Predictive maintenance and early warning method and device based on commercial vehicle battery swap station motor

Abstract

The invention discloses a predictive maintenance and early warning method and device based on a commercial vehicle battery swap station motor, and belongs to the technical field of new energy battery swap stations, and the method comprises a standard reference data set M1 obtained through detection of a normal motor in a normal working state and an actual data set M2 obtained through detection of the motor in the same working environment and path. And setting an over-limit interval, and comparing the data set M1 with the data set M2 to monitor the performance of the motor. According to the method, the running state of the motor is monitored by setting the over-limit interval, comparing the collected torque set M2 of the motor with the torque set M1, and setting the threshold value of the over-limit torque quantity in the over-limit interval or comparing the similarity between the torque set M1 and the torque set M2, so that the early warning of the motor performance is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy battery swap stations, and in particular, relates to a predictive maintenance and early warning method and device based on motors of commercial vehicle battery swap stations. Background Art

[0002] In the new energy battery swap industry, the core component of a commercial vehicle battery swap station during operation is the battery swap robot. Once the motor shaft of the robot is damaged, it will directly affect the normal use of the battery swap station, and will even affect the safety performance of the mechanical structure and the safe use of the battery swap station.

[0003] Moreover, under the general environment of cost management and automation, the working space of battery swap station equipment is gradually developing towards unattended operation. This means that if the management personnel cannot find and deal with the damage of the motor shaft of the robot in the battery swap station in time, it will seriously affect the subsequent processes. Therefore, it is important to have a method that can provide timely feedback on the mechanical status of the battery swap station during operation, so that the management personnel can arrange repairs or deal with the motor shaft with hidden dangers in time. Summary of the invention

[0004] The purpose of the present invention is to provide a predictive maintenance and early warning method and device based on a motor of a commercial vehicle battery swap station to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above purpose, a technical solution adopted by the present invention is: a predictive maintenance and early warning method based on a motor of a commercial vehicle battery swap station, comprising the following steps:

[0006] S01: obtaining a control group torque collection M1 at various positions during the operation of the motor, wherein the collection M1 is a collection of standard reference data obtained by detecting a normal motor under normal working conditions;

[0007] S02: obtaining a torque collection M2 of the experimental group at various positions during the operation of the motor, wherein the collection M2 is a collection of actual data detected under the same working conditions and paths of the motor;

[0008] S03: Determine the current performance level of the motor by comparing the data of the collection M2 and the collection M1 at the same position;

[0009] The specific method of the early warning is any of the following methods:

[0010] Method 1: S11: setting a plurality of over-limit intervals connected in sequence, wherein the over-limit interval is a percentage range, and the over-limit interval is the absolute value of the percentage value of the value of the set M2 exceeding the value of the set M1 when the motor is in the same position;

[0011] S12: setting a threshold value of the number of torques exceeding the limit in each exceeding limit interval in the collection M2;

[0012] S13: When the torque in any over-limit interval exceeds the set threshold, an early warning reminder is issued;

[0013] Method 2: S21: setting a plurality of over-limit intervals connected in sequence, wherein the over-limit interval is a percentage range, and the over-limit interval is the absolute value of the percentage value of the value of the set M2 exceeding the value of the set M1 when the motor is in the same position;

[0014] S22: Compare the similarity between the torque collection M2 of the experimental group and the torque collection M1 of the control group, set a similarity threshold, and issue an early warning reminder when the similarity between the two is less than the similarity threshold.

[0015] Preferably, the calculation formula for similarity in method 2 is as follows:

[0016]

[0017] IS=SUM / CT*IN max

[0018] Wherein, Sim is the similarity described in S22, IS is a single over-limit interval, n represents the number of over-limit intervals, SUM represents the sum of the absolute values ​​of the percentage values ​​of all M2 exceeding M1 in the over-limit interval, CT represents the number of M2 in the collection in the over-limit interval, and IN represents the number of M2 in the collection in the over-limit interval. max Indicates the maximum value of the interval that exceeds the limit.

[0019] Preferably, the calculation formula for similarity in method 2 is as follows:

[0020]

[0021] Among them, Sim is the similarity described in S22, SUM represents the sum of the absolute values ​​of the percentage values ​​of all M2 exceeding M1 in the excess interval, AM represents the weight of the excess interval for the overall calculation, CT represents the number of collections M2 in the excess interval, INmax represents the maximum value of the excess interval, and x is a value selected by yourself.

[0022] Preferably, the motor operating position is set to a plurality of position intervals, and the values ​​obtained by the collection M1 and the collection M2 are respectively the median value of the position interval and the average value of the torque collected in the position interval.

[0023] Preferably, if the torque value collected in the position interval is empty, the average value of all torque values ​​in the same number of position intervals before and after the position interval is selected as the torque value in the position interval.

[0024] A device for predictive maintenance and early warning of motors in commercial vehicle battery swap stations, using the above method, specifically comprises: a data acquisition module for collecting torque values ​​during motor operation;

[0025] A data storage module for storing data of collections M1 and M2;

[0026] A calculation processing module for processing the data of the calculation set M1;

[0027] An early warning module used to display the calculation results of the operation processing module.

[0028] Beneficial effects of the present invention: The present invention sets an over-limit interval, compares the collected motor torque collection M2 with the torque collection M1, sets a threshold for the number of over-limit torques in the over-limit interval, or compares the similarity between the torque collection M1 and the torque collection M2, so that the running state of the motor can be monitored, thereby realizing early warning of the motor performance;

[0029] Among them, since the position data collected in actual production is not consistent every time, it changes according to the scanning cycle of the PLC, the data collected in actual production is divided into position intervals to enhance the feasibility and reliability of the comparison. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the connection relationship between the modules in the predictive maintenance and early warning device of the motor in the present invention. DETAILED DESCRIPTION

[0031] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0032] Example:

[0033] See also Figure 1 ,The predictive maintenance and early warning method based on the motor of the commercial vehicle battery swap station includes the following steps:

[0034] S01: Obtain a control group torque collection M1 at various positions during the operation of the motor, wherein the collection M1 is a standard reference data collection obtained by detecting a normal motor under a normal working state, and the standard reference data collection may be an average value of torque collections collected multiple times;

[0035] S02: obtaining a torque collection M2 of the experimental group at various positions during the operation of the motor, wherein the collection M2 is a collection of actual data detected under the same working conditions and paths of the motor, wherein if the motors for obtaining the torque collection M1 of the control group and the torque collection M2 of the experimental group are of the same model, the prediction accuracy of this scheme will be more accurate;

[0036] S03: Determine the current performance level of the motor by comparing the data of the collection M2 and the collection M1 at the same position;

[0037] The specific method of the early warning is any of the following methods:

[0038] Method 1: S11: setting a plurality of over-limit intervals connected in sequence, wherein the over-limit interval is a percentage range, and the over-limit interval is the absolute value of the percentage value of the value of the set M2 exceeding the value of the set M1 when the motor is in the same position;

[0039] S12: setting a threshold value of the number of torques exceeding the limit in each exceeding limit interval in the collection M2;

[0040] S13: When the torque in any over-limit interval exceeds the set threshold, an early warning reminder is issued;

[0041] For example, assuming that there are 5 over-limit intervals, namely 0%-1%, 1%-5%, 5%-10%, 10%-20%, and 20%-30%, the percentage value range of each over-limit interval includes the tail value but does not include the head value. At the same time, assuming that the number of torques in the collection M2 that fall into 0%-1% is greater than 70, a warning reminder is given, the number of torques in the collection M2 that fall into 1%-5% is greater than 50, a warning reminder is given, and the number of torques in the collection M2 that fall into 5%-10% is greater than 30, a warning reminder is given. With this setting, the preferred setting method is as described above. The larger the value of the over-limit interval, the smaller the threshold value of the number of torques in the collection M2 that fall into it should be. The above is the specific content of method one.

[0042] Method 2: S21: setting a plurality of over-limit intervals connected in sequence, wherein the over-limit interval is a percentage range, and the over-limit interval is the absolute value of the percentage value of the value of the set M2 exceeding the value of the set M1 when the motor is in the same position;

[0043] S22: comparing the similarity between the torque collection M2 of the experimental group and the torque collection M1 of the control group, setting a similarity threshold, and issuing an early warning when the similarity between the two is less than the similarity threshold;

[0044] The calculation formula for similarity in method 2 can be any of the following two. The first calculation formula is as follows:

[0045]

[0046] IS=SUM / CT*IN max

[0047] Wherein, Sim is the similarity described in S22, IS is a single over-limit interval, n represents the number of over-limit intervals, SUM represents the sum of the absolute values ​​of the percentage values ​​of all M2 exceeding M1 in the over-limit interval, CT represents the number of M2 in the collection in the over-limit interval, and IN represents the number of M2 in the collection in the over-limit interval. max Indicates the maximum value of the interval of the over-limit interval;

[0048] To illustrate the formula, assume that there are 10 over-limit intervals, and at the same time, there is an over-limit interval of 1%-5%, and there are 4 data in the collection M2, 1%, 2%, 2%, and 3%, which fall within the over-limit interval of 1%-5%, and the other data in the collection M2 do not exceed the limit. In this way, substituting into the formula, we can get Assuming the similarity threshold is 90%, this value does not meet the standard for early warning. If the calculated result is less than 90%, an early warning will be issued;

[0049] The second calculation formula for similarity in method 2 is as follows:

[0050]

[0051] Wherein, Sim is the similarity described in S22, SUM represents the sum of the absolute values ​​of the percentage values ​​of all M2 exceeding M1 in the exceeding interval, AM represents the weight of the exceeding interval for the overall operation, CT represents the number of sets M2 in the exceeding interval, INmax represents the interval maximum value of the exceeding interval, and x is a value selected by oneself;

[0052] Compared with the first calculation formula, new codes AM and x appear in this formula. In this formula, AM is an independently set value based on actual use needs. The setting rule is that the larger the value of the out-of-limit interval is, the larger the value of AM is; x is a self-selected value. The larger the selected value is, the higher the tolerance for the collection M2 data in the calculation rule. For example, in the same collection M2, there are only 4 data 1%, 2%, 2%, and 3% that fall into the out-of-limit interval 1%-5%, and the rest of the data are normal. Then, when it is assumed that x is 3, the similarity result is 93%. Under the same conditions, if x is 5, the similarity result may be 97%.

[0053] Among them, the motor running position is set to several position intervals, and the values ​​obtained by the collection M1 and the collection M2 are the median value of the position interval and the average value of the torque collected in the position interval, respectively. For example, starting from 0, every 1 cm is regarded as a position interval. Assume that the data collected by M1 are {(0.1, 1.2), (0.8, 1.5), (1.2, 1.7), (1.8, 2.1) ...}, and the two items of each group of data in the above data set represent the position information and the torque information, respectively. According to the information of the data set, it can be obtained that the collected position intervals should be 0.5 cm, 1.5 cm, 2.5 cm ..., respectively, and the corresponding torque data are (1.2 + 1.5) / 2 = 1.35, (1.8 + 2.12 = 1.95, and the processed data should be {(0.5, 1.35), (1.5, 1.95)}.

[0054] Among them, since the running speed of each axis during battery replacement will also affect the collected data, there may be a situation where the torque value collected in the position interval is empty. For this situation, the average value of all torque values ​​in the same number of position intervals before and after the position interval is selected as the torque value in the position interval. For example, the average value of the torque values ​​in the first two position intervals and the last two position intervals at the position can be used as a supplement to the empty value. For continuously empty position intervals, the continuous position space can be taken as a whole, and the average value of the front and rear position spaces can be taken as a supplement to the empty value of each position in the continuous position space.

[0055] In addition, the present application also proposes a device for predictive maintenance and early warning of motors in commercial vehicle battery swap stations, using the above method, which specifically includes: a data acquisition module for collecting torque values ​​during motor operation;

[0056] A data storage module for storing collection M1 data;

[0057] An operation processing module for processing and calculating data of the collection M1 and the collection M2;

[0058] An early warning module for displaying the calculation results of the operation processing module;

[0059] Specifically, torque data can be collected through PLC, and PLC sends the torque data to the host computer. The host computer reads the data and compares it with the data in the data storage module. The warning result is obtained through comparison and calculation of the operation processing module. If the warning value is reached, the warning value will be output to the host computer and sent to the client by the host computer.

[0060] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A predictive maintenance and early warning method based on a motor in a commercial vehicle battery swap station, characterized in that: The following steps are involved: S01: obtaining a control group torque collection M1 at various positions during the operation of the motor, wherein the collection M1 is a collection of standard reference data obtained by detecting a normal motor under normal working conditions; S02: obtaining a torque collection M2 of the experimental group at various positions during the operation of the motor, wherein the collection M2 is a collection of actual data obtained by detecting the motor under the same working conditions and paths; S03: Determine the current performance level of the motor by comparing the data of the collection M2 and the collection M1 at the same position; The specific method of the early warning is any of the following methods: Method 1: S11: setting a plurality of over-limit intervals connected in sequence, wherein the over-limit interval is a percentage range, and the over-limit interval is the absolute value of the percentage value of the value of the set M2 exceeding the value of the set M1 when the motor is in the same position; S12: setting a threshold value of the number of torques exceeding the limit in each exceeding limit interval in the collection M2; S13: When the torque in any over-limit interval exceeds the set threshold, an early warning reminder is issued; Method 2: S21: setting a plurality of over-limit intervals connected in sequence, wherein the over-limit interval is a percentage range, and the over-limit interval is the absolute value of the percentage value of the value of the set M2 exceeding the value of the set M1 when the motor is in the same position; S22: Compare the similarity between the torque collection M2 of the experimental group and the torque collection M1 of the control group, set a similarity threshold, and issue an early warning reminder when the similarity between the two is less than the similarity threshold.

2. The predictive maintenance and early warning method based on the motor of the commercial vehicle battery swap station according to claim 1 is characterized by: The calculation formula for similarity in method 2 is as follows: IS=SUM / CT*IN max Wherein, Sim is the similarity described in S22, IS is a single over-limit interval, n represents the number of over-limit intervals, SUM represents the sum of the absolute values ​​of the percentage values ​​of all M2 exceeding M1 in the over-limit interval, CT represents the number of M2 in the collection in the over-limit interval, and IN represents the number of M2 in the collection in the over-limit interval. max Indicates the maximum value of the interval that exceeds the limit.

3. The predictive maintenance and early warning method based on the motor of the commercial vehicle battery swap station according to claim 1 is characterized in that: The calculation formula for similarity in method 2 is as follows: Among them, Sim is the similarity described in S22, SUM represents the sum of the absolute values ​​of the percentage values ​​of all M2 exceeding M1 in the excess interval, AM represents the weight of the excess interval for the overall calculation, CT represents the number of collections M2 in the excess interval, INmax represents the maximum value of the excess interval, and x is a value selected by yourself.

4. The predictive maintenance and early warning method based on the motor of the commercial vehicle battery swap station according to claim 1 is characterized in that: The motor operating position is set to a number of position intervals, and the values ​​obtained by the collection M1 and the collection M2 are the median value of the position interval and the average value of the torque collected in the position interval, respectively.

5. The predictive maintenance and early warning method based on the motor of the commercial vehicle battery swap station according to claim 4 is characterized in that: If the torque value collected in the position interval is empty, the average value of all torque values ​​in the position intervals with the same number of positions before and after the position interval is selected as the torque value in the position interval.

6. A device for predictive maintenance and early warning of motors in commercial vehicle battery swap stations, using the method described in claims 1-5, characterized in that: include: A data acquisition module for collecting torque values ​​during motor operation; A data storage module for storing collection M1 data; An operation processing module for processing and calculating data of the collection M1 and the collection M2; An early warning module used to display the calculation results of the operation processing module.

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