KR200486953Y1 - Apparatus for monitoring condition of electric motros - Google Patents

Abstract

According to an embodiment of the present invention, an intelligent electric motor condition diagnosis apparatus includes a current-voltage measurement unit for measuring a current-voltage according to power supplied to each of a plurality of motors and leakage power; A power frequency obtaining unit connected to the current-voltage measuring unit to periodically obtain a power frequency of power supplied to each of the plurality of motors; A vibration value obtaining unit for detecting a vibration value according to the rotation of the electric motor; And a monitoring unit for comparing the power frequency with a reference power frequency to predict a load state of the electric motor and predicting an abnormal state and a replacement time of the electric motor using the load state, deterioration, leakage power, and insulation resistance.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to an apparatus,

The present invention relates to an intelligent electric motor condition diagnosis apparatus.

Various defects such as dynamic eccentricity of the rotor, static eccentricity, rotor bar short circuit, stator winding insulation fault, bearing failure, etc. may occur during the operation of the induction motor in the unexpected failure of the induction motor used in various industrial fields, If such defects occur, it leads to paralysis of whole or part of the process, leading to fatal accidents and economic losses, and may lead to safety accidents. Therefore, there are various methods of diagnosing induction motors that can accurately diagnose the state of the induction motor .

The fault diagnosis method of high voltage induction motor has been mainly carried out for early detection of equipment faults and reduction of equipment downtime by using vibration technique. However, since it is difficult to detect the fault of motor and precisely analyze the equipment by the vibration technique alone, There is a need for precise diagnosis of rotating machines by analyzing characteristics such as current, voltage and magnetic flux characteristics.

Therefore, data such as current, voltage, magnetic flux, temperature, and vibration signal of the induction motor used in various industrial fields are acquired in the field to diagnose insulation failure of the motor stator winding, breakage of the rotor, instability of the gap, New diagnostic techniques are being developed that utilize a variety of techniques to monitor bearings or shaft damage due to currents and to perform accurate diagnostics, life prediction, and early detection of defects.

The MCSA (Motor Current Signature Analysis) technique for diagnosing an induction motor using a frequency component of an electric current, the ESA (Electrical Signature Analysis) using a current and a voltage signal at the same time, And a method of using an electrical signal such as a stator current analysis have been applied. However, it is impossible to diagnose a composite defect state in which various defects are generated in a complex manner, There is a problem that it is difficult to precisely diagnose the abnormal operation state of the actual motor because it is impossible to classify the degree of the defect and only the presence or absence of the defect is judged in the fault diagnosis. In particular, Not only the accuracy of the measurement is reduced, but also there is a safety problem.

Japanese Patent Application Laid-Open No. 2009-098147

The object of the present invention is to provide an intelligent motor condition diagnosing device capable of accurately diagnosing an abnormal operation state of an electric motor.

According to an embodiment of the present invention, an intelligent electric motor condition diagnosis apparatus includes a current-voltage measurement unit for measuring a current-voltage according to power supplied to each of a plurality of motors and leakage power; A power frequency obtaining unit connected to the current-voltage measuring unit to periodically obtain a power frequency of power supplied to each of the plurality of motors; And a monitoring unit for comparing the power frequency with a reference power frequency to predict a load state of the electric motor and predicting an abnormal state and a replacement time of the electric motor using the load state, deterioration, leakage power, and insulation resistance, Wherein the power frequency obtaining unit further includes a synchronization control unit for synchronizing an acquisition period of the power frequency of each of the plurality of electric motors so as to easily acquire a change in power frequency of each of the plurality of electric motors over time, The monitoring unit includes a deterioration diagnosing unit for diagnosing deterioration using the power frequency of the motor; A leakage power diagnosis unit for determining whether an abnormality of a difference voltage, a difference current, and a grounding current of the motor is detected using a current-voltage corresponding to the leakage power; And an insulation resistance state diagnostic unit for predicting and diagnosing an insulation resistance state of the motor based on the deterioration and the leakage power; A fault diagnosis unit for diagnosing frequency-domain faults of the motor; And a replacement time predicting unit for diagnosing a state abnormality by using the result of each diagnosis unit and predicting a replacement time of the electric motor according to the state abnormality, wherein the insulation resistance state diagnosis unit comprises: The insulation resistance of the motor is diagnosed using the effective insulation resistance value calculated using the leakage current and the output current of the motor and the sum of the insulation resistance calculated using the deterioration.

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In one embodiment, the deterioration diagnosis unit diagnoses a deterioration progress state of the motor based on whether a specific frequency is generated according to a state change of the motor, and the specific frequency is a power frequency of the motor, a slot of the stator, Number of poles, number of poles, number of poles, number of bars and number of rotors, and frequency of rotation.

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The solution of the above-mentioned problems does not list all features of the present invention. Various means for solving the problems of the present invention can be understood in detail with reference to specific embodiments of the following detailed description.

According to one embodiment of the present invention, the intelligent electric motor condition diagnosis apparatus tracks and monitors changes in the characteristics of the electric wire power, deterioration, and insulation, which occur while operating each motor, to the database, And can easily and accurately diagnose the condition of the motor.

In addition, the embodiment of the present invention has an advantage that the motor monitoring function can be realized by only electric elements without mechanical elements, simplification of configuration, reduction of investment cost of sensing and control facilities, and reuse of existing resources.

Through the advantages described above, it is possible to prevent and diagnose the process equipment electrically connected to the motor operated by the motor at the minimum cost, and to improve the convenience of use by integrating the motor operation system and the management system.

1 is an apparatus diagram of an intelligent electric motor condition diagnosis apparatus according to an embodiment of the present invention.
2 is a flowchart illustrating an intelligent motor condition diagnosis method according to an embodiment of the present invention.
FIG. 3 is a flow chart illustrating S740 shown in FIG. 2 in more detail.
4 is a diagram illustrating an exemplary computing environment in which one or more embodiments disclosed herein may be implemented.

In order to fully understand the advantages of the present invention, its operational advantages, and the objects attained by the practice of the present invention, reference should be made to the accompanying drawings, which illustrate preferred embodiments of the present invention, and to the description in the accompanying drawings. DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the preferred embodiments of the present invention with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the described embodiments. In order to clearly explain the present invention, parts not related to the description are omitted, and the same reference numerals in the drawings denote the same members.

Throughout the specification, when an element is referred to as " including " an element, it does not exclude other elements unless specifically stated to the contrary.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An intelligent motor condition diagnosing apparatus according to the present invention will be described in detail below with reference to the accompanying drawings.

1 is an apparatus diagram showing an intelligent electric motor condition diagnosis apparatus according to the present invention.

Referring to FIG. 1, an intelligent motor state diagnosing apparatus 100 according to an embodiment of the present invention includes a current-voltage measuring unit 110, a power frequency obtaining unit 120, a motor state measuring unit 130, (140). ≪ / RTI >

The current-voltage measuring unit 110 measures current-voltage according to power supplied to each of the plurality of motors and leakage power.

The leakage power (leakage current) is detected by using a load resistance detected by a line of the electric motor M. [

The power frequency acquiring unit 120 is connected to the current-voltage measuring unit 110 and periodically acquires the power frequency of the power supplied to each of the plurality of motors M.

The power frequency acquiring unit 120 may further include a synchronization controller 121 for synchronizing an acquisition cycle of the power frequency of each of the plurality of motors M, The change of the power frequency of each of the plurality of electric motors M can be obtained more easily.

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The monitoring unit 140 predicts a load state of the motor by comparing the power frequency with a reference power frequency and predicts an abnormal state and a replacement time of the motor using the load state, deterioration, leakage power, do.

More specifically, the monitoring unit 140 includes a deterioration diagnosis unit 141, a leakage power diagnosis unit 142, an insulation resistance state diagnosis unit 143, a defect diagnosis unit 144, and a replacement time prediction unit 145 .

The deterioration diagnosis unit 141 diagnoses the deterioration using the change of the power frequency according to the state change of each electric motor M. [

For example, the deterioration diagnosis unit 141 diagnoses deterioration progress information of the motor based on whether a specific frequency is generated according to a change in the state of the motor, and the specific frequency is a power frequency of the motor, The number of rotors, and the number of bars, and the rotation frequency, and judges the good state and the bad state according to the deterioration of the motor according to the energy magnitude of the specific frequency.

For this purpose, it is possible to determine the state of the power load of each branch by analyzing the harmonics included in each quarter current.

Next, the leakage power diagnosis unit 142 uses the current-voltage corresponding to the leakage power to determine whether an abnormality has occurred in the difference voltage, the difference current, and the ground current of the motor.

The insulation resistance state diagnosis unit 143 predicts and diagnoses the insulation resistance state of the motor based on the deterioration and the leakage power.

The insulation resistance state diagnosis unit 143 calculates the sum of the effective insulation resistance value calculated using the leakage current detected from the line of the motor and the output current of the motor and the insulation resistance calculated using the deterioration progress information Thereby diagnosing the insulation resistance state of the electric motor.

The defect diagnosis unit 144 calculates a slip and a rotation frequency of the induction motor by using the current-voltage and the vibration value, calculates a slip and a rotation frequency of the induction motor, To diagnose the fault factor according to the power frequency.

Next, the replacement time predicting unit 145 predicts the replacement point of the electric motor using the result of each diagnosis unit.

At this time, the replacement-time predictor 145 estimates the replacement point predictor 145 based on the MLR (Multiple Linear Regression), PLS (Partial Least Squares), RIDGE, LASSO (Smoothly Clipped Absolute Deviation) ), SVM (Support Vector Machine), Bagging, Boosting, and Random Forest.

In addition, the replacement-point prediction unit 145 may include a data mining algorithm, for example, a classification algorithm that predicts one or more discrete variables based on other characteristics of the data set, A regression algorithm that predicts continuous variables, a segmentation algorithm that divides data into groups or clusters of similar items of property, and a connection algorithm that finds correlations between various characteristics in the data set.

In addition, a decision tree algorithm, an artificial neural network algorithm, and a cluster analysis algorithm can be used.

For reference, data mining refers to a series of analysis processes that extract useful knowledge by searching, finding and modeling relationships, patterns, rules, etc. existing in these data from a large amount of data.

The functions of data mining include Classification, Estimation, Predecation, Affinity Grouping, Clustering, and Descriotion.

 The process of extracting data from data mining can be divided into ① sampling ② exploration ③ modification / modification ④ modeling ⑤ evaluation.

(1) Sampling is the process of extracting an appropriate amount of sample from raw data; (2) Exploring is the search of various data to obtain basic information such as basic statistical data, frequency distribution tables, average, variance, (3) Modification / conversion is a process of transforming data through conversion, quantification, and grouping of variables for efficient use of data. (4) Modeling is the process of using appropriate techniques (5) Assessment is the process of evaluating the reliability and usefulness of the results of modeling.

2 is a flowchart illustrating an intelligent motor condition diagnosis method (S700) according to an embodiment of the present invention.

Referring to FIG. 2, the intelligent motor state diagnosis method (S700) according to an embodiment of the present invention includes a dust, a current-voltage measurement unit 110, (S710), and the leakage power (leakage current) is detected by using a load resistance detected by the line of the motor M.

Next, the power frequency acquisition unit 120 is connected to the current-voltage measurement unit 110 to periodically acquire the power frequency of the power supplied to each of the plurality of motors M (S720). Here, the step S720 may include a step of synchronizing an acquisition period of the power frequency of each of the plurality of motors M.

Thereafter, the monitoring unit 140 compares the actually measured power frequency with the reference power frequency, predicts the load state of the motor, and detects the abnormal state of the motor (defective state) by using the load state, deterioration, leakage power, Diagnosis) (S740) and predicts the replacement time (S750).

Referring to FIG. 3, in operation S740, the deterioration diagnosis unit 141 is used to diagnose the deterioration using the load state and the state information of the motor. In the leakage power diagnosis unit 142, a current corresponding to the leakage power - the voltage difference is used to determine whether an abnormality has occurred in the difference voltage, the difference current, and the ground current of the motor, and the insulation resistance state diagnosis section (143) diagnoses the insulation resistance state of the motor based on the deterioration and the leakage power do.

Here, the insulation resistance state diagnosis may be performed by using an effective insulation resistance value calculated by using a leakage current detected from a line of the motor and an output current of the motor, and a sum of an insulation resistance calculated using the deterioration progress information And diagnosing the insulation resistance state of the motor.

Next, the fault diagnosis unit 144 calculates the slip and the rotation frequency of the motor using the current-voltage and the vibration value, and calculates the slip and rotation frequency of the induction motor and the diagnosis parameters To diagnose the fault factor according to the power frequency.

Next, the replacement time prediction unit 145 may include a step of estimating the replacement time of the electric motor using the result of each diagnosis unit.

Meanwhile, the deterioration diagnosis process may be a process of diagnosing deterioration progress information of the electric motor based on whether a specific frequency is generated according to a change in the state of the electric motor.

The specific frequency is calculated according to the power frequency of the motor, the number of slots and poles of the stator, the number of rotator bars, and the rotation frequency, and is determined according to the energy level of the specific frequency. State.

To do this, we analyze the harmonics included in each quarter current to determine the state of the quarterly power load.

On the other hand, the intelligent electric motor condition diagnosis apparatus 100 according to an embodiment of the present invention may include circuits that connect the components to each other and communicate communication (e.g., control messages and / or data) between the components have.

In addition, the intelligent electric motor condition diagnosis apparatus 100 according to an embodiment of the present invention may include a communication interface.

For example, communication can be established between the administrator terminal and the management server. Wireless communications may include, for example, cellular communication protocols such as long-term evolution (LTE), LTE Advance (LTE), code division multiple access (CDMA), wideband CDMA (WCDMA) mobile telecommunications system, WiBro (Wireless Broadband), or Global System for Mobile Communications (GSM). Further, the wireless communication may include, for example, local communication. The local area communication may include at least one of, for example, wireless fidelity (WiFi), Bluetooth, near field communication (NFC), or global navigation satellite system (GNSS). The GNSS may include at least one of Global Positioning System (GPS), Global Navigation Satellite System (Glonass), Beidou Navigation Satellite System (Beidou) or Galileo, the European global satellite-based navigation system . ≪ / RTI > In this document, GPS can be used interchangeably with GNSS. The wired communication may include at least one of, for example, a universal serial bus (USB), a high definition multimedia interface (HDMI), a recommended standard 232 (RS-232), or plain old telephone service (POTS). The network may include at least one of a telecommunications network, e.g., a computer network (e.g., a LAN or WAN), the Internet, or a telephone network.

Meanwhile, the intelligent electric motor condition diagnosis apparatus according to an embodiment of the present invention can be interlocked with an electric motor switchboard, and the electric motor switchboard can be used for a variety of purposes such as water supply, sewage, and wastewater treatment facilities, Can be applied to production processes including heating

For reference, the electric motor control panel may be designed to have components inside the case adjusted by a polypropylene resin composition in order to protect the components from the external environment. Such a polypropylene resin composition is a composition having excellent whitening resistance and impact resistance. The polypropylene resin composition comprises 75 to 95% by weight of an ethylene-propylene-alphaolefin random copolymer and 5 to 25% by weight of an ethylene-propylene block copolymer having an ethylene content of 20 to 50% %, And the intrinsic viscosity ratio of the ethylene-propylene-alpha olefin random copolymer to the above-mentioned ethylene-propylene block copolymer may be 0.3 to 1.

More specifically, the polypropylene random block copolymer is preferably composed of 75 to 95% by weight of the ethylene-propylene-alphaolefin random copolymer and 5 to 25% by weight of the ethylene-propylene block copolymer, When the olefin random copolymer is less than 75% by weight, the stiffness is deteriorated. When the olefin random copolymer is more than 95% by weight, the impact resistance is decreased. When the olefin random copolymer is less than 5% by weight, The rigidity is lowered.

In the polypropylene random block copolymer, the intrinsic viscosity ratio of the ethylene-propylene-alpha olefin random copolymer to the ethylene-propylene block copolymer is preferably 0.3 to 1. When the ratio is less than 0.3, the molecular weight of the ethylene-propylene block copolymer Is relatively low than the random copolymer of ethylene-propylene-alpha olefin, which makes it difficult to absorb the impact. When the content exceeds 1, the size of the dispersed phase of the ethylene-propylene block copolymer increases and the whitening resistance may be lowered.

Wherein the ethylene-propylene-alpha olefin random copolymer comprises 0.5 to 7% by weight of ethylene and 1 to 15% by weight of an alpha-olefin having 4 to 5 carbon atoms and improves mechanical stiffness and heat resistance of the polypropylene resin composition, As shown in Fig. The ethylene content is preferably from 0.5 to 5% by weight, more preferably from 1 to 3% by weight. When the content of ethylene is less than 0.5% by weight, the whitening resistance is deteriorated. When the content is more than 7% by weight, .

The alpha olefin means any alpha olefin except ethylene and propylene, preferably butene. In addition, when the number of carbon atoms is less than 4 or more than 5, the reactivity of the alpha olefin with the comonomer is low during the production of the random copolymer, making it difficult to produce the copolymer. Also, it contains 1 to 15% by weight, preferably 1 to 10% by weight, and more preferably 3 to 9% by weight of the alpha olefin. If the amount of the alpha-olefin is less than 1% by weight, the crystallinity becomes higher than necessary and the transparency is lowered. When the amount of the alpha-olefin is more than 15% by weight, the crystallinity and rigidity are lowered and the heat resistance is significantly lowered.

The ethylene-propylene block copolymer contains 20 to 50% by weight of ethylene and imparts impact resistance to the polypropylene resin composition and allows fine dispersion of the polypropylene resin composition to impart both whitening resistance and transparency. The ethylene content may be preferably 20 to 40% by weight, and if it is less than 20% by weight, impact resistance is deteriorated. If it exceeds 50% by weight, impact resistance and whitening resistance may be deteriorated.

According to one embodiment of the present invention, the intelligent electric motor condition diagnosis apparatus tracks and monitors changes in the characteristics of the electric wire power, deterioration, and insulation, which occur while operating each motor, to the database, And can easily and accurately diagnose the condition of the motor.

In addition, the embodiment of the present invention has an advantage that the motor monitoring function can be realized by only electric elements without mechanical elements, simplification of configuration, reduction of investment cost of sensing and control facilities, and reuse of existing resources.

Through the advantages described above, it is possible to prevent and diagnose the process equipment electrically connected to the motor operated by the motor at the minimum cost, and to improve the convenience of use by integrating the motor operation system and the management system.

FIG. 4 is a diagram illustrating an exemplary computing environment in which one or more embodiments disclosed herein may be implemented, and is illustrative of a system 1000 including a computing device 1100 configured to implement one or more of the embodiments described above. / RTI > For example, the computing device 1100 may be a personal computer, a server computer, a handheld or laptop device, a mobile device (mobile phone, PDA, media player, etc.), a multiprocessor system, a consumer electronics device, A distributed computing environment including any of the above-described systems or devices, and the like.

The computing device 1100 may include at least one processing unit 1110 and memory 1120. [ The processing unit 1110 may include, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array And may have a plurality of cores. The memory 1120 can be a volatile memory (e.g., RAM, etc.), a non-volatile memory (e.g., ROM, flash memory, etc.) or a combination thereof. In addition, the computing device 1100 may include additional storage 1130. Storage 1130 includes, but is not limited to, magnetic storage, optical storage, and the like. The storage 1130 may store computer readable instructions for implementing one or more embodiments as disclosed herein, and other computer readable instructions for implementing an operating system, application programs, and the like. The computer readable instructions stored in storage 1130 may be loaded into memory 1120 for execution by processing unit 1110. In addition, computing device 1100 may include input device (s) 1140 and output device (s) 1150.

Here, input device (s) 1140 may include, for example, a keyboard, a mouse, a pen, a voice input device, a touch input device, an infrared camera, a video input device, or any other input device. Also, output device (s) 1150 can include, for example, one or more displays, speakers, printers, or any other output device. In addition, computing device 1100 may use an input device or output device included in another computing device as input device (s) 1140 or output device (s) 1150. [ The computing device 1100 may also include communication connection (s) 1160 that allow the computing device 1100 to communicate with other devices (e.g., computing device 1300).

(S) 1160 may include a modem, a network interface card (NIC), an integrated network interface, a radio frequency transmitter / receiver, an infrared port, a USB connection or other Interface. Also, the communication connection (s) 1160 may include a wired connection or a wireless connection. Each component of the computing device 1100 described above may be connected by various interconnects (e.g., peripheral component interconnect (PCI), USB, firmware (IEEE 1394), optical bus architecture, etc.) And may be interconnected by the network 1200. As used herein, the terms "component," "system," and the like generally refer to a computer-related entity, which is hardware, a combination of hardware and software, software, or software in execution.

For example, an element may be, but is not limited to being, a processor, an object, an executable, an executable thread, a program and / or a computer running on a processor. For example, both the application running on the controller and the controller may be components. One or more components may reside within a process and / or thread of execution, and the components may be localized on one computer and distributed among two or more computers.

It will be understood by those skilled in the art that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the invention as defined by the appended claims. will be. Therefore, the embodiments disclosed in the present invention and the accompanying drawings are intended to illustrate and not to limit the technical idea of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments and the accompanying drawings . The scope of protection of the present invention should be construed according to the following claims, and all technical ideas which are within the scope of the same should be interpreted as being included in the scope of the present invention.

100: Intelligent motor condition diagnosis device
110: current-voltage measuring section
120: Power frequency acquisition unit
130: motor condition measuring unit
140:
141: Deterioration diagnosis unit
142: Leakage power diagnosis unit
143: Insulation resistance state diagnosis part
144:

Claims (5)

A current-voltage measuring unit for measuring a current-voltage corresponding to power supplied to each of the plurality of motors and leakage power;
A power frequency obtaining unit connected to the current-voltage measuring unit to periodically obtain a power frequency of power supplied to each of the plurality of motors; And
And a monitoring unit for comparing the power frequency with a reference power frequency to predict a load state of the electric motor and predict an abnormal state and a replacement time of the electric motor using the load state, deterioration, leakage power, and insulation resistance,
The power frequency obtaining unit
Further comprising a synchronization control unit for synchronizing an acquisition cycle of the power frequency of each of the plurality of electric motors so as to easily acquire a change in the power frequency of each of the plurality of electric motors over time,
The monitoring unit
A deterioration diagnosis unit for diagnosing deterioration using the power frequency of the electric motor;
A leakage power diagnosis unit for determining whether an abnormality of a difference voltage, a difference current, and a grounding current of the motor using the current-voltage corresponding to the leakage power is detected; And
An insulation resistance state diagnostic unit for predicting and diagnosing an insulation resistance state of the motor based on the deterioration and the leakage power;
A fault diagnosis unit for diagnosing frequency-domain faults of the motor; And
And a replacement time predicting unit for diagnosing a state abnormality using the result of each diagnosis unit and predicting a replacement time of the electric motor according to the state abnormality,
The insulation resistance state diagnosis unit
An insulation resistance state of the motor is calculated using an effective insulation resistance value calculated by using a leakage current detected from a line connected between the motor and the distribution board and an output current of the motor and an insulation resistance value calculated using the deterioration, Diagnosis device for intelligent electric motor diagnosing.
delete delete The method according to claim 1,
The degradation diagnosis unit
Diagnosing a deterioration progress state of the electric motor based on whether a specific frequency is generated according to a state change of the electric motor,
Wherein the specific frequency is a frequency calculated by a power frequency of the motor, a number of slots and poles of the stator, a number of bar rotors, and a rotation frequency.

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