A fully automatic PLC industrial equipment fault data signal monitoring method and system

Abstract

The present invention relates to the field of electronic digital data processing technology, and specifically to a fully automatic PLC industrial equipment fault data signal monitoring method and system, including: obtaining vibration signal data when a grinding machine component is working, screening abnormal fluctuations according to fluctuation data generated when the working component of the grinding machine is working, presetting a reference time period, obtaining the abnormal severity of the abnormal fluctuation in the reference time period according to the number of fluctuations and abnormality parameters in the reference time period, classifying the abnormal fluctuations based on the abnormal severity, obtaining abnormal fluctuations caused by abnormal problems of the equipment component itself, combining the value, abnormal severity and number of the fluctuations, determining the possibility that the abnormal fluctuations caused by the abnormal problems of the equipment component itself are caused by tool wear, thereby determining the cause of the abnormal fluctuations. The present invention improves the accuracy of abnormal signal monitoring and abnormal diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to a method and system for monitoring fault data signals of fully automatic PLC industrial equipment. Background Art

[0002] As a key equipment for precision machining, grinders can use high-speed rotating grinding wheels or other grinding tools to perform high-precision grinding on the workpiece surface, effectively removing tiny uneven parts, thereby achieving extremely high surface finish and dimensional accuracy. However, once the grinder has an abnormality or failure, it will directly have a significant impact on the quality of the ground products, and may lead to problems such as product failure.

[0003] The traditional method is often to detect abnormalities and perform maintenance only when the grinder has already failed or the ground products fail to meet the established standards. This method cannot monitor the equipment failure data signal and discover abnormalities in a timely manner, affecting the continuity of equipment production and causing a decrease in production efficiency. The downtime and repair costs after the failure may lead to higher maintenance costs and production costs, which is not conducive to the continuity and efficiency of industrial equipment production. Summary of the invention

[0004] The present invention provides a full-automatic PLC industrial equipment fault data signal monitoring method and system to solve the existing problems.

[0005] The present invention provides a fully automatic PLC industrial equipment fault data signal monitoring method and system using the following technical solutions:

[0006] An embodiment of the present invention provides a fully automatic PLC industrial equipment fault data signal monitoring method, the method comprising the following steps:

[0007] Acquire vibration signal data of a working part of the grinding machine when it is working, wherein the vibration signal data includes a plurality of fluctuations, each of which corresponds to a fluctuation amplitude and a fluctuation period;

[0008] According to the vibration signal data generated by the working parts of the grinding machine during operation, the abnormal degree parameters of the fluctuations are obtained, and the abnormal fluctuations are screened according to the abnormal degree parameters of the fluctuations; a reference time period is preset, and according to the number of fluctuations and the abnormal degree parameters in the reference time period, the abnormal severity of the abnormal fluctuations in the reference time period is obtained, and the abnormal fluctuations are classified based on the abnormal severity, so as to obtain the abnormal fluctuations caused by the abnormal problems of the equipment parts themselves;

[0009] Combined with the severity of the abnormality, the value of the fluctuation and the number of fluctuations in the reference time period during which the abnormal fluctuations caused by the abnormal problems of the equipment components themselves are located, it is possible that the abnormal fluctuations caused by the abnormal problems of the equipment components themselves are caused by tool wear, thereby determining the cause of the abnormal fluctuations.

[0010] Furthermore, the method of obtaining the abnormality degree parameter of the fluctuation and screening the abnormal fluctuation according to the abnormality degree parameter of the fluctuation includes the following specific methods:

[0011] According to the amplitude and fluctuation period of adjacent fluctuations of the working parts of the grinding machine during operation, the abnormal degree parameter of the fluctuations generated by the working parts of the grinding machine during operation is determined;

[0012] A first threshold is preset, and fluctuations whose abnormality degree parameter is greater than the preset first threshold are recorded as abnormal fluctuations.

[0013] Furthermore, the specific calculation method of the abnormal degree parameter of the fluctuation generated when the working part of the grinding machine is working is:

[0014]

[0015] In the formula, Indicates the first The abnormality degree parameter of the fluctuation; Indicates the first The amplitude of a fluctuation; Indicates the first The amplitude of a fluctuation; Indicates the first The fluctuation period of a fluctuation; Indicates the first The fluctuation period of a fluctuation; Indicates taking the absolute value.

[0016] Furthermore, the specific method of obtaining the severity of the abnormality within the reference time period where the abnormal fluctuation occurs is as follows:

[0017] The period of time after any abnormal fluctuation occurs is recorded as the reference time period. The reference time period is preset, and the first The severity of the abnormality within the reference time period of the abnormal fluctuation.

[0018] Furthermore, the The specific calculation method for the severity of anomalies within the reference time period of an abnormal fluctuation is as follows:

[0019]

[0020] In the formula, Indicates The severity of the abnormality within the reference time period of the abnormal fluctuation; Indicates An abnormal degree parameter of abnormal fluctuation; Indicates The number of fluctuations in the reference time period where the abnormal fluctuation occurs; Indicates The abnormal fluctuation occurs within the reference time period. The abnormality degree parameter of the fluctuation; Indicates The abnormal fluctuation occurs within the reference time period. The abnormality degree parameter of a fluctuation.

[0021] Furthermore, the abnormal fluctuations are classified based on the severity of the abnormality to obtain the abnormal fluctuations caused by the abnormal problems of the equipment components themselves, including the specific method of:

[0022] The severity of the abnormality within the reference time period of each abnormal fluctuation is calculated and a reference range is preset. When the severity of the abnormality within the reference time period of the abnormal fluctuation is not within the reference range, it is determined that the abnormal fluctuation is caused by an abnormal problem of the equipment component itself.

[0023] Furthermore, the possibility of determining that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear includes the following specific methods:

[0024] Among all the fluctuations generated by the working parts of the grinder during operation, the maximum value of all fluctuations except the abnormal fluctuations is recorded as the value of the normal signal. Based on the severity of the abnormality within the reference time period where the abnormal fluctuation is located, the values ​​of normal and abnormal fluctuations, and the number of fluctuations, the possibility that the abnormal fluctuation caused by the abnormal problems of the equipment parts themselves is caused by tool wear is determined.

[0025] Furthermore, the specific calculation method for the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is:

[0026]

[0027] In the formula, Indicates The possibility that abnormal fluctuations caused by abnormal problems of equipment components themselves are caused by tool wear; Indicates The severity of abnormal fluctuations caused by abnormal problems of equipment components within the reference time period; Indicates The absolute value of the difference between the maximum value of an abnormal fluctuation caused by an abnormal problem of a device component itself and the value of a normal signal within a reference time period; Indicates The number of fluctuations before the maximum value in the reference time period caused by abnormal problems of equipment components themselves; Represents the normalization function.

[0028] Furthermore, the specific method of determining the cause of the abnormal fluctuation includes:

[0029] Calculate and obtain the possibility that each abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear, preset a second threshold, when the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is greater than or equal to the second threshold, determine that the current abnormal fluctuation is caused by tool wear; when the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is less than the second threshold, determine that the current abnormal fluctuation is caused by component aging and looseness.

[0030] A fully automatic PLC industrial equipment fault data signal monitoring system comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any one of the steps of the fully automatic PLC industrial equipment fault data signal monitoring method is implemented.

[0031] The beneficial effects of the technical solution of the present invention are as follows: according to the fluctuations generated by the working parts of the grinding machine during operation, the abnormal degree parameters of the fluctuations are obtained, and the abnormal fluctuations are screened according to the abnormal degree parameters of the fluctuations, which is helpful to accurately monitor the abnormal fluctuations; a reference time period is preset, and according to the number of fluctuations in the reference time period and the abnormal degree parameters, the abnormal severity of the abnormal fluctuations in the reference time period is obtained, and the abnormal fluctuations are classified based on the abnormal severity to obtain the abnormal fluctuations caused by the abnormal problems of the equipment parts themselves, which is helpful to distinguish the abnormal fluctuations caused by different reasons; combined with the abnormal severity, the value of the fluctuation and the number of fluctuations in the reference time period where the abnormal fluctuations caused by the abnormal problems of the equipment parts themselves are located, it is determined that the possibility that the abnormal fluctuations caused by the abnormal problems of the equipment parts themselves are caused by tool wear is determined, thereby determining the cause of the abnormal fluctuations, further clarifying the cause of the abnormal fluctuations, helping to adopt different processing methods for different abnormal signals, improving the accuracy of monitoring the equipment fault data signal, and accurately analyzing the cause of the fault, which is helpful to take effective repair measures for the equipment fault. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 The present invention is a flowchart of the steps of a fully automatic PLC industrial equipment fault data signal monitoring method. DETAILED DESCRIPTION

[0034] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method and system for monitoring fault data signals of fully automatic PLC industrial equipment proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0036] The specific scheme of a fully automatic PLC industrial equipment fault data signal monitoring method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0037] See also Figure 1 , which shows a step flow chart of a method for fully automatic PLC industrial equipment fault data signal monitoring provided by an embodiment of the present invention, the method comprising the following steps:

[0038] Step S001: Obtain vibration signal data of a working part of a grinding machine during operation, wherein the vibration signal data includes a plurality of fluctuations, each of which corresponds to an amplitude and a fluctuation period.

[0039] Specifically, in order to implement the fully automatic PLC industrial equipment fault data signal monitoring method proposed in this embodiment, it is first necessary to collect equipment data information. The specific process is as follows:

[0040] A vibration sensor is installed on a grinder with a PLC device. After the grinder starts working, the vibration sensor collects the fluctuation data of the working parts on the grinder in real time, and transmits the collected signal data to the PLC data center through a transmission device. The PLC device arranges the fluctuation data according to the collection time of the fluctuation data, and records the amplitude and fluctuation period generated by each vibration. The horizontal axis of the fluctuation data is time, and the vertical axis is the acceleration of the vibration. Each fluctuation is a complete fluctuation period in the vibration signal.

[0041] So far, the vibration signal data of the working parts of the grinding machine during operation are obtained through the above method.

[0042] Step S002: According to the vibration signal data generated by the working parts of the grinding machine during operation, obtain the abnormality degree parameters of the fluctuations, and filter the abnormal fluctuations according to the abnormality degree parameters of the fluctuations; preset a reference time period, and according to the number of fluctuations in the reference time period and the abnormality degree parameters, obtain the abnormal severity of the abnormal fluctuations in the reference time period, classify the abnormal fluctuations based on the abnormal severity, and obtain the abnormal fluctuations caused by abnormal problems of the equipment parts themselves.

[0043] Step (2.1): According to the vibration signal data generated by the working parts of the grinding machine during operation, obtain the abnormal degree parameter of the fluctuation, and screen the abnormal fluctuation according to the abnormal degree parameter of the fluctuation.

[0044] It should be noted that the grinder is mainly used to grind and grind the products to be processed, so vibration will be generated during the operation of the grinder. When there is no interference or abnormal situation, the vibration frequency and amplitude of the working parts of the grinder are generally fixed or approximately fixed. Therefore, by analyzing the detected fluctuations, the normal fluctuations of the grinder when processing the product can be obtained.

[0045] Specifically, in the acquired equipment data information, according to the amplitude and fluctuation period of adjacent fluctuations of the working parts of the grinding machine during operation, the abnormality degree parameter of the fluctuations generated when the working parts of the grinding machine are working is determined.

[0046] As an example, The specific calculation method of the abnormal degree parameter of a fluctuation is:

[0047]

[0048] In the formula, Indicates the first The abnormality degree parameter of the fluctuation; Indicates the first The amplitude of a fluctuation; Indicates the first The amplitude of a fluctuation; Indicates the first The fluctuation period of a fluctuation; Indicates the first The fluctuation period of a fluctuation; Indicates taking the absolute value.

[0049] It should be noted that and They represent the first and The ratio of the amplitude of a fluctuation to the fluctuation period. When the working parts of the grinding machine are working normally, and The value of is close to 1, so when The closer the value is to 1, the more the working parts of the grinder will produce the first The more normal a fluctuation is, the more abnormal it is.

[0050] Calculate the abnormality degree parameters of all fluctuations generated by the working parts of the grinding machine during operation, preset a first threshold, and record the fluctuations with abnormality degree parameters greater than the preset first threshold as abnormal fluctuations.

[0051] It should be noted that the preset first threshold is 0.2, which can be adjusted according to actual conditions and is not specifically limited in this embodiment.

[0052] Step (2.2): Preset a reference time period, obtain the severity of the abnormal fluctuations in the reference time period according to the number of fluctuations and the abnormality degree parameters in the reference time period, classify the abnormal fluctuations based on the severity of the abnormality, and obtain the abnormal fluctuations caused by the abnormal problems of the equipment components themselves.

[0053] It should be noted that when the working parts of the grinder come into contact with the harder parts of the product, the fluctuation amplitude may increase because the parts are subjected to greater reaction force when in contact with the hard material. Then, as the contact continues, the fluctuation will gradually stabilize and gradually return to the normal fluctuation range. Because the tool will gradually adapt or wear to a stable state, the process of fluctuation from change to return to normal occurs gradually. At the same time, the hardness change of the product surface may affect the duration and intensity of the fluctuation. Therefore, by analyzing the abnormal fluctuation and comparing the changes between the abnormal fluctuation and the adjacent fluctuation after the abnormal fluctuation occurs, if the fluctuation frequency, amplitude, etc. of any abnormal fluctuation are similar to those of the adjacent fluctuation after its occurrence, it can be considered that the abnormal fluctuation is a sudden abnormality caused by the material, rather than an abnormality of the grinding machine equipment; otherwise, it may be caused by an abnormality of the equipment itself.

[0054] Specifically, a period of time after any abnormal fluctuation occurs is recorded as a reference time period, and a reference time period is preset. The first The severity of the abnormality within the reference time period of the abnormal fluctuation.

[0055] As an example, The specific calculation method of the abnormal severity within the reference time period of an abnormal fluctuation is:

[0056]

[0057] In the formula, Indicates The severity of the abnormality within the reference time period of the abnormal fluctuation; Indicates An abnormal degree parameter of abnormal fluctuation; Indicates The number of fluctuations in the reference time period where the abnormal fluctuation occurs; Indicates The abnormal fluctuation occurs within the reference time period. The abnormality degree parameter of the fluctuation; Indicates The abnormal fluctuation occurs within the reference time period. The abnormality degree parameter of a fluctuation.

[0058] It should be noted that during the working process of the component, when the tool touches the harder part of the processed product, a rebound phenomenon will occur, which makes the fluctuation at this moment very different from the fluctuation at the previous moment, and then gradually stabilizes to normal fluctuations, that is, the abnormality gradually stabilizes. Indicates The abnormal fluctuation occurs within the reference time period. and The sum of the abnormality degree parameters of the fluctuations is used to indicate the change in the abnormality degree parameters between the abnormal fluctuation and the fluctuation after the abnormal fluctuation when an abnormal fluctuation occurs. Indicates The abnormal degree parameter of the abnormal fluctuation and the The ratio of the abnormal degree parameters of adjacent fluctuations in the reference time period after an abnormal fluctuation occurs. The closer the value of is to 1, the If an abnormal fluctuation returns to normal after a reference period of time, otherwise, it means that An abnormal fluctuation remains abnormal after the reference time period.

[0059] It should be noted that the preset reference time period is 2 seconds, which can be adjusted according to time conditions and is not specifically limited in this embodiment.

[0060] The severity of the abnormality within the reference time period of each abnormal fluctuation is calculated and a reference range is preset. When the severity of the abnormality within the reference time period of the abnormal fluctuation is not within the reference range, it is considered that the abnormal fluctuation is caused by abnormal problems of the equipment components themselves; when the severity of the abnormality within the reference time period of the abnormal fluctuation is within the reference range, it is considered that the abnormal fluctuation is caused by processed products.

[0061] It should be noted that the preset reference range is , can be adjusted according to actual conditions, and this embodiment does not make any specific limitation.

[0062] So far, the abnormal fluctuations caused by abnormal problems of the equipment components themselves have been obtained through the above method.

[0063] Step S003: Based on the severity of the abnormality, the value of the fluctuation and the number of fluctuations within the reference time period, determine the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear, thereby determining the cause of the abnormal fluctuation.

[0064] It should be noted that abnormal fluctuations caused by abnormalities of equipment components themselves (such as component aging, looseness or tool wear, etc.) will continue to exist, and the degree of fluctuation will gradually increase until it stabilizes, and may eventually completely cover up normal fluctuations. In the case of abnormal equipment components, whether it is component aging and looseness or tool wear, abnormal fluctuations may continue to occur. However, compared with the aging and looseness of equipment components, abnormal fluctuations caused by tool wear are often more rapid and violent. This is because during use, as the use time increases, tool wear is more common and changes faster than aging and loosening of equipment components. Therefore, the abnormal fluctuations can be further analyzed in combination with the severity of the abnormality in the reference time period where the abnormal fluctuations are located, and the causes of the abnormal fluctuations can be determined.

[0065] Specifically, among all fluctuations generated by the working parts of the grinder during operation, the maximum value of all fluctuations except the abnormal fluctuations is recorded as the value of the normal signal. Based on the severity of the abnormality within the reference time period where the abnormal fluctuation is located, the values ​​of normal and abnormal fluctuations, and the number of fluctuations, the possibility that the abnormal fluctuation caused by the abnormal problems of the equipment parts themselves is caused by tool wear is determined.

[0066] It should be noted that the value at each moment in the fluctuation is the acceleration of the vibration of the working parts of the grinding machine during operation.

[0067] As an example, The specific calculation method for the possibility that an abnormal fluctuation caused by abnormal problems of equipment components itself is caused by tool wear is:

[0068]

[0069] In the formula, Indicates The possibility that abnormal fluctuations caused by abnormal problems of equipment components themselves are caused by tool wear; Indicates The severity of abnormal fluctuations caused by abnormal problems of equipment components within the reference time period; Indicates The absolute value of the difference between the maximum value of an abnormal fluctuation caused by an abnormal problem of a device component itself and the value of a normal signal within a reference time period; Indicates The number of fluctuations before the maximum value in the reference time period caused by abnormal problems of equipment components themselves; Represents the normalization function.

[0070] It should be noted that Indicates the appearance of The rate of change of abnormal fluctuations caused by abnormal problems of equipment components themselves, The larger the value, the The greater the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment components themselves is caused by tool wear, that is, The larger the value of .

[0071] The possibility that each abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is calculated, and a second threshold is preset. When the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is greater than or equal to the second threshold, it is considered that the current abnormal fluctuation is caused by tool wear. When the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is less than the second threshold, it is considered that the current abnormal fluctuation is caused by aging and looseness of components.

[0072] It should be noted that the preset second threshold is 0.6, which can be adjusted according to actual conditions and is not specifically limited in this embodiment.

[0073] By determining the causes of abnormal fluctuations and taking corresponding treatment measures for the equipment according to the causes, equipment failures can be reduced.

[0074] Through the above steps, the monitoring of abnormal fluctuations and the determination of the causes of abnormal fluctuations are completed.

[0075] It should be noted that, in this embodiment, when the denominator in the formula is 0, the denominator is set to 1 to ensure that the formula is valid, and this example is used for description.

[0076] An embodiment of the present invention also provides a fully automatic PLC industrial equipment fault data signal monitoring system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, steps S001 to S003 of a fully automatic PLC industrial equipment fault data signal monitoring method are implemented.

[0077] This embodiment obtains fluctuation abnormality parameters based on fluctuation data generated by the working parts of the grinding machine during operation, and screens abnormal fluctuations based on the fluctuation abnormality parameters, which helps to accurately monitor abnormal fluctuations; a reference time period is preset, and the abnormal severity within the reference time period where the abnormal fluctuations are located is obtained based on the number of fluctuations and the abnormality parameters within the reference time period, and the abnormal fluctuations are classified based on the abnormal severity to obtain abnormal fluctuations caused by abnormal problems of the equipment parts themselves, which helps to distinguish abnormal fluctuations caused by different reasons; based on the abnormal severity, fluctuation value and number of fluctuations within the reference time period where the abnormal fluctuations caused by the abnormal problems of the equipment parts themselves are located, it is determined that the possibility that the abnormal fluctuations caused by the abnormal problems of the equipment parts themselves are caused by tool wear is possible, thereby determining the cause of the abnormal fluctuations, further clarifying the cause of the abnormal fluctuations, helping to adopt different processing methods for different abnormal signals, improving the accuracy of monitoring equipment fault data signals, and accurately analyzing the cause of the fault, which helps to take effective repair measures for the equipment fault.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A fully automatic PLC industrial equipment fault data signal monitoring method, characterized in that: The method comprises the following steps: Acquire vibration signal data of a working part of the grinding machine when it is working, wherein the vibration signal data includes a plurality of fluctuations, each of which corresponds to a fluctuation amplitude and a fluctuation period; According to the vibration signal data generated by the working parts of the grinding machine during operation, the abnormal degree parameters of the fluctuations are obtained, and the abnormal fluctuations are screened according to the abnormal degree parameters of the fluctuations; a reference time period is preset, and according to the number of fluctuations and the abnormal degree parameters in the reference time period, the abnormal severity of the abnormal fluctuations in the reference time period is obtained, and the abnormal fluctuations are classified based on the abnormal severity, so as to obtain the abnormal fluctuations caused by the abnormal problems of the equipment parts themselves; Combined with the severity of the abnormality, the value of the fluctuation and the number of fluctuations in the reference time period of the abnormal fluctuation caused by the abnormal problem of the equipment component itself, determine the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear, so as to determine the cause of the abnormal fluctuation; The method of obtaining the abnormality degree parameter of the fluctuation and screening the abnormal fluctuation according to the abnormality degree parameter of the fluctuation includes the following specific methods: According to the amplitude and fluctuation period of adjacent fluctuations of the working parts of the grinding machine during operation, the abnormal degree parameter of the fluctuations generated by the working parts of the grinding machine during operation is determined; A first threshold is preset, and fluctuations whose abnormality degree parameter is greater than the preset first threshold are recorded as abnormal fluctuations; The specific calculation method of the abnormal degree parameter of the fluctuation generated when the working parts of the grinding machine are working is: In the formula, Indicates the first The abnormality degree parameter of the fluctuation; Indicates the first The amplitude of a fluctuation; Indicates the first The amplitude of a fluctuation; Indicates the first The fluctuation period of a fluctuation; Indicates the first The fluctuation period of a fluctuation; Indicates taking the absolute value; The specific method of obtaining the severity of the abnormality within the reference time period where the abnormal fluctuation occurs is as follows: The period of time after any abnormal fluctuation occurs is recorded as the reference time period. The reference time period is preset, and the first The severity of the abnormality within the reference time period of the abnormal fluctuation; The said The specific calculation method for the severity of anomalies within the reference time period of an abnormal fluctuation is as follows: In the formula, Indicates The severity of the abnormality within the reference time period of the abnormal fluctuation; Indicates An abnormal degree parameter of abnormal fluctuation; Indicates The number of fluctuations in the reference time period where the abnormal fluctuation occurs; Indicates The abnormal fluctuation occurs within the reference time period. The abnormality degree parameter of the fluctuation; Indicates The abnormal fluctuation occurs within the reference time period. The abnormality degree parameter of the fluctuation; The specific method of determining the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear includes: Among all fluctuations generated by the working parts of the grinder during operation, the maximum value of all fluctuations except the abnormal fluctuation is recorded as the value of the normal signal, and based on the severity of the abnormality within the reference time period where the abnormal fluctuation is located, the values ​​of the normal and abnormal fluctuations, and the number of fluctuations, the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment parts themselves is caused by tool wear is determined; The specific calculation method for the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is: In the formula, Indicates The possibility that abnormal fluctuations caused by abnormal problems of equipment components themselves are caused by tool wear; Indicates The severity of abnormal fluctuations caused by abnormal problems of equipment components within the reference time period; Indicates The absolute value of the difference between the maximum value of an abnormal fluctuation caused by an abnormal problem of a device component itself and the value of a normal signal within a reference time period; Indicates The number of fluctuations before the maximum value in the reference time period caused by abnormal problems of equipment components themselves; Represents the normalization function.

2. According to claim 1, a fully automatic PLC industrial equipment fault data signal monitoring method is characterized in that: The abnormal fluctuations are classified based on the severity of the abnormality to obtain the abnormal fluctuations caused by the abnormal problems of the equipment components themselves, including the specific method of: The severity of the abnormality within the reference time period of each abnormal fluctuation is calculated and a reference range is preset. When the severity of the abnormality within the reference time period of the abnormal fluctuation is not within the reference range, it is determined that the abnormal fluctuation is caused by an abnormal problem of the equipment component itself.

3. According to claim 1, a fully automatic PLC industrial equipment fault data signal monitoring method is characterized in that: The specific method of determining the cause of abnormal fluctuations includes: Calculate and obtain the possibility that each abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear, preset a second threshold, when the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is greater than or equal to the second threshold, determine that the current abnormal fluctuation is caused by tool wear; when the possibility that the abnormal fluctuation caused by the abnormal problem of the equipment component itself is caused by tool wear is less than the second threshold, determine that the current abnormal fluctuation is caused by component aging and looseness.

4. A fully automatic PLC industrial equipment fault data signal monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a fully automatic PLC industrial equipment fault data signal monitoring method as described in any one of claims 1 to 3 are implemented.

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