CN112748336A - Error-proofing alarm system and method for production detection station of motor - Google Patents
Error-proofing alarm system and method for production detection station of motor Download PDFInfo
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Abstract
The invention discloses a mistake-proofing alarm system and method for a production detection station of a motor, which relate to the technical field of motors, and the system comprises the following components: the system comprises a data acquisition part, a data processing part and a data analysis part; the data acquisition part acquires the running state data of the motor through the sensor and sends the acquired running state data of the motor to the data processing part; the data processing part is used for processing the collected running state data and sending the processed data to the data analysis part; the data analysis part analyzes according to the processed data and judges whether the motor operates normally. Through data processing and data analysis, the running state of the motor is accurately obtained, and early warning is timely carried out.
Description
Technical Field
The invention relates to the technical field of motors, in particular to a system and a method for preventing a mistake in a production detection station of a motor.
Background
An electric Motor (Motor) is a device that converts electrical energy into mechanical energy. The electromagnetic power generator utilizes an electrified coil (namely a stator winding) to generate a rotating magnetic field and acts on a rotor (such as a squirrel-cage closed aluminum frame) to form magnetoelectric power rotating torque. The motors are divided into direct current motors and alternating current motors according to different power supplies, most of the motors in the power system are alternating current motors, and can be synchronous motors or asynchronous motors (the rotating speed of a stator magnetic field of the motor is different from the rotating speed of a rotor to keep synchronous speed). The motor mainly comprises a stator and a rotor, and the direction of the forced movement of the electrified conducting wire in a magnetic field is related to the current direction and the direction of a magnetic induction line (magnetic field direction). The working principle of the motor is that the magnetic field exerts force on current to rotate the motor.
With the development of society and the innovation of science and technology, the informatization degree of each industry is higher and higher, and data is not only reflected by results but also reflected by the running states of various systems. Today in the twenty-first century, the value of data has attracted sufficient attention from all societies, because data is a record of historical state, and through analysis of historical data, the reason for the current result can be found, and the future happening can be predicted, even a nice future can be created. Various complex information systems currently form the digital world on which we rely, and from the viewpoint of data flow, the information systems can be divided into four main steps of data generation, data acquisition, data processing and data analysis. The development of sensing technology and the increase of information systems provide more data sources for data generation, meanwhile, the progress of data acquisition technology is promoted, the dramatic increase of the data volume prevents people from processing mass data on a single server in real time, so that the technology of 'big data' and 'cloud computing' is rapidly developed and widely applied once the technology appears, in addition, artificial intelligence based on the big data and the cloud computing is also developed in a fierce manner, and the technology is innovative and even replaces numerous mechanical industries.
In the face of structured, semi-structured and unstructured data with multiple kinds and large size, the data quality is not ideal, and missing and abnormal data with different degrees exist. Practical project experience also tells that data preprocessing is an indispensable link and work in the project development process, and the processing quality is directly related to the data analysis result and is directly related to the success or failure of the project.
Disclosure of Invention
In view of the above, the invention aims to provide a motor production detection station error-proofing alarm system and method, which can accurately obtain the running state of a motor through data processing and data analysis and perform early warning in time.
In order to achieve the purpose, the invention adopts the following technical scheme:
a motor production inspection station error proofing alarm system, the system comprising: the system comprises a data acquisition part, a data processing part and a data analysis part; the data acquisition part acquires the running state data of the motor through the sensor and sends the acquired running state data of the motor to the data processing part; the data processing part is used for processing the collected running state data and sending the processed data to the data analysis part; the data analysis part analyzes according to the processed data and judges whether the motor operates normally.
Further, the data acquisition section includes: a sensor unit and a data conversion unit; the sensor unit includes: a temperature sensor, a sound sensor and a rotation speed sensor; the sensor unit sends the detected simulated temperature data, sound data and rotating speed data of the motor in operation to the data conversion unit; and the data conversion unit is used for converting the received temperature data, sound data and rotating speed data into digital data.
Further, the data processing section includes: the data cleaning unit and the data abnormal value processing unit; the data cleaning unit is used for cleaning the received data to obtain cleaning data; the data abnormal value processing unit is used for processing the data abnormal value of the cleaning data obtained after the data cleaning and eliminating the abnormal value in the data; the data outlier processing unit includes: the system comprises a window setting subunit, a distance calculating subunit, a coefficient calculating subunit, a judging subunit and a traversing subunit; the window setting subunit is used for setting a sliding window; the distance calculating subunit is configured to calculate an outlier distance of the data in the current sliding window; the coefficient calculating subunit is used for calculating the outlier coefficient of each data in the current sliding window; the judgment subunit is used for judging and correcting abnormal values; and the traversal subunit is used for moving the sliding window backwards by one datum until the whole data set is traversed, and completing the processing of the abnormal value.
Further, the specific method for correcting the data abnormal value in the sliding window comprises the following steps: and if the number of the data with the maximum outlier coefficient in the current sliding window is more than one, taking the average value of the data with the maximum outlier coefficient in the current sliding window as the corrected value.
Further, the data analysis part compares the data processed by the data processing part, and the specific process is as follows: and comparing the data processed by the data processing part with the stored template data to obtain a difference value between the data and the stored template data, comparing the difference value with a preset threshold value, judging that the motor runs abnormally if the difference value exceeds the preset threshold value, and sending an early warning signal, or judging that the running state of the motor is normal if the difference value does not exceed the preset threshold value.
A mistake-proofing alarm method for a production detection station of a motor comprises the following steps: the data acquisition part acquires the running state data of the motor through the sensor and sends the acquired running state data of the motor to the data processing part; the data processing part is used for processing the collected running state data and sending the processed data to the data analysis part; the data analysis part analyzes according to the processed data and judges whether the motor operates normally.
Further, the data processing part performs the following steps to the collected operation state data: the data cleaning unit is used for cleaning the received data to obtain cleaning data; the data abnormal value processing unit is used for processing the data abnormal value of the cleaning data obtained after the data cleaning and eliminating the abnormal value in the data; the method for processing the abnormal value by the abnormal value processing unit comprises the following steps: setting a sliding window, wherein the number of numerical values contained in the sliding window is odd, and the initial position of the sliding window is positioned at the starting end of the time sequence; calculating the outlier distance of the data in the current sliding window; calculating an outlier coefficient of each data in the current sliding window; setting a threshold value of an outlier coefficient, if the outlier coefficient of the data positioned at the middle point of the sliding window is smaller than the threshold value, judging that the value of the outlier coefficient is an abnormal value and correcting the outlier coefficient; otherwise, judging the numerical value to be a normal value without correction; and moving the sliding window backwards by one datum until the whole data set is traversed, and finishing the processing of the abnormal value.
Further, the specific method for correcting the data abnormal value in the sliding window comprises the following steps: and if the number of the data with the maximum outlier coefficient in the current sliding window is more than one, taking the average value of the data with the maximum outlier coefficient in the current sliding window as the corrected value.
Compared with the prior art, the invention has the following beneficial effects: through data preprocessing, the accuracy and the effectiveness of the collected data during analysis are guaranteed, and the accuracy and the effectiveness of final early warning are improved.
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The invention is described in further detail below with reference to the following figures and detailed description:
fig. 1 is a schematic structural diagram of a system of a mistake-proofing alarm system for a production detection station of a motor according to an embodiment of the invention.
Fig. 2 is a schematic flow chart of a method for preventing a mistake and alarming in a production detection station of a motor according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Please refer to fig. 1. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Example 1
A motor production inspection station error proofing alarm system, the system comprising: the system comprises a data acquisition part, a data processing part and a data analysis part; the data acquisition part acquires the running state data of the motor through the sensor and sends the acquired running state data of the motor to the data processing part; the data processing part is used for processing the collected running state data and sending the processed data to the data analysis part; the data analysis part analyzes according to the processed data and judges whether the motor operates normally.
Example 2
On the basis of the above embodiment, the data acquisition section includes: a sensor unit and a data conversion unit; the sensor unit includes: a temperature sensor, a sound sensor and a rotation speed sensor; the sensor unit sends the detected simulated temperature data, sound data and rotating speed data of the motor in operation to the data conversion unit; and the data conversion unit is used for converting the received temperature data, sound data and rotating speed data into digital data.
Specifically, data cleansing, also known by name as "washing out" of "dirty", refers to the last procedure to find and correct recognizable errors in a data file, including checking data consistency, handling invalid and missing values, and the like. Because the data in the data warehouse is a collection of data oriented to a certain subject, the data is extracted from a plurality of business systems and contains historical data, so that the condition that some data are wrong data and some data conflict with each other is avoided, and the wrong or conflicting data are obviously unwanted and are called as 'dirty data'. We need to "wash" dirty data according to certain rules, which is data washing. The task of data cleaning is to filter the data which do not meet the requirements, and the filtered result is sent to a business administration department to confirm whether the data are filtered or corrected by a business unit and then extracted. The data which is not qualified is mainly three categories of incomplete data, error data and repeated data. Data cleaning is different from questionnaire examination, and data cleaning after entry is generally completed by a computer instead of a human.
Example 3
On the basis of the above embodiment, the data processing section includes: the data cleaning unit and the data abnormal value processing unit; the data cleaning unit is used for cleaning the received data to obtain cleaning data; the data abnormal value processing unit is used for processing the data abnormal value of the cleaning data obtained after the data cleaning and eliminating the abnormal value in the data; the data outlier processing unit includes: the system comprises a window setting subunit, a distance calculating subunit, a coefficient calculating subunit, a judging subunit and a traversing subunit; the window setting subunit is used for setting a sliding window; the distance calculating subunit is configured to calculate an outlier distance of the data in the current sliding window; the coefficient calculating subunit is used for calculating the outlier coefficient of each data in the current sliding window; the judgment subunit is used for judging and correcting abnormal values; and the traversal subunit is used for moving the sliding window backwards by one datum until the whole data set is traversed, and completing the processing of the abnormal value.
Example 4
On the basis of the above embodiment, the specific method for correcting the data outlier in the sliding window is as follows: and if the number of the data with the maximum outlier coefficient in the current sliding window is more than one, taking the average value of the data with the maximum outlier coefficient in the current sliding window as the corrected value.
Specifically, consistency check (consistency check) is to check whether data is satisfactory or not according to a reasonable value range and a mutual relationship of each variable, and find data which is out of a normal range, logically unreasonable or contradictory. For example, a variable measured on a scale of 1-7 with a value of 0 and a negative weight should be considered as outside the normal range. Computer software such as SPSS, SAS, Excel and the like can automatically identify variable values of each out-of-range according to the defined value range. Answers with logical inconsistencies may appear in a number of forms: for example, many panelists say themselves are driving to work and report that there is no car; or the panelist reports that he or she is a heavy buyer and user of a certain brand, but at the same time gives a very low score on the familiarity scale. When the inconsistency is found, the questionnaire serial number, the record serial number, the variable name, the error category and the like are listed, so that further checking and correction are facilitated.
Example 5
On the basis of the previous embodiment, the data analysis part compares the data processed by the data processing part, and the specific process is as follows: and comparing the data processed by the data processing part with the stored template data to obtain a difference value between the data and the stored template data, comparing the difference value with a preset threshold value, judging that the motor runs abnormally if the difference value exceeds the preset threshold value, and sending an early warning signal, or judging that the running state of the motor is normal if the difference value does not exceed the preset threshold value.
Example 6
As shown in fig. 2, a method for preventing error alarm of a production detection station of a motor performs the following steps: the data acquisition part acquires the running state data of the motor through the sensor and sends the acquired running state data of the motor to the data processing part; the data processing part is used for processing the collected running state data and sending the processed data to the data analysis part; the data analysis part analyzes according to the processed data and judges whether the motor operates normally.
Specifically, the incomplete data is mainly information missing which should be existed, such as the name of a supplier, the name of a branch company, the regional information missing of a client, the unmatched main list and detail list in a business system, and the like. And filtering the data, respectively writing different Excel files according to the missing content, submitting the Excel files to the client, and requiring completion within the specified time. And writing the data into a data warehouse after completion. The reason for the error data is that the service system is not sound enough and the data is not judged after receiving the input and is directly written into the background database, for example, the numerical data is input into full-angle digital characters, the return operation is carried out after the character string data, the date format is incorrect, the date is out of range, and the like. The data is also classified, and for the problems of full-angle characters and invisible characters before and after the data, the data can be found only by writing SQL sentences, and then the data is required to be extracted by customers after the business system is corrected. Errors such as incorrect date format or overrange date can cause ETL operation failure, and the errors need to be picked up by a business system database in an SQL mode, are submitted to a business administration department to require time limit correction, and are extracted after correction
Example 7
On the basis of the above embodiment, the data processing section, the method of performing data processing on the collected operation state data, performs the steps of: the data cleaning unit is used for cleaning the received data to obtain cleaning data; the data abnormal value processing unit is used for processing the data abnormal value of the cleaning data obtained after the data cleaning and eliminating the abnormal value in the data; the method for processing the abnormal value by the abnormal value processing unit comprises the following steps: setting a sliding window, wherein the number of numerical values contained in the sliding window is odd, and the initial position of the sliding window is positioned at the starting end of the time sequence; calculating the outlier distance of the data in the current sliding window; calculating an outlier coefficient of each data in the current sliding window; setting a threshold value of an outlier coefficient, if the outlier coefficient of the data positioned at the middle point of the sliding window is smaller than the threshold value, judging that the value of the outlier coefficient is an abnormal value and correcting the outlier coefficient; otherwise, judging the numerical value to be a normal value without correction; and moving the sliding window backwards by one datum until the whole data set is traversed, and finishing the processing of the abnormal value.
Specifically, data cleansing is a repetitive process, which cannot be completed within a few days, and only the problem is discovered and solved continuously. Whether filtering is performed or not, whether correction is performed or not generally requires customer confirmation, filtered data is written into an Excel file or a data table, and mails for filtering data can be sent to business units every day in the initial stage of ETL development to prompt the business units to correct errors as soon as possible, and meanwhile, the filtered data can also be used as a basis for verifying data in the future. Data cleansing requires care not to filter out useful data, to verify carefully for each filtering rule, and to confirm by the user.
Example 8
On the basis of the above embodiment, the specific method for correcting the data outlier in the sliding window is as follows: and if the number of the data with the maximum outlier coefficient in the current sliding window is more than one, taking the average value of the data with the maximum outlier coefficient in the current sliding window as the corrected value.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (8)
1. A motor production detection station mistake proofing alarm system, characterized in that, the system includes: the system comprises a data acquisition part, a data processing part and a data analysis part; the data acquisition part acquires the running state data of the motor through the sensor and sends the acquired running state data of the motor to the data processing part; the data processing part is used for processing the collected running state data and sending the processed data to the data analysis part; the data analysis part analyzes according to the processed data and judges whether the motor operates normally.
2. The system of claim 1, wherein the data acquisition portion comprises: a sensor unit and a data conversion unit; the sensor unit includes: a temperature sensor, a sound sensor and a rotation speed sensor; the sensor unit sends the detected simulated temperature data, sound data and rotating speed data of the motor in operation to the data conversion unit; and the data conversion unit is used for converting the received temperature data, sound data and rotating speed data into digital data.
3. The system of claim 2, wherein the data processing section comprises: the data cleaning unit and the data abnormal value processing unit; the data cleaning unit is used for cleaning the received data to obtain cleaning data; the data abnormal value processing unit is used for processing the data abnormal value of the cleaning data obtained after the data cleaning and eliminating the abnormal value in the data; the data outlier processing unit includes: the system comprises a window setting subunit, a distance calculating subunit, a coefficient calculating subunit, a judging subunit and a traversing subunit; the window setting subunit is used for setting a sliding window; the distance calculating subunit is configured to calculate an outlier distance of the data in the current sliding window; the coefficient calculating subunit is used for calculating the outlier coefficient of each data in the current sliding window; the judgment subunit is used for judging and correcting abnormal values; and the traversal subunit is used for moving the sliding window backwards by one datum until the whole data set is traversed, and completing the processing of the abnormal value.
4. The system of claim 3, wherein the data outliers in the sliding window are corrected by: and if the number of the data with the maximum outlier coefficient in the current sliding window is more than one, taking the average value of the data with the maximum outlier coefficient in the current sliding window as the corrected value.
5. The system of claim 4, wherein the data analysis part performs data comparison on the data processed by the data processing part, and the specific process is as follows: and comparing the data processed by the data processing part with the stored template data to obtain a difference value between the data and the stored template data, comparing the difference value with a preset threshold value, judging that the motor runs abnormally if the difference value exceeds the preset threshold value, and sending an early warning signal, or judging that the running state of the motor is normal if the difference value does not exceed the preset threshold value.
6. A motor production inspection station error-proofing alarm method based on the system of any one of claims 1 to 5, characterized in that the method performs the following steps: the data acquisition part acquires the running state data of the motor through the sensor and sends the acquired running state data of the motor to the data processing part; the data processing part is used for processing the collected running state data and sending the processed data to the data analysis part; the data analysis part analyzes according to the processed data and judges whether the motor operates normally.
7. The method according to claim 6, wherein the data processing section performs the following steps for the method of processing the collected operation state data: the data cleaning unit is used for cleaning the received data to obtain cleaning data; the data abnormal value processing unit is used for processing the data abnormal value of the cleaning data obtained after the data cleaning and eliminating the abnormal value in the data; the method for processing the abnormal value by the abnormal value processing unit comprises the following steps: setting a sliding window, wherein the number of numerical values contained in the sliding window is odd, and the initial position of the sliding window is positioned at the starting end of the time sequence; calculating the outlier distance of the data in the current sliding window; calculating an outlier coefficient of each data in the current sliding window; setting a threshold value of an outlier coefficient, if the outlier coefficient of the data positioned at the middle point of the sliding window is smaller than the threshold value, judging that the value of the outlier coefficient is an abnormal value and correcting the outlier coefficient; otherwise, judging the numerical value to be a normal value without correction; and moving the sliding window backwards by one datum until the whole data set is traversed, and finishing the processing of the abnormal value.
8. The method of claim 7, wherein the data outliers in the sliding window are corrected by: and if the number of the data with the maximum outlier coefficient in the current sliding window is more than one, taking the average value of the data with the maximum outlier coefficient in the current sliding window as the corrected value.
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