A spare parts management method for maintenance of a three-phase asynchronous motor

Abstract

The present application belongs to the technical field of spare parts management, and discloses a spare parts management method for maintenance of a three-phase asynchronous motor, which comprises the following steps: preparing basic data information, establishing a standard subordinate relationship between the three-phase asynchronous motor and the spare parts; processing the basic data and arranging them in order; spare parts classification and control, combining the AHP-ABC method and the characteristic data clustering method; screening the number of important three-phase asynchronous motors on the production line; and inventory control and maintenance plan of important motor spare parts. The present application improves the reliability and accuracy of the three-phase asynchronous motor on the production line, and has good stability.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of spare parts management, and particularly relates to a spare parts management method for maintenance of a three-phase asynchronous motor. BACKGROUND

[0002] The three-phase asynchronous motor is widely used in various links of pulp and paper production as a driving device, and is centrally controlled by a DCS distributed control system to run. In order to ensure that the production equipment can run safely and stably, all three-phase asynchronous motors need to be kept in a good and reliable running state at all times. However, the high-temperature, high-humidity and heavy-load production environment causes a high failure rate of the three-phase asynchronous motor, which seriously affects the operation efficiency of the production line. The conventional motor maintenance methods of "blowing", "sweeping" and "cleaning" cannot completely overcome the adverse effects of the production environment on the three-phase asynchronous motor, and a large number of motor spare parts and returned motors are stored in the warehouse.

[0003] At present, the spare parts management method of the equipment mainly relies on establishing a multi-index spare parts value classification model, fitting a probability distribution function according to historical data or using a machine learning prediction algorithm to calculate the spare parts demand under ideal conditions, and finally formulating a spare parts inventory control strategy according to the prediction results. The classification results and control degree of the spare parts will be very different due to the difference in the classification model. The fault points and fault frequency of the three-phase asynchronous motor on the production line actually show the characteristics of discretization and randomness, and the probability distribution function or the machine learning algorithm is not suitable for predicting the demand of such spare parts, so the result of the spare parts inventory control is poor. The maintenance method of the three-phase asynchronous motor mainly relies on regular inspection and cleaning, but the temperature and humidity changes in the production environment will seriously affect the service life of the motor. The conventional maintenance method cannot well solve the problem of high failure rate of the three-phase asynchronous motor on the production line, and the TPM maintenance mode can early detect potential problems of the motor, but requires high judgment experience of the on-duty personnel. SUMMARY

[0004] The purpose of the present application is to provide a spare parts management method for maintenance of a three-phase asynchronous motor, so as to realize accurate maintenance of important spare parts of the three-phase asynchronous motor and improve the reliability of important three-phase asynchronous motors on a continuous production line.

[0005] The technical solution for achieving the purpose of the present application is as follows:

[0006] A spare parts management method for maintenance of a three-phase asynchronous motor, comprising the following steps:

[0007] S1, preparing basic data information, establishing standard dependency: according to the item name, specification and model, recommended service life of three-phase asynchronous motor in enterprise material management system and the item name, specification and model, recommended service life of its spare parts, the standard dependency between three-phase asynchronous motor and spare parts is established;

[0008] S2, processing basic data: arranging in order, supplementing data information, and discarding the data information which cannot be supplemented;

[0009] S3, spare parts classification and control: according to the standard dependency between motor and spare parts, the AHP-ABC method of'spare parts failure times' and'spare parts recommended service life' indexes is used to calculate the importance of the detected spare parts, the characteristic data clustering method is used to further classify the spare parts specification and model in the important spare parts, and the most important spare parts are obtained;

[0010] S4, screening the number of important three-phase asynchronous motors on the production line;

[0011] S5, inventory control and maintenance plan of important motor spare parts.

[0012] Compared with the prior art, the present application has the following advantages:

[0013] (1) the AHP-ABC method combined with the characteristic data clustering method is used to realize differentiated hierarchical management of multi-specification and model spare parts;

[0014] (2) the standard dependency graph is used to realize quick positioning, selection and replacement of spare parts specification and model;

[0015] (3) the position of the most important three-phase asynchronous motor on the production line is accurately positioned, the important spare parts are replaced regularly, and the stability of the continuous production line is realized. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of a spare parts management method for three-phase asynchronous motor maintenance provided by the present application.

[0017] Figure 2 is Figure 1 the method for establishing the standard dependency between three-phase asynchronous motor and spare parts in DETAILED DESCRIPTION

[0018] The present application will be further described below in combination with the drawings and specific embodiments.

[0019] In combination with Figure 1 and Figure 2 , the present application provides a spare parts management method for three-phase asynchronous motor maintenance, which mainly includes the following steps:

[0020] S1. Prepare basic data and establish standard hierarchical relationships: Based on the item name, specifications, and recommended service life of three-phase asynchronous motors in the enterprise material management system, as well as the name, specifications, and recommended service life of their supplied spare parts, establish standard hierarchical relationships between three-phase asynchronous motors and spare parts. The specific meaning of standard hierarchical relationships refers to the correspondence between each model of three-phase asynchronous motor and spare parts, resulting in ten main components of three-phase asynchronous motors; Based on the material requisition data of three-phase asynchronous motors and spare parts in the warehouse-level material system, obtain material requisition data for the past three years; Based on the material requisition data in the maintenance department's material system, obtain material requisition data for three-phase asynchronous motors and spare parts for the past three years.

[0021] S2, Processing Basic Data: The material requisition data of the maintenance department includes purely manual paper records and paper archives, as well as manually entered archives into the computer. After the basic data information is arranged in order, the manual paper records and paper archives are compared with the computer archives. For information data that is missing one of the following: item name or spare part name, specifications, material requisition time, cause of failure, or location of failure, it is supplemented. For data that is missing two or more of the following: item name or spare part name, specifications, material requisition time, cause of failure, or location of failure, it is discarded.

[0022] S3, Spare Parts Classification and Control: Based on the standard subordinate relationship between the motor and spare parts, the AHP-ABC method is used with the indicators of 'number of spare part failures' and 'recommended service life of spare parts' to calculate and obtain Class A spare parts (important spare parts), Class B spare parts (minor spare parts), and Class C spare parts (ordinary spare parts). Among them, Class A spare parts are three-phase asynchronous motor coils and bearings. The spare parts specifications and models in Class A spare parts are further classified using the feature data clustering method to obtain the more important Class A+ spare parts (most important spare parts).

[0023] The specific steps are as follows:

[0024] Step 301, define variable parameters.

[0025] (1)m ij =(x i ,y j );

[0026] In the formula —m ij For variable parameters, it refers to the number of failures of the spare part with the i-th characteristic parameter in the j-th year;

[0027] —x i For spare parts, refer to the characteristics of the i-th spare part, such as coil diameter, end cap size, hole specifications, and frame code;

[0028] —y j The number of times a spare part fails refers to the number of times a spare part fails in year j.

[0029] (2)H k ={m k1 ,m k2 ,...,m kj ,...m kn}

[0030] In the formula —H k The feature data set refers to the number of failures of the k-th spare part feature over the years;

[0031] —kj refers to the number of failures in the j-th year for the k-th type of spare part;

[0032] —kn refers to the total number of n failures for the kth type of spare part;

[0033] (3) Based on the standard subordinate relationship, cluster the characteristics of the same spare parts for different three-phase asynchronous motors so that:

[0034] M c ={H k H k+1 H k+2 ,...H k+λ}

[0035] In the formula —M c This is a data set of spare parts specifications and models, referring to the name of spare part type C. The data set includes spare parts specifications and models and the number of failures over the years.

[0036] —k+λ refers to the set of data in the feature data clustering where a total of λ spare part features are found that have the same spare part name but different spare part specifications and models as the kth spare part feature;

[0037] Step 302, calculate each characteristic parameter x in type A spare parts. i The total number of failures, as given by formula (1) is:

[0038]

[0039] In the formula —Y im The total number of failures refers to the characteristic parameter x. i The total number of spare parts failures over m years;

[0040] —j indicates the year in which the spare part failed;

[0041] Step 303: Calculate the total number of failures for different specifications and models of spare parts with the same name in Class A spare parts, using formula (2);

[0042]

[0043] In the formula —G c The total number of failures refers to the number of failures under the same spare part name, based on the characteristic parameter x.k To x k+λ The total number of failures of all spare parts in year m;

[0044] —c is the spare part name number, referring to the c-th spare part name;

[0045] Record the quantity of spare parts with different spare parts names, and record the total quantity of spare parts after summing them as z;

[0046] Step 304, calculate the average number of failures for Class A spare parts, using formula (3):

[0047]

[0048] In the formula— The average number of failures for Category A spare parts;

[0049] —n represents the number of spare parts names, indicating the number of spare parts names to be accumulated;

[0050] —z represents the quantity of spare parts specifications and models, specifically the quantity of Class A spare parts specifications and models;

[0051] Step 305, in step 302, the total number of failures Y for each specification and model of spare parts is obtained. hm Decrease the average number of failures of Class A spare parts in sequence When the result is greater than zero, it is an A+ category spare part specification model and name.

[0052] Table 1 shows the detailed operation results:

[0053]

[0054] S4, Screen the number of important three-phase asynchronous motors on the production line: Based on the classification results obtained in step S3, count the number of three-phase asynchronous motors corresponding to A+ category spare parts in the enterprise, and refer to the outdoor temperature and humidity as the standard. Three-phase asynchronous motors in the standard temperature and humidity environment are not subject to special management. Record the number of three-phase asynchronous motors that are more likely to fail when in the standard temperature and humidity environment.

[0055] S5, Inventory control and maintenance plan for important motor spare parts: The demand for A+ category spare parts in the next year is calculated using a weighted moving average method as the safety stock quantity, see Table 2.

[0056] Table 2 Classification Results of A+ Category Spare Parts

[0057]

Claims

1. A spare parts management method for the maintenance of a three-phase asynchronous motor, characterized in that, Includes the following steps: S1. Prepare basic data and establish standard subordinate relationships: Based on the item name, specifications, and recommended service life of the three-phase asynchronous motor in the enterprise material management system, as well as the name, specifications, and recommended service life of the supplied spare parts, establish standard subordinate relationships between the three-phase asynchronous motor and the spare parts. S2, Processing basic data: Arrange in order, complete data information, and discard data that cannot be completed. S3, Spare Parts Classification and Control: Based on the standard subordinate relationship between the motor and spare parts, the AHP-ABC method is used with the indicators of 'number of spare part failures' and 'recommended service life of spare parts' to calculate and classify the importance of the spare parts. Then, feature data clustering is used to further classify the specifications and models of spare parts in the important category to obtain the most important spare parts. Specifically, this includes the following steps: 301, Define variable parameters: m ij =(x i ,y j ); m ij The number of failures in the j-th year for the spare part with the i-th characteristic parameter; x i y is a characteristic parameter, referring to the characteristic of the i-th spare part; j The number of spare parts failures in year j; H k ={m k1 ,m k2 ,...,m kj ,...m kn } H k m represents the number of failures of the k-th spare part characteristic over the years. kj The number of failures in the j-th year for k types of spare parts; M c ={H k ,H k+1 ,H k+2 ,...H k+λ } M c λ represents the set of spare parts specifications and models; λ is the total number of part features.

302. Calculate each characteristic parameter x in type A spare parts. i Total number of failures: Y im The total number of failures refers to the characteristic parameter x. i The total number of spare parts failures over m years; 303. Calculate the total number of failures for different specifications and models of the same type of spare parts under category A: G c The total number of failures refers to the number of failures under the same spare part name, based on the characteristic parameter x. k To x k+λ The total number of failures of all spare parts in year m; 304. Calculate the average number of failures for critical spare parts: The average number of failures for Class A spare parts; n is the number of spare part names, referring to the cumulative number of spare part names. z represents the quantity of spare parts specifications and models, specifically the quantity of important spare parts specifications and models; Step 305, obtain the total number of failures Y for each specification and model of spare parts. hm Decrease the average number of failures for important spare parts in sequence. When the result is greater than zero, it represents the most important spare part specifications and name; S4, Screen the number of important three-phase asynchronous motors on the production line; The specific process is as follows: Based on the classification results obtained in step S3, count the number of three-phase asynchronous motors corresponding to the most important spare parts in the enterprise, and refer to the outdoor temperature and humidity as the standard. Three-phase asynchronous motors in the standard temperature and humidity environment are not subject to special management. Record the number of three-phase asynchronous motors that are more likely to fail when in the standard temperature and humidity. S5, Inventory control and maintenance plan for critical motor spare parts; the specific process is as follows: the demand for the most important spare parts in the next year is calculated using a weighted moving average method as the safety stock quantity.

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