detection method based on acoustic emission indicators as described in any one of claims 1 to 8, wherein the online damage detection system based on acoustic emission indicators comprises an acoustic emission acquisition module, an acoustic emission signal processing module, and a damage identification module; The acoustic emission acquisition module includes an acoustic emission sensor, which is installed on the device to be detected to collect the AE signal released during its operation and convert the sound signal into an electrical signal; The acoustic emission signal processing module includes a preamplifier and an acoustic emission instrument. The input end of the preamplifier is connected to the output end of the acoustic emission sensor through a signal cable; the acoustic emission instrument can filter and perform analog-to-digital conversion on the amplified signal, and generate a waveform file for transmission to the host; The damage identification module includes a host, which is used to obtain the original acoustic emission waveform file and install an online damage detection method based on acoustic emission indicators for detection.

10. The online damage detection system based on acoustic emission index according to claim 9, characterized in that: The damage identification module is provided with a signal light warning device, which is connected to the host and warns of different damage levels of the equipment to be detected through different states of the signal light.

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