Predictive maintenance system of construction machinery

Abstract

The present invention relates to a predictive maintenance system of construction machinery. The present invention comprises: mechanical equipment having a rotary shaft; a power source for transmitting current to the mechanical equipment; sensor units, attached to a plurality of points on the mechanical equipment, for obtaining vibration data in three axial directions; and a processor for receiving the vibration data from the sensor units and diagnosing faults, wherein the processor converts the vibration data to the frequency domain, extracts from the converted frequency domain a frequency peak of a preset first band and a frequency peak of a second band different from the first band, and compares the extracted frequency peaks of the first and second bands.

Description

Predictive maintenance system for construction machinery equipment

[0001] An embodiment of the present invention relates to a predictive maintenance system and a predictive maintenance method for construction machinery equipment.

[0002] This research was supported by the Ministry of Trade, Industry & Energy (MOTIE) and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) (No. 20212020800120).

[0003] Machinery and electronic equipment used in critical industries like construction, manufacturing, defense, and information and communications technology (ICT) frequently experience failures. Because failures that occur unexpectedly can be fatal, prognostics and health management (PHM) technology is crucial.

[0004] In particular, various mechanical equipment is used on construction sites, and devices such as electric motors with rotating shafts are essential for construction sites. However, due to the unique characteristics of each site and the large size of construction machinery, it has been difficult to predict or diagnose mechanical equipment failures appropriately for construction sites.

[0005] However, fault diagnosis technology that can prevent accidents caused by failures in construction machinery is emerging as an important aspect, along with maintenance. Korean Patent Publication No. 10-2005-0063441 discloses a device for diagnosing abnormal conditions in induction motors. However, this diagnostic device has difficulty detecting abnormal conditions due to noise for certain low-energy faults, such as bearing failures, and thus, makes it difficult to accurately predict and maintain the condition.

[0006] The present invention relates to a system and method for accurately and quickly performing predictive maintenance on a construction machinery facility based on vibration data measured from the construction machinery facility having a rotating shaft.

[0007] An embodiment of the present invention provides a predictive maintenance system for a building mechanical equipment, comprising: a mechanical equipment having a rotational axis; a power source for transmitting current to the mechanical equipment; a sensor unit mounted at multiple points of the mechanical equipment to obtain vibration data in three-axis directions; and a processor receiving the vibration data from the sensor unit and diagnosing a fault, wherein the processor converts the vibration data into a frequency domain, extracts a frequency peak of a preset first band and a frequency peak of a second band different from the first band from the converted frequency domain, and compares the extracted frequency peak of the first band with the frequency peak of the second band.

[0008] A predictive maintenance system and method for building mechanical equipment according to one embodiment of the present invention can diagnose abnormalities in mechanical equipment or predict failures and perform predictive maintenance by comparing a frequency peak in a normal state with a monitored and measured frequency peak.

[0009] A predictive maintenance system and method for building mechanical equipment according to one embodiment of the present invention can diagnose abnormalities in mechanical equipment or predict failures and perform predictive maintenance by comparing a frequency peak according to a frequency band in a normal state with a frequency peak according to a measured frequency band or patterning the same.

[0010] FIG. 1 is a drawing illustrating a predictive maintenance system for construction machinery equipment according to one embodiment of the present invention.

[0011] Figure 2 is a cross-sectional view showing the mechanical equipment of Figure 1.

[0012] Figure 3 is a drawing showing a part of the configuration of Figure 1.

[0013] FIG. 4 is a block diagram illustrating a portion of the processor of FIG. 3.

[0014] FIG. 5 and FIG. 6 are block diagrams illustrating a modified example of the processor of FIG. 4.

[0015] Figure 7 is a graph showing vibration data measured by the first sensor in a normal state.

[0016] Figures 8 and 9 are graphs showing vibration data measured by the first sensor in an abnormal state.

[0017] Figure 10 is a graph showing vibration data measured by the fourth sensor in an abnormal state.

[0018] Figure 11 is a graph showing vibration data measured by the second sensor in a normal state.

[0019] Figures 12 and 13 are graphs showing vibration data measured by the second sensor in an abnormal state.

[0020] Figures 14 and 15 are graphs showing vibration data measured by the fourth sensor in an abnormal state.

[0021] Figure 16 is a graph showing data measured by the fifth sensor in an abnormal state.

[0022] An embodiment of the present invention provides a predictive maintenance system for a building mechanical equipment, comprising: a mechanical equipment having a rotational axis; a power source for transmitting current to the mechanical equipment; a sensor unit mounted at multiple points of the mechanical equipment to obtain vibration data in three-axis directions; and a processor receiving the vibration data from the sensor unit and diagnosing a fault, wherein the processor converts the vibration data into a frequency domain, extracts a frequency peak of a preset first band and a frequency peak of a second band different from the first band from the converted frequency domain, and compares the extracted frequency peak of the first band with the frequency peak of the second band.

[0023] Additionally, the processor may have a different number of frequency peaks extracted from the first band and a different number of frequency peaks extracted from the second band.

[0024] Additionally, the processor can compare a first value obtained by averaging frequency peaks extracted from the first band with a second value obtained by averaging frequency peaks extracted from the second band, and diagnose a failure if the difference between the first value and the second value is outside a preset range.

[0025] Additionally, the processor can merge three-axis vibration data received from the sensor unit and convert the merged vibration data into a frequency domain.

[0026] The present invention is capable of various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. The effects and features of the present invention, as well as the methods for achieving them, will become clearer with reference to the embodiments described in detail below, along with the drawings. However, the present invention is not limited to the embodiments disclosed below and can be implemented in various forms.

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. When describing with reference to the drawings, identical or corresponding components are given the same reference numerals and redundant descriptions thereof will be omitted.

[0028] In the examples below, the terms first, second, etc. are not used in a limiting sense, but are used for the purpose of distinguishing one component from another.

[0029] In the examples below, singular expressions include plural expressions unless the context clearly indicates otherwise.

[0030] In the examples below, terms such as “include” or “have” mean that a feature or component described in the specification is present, and do not preclude the possibility that one or more other features or components may be added.

[0031] In the following examples, when a part such as a film, region, component, etc. is said to be on or above another part, it includes not only a case where it is directly on top of the other part, but also a case where another film, region, component, etc. is interposed in between.

[0032] For convenience of explanation, the sizes of components in the drawings may be exaggerated or reduced. For example, the sizes and thicknesses of each component shown in the drawings are arbitrarily indicated for convenience of explanation, and thus the present invention is not necessarily limited to what is shown.

[0033] In some embodiments, where implementations are otherwise feasible, specific process sequences may be performed in a different order than described. For example, two processes described in succession may be performed substantially simultaneously, or in a reverse order from the described order.

[0034] In the following examples, when it is said that a film, region, component, etc. are connected, it includes not only cases where the films, regions, and components are directly connected, but also cases where other films, regions, and components are interposed between the films, regions, and components and thus indirectly connected. For example, when it is said in this specification that a film, region, component, etc. are electrically connected, it includes not only cases where the films, regions, and components are directly electrically connected, but also cases where other films, regions, and components are interposed between them and thus indirectly electrically connected.

[0035] FIG. 1 is a drawing illustrating a predictive maintenance system for a construction machinery facility according to one embodiment of the present invention, FIG. 2 is a cross-sectional view illustrating the machinery facility of FIG. 1, and FIG. 3 is a drawing illustrating a part of the configuration of FIG. 1.

[0036] Referring to FIGS. 1 to 3, a predictive maintenance system (1) for a construction machine facility can predict and maintain the presence or absence of an abnormality in the machine facility (100) based on data measured in the machine facility (100).

[0037] In one embodiment of the present invention, a predictive maintenance system (1) for a construction machinery facility may include a machinery facility (100), a processor (200), a sensor unit (300), a power source (400), a data storage unit (500), and a user interface device (600).

[0038] Mechanical equipment (100) is defined as equipment used in the construction industry, and may be, in particular, a rotating machine. Mechanical equipment (100) having a rotating shaft generates vibration or noise due to rotation, and this can be monitored and judged for predictive maintenance.

[0039] Referring to FIG. 2, the mechanical equipment (100) may include a driver (110), a rotating shaft (120), a bearing unit (130), a fan (140), and a housing (150).

[0040] The driver (110) can receive current from the power source (400) and transmit driving force to the rotation shaft (120). In one embodiment, the driver (110) may be an electric motor. However, the present invention is not limited thereto and may be various mechanical devices that generate rotational force.

[0041] The rotation shaft (120) can be connected to the driver (110). Another mechanical device (not shown) can be connected to the rotation shaft (120), so that the power of the mechanical equipment (100) can be transmitted to the mechanical device.

[0042] The rotation axis (120) may have a first rotation axis (121) arranged at the front end of the driver (110) and a second rotation axis (122) arranged at the rear end of the driver (110).

[0043] The first rotation shaft (121) is connected to the first bearing (131) and can be connected to an external mechanical device. The first rotation shaft (121) is arranged so that at least a portion of it is exposed to the outside, so that it can be connected to the mechanical device.

[0044] The second rotation shaft (122) is connected to the second bearing (132) and can be placed inside the housing (150). The second rotation shaft (122) is equipped with a fan (140) and can drive the fan (140).

[0045] The bearing unit (130) can be inserted into the rotation shaft (120) so that the rotation shaft (120) can be installed rotatably. The first bearing (131) can be mounted on the first rotation shaft (121), and the second bearing (132) can be mounted on the second rotation shaft (122).

[0046] A fan (140) can cool the mechanical equipment (100). The fan (140) is mounted at the rear of the driver (110) and can cause external air to flow to the driver (110) by the rotation of the second rotation shaft (122). A second bearing (132) can be mounted on one side of the fan (140).

[0047] The housing (150) forms the exterior of the mechanical equipment (100), and a driver (110), a rotation shaft (120), a bearing unit (130), and a fan (140) can be placed in the internal space.

[0048] The housing (150) may be divided into a plurality of parts. For example, the housing (150) may include a first bracket (151), a second bracket (152), and a main body (153). The first bracket (151) covers the first rotational axis (121), and a first bearing (131) may be arranged inside the first bracket (151). The second bracket (152) covers the second rotational axis (122), and a second bearing (132) may be arranged inside the second bracket (152). The main body (153) may be arranged between the first bracket (151) and the second bracket (152), and may cover the outside of the driver (110).

[0049] The housing (150) can be equipped with multiple sensors. A sensor unit (300) is mounted in a preset area of ​​the housing (150) to measure vibrations transmitted to the housing (150).

[0050] The processor (200) can receive data from the sensor unit (300) and diagnose a failure. The processor (200) can diagnose a failure of the mechanical equipment (100) based on data on vibration, noise, or current changes measured by the sensor unit (300).

[0051] The processor (200) is electrically connected to the data storage unit (500), and can store data measured by the sensor unit (300) in the data storage unit (500), or receive data from the data storage unit (500) and use it as data for machine learning.

[0052] The processor (200) may be connected to a user interface device (600). The user interface device (600) may receive an input signal from a user and transmit it to the processor (200), or display an alarm signal generated by the processor (200) so that the user may recognize it.

[0053] In an optional embodiment, the processor (200) may be connected to a communication unit (50) and may be connected to an external diagnostic management server (2). The diagnostic management server (2) may be configured to record diagnostic result information received through the communication unit (50) as diagnostic history information in a database (not shown) corresponding to the unique identification information of the processor (200) and provide it in a viewable manner, and to transmit diagnostic information to an administrator terminal (not shown) when an emergency notification is required to an administrator based on received failure information.

[0054] The sensor unit (300) is mounted on one side of the mechanical equipment (100), measures the status of the mechanical equipment (100), and can transmit the measured data to the processor (200). For example, the sensor unit (300) can measure vibration data, noise data, or current data generated from the mechanical equipment (100).

[0055] In one embodiment, the sensor unit (300) may include a first sensor (310), a second sensor (320), and a third sensor (330) that measure vibration. The first sensor (310), the second sensor (320), and the third sensor (330) may be mounted at preset locations of the mechanical equipment (100).

[0056] The first sensor (310) may be installed on the front side of the mechanical equipment (100). The first sensor (310) may be positioned adjacent to the first rotation axis (121) of the mechanical equipment (100). The first sensor (310) may be installed on one side of the first bracket (151) and may measure vibration occurring near the first rotation axis (121) of the mechanical equipment (100).

[0057] The second sensor (320) may be installed at the rear side of the mechanical equipment (100). The second sensor (320) may be positioned adjacent to the second rotation axis (122) of the mechanical equipment (100). The second sensor (320) may be installed at one side of the second bracket (152) to measure vibration occurring near the second rotation axis (122) of the mechanical equipment (100).

[0058] The third sensor (330) can be installed in the middle of the mechanical equipment (100). The second sensor (320) can be installed in the main body (153) of the mechanical equipment (100) to measure vibrations generated from the driver (110) of the mechanical equipment (100).

[0059] The first sensor (310), the second sensor (320), and the third sensor (330) can measure vibration data in three-axis directions. Referring to FIG. 7, the first sensor (310) can measure vibration data in the X-axis, Y-axis, and Z-axis and transmit the same to the processor (200). In addition, referring to FIG. 11, the second sensor (320) can measure vibration data in the X-axis, Y-axis, and Z-axis and transmit the same to the processor (200). Since the first sensor (310), the second sensor (320), and the third sensor (330) measure vibration data in the three-axis directions, the mechanical equipment (100) can be accurately maintained.

[0060] The fourth sensor (340) can measure noise generated from the mechanical equipment (100). The fourth sensor (340) is mounted at a preset location of the mechanical equipment (100) and can acquire noise data generated from the mechanical equipment (100). In FIG. 3, an embodiment in which the fourth sensor (340) is mounted on the main body (153) is shown, but the fourth sensor (340) is not limited thereto and may be installed on the first bracket (151) or the second bracket (152). In addition, the fourth sensor (340) may be installed in the internal space of the mechanical equipment (100), i.e., inside the housing (150).

[0061] The fifth sensor (350) is installed between the mechanical equipment (100) and the power source (400) and can measure the amount of current provided from the power source (400) to the mechanical equipment (100). The fifth sensor (350) can measure the amount of current provided to the mechanical equipment (100) in order to diagnose whether there is a phase short circuit in the mechanical equipment (100).

[0062] Specifically, current is supplied to the mechanical equipment (100) through three lines, and if any one of the three is defective, the current flowing into the remaining two lines increases. At this time, even if current is supplied to two lines, the mechanical equipment (100) operates, but there is a risk of damage or malfunction, so predictive maintenance is required. The fifth sensor (350) can measure data on the current supplied to the mechanical equipment (100) and perform predictive maintenance to determine whether a short circuit has occurred.

[0063] The power source (400) can provide current to drive the mechanical equipment (100). A fifth sensor (350) is arranged between the power source (400) and the mechanical equipment (100) to sense changes in the current.

[0064] The data storage unit (500) is connected to the processor (200) and can store data measured by the sensor unit (300). In addition, by transmitting the data stored in the processor (200), the processor (200) can provide basic data for predictive maintenance.

[0065] The user interface device (600) can provide a signal input from the user and inform the user of the results diagnosed by the processor (200). The user interface device (600) can receive the user's input signal by including a touch screen, a keyboard, etc. In addition, the user interface device (600) can include an alarm such as an LED, a microphone, a vibration element, etc., and can convey the diagnosis result or alarm signal to the user.

[0066] FIG. 4 is a block diagram illustrating a part of the processor of FIG. 3, and FIGS. 5 and 6 are block diagrams illustrating modified examples of the processor of FIG. 4.

[0067] Referring to FIGS. 3 and 4, the processor (200) converts data measured by the sensor unit (300) into a frequency domain, extracts a frequency peak of a preset first band and a frequency peak of a second band different from the first band from the converted frequency domain, and compares the extracted frequency peak of the first band with the frequency peak of the second band.

[0068] The processor (200) may have different numbers of frequency peaks extracted from the first band and different numbers of frequency peaks extracted from the second band.

[0069] In addition, the processor (200) can compare a first value obtained by averaging frequency peaks extracted from the first band with a second value obtained by averaging frequency peaks extracted from the second band, and can diagnose a failure if the difference between the first value and the second value is outside a preset range.

[0070] In one embodiment, the processor (200) may include a filter unit (210), an FFT transform unit (220), and a diagnostic unit (230).

[0071] The filter unit (210) can filter data measured by the sensor unit (300). The filter unit (210) is a band-pass filter that can pass only the frequency band of interest in the digital signal. The filter unit (210) filters a range corresponding to noise, and the cutoff frequency characteristics can change and be adjusted depending on the characteristics of vibration, noise, and current signals.

[0072] The FFT conversion unit (220) converts the received data into frequency domain data through a fast Fourier transform process. For example, through the FFT conversion unit (220), data can be generated as converted data as shown in FIGS. 7 to 16.

[0073] The diagnostic unit (230) can perform predictive maintenance of the mechanical equipment (100) based on preprocessed data. The diagnostic unit (230) can determine whether the mechanical equipment (100) is in a normal or abnormal state. Furthermore, in an abnormal state, the diagnostic unit (230) can diagnose whether the mechanical equipment (100) requires additional diagnosis or whether a failure has been confirmed and repairs are required.

[0074] The diagnostic unit (230) may have a peak extraction unit (231) that distinguishes preset bands and extracts frequency peaks in each band, a comparison unit (232) that compares frequency peaks in each band, and a judgment unit (233) that judges a result based on the result judged by the comparison unit.

[0075] The peak extraction unit (231) can distinguish bands for comparison. Each band can be set in various ways depending on the target output and installation location of the machine equipment (100).

[0076] For example, the peak extraction unit (231) can distinguish between a first band and a second band. The first band can be set to be above 0 Hz and below 2000 Hz, and the second band can be set to be above 2000 Hz and below 5000 Hz.

[0077] At this time, the peak extraction unit (231) can extract a preset number of frequency peaks from the first band. In addition, the peak extraction unit (231) can extract each frequency peak from the three axes.

[0078] For example, the peak extraction unit (231) can extract two peaks. Referring to FIG. 7, the peak extraction unit (231) can extract one frequency peak between 300 Hz and 800 Hz, and one frequency peak between 1000 Hz and 1500 Hz. At this time, the peak extraction unit (231) can extract frequency peaks from data on the X-axis, Y-axis, and Z-axis, respectively.

[0079] The peak extraction unit (231) can extract a preset number of frequency peaks in the second band. In addition, the peak extraction unit (231) can extract frequency peaks in each of the three axes.

[0080] For example, the peak extraction unit (231) can extract three peaks. Referring to FIG. 7, the peak extraction unit (231) can extract one frequency peak between 2300 Hz and 3300 Hz, one frequency peak between 3300 Hz and 4000 Hz, and one frequency peak between 4000 Hz and 5000 Hz. At this time, the peak extraction unit (231) can extract frequency peaks from the data of the X-axis, Y-axis, and Z-axis, respectively.

[0081] The comparison unit (232) can compare the frequency peaks extracted from the peak extraction unit (231).

[0082] The comparison unit (232) can calculate a first value that is an average of two frequency peaks extracted from the first band, and a second value that is an average of three frequency peaks extracted from the second band.

[0083] Table 1 below shows the average of peak values ​​extracted from the first and second bands of the three-axis vibration data. While the average value in the second band is greater than the average value in the first band, the deviation is within a certain range.

[0084] X-axisY-axisZ-axis1st band0.0251g0.017785g0.0198g2nd band0.0362g0.0624g0.022g

[0085] The judgment unit (233) can determine whether there is an abnormality in the mechanical equipment by judging the difference in the frequency peak. The judgment unit (233) can use the data in the normal state as reference data and compare the reference data with the monitored data to determine whether there is an abnormality. For example, since the data in FIG. 7 is data measured when the mechanical equipment is operating in a normal state, the data in FIG. 7 can be used as reference data.

[0086] In one embodiment, the judgment unit (233) compares the average values ​​of the frequency peaks of the first band and the second band, and if the difference is outside the preset range in the normal state, it can diagnose an abnormal state.

[0087] Specifically, the judgment unit (233) can compare a first value, which is an average value of frequency peaks in the first band of the reference data, with a second value, which is an average value of frequency peaks in the second band, to set an error range in a normal state. Thereafter, the comparison unit calculates a difference between the average value of frequency peaks in the first band of the data measured by the sensor unit (300) and the average value of frequency peaks in the second band, and if the result falls outside the preset error range, it can be diagnosed as an abnormal state.

[0088] In one embodiment, the judgment unit (233) compares the deviation of the frequency peak of the first band with the deviation of the frequency peak of the second band, and if the difference deviates from the deviation in the normal state, it can diagnose an abnormal state.

[0089] Specifically, the comparison unit (232) calculates the deviation of two frequency peaks in the first band of the reference data and the deviation of three frequency peaks in the second band, and the judgment unit (233) can determine that an abnormal state exists if each deviation is outside a preset range. In a normal state, the deviation of each frequency peak has a preset error as shown in Fig. 7, but in an abnormal state, the error range is measured to be large as shown in Figs. 8 and 9.

[0090] In one embodiment, the judgment unit (233) can determine whether the frequency peak of the first band and the frequency peak of the second band can be extracted, and if the frequency peak cannot be extracted, it can diagnose that there is an abnormality.

[0091] Specifically, in a normal state, as shown in FIG. 7, a frequency peak can be extracted from a preset band by a peak extraction unit (231). However, in an abnormal state, as shown in FIGS. 8 and 9, it is difficult to extract a frequency peak from a preset band, and the number of frequency peaks does not correspond to the preset number. Accordingly, the determination unit (233) can determine that a state is abnormal if it cannot extract a frequency peak or if the number of extracted peaks does not meet the required number.

[0092] In one embodiment, the judgment unit (233) may compare the average value of the frequency peak of the first band in the reference data with the average value of the frequency peak of the first band in the measured data, and diagnose an abnormality if the difference is outside a preset range. In addition, the judgment unit (233) may compare the average value of the frequency peak of the second band in the reference data with the average value of the frequency peak of the second band in the measured data, and diagnose an abnormality if the difference is outside a preset range.

[0093] Specifically, the average value of the frequency peak in the first band in the normal state of Fig. 7 can be compared with the average value of the frequency peak in the first band of the measured data. If the measured data is measured as in Fig. 8 or Fig. 9, the difference in the average value is outside the preset range, so it can be diagnosed as an abnormal state. In addition, the average value of the frequency peak in the second band in the normal state of Fig. 7 can be compared with the average value of the frequency peak in the second band of the measured data. If the measured data is measured as in Fig. 8 or Fig. 9, the difference in the average value is outside the preset range, so it can be diagnosed as an abnormal state.

[0094] Referring to FIG. 5, the diagnostic unit (230A) of the modified example of the present invention may include a merging unit (231AP), a peak extraction unit (231A), a comparison unit (232A), and a judgment unit (233A). The peak extraction unit (231A), the comparison unit (232A), and the judgment unit (233A) are substantially the same as the peak extraction unit (231), the comparison unit (232), and the judgment unit (233) of the aforementioned practical example, and thus, a detailed description thereof will be omitted or briefly described.

[0095] The processor can merge three-axis vibration data received from the sensor unit (300) and convert the merged vibration data into a frequency domain. Referring to FIG. 7, three-axis data measured by the sensor unit (300) can be merged. In a normal state, each frequency peak in the three axes is formed within a preset range.

[0096] Comparing the three-axis data in Fig. 7, the two frequency peaks in the first band have similar extracted bands, and the three frequency peaks in the second band have similar extracted bands. Therefore, since the extracted bands of each frequency peak are similar, even when the three-axis data are merged, the frequency range in the steady state remains within the preset range, and thus the steady-state frequency characteristics can be obtained.

[0097] The merger unit (231AP) merges three-axis frequency data to enhance the frequency characteristics in a steady state. Since the merger unit (231AP) merges data in the three axes in a steady state, the number of reference data used as a comparison standard is reduced, enabling rapid computational processing. In addition, the merger unit (231AP) merges data from the three axes that are being monitored and measured.

[0098] The peak extraction unit (231A) extracts frequency peaks from the merged reference data and the merged measurement data. The comparison unit (232A) compares each band of the merged reference data and the merged measurement data, and the judgment unit (233A) can determine a normal state and an abnormal state based on the compared results.

[0099] Referring to FIG. 6, the diagnostic unit (230B) of the modified example of the present invention may include a patterning unit (231B), a pattern comparison unit (232B), and a judgment unit (233B).

[0100] The patterning unit (231B) can pattern reference data or measured data. Since frequency peaks are extracted from a specific band in a normal state, frequency peaks in a specific band can be patterned. Additionally, for monitoring purposes, data measured by the sensor unit can also be patterned into peak values ​​in the specific band.

[0101] The pattern comparison unit (232B) can compare the pattern of reference data with the pattern of measured data to determine whether there is similarity.

[0102] The judgment unit (233B) can determine whether the machine equipment is in a normal or abnormal state based on the matching rate measured by the pattern comparison unit (232B). If the matching rate is high compared to the reference pattern, which is in a normal state, the machine equipment is judged to be in a normal state, but if the matching rate is low, the machine equipment is judged to be in an abnormal state.

[0103] The diagnostic unit (230B) can quickly and simply determine or diagnose whether there is an abnormality in the mechanical equipment (100) by patterning the reference data and measured data. In a normal state, a frequency peak is observed in a preset frequency band, so a reference pattern is generated by patterning the frequency band and peak, and the measured data is also patterned for the preset frequency band and peak, and this is compared with the reference pattern to diagnose whether there is an abnormality in the mechanical equipment (100).

[0104] Fig. 7 is a graph showing vibration data measured by the first sensor in a normal state, and Figs. 8 and 9 are graphs showing vibration data measured by the first sensor in an abnormal state.

[0105] Fig. 8 is data measured in a state where replacement is required due to a defect in the first rotation shaft (121), and Fig. 9 is data measured in a state where replacement is required due to a defect in the first bracket (151) and the second rotation shaft (122). Fig. 7 is assumed to be reference data, and Figs. 8 and 9 are assumed to be data measured when the mechanical equipment is in an abnormal state.

[0106] The graphs of FIGS. 7 to 9 can be divided into a first band (above 0 Hz and below 2000 Hz) and a second band (above 2000 Hz and below 5000 Hz). The first band can be set to , and the second band can be set to .

[0107] The processor (200) extracts frequency peaks of the first band and the second band.

[0108] In one embodiment, in the normal state of FIG. 7, when comparing the three-axis data, the frequency peak can be extracted within a preset error range. However, in an abnormal state, such as FIG. 8 or FIG. 9, when comparing the three-axis data, the frequency peak has a large error range, so the processor (200) can diagnose an abnormal state by comparing it with reference data.

[0109] For example, in the normal state of Fig. 7, two preset frequency peaks can be extracted from the first band, and three preset frequency peaks can be extracted from the second band. However, in an abnormal state such as Fig. 8 or Fig. 9, two frequencies are not extracted from the first band, and three frequency peaks are not extracted from the third band. Even if the preset numbers are extracted from the first and second bands, the deviation of the peaks is large, and they are not extracted from the preset frequency bands (for example, the first band should extract one frequency peak between 300 Hz and 800 Hz, and one frequency peak between 1000 Hz and 1500 Hz, but they are not extracted from each frequency band).

[0110] For example, the processor can calculate a first value by averaging the frequency peaks of the first band, calculate a second value by averaging the frequency peaks of the second band, and compare these to diagnose whether there is an abnormality. Referring to FIG. 7 and Table 1 above, the average values ​​in the first and second bands may have a set error range. However, referring to FIG. 8 and FIG. 9, the average values ​​of the frequency peaks extracted from the first and second bands have a large error range, and thus an abnormality can be diagnosed based on this.

[0111] Figure 10 is a graph showing vibration data measured by the fourth sensor in an abnormal state. Specifically, it is data measured in a state where replacement is required due to a defect in the first bearing (131).

[0112] Referring to Fig. 10, noise data measured by the fourth sensor can also be divided into a first band and a second band.

[0113] The processor (200) extracts frequency peaks of the first and second bands as described above. At this time, it is possible to determine whether or not the mechanical equipment is abnormal by determining whether or not the frequency peaks can be extracted, whether the values ​​of the extracted frequency peaks fall within the error range, and whether the difference between the average values ​​of the extracted frequency peaks falls within the error range.

[0114] Fig. 11 is a graph showing vibration data measured by the second sensor in a normal state, and Figs. 12 and 13 are graphs showing vibration data measured by the second sensor in an abnormal state. Specifically, Fig. 12 is data measured in a state where replacement is required due to a defect in the first rotation shaft (121) and the second bracket (152), and Fig. 13 is data measured in a state where replacement is required due to a defect in the first bracket (151) and the second rotation shaft (122).

[0115] Referring to FIGS. 11 to 13, vibration data measured by the second sensor (320) can also be divided into a first band and a second band.

[0116] The processor (200) extracts frequency peaks of the first and second bands as described above. At this time, it is possible to determine whether or not the mechanical equipment is abnormal by determining whether or not the frequency peaks can be extracted, whether the values ​​of the extracted frequency peaks fall within the error range, and whether the difference between the average values ​​of the extracted frequency peaks falls within the error range.

[0117] Figures 14 and 15 are graphs showing vibration data measured by the fourth sensor in an abnormal state, and Figure 16 is a graph showing data measured by the fifth sensor in an abnormal state.

[0118] Specifically, Fig. 14 is data measured in a state requiring replacement due to a defect in the second bearing (132), and Fig. 15 is data measured in a state requiring replacement due to a defect in the fan (140). In addition, Fig. 16 is data on current measured by the fifth sensor in a state of a phase-open short circuit.

[0119] The processor (200) extracts frequency peaks of the first and second bands as described above. At this time, it is possible to determine whether or not the mechanical equipment is abnormal by determining whether or not the frequency peaks can be extracted, whether the values ​​of the extracted frequency peaks fall within the error range, and whether the difference between the average values ​​of the extracted frequency peaks falls within the error range.

[0120] While the present invention has been described with reference to the embodiments illustrated in the drawings, these are merely exemplary, and those skilled in the art will appreciate that various modifications and variations of the embodiments are possible. Therefore, the true scope of technical protection of the present invention should be determined by the technical spirit of the appended claims.

[0121] -National Research and Development Project that supported this invention

[0122] - Assignment ID: 1415186571

[0123] - Assignment number: 20212020800120

[0124] - Ministry name: Ministry of Trade, Industry and Energy

[0125] - Project Management (Specialist) Organization Name: Korea Institute of Energy Technology Evaluation and Planning

[0126] - Research Project Name: Development of Core Energy Demand Management Technology (E-Tech)

[0127] - Research Project Name: Development of an Intelligent Building Energy and Environment Integrated Management System (iBEEMS) based on Autonomous Driving

[0128] - Contribution rate: 1 / 1

[0129] - Project implementation organization name: Dankook University Industry-Academic Cooperation Foundation

[0130] Research period: January 1, 2023 - December 31, 2023

Claims

1. Mechanical equipment having a rotating shaft; A power source that transmits current to the above mechanical equipment; A sensor unit mounted at multiple points of the above mechanical equipment to obtain vibration data in three-axis directions; and A processor that receives vibration data from the sensor unit and diagnoses a fault; The above processor Convert the above vibration data into the frequency domain, In the converted frequency domain, the frequency peak of the preset first band and the frequency peak of the second band different from the first band are extracted, A predictive maintenance system for building mechanical equipment, which compares the frequency peak of the extracted first band with the frequency peak of the extracted second band.

2. In paragraph 1, The above processor A predictive maintenance system for building mechanical equipment, wherein the number of frequency peaks extracted from the first band and the number of frequency peaks extracted from the second band are different.

3. In paragraph 1, The above processor Compare a first value obtained by averaging frequency peaks extracted from the first band with a second value obtained by averaging frequency peaks extracted from the second band, A predictive maintenance system for construction machinery equipment, which diagnoses a failure when the difference between the first value and the second value exceeds a preset range.

4. In paragraph 1, The above processor A predictive maintenance system for building mechanical equipment that merges three-axis vibration data received from the above sensor unit and converts the merged vibration data into a frequency domain.

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