WO2022176730A1 - Système de diagnostic, procédé de diagnostic, et programme - Google Patents

Système de diagnostic, procédé de diagnostic, et programme Download PDF

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Publication number
WO2022176730A1
WO2022176730A1 PCT/JP2022/005064 JP2022005064W WO2022176730A1 WO 2022176730 A1 WO2022176730 A1 WO 2022176730A1 JP 2022005064 W JP2022005064 W JP 2022005064W WO 2022176730 A1 WO2022176730 A1 WO 2022176730A1
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WIPO (PCT)
Prior art keywords
diagnostic
frequency
equipment
value
analysis
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PCT/JP2022/005064
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English (en)
Japanese (ja)
Inventor
稔 杉浦
靖 森下
具承 増田
智城 笹森
英 酒田
和弘 露木
Original Assignee
三菱パワー株式会社
三菱重工業株式会社
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Publication of WO2022176730A1 publication Critical patent/WO2022176730A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/02—Gearings; Transmission mechanisms
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring

Definitions

  • the present disclosure relates to diagnostic systems, diagnostic methods and programs.
  • the present disclosure claims priority based on Japanese Patent Application No. 2021-025194 filed in Japan on February 19, 2021, the contents of which are incorporated herein.
  • a diagnostic method sets a threshold for the value of the state quantity of the device, and diagnoses an abnormality when the value exceeds the threshold.
  • the measured values of a plurality of state quantities are acquired, the range of values that each state quantity can take after a predetermined time is predicted, and a plurality of A state analysis device is disclosed that displays a figure having a shape corresponding to the range of possible values of the state quantity.
  • a graphic indicating the range of possible values of the state quantity is compared with a threshold to diagnose whether the device can transition to an abnormal state in the future.
  • the present disclosure provides a diagnostic system, diagnostic method, and program that can solve the above problems.
  • a diagnostic system of the present disclosure includes a measurement result acquisition unit that acquires a measurement value of a current supplied to each of a plurality of facilities, an analysis unit that performs frequency analysis on the measurement value, and a predetermined frequency from the analysis result of the frequency analysis. as an evaluation value indicating the state of the equipment, and an output unit for outputting the evaluation values for each equipment for comparison.
  • a diagnostic system of the present disclosure is a diagnostic system comprising a terminal device and a diagnostic device capable of communicating with the terminal device, wherein the terminal device includes request means for requesting diagnosis of the state of each of a plurality of facilities; an output unit for outputting to compare the evaluation values for each of the facilities extracted by the extracting unit of the diagnostic device, wherein the diagnostic device outputs current supplied to each of the plurality of facilities in response to a request from the terminal device a measurement result acquisition unit that acquires the measured value of; an analysis unit that performs frequency analysis on the measured value; and
  • the diagnostic method of the present disclosure includes the steps of obtaining measured values of currents supplied to each of a plurality of pieces of equipment, performing frequency analysis on the measured values, and frequency component values of a predetermined frequency from the analysis results of the frequency analysis. as an evaluation value indicating the state of the equipment, and a step of outputting the evaluation values extracted for each equipment for comparison.
  • a diagnostic method of the present disclosure is a diagnostic method using a terminal device and a diagnostic device capable of communicating with the terminal device, wherein the terminal device requests diagnosis of the state of each of a plurality of facilities; and an output step of outputting for comparison the evaluation values for each of the facilities extracted by the diagnostic device extraction step, and the diagnostic device is supplied to each of the plurality of facilities in response to a request from the terminal device.
  • a measurement result acquisition step of acquiring a measured value of current, an analysis step of frequency-analyzing the measured value, and extracting a frequency component value of a predetermined frequency from the analysis result of the frequency analysis as an evaluation value indicating the state of the equipment. perform an extraction step;
  • the program of the present disclosure provides a computer with a step of acquiring a measured value of a current supplied to each of a plurality of facilities, a step of frequency analysis of the measured value, and a frequency of a predetermined frequency from the analysis result of the frequency analysis A step of extracting a component value as an evaluation value indicating the state of the equipment, and a step of outputting the extracted evaluation value for each equipment for comparison are executed.
  • the diagnostic target can be diagnosed appropriately.
  • FIG. 4 is a first diagram showing an example of a diagnostic graph according to the first embodiment; It is a second diagram showing an example of a diagnostic graph according to the first embodiment.
  • FIG. 11 is a third diagram showing an example of a diagnostic graph according to the first embodiment;
  • FIG. 14 is a fourth diagram showing an example of a diagnostic graph according to the first embodiment;
  • FIG. 15 is a fifth diagram showing an example of a diagnostic graph according to the first embodiment;
  • FIG. 11 is a sixth diagram showing an example of a diagnostic graph according to the first embodiment; It is a flow chart which shows an example of operation of a diagnosis system concerning a first embodiment.
  • FIG. 1 is a block diagram showing an example of a diagnostic system according to an embodiment.
  • the diagnostic system 100 includes diagnostic target facilities 10A, 10B, . . .
  • the equipment 10A and the equipment 10B have the same specifications.
  • the same specification means that the specifications of the equipment provided in the facility 10A and the equipment provided in the facility 10B are the same, or that the specifications of each device are only different enough to be considered the same.
  • the specifications are, for example, the material, size, model, function and performance of the device.
  • the facility 10A includes a power source 1A, an electric motor 2A, a transmission device 3A, a load device 4A, and a current measuring device 5A.
  • the electric motor 2A and the transmission device 3A are connected by a shaft 6A, and the transmission device 3A and the load device 4A are connected by a shaft 7A.
  • Power source 1A supplies current to electric motor 2A via wire 8A.
  • the electric motor 2A is rotationally driven by the current supplied from the power source 1A to rotate the shaft 6A.
  • the transmission device 3A transmits the rotation of the shaft 6A to the shaft 7A.
  • the rotation of the shaft 7A drives the load device 4A.
  • the transmission device 3A is, for example, a speed reducer or a belt drive device.
  • the load device 4A is, for example, a fan or a centrifuge.
  • the current measuring device 5A measures the current flowing through the electric wire 8A and transmits the current measurement result to the diagnostic device 20 .
  • the current measuring device 5A converts the measured analog current waveform into digital current data and transmits the digital current data to the diagnostic device 20 .
  • the power source 1A, the electric motor 2A, the transmission device 3A, the load device 4A, the current measuring device 5A, the shaft 6A and the shaft 7A are examples of equipment.
  • the facility 10B also includes the power source 1A, the electric motor 2A, the transmission device 3A, the load device 4A, the current measuring device 5A, the shafts 6A and 7A, and the electric power source 1B, the electric motor 2B, and the respective shafts 6A and 7A of the facility 10A.
  • a transmission device 3B, a load device 4B, a current measuring device 5B, shafts 6B and 7B are provided, and these are configured in the same manner as the facility 10A.
  • the current measuring device 5B of the facility 10B transmits the current measurement result to the diagnostic device 20 .
  • the diagnostic system 100 may include three or more facilities 10 with the same specifications. For example, if the diagnostic system 100 includes four facilities, each is described as facilities 10A, 10B, 10C, and 10D.
  • the facilities 10A, 10B, etc. may be referred to as facilities 10
  • the electric motors 2A, 2B, etc. may be referred to as electric motors 2 when there is no need to distinguish them. The same applies to other devices.
  • the diagnostic device 20 includes a measurement result acquisition unit 21 , an analysis unit 22 , a diagnosis processing unit 23 , an input unit 24 , a display unit 25 and a storage unit 26 .
  • the measurement result acquisition unit 21 acquires the measurement result of the current flowing through the electric motor 2 .
  • the measurement result acquisition unit 21 is configured using an input interface and a communication interface for inputting measurement results by the current measurement device 5 and acquires digital current data transmitted from the current measurement device 5 .
  • the analysis unit 22 decomposes the measurement result acquired by the measurement result acquisition unit 21 into a plurality of frequency components by FFT (Fast Fourier Transform).
  • FFT Fast Fourier Transform
  • the diagnosis processing unit 23 executes processing for diagnosing the state of the equipment (for example, the electric motor 2, the transmission device 3, the load device 4) provided in the facility 10.
  • the diagnostic processing unit 23 extracts information indicating the state of the electric motor 2 ⁇ /b>A and the like from the analysis result by the analysis unit 22 and displays it on the display unit 25 .
  • the frequency components include the equipment or a part constituting the equipment (referred to as a specific part) ) is reflected.
  • a change representing the state of the load device 4 occurs in the rotation frequency generated by the sideband wave from the power supply frequency of the load device 4 among the frequency components included in the current measurement value. For example, when the condition of the load device 4 deteriorates, the frequency component value of the rotation frequency of the load device 4 increases.
  • the diagnostic processing unit 23 uses this property to extract frequency component values that are useful for diagnosing each device from the analysis result of the analysis unit 22 .
  • the diagnosis processing unit 23 extracts a frequency component value near the frequency f1 from the FFT analysis result, and displays this value on the display unit 25 as an evaluation value for diagnosing the state of the electric motor 2 .
  • the diagnostic processing unit 23 creates diagnostic graphs exemplified in FIGS. 2A to 2F described later and displays them on the display unit 25 .
  • the input unit 24 is configured using an input device such as a keyboard, mouse, or touch panel.
  • the input unit 24 receives an input for operating the diagnostic device 20 .
  • the input unit 24 outputs the content of the received operation input to the diagnostic processing unit 23 .
  • the display unit 25 is configured using a display device such as a liquid crystal display or an organic EL (Electro-luminescence) display.
  • the display unit 25 displays arbitrary information based on instructions from the diagnostic processing unit 23 .
  • the storage unit 26 is configured using a storage device such as an HDD or flash memory.
  • the storage unit 26 stores various information necessary for diagnosing the state of the device, for example, information defining the frequency of the current measurement value indicating the state of the device for each device.
  • the storage unit 26 stores information such as the frequency f1 for the electric motor 2, the frequency f2 for the transmission device 3, and the frequency f3 for the load device 4.
  • FIG. Two or more frequencies may be set for one device, or a frequency may be set for each specific part constituting the device.
  • the storage unit 26 stores the frequency f11 for diagnosing the state of the rotor bar of the electric motor 2 and the frequency f12 for diagnosing the state of the rotating shaft of the electric motor 2, respectively. child bar, rotation axis) are associated with each other and stored.
  • FIGS. 2A to 2F are first to sixth diagrams respectively showing examples of diagnostic graphs according to the embodiment.
  • the vertical axis of the graphs shown in FIGS. 2A to 2F is the frequency component value (dB) of the specific portion based on the power supply frequency component value, and the horizontal axis is the equipment for each facility.
  • FIG. 2A is a diagnostic graph showing current frequency component values reflecting the states of the planetary gears of the speed reducers ac.
  • the speed reducer a is an example of the transmission device 3 of the facility 10A
  • the speed reducer b is an example of the transmission device 3B of the facility 10B
  • the speed reducer c is an example of the transmission device 3C of the facility 10C.
  • a planetary gear is an example of a specific portion.
  • a measured value measured by the current measuring device 5A is described as a measured value A.
  • the diagnostic processing unit 23 extracts the peak of the frequency component value of the frequency f2 from the analysis result of the measurement value A by the analysis unit 22, and extracts the peak of the frequency component value of the frequency f2 from the analysis result of the measurement value B by the analysis unit 22. Then, the peak of the frequency component value of frequency f2 is extracted from the analysis result of the measurement value C by the analysis unit 22 .
  • the selected values are evaluation values indicating the states of the planetary gears of the speed reducer a, speed reducer b, and speed reducer c, respectively.
  • the diagnostic processing unit 23 creates the diagnostic graph of FIG.
  • the magnitude of the frequency component value reflects the state of the planetary gears of the speed reducers ac. Specifically, the worse the condition, the larger the frequency component value. This is the same for other devices and other specific parts.
  • the engineer determines that there is a high possibility that an abnormality will occur in the speed reducer c whose frequency component value is clearly larger than the others, and selects it as a candidate for which maintenance is recommended. In this example, one device was selected as a recommended maintenance candidate. Machine c may be selected as a recommended maintenance candidate.
  • FIG. 2B is a diagnostic graph showing frequency component values of currents that reflect the states of rotor bars of motors a to b.
  • the electric motor a is an example of the electric motor 2A of the facility 10A
  • the electric motor b is an example of the electric motor 2B of the facility 10B.
  • the diagnostic processing unit 23 extracts the peak of the frequency component value of the frequency f11 from the analysis result of the measurement value A by the analysis unit 22, and extracts the peak of the frequency component value of the frequency f11 from the analysis result of the measurement value B by the analysis unit 22. Then, the diagnostic graph of FIG. 2B is created and output to the display unit 25 .
  • the engineer refers to the diagnostic graph in FIG. 2B and selects the electric motor a, which has a clearly larger frequency component value than the others, as a maintenance-recommended equipment candidate.
  • FIG. 2C is a diagnostic graph showing frequency component values of currents that reflect the states of the rotating shafts of centrifuges a to g.
  • the centrifuge a is an example of the load device 4A of the facility 10A
  • the centrifuge b is an example of the load device 4B of the facility 10B.
  • the diagnostic processing unit 23 extracts the peak of the frequency component value of the frequency f3 from the analysis results of the measurement values A to G by the analysis unit 22, creates the diagnostic graph of FIG. 2C, and outputs it to the display unit 25.
  • the engineer refers to the diagnostic graph in FIG. 2C and selects the centrifuge d whose frequency component value is clearly larger than the others as a candidate for maintenance recommended equipment.
  • FIG. 2D is a diagnostic graph showing the frequency component values (dB) of the current reflecting the state of the belts of fans ac.
  • the fan a is an example of the load device 4A of the facility 10A
  • the belt a of the fan a is the belt portion of the belt driving device, which is an example of the specific portion of the transmission device 3A.
  • the diagnostic processing unit 23 extracts the peak of the frequency component value of the frequency f21 from the analysis results of the measurement values A to C by the analysis unit 22, creates the diagnostic graph of FIG. 2C, and outputs it to the display unit 25.
  • f21 is, for example, the frequency of rotation of fans ac.
  • the frequency of rotation of fans ac is the same.
  • the frequency component values are generally higher than those for the other devices illustrated in FIGS. 2A-2C.
  • FIG. 2E is generated by the diagnostic processing unit 23 by extracting the peak of the frequency component value of twice the power supply frequency when f21 is the frequency of the sideband wave of the power supply frequency from the analysis result by the analysis unit 22. Shows a diagnostic graph.
  • FIG. 2F shows a diagnostic graph created by the diagnostic processing unit 23 by extracting the peak of the frequency component value of the frequency three times the power supply frequency from the analysis result by the analysis unit 22 .
  • a diagnostic graph is created for harmonics from the 1st order to the nth order (preferably 1st to 10th order), and comparison is performed to comprehensively determine the state of the belt. evaluate.
  • the engineer comprehensively evaluates the diagnostic graphs for the 1st to nth harmonics to identify the belt whose condition is deteriorating.
  • the technician can see from the mutual comparison of the diagnostic graphs of FIGS. It is bad and judges that maintenance is necessary.
  • FIG. 3 is a flow chart showing an example of the operation of the diagnostic system according to the embodiment.
  • a user of the diagnostic system 100 sets information designating a diagnostic target in the diagnostic device 20 in advance.
  • the user can specify the diagnosis target as the planetary gear of the speed reducer (transmission device 3), the rotor bar of the electric motor 2, the rotating shaft of the centrifuge (load device 4), the fan (load device 4)
  • a belt (a part of the transmission device 3) or the like is set in the diagnostic device 20.
  • the storage unit 26 stores information indicating these diagnosis targets.
  • the storage unit 26 stores frequency information (frequency f11 or the like for the rotor bar of the electric motor 2) that reflects the state of the object to be diagnosed.
  • the measurement result acquiring unit 21 acquires the measurement result from the current measuring device 5 provided in each of the multiple pieces of equipment 10 having the same specification (step S1).
  • the measurement result acquiring unit 21 is connected online to the current measuring device 5A of the facility 10A and the current measuring device 5B of the facility 10B, and constantly acquires digital current data from the facilities 10A and 10B.
  • the measurement result acquisition unit 21 records the acquired measurement results in the storage unit 26 for each facility.
  • the analysis unit 22 performs FFT analysis on the measurement result obtained in step S1 (step S2).
  • the analysis unit 22 reads the measurement results for a predetermined period of time for each of the facilities 10A and 10B from the storage unit 26, performs FFT analysis on each, and records the analysis results in the storage unit 26 for each facility.
  • the diagnostic processing unit 23 extracts the peak of the frequency component value to be diagnosed (step S3).
  • the diagnostic processing unit 23 reads the FFT analysis result from the storage unit 26 for each facility.
  • the diagnostic processing unit 23 reads information on the frequency corresponding to the diagnosis target from the storage unit 26 .
  • the diagnostic processing unit 23 uses the frequency f2 for the speed reducer, the frequency f11 for the rotor bar of the electric motor 2, the frequency f3 for the centrifuge, the frequency f21 for the belt of the belt drive device, and its harmonics (1 to n order) and so on are read out from the storage unit 26 .
  • the diagnosis processing unit 23 refers to the data of the frequency f2 among the FFT analysis results of the current measurement value of the equipment 10A, and extracts the peak value therefrom. This value corresponds to the frequency component value of the speed reducer a in the diagnostic graph of FIG. 2A. Similarly, the diagnostic processing unit 23 extracts the peak of the corresponding frequency component value for the reduction gear b of the facility 10B.
  • the diagnosis processing unit 23 refers to the data of the frequency f21 among the FFT analysis results of the current measurement value of the equipment 10A, and extracts the peak value therefrom. Further, the diagnostic processing unit 23 refers to the data of f21 ⁇ 2 times frequency, f21 ⁇ 3 times frequency, . Extract. The diagnosis processing unit 23 also extracts the peak of the frequency component value of the frequency corresponding to each diagnosis target for the remaining facilities 10B and the like.
  • the diagnostic processing unit 23 creates a diagnostic graph using the peaks of the frequency component values extracted for each diagnostic target for each facility in step S3 (step S4). For example, the diagnostic processing unit 23 creates a diagnostic graph by arranging the peaks of the frequency component values extracted for a certain diagnostic target for all the equipment.
  • the diagnostic processing unit 23 outputs the created diagnostic graph to the display unit 25 (step S5).
  • the display unit 25 displays diagnostic graphs as exemplified in FIGS. 2A to 2F.
  • the diagnostic processing unit 23 may output the created diagnostic graph to an electronic file, paper (printer), or other computer.
  • the diagnostic system 100 the current measurement value supplied to the electric motor 2 is used to analyze the evaluation value indicating the state of the equipment (the electric motor 2 and the equipment connected to the electric motor 2).
  • This processing is executed for a plurality of devices with the same specifications, and the states are ranked according to mutual comparison to evaluate the state of the devices.
  • the state of equipment can be evaluated without setting thresholds for each failure mode.
  • the diagnostic processing unit 23 may output the comparison result in another format.
  • the diagnostic processing unit 23 arranges the peaks of the frequency component values of each device in descending order, displays the peak values of the frequency component values of each device, and displays the peak values of the device and the device one rank lower than that device. You may output the information which displayed the difference of between to the display part 25.
  • FIG. The diagnostic processing unit 23 calculates the difference between the maximum and minimum peak frequency component values for each device. It may be determined, and the comparison result (for example, the device with a large difference value and its evaluation value) may be output to the display unit 25 .
  • the diagnosis method of the present embodiment includes broken rotor bars of the electric motor 2, eccentricity of the rotating shaft of the electric motor 2, abnormal bearings of the electric motor 2, misalignment of the coupling center of the transmission device 3, wear of gears of the reduction gear, and wear of the belt drive device. It can be applied to diagnosis of belt looseness, shaft contact, bending, unbalance, etc. of the load side machine.
  • devices with the same specifications are compared with each other. good.
  • the same operating conditions include not only the case where the operating conditions are completely the same, but also the range in which the evaluation values can be regarded as the same in mutual comparison, and the case where the difference is as small as not to lose the meaning of the comparison. For example, if there are multiple facilities with the same specifications in the same power plant (coal-fired power, gas-fired power, onshore wind turbines, offshore wind turbines, geothermal power), the operating conditions (temperature, humidity, location conditions, etc.) of these facilities with the same specifications are the same.
  • FIG. 1 A diagnostic system 100' according to the second embodiment will be described below with reference to FIGS. 4 to 7.
  • FIG. the states of the devices are evaluated without thresholds by comparing the evaluation values (peaks of frequency component values) representing the states of the devices between the devices.
  • the diagnostic device 20' in the second embodiment has a function of acquiring threshold values through learning.
  • FIG. 4 is a block diagram showing an example of a diagnostic system according to the second embodiment.
  • a diagnostic system 100' according to the second embodiment includes diagnostic target facilities 10A, 10B, . . . and a diagnostic device 20'.
  • the diagnostic device 20 ′ includes a measurement result acquisition unit 21 , an analysis unit 22 , a diagnostic processing unit 23 ′, an input unit 24 , a display unit 25 , a storage unit 26 and a learning unit 27 .
  • the learning unit 27 performs machine learning or the like on learning data with labels such as "abnormal”, “caution”, and "normal” for peaks of frequency component values extracted by the same method as in the first embodiment. Threshold values for determining whether the peak of the frequency component value is considered to be in an abnormal state or a state requiring attention (respectively, state determination thresholds 1 and 2 ).
  • the diagnostic processing unit 23' has a function of creating and displaying a diagnostic graph including the threshold set by the learning unit 27, in addition to the functions of the diagnostic processing unit 23 of the first embodiment. Alternatively, the diagnostic processing unit 23 ′ may determine the state of the device based on the threshold set by the learning unit 27 .
  • FIG. 5 is a first flow chart showing an example of the operation of the diagnostic system according to the second embodiment.
  • an engineer performs evaluation and the like to create learning data (step S11).
  • a maintenance plan is created by the diagnostic method of the first embodiment.
  • the maintenance person performs the planned maintenance of the equipment and creates a maintenance record.
  • the maintenance record records the results of various measurements and inspections performed on the equipment.
  • an engineer who has the knowledge to evaluate the condition of the equipment refers to the maintenance record and evaluates the condition of the equipment on which the maintenance has been performed.
  • the engineer evaluates the equipment to be maintained or the specific part of the equipment as “abnormal”, “caution”, or "normal” based on his/her knowledge.
  • the user of the diagnostic system 100' receives the evaluation result of the state of the equipment by the engineer, and associates the evaluation result with the peak of the frequency component value of the maintenance target equipment. This creates learning data.
  • the user inputs the learning data to the diagnostic device 20'.
  • the input unit 24 acquires the input learning data (step S12).
  • the diagnostic processing unit 23' writes the learning data acquired by the input unit 24 to the storage unit 26 and stores the learning data. When a certain amount of learning data is accumulated, the user performs an operation to instruct threshold learning.
  • the diagnostic processing unit 23' causes the learning unit 27 to learn the learning data based on the instruction operation of the user.
  • the learning unit 27 reads learning data labeled with any one of “abnormal”, “caution”, and “normal” from the storage unit 26, performs machine learning, and obtains a state determination threshold value 1 (abnormality determination threshold value). ), and state determination threshold 2 (determination threshold for determining whether or not the state requires attention) are set (step S13).
  • the learning unit 27 saves the set threshold in the storage unit 26 .
  • FIG. 6 is a second flow chart showing an example of the operation of the diagnostic system according to the second embodiment.
  • FIG. 7 is a diagram showing an example of a diagnostic graph according to the second embodiment.
  • the process described with reference to FIG. (threshold for determining whether or not the user is in a state that requires attention) and state determination threshold 2 (for example, a threshold for determining whether or not the user is in a state requiring attention) are set.
  • the measurement result acquisition unit 21 acquires measurement results from the current measurement devices 5 provided in each of the plurality of facilities 10 having the same specifications or the same specifications and the same operating conditions (step S1).
  • the analysis unit 22 performs FFT analysis on the measurement result obtained in step S1 (step S2).
  • the diagnostic processing unit 23' extracts the peak of the frequency component value to be diagnosed (step S3).
  • the diagnostic processing unit 23' extracts peaks of frequency component values that reflect the states of the planetary gears of the reduction gears ac by performing the same processing as the processing described with reference to FIG. 2A.
  • the diagnostic processing unit 23' creates a diagnostic graph with a threshold using the peaks of the frequency component values extracted in step S3 (step S4'). For example, the diagnostic processing unit 23′ reads the peak of the frequency component value of the speed reducers a to c and the state determination threshold 1 and the state determination threshold 2 set for the speed reducers a to c from the storage unit 26, and determines the state. A diagnostic graph displaying the threshold 1 and the state determination threshold 2 is created. Next, the diagnostic processing unit 23' outputs the created diagnostic graph to the display unit 25 (step S5'). A diagnostic graph as illustrated in FIG. 7 is displayed on the display unit 25 . TH1 in FIG. 7 is the state determination threshold value 1, and TH2 is the state determination threshold value 2. As shown in FIG.
  • the engineer looks at the diagnosis graph in FIG. 7, compares the frequency component values with each other, and compares them with the state determination threshold 1 and the state determination threshold 2, and selects maintenance recommendation candidate equipment. For example, in the case of the example of FIG. 7, as a result of the mutual comparison of the frequency component values, the engineer finds that the value of the speed reducer c is clearly larger than the others, and that the frequency component value of the speed reducer c exceeds the caution threshold.
  • the reduction gear c is selected as a maintenance-recommended equipment candidate.
  • the diagnostic processing unit 23' may determine "normal”, “abnormal”, “caution”, etc. based on the set threshold.
  • the determination result may be displayed.
  • the diagnostic accuracy is improved by repeatedly learning the threshold each time maintenance is performed. can do. For example, even if it is determined that the frequency component value exceeds the state determination threshold value 1, if no abnormality is recognized according to the actual maintenance records, the frequency component value is classified as "normal” or "caution”. It becomes the training data labeled with . By learning this learning data, the current state determination threshold 1 and state determination threshold 2 are adjusted.
  • FIG. 8 is a diagram illustrating an example of a hardware configuration of a diagnostic system according to the embodiment.
  • a computer 900 includes a CPU 901 , a main memory device 902 , an auxiliary memory device 903 , an input/output interface 904 and a communication interface 905 .
  • the diagnostic devices 20 , 20 ′ described above are implemented in a computer 900 .
  • Each function described above (the analysis unit 22, the diagnostic processing units 23 and 23', and the learning unit 27) is stored in the auxiliary storage device 903 in the form of a program.
  • the CPU 901 reads out the program from the auxiliary storage device 903, develops it in the main storage device 902, and executes the above processing according to the program.
  • the CPU 901 secures a storage area in the main storage device 902 according to the program.
  • the CPU 901 secures a storage area for storing data being processed in the auxiliary storage device 903 according to the program.
  • the diagnostic system described above may be configured by a system comprising a plurality of computers such as a client terminal (not shown) and diagnostic device 20 .
  • the configuration may be such that some functions of the diagnostic device 20 (for example, the input unit 24 and the display unit 25) are provided in the client terminal, and other functions are provided in the diagnostic device 20 that can communicate with the client terminal.
  • the configuration may be such that each function of the diagnostic device 20 is executed in accordance with a diagnostic request from the client terminal.
  • the "computer system” here includes hardware such as an OS and peripheral devices.
  • the "computer system” includes the home page providing environment (or display environment) if the WWW system is used.
  • the term "computer-readable recording medium” refers to portable media such as CDs, DVDs, and USBs, and storage devices such as hard disks built into computer systems.
  • the diagnostic system 100, 100' includes a measurement result acquisition unit 21 that acquires a measurement value of the current supplied to each of a plurality of pieces of equipment, and an analysis unit 22 that performs frequency analysis on the measurement value.
  • an extracting unit for extracting a frequency component value of a predetermined frequency (f1, f2, etc.) from the analysis result of the frequency analysis as an evaluation value indicating the state of the equipment, and the evaluation for each equipment an output unit (diagnostic processing unit 23) that outputs the results of comparing values (graphs of FIGS. 2A to 2F, information on devices whose difference between the maximum value and the minimum value of evaluation values is equal to or greater than a predetermined value, etc.) .
  • the diagnostic system 100, 100' is the diagnostic system 100, 100' of (1), wherein the extraction unit, in addition to the frequency component value of the predetermined frequency, A harmonic component value (for example, in addition to the 1st order, the 2nd to nth order, preferably the 1st to 10th order) is extracted as the evaluation value.
  • a harmonic component value for example, in addition to the 1st order, the 2nd to nth order, preferably the 1st to 10th order
  • By performing mutual comparison including the harmonic component values it is possible to improve the accuracy of diagnosing the state of equipment.
  • the diagnostic system 100, 100' is the diagnostic system 100, 100' of (1) to (2), wherein the output unit includes the evaluation value extracted for each facility Graphs (FIGS. 2A to 2F) in which the sizes of are arranged so as to be comparable are created and output.
  • An engineer who diagnoses the state of equipment by referring to the diagnostic graph can visually evaluate the state of each piece of equipment while comparing it with others.
  • the diagnostic system 100, 100' is the diagnostic system 100, 100' of (1) to (4), wherein the evaluation value and the evaluation of the state of the equipment indicated by the evaluation value
  • a learning data acquisition unit (input unit 24) that acquires learning data combined with results, and a learning unit 27 that sets a threshold value for determining the state of the equipment by machine learning using the learning data.
  • the output unit outputs the evaluation value for each facility in a comparable manner, and outputs the threshold value in a manner comparable to the evaluation value.
  • Learning data is created by attaching the evaluation results of the equipment status based on the inspection results when actually performing inspections, etc. to the evaluation value, and by learning the learning data, the equipment status can be determined in any way.
  • the diagnostic system 100, 100' is the diagnostic system 100, 100' of (1) to (5), wherein the facility is one or more devices (motor 2, transmission device 3.
  • the load device 4) is included, and the extraction unit extracts the frequency component value of the predetermined frequency determined for each type of failure that occurs in the device or the device (broken rotor bar, eccentricity of the rotating shaft, etc.) do.
  • the extraction unit extracts the frequency component value of the predetermined frequency determined for each type of failure that occurs in the device or the device (broken rotor bar, eccentricity of the rotating shaft, etc.) do.
  • the broken rotor bar of the electric motor 2 when diagnosing a facility driven by the electric motor 2, the broken rotor bar of the electric motor 2, the eccentricity of the rotating shaft of the electric motor 2, the bearing abnormality of the electric motor 2, the misalignment of the coupling center of the transmission device 3, and the gear of the reduction gear. It is possible to diagnose wear, loosening of the belt of the belt driving device, occurrence of axial contact, bending, imbalance, etc. of the load device 4 such as a fan based on the frequency component value (evaluation value).
  • a diagnostic method is a diagnostic system comprising a terminal device and a diagnostic device capable of communicating with the terminal device, wherein the terminal device is connected to each of a plurality of facilities.
  • requesting means for requesting state diagnosis; and an output unit for outputting to compare evaluation values for each facility extracted by the extracting unit of the diagnostic device;
  • a measurement result acquisition unit that acquires a measurement value of the current supplied to each of the plurality of facilities, an analysis unit that performs frequency analysis on the measurement value, and a frequency component value of a predetermined frequency from the analysis result of the frequency analysis.
  • an extraction unit for extracting as an evaluation value indicating the state of the equipment.
  • a diagnostic method includes a step of acquiring a measured value of a current supplied to each of a plurality of pieces of equipment, a step of performing frequency analysis on the measured value, and a predetermined value from the analysis result of the frequency analysis. and outputting a comparison result of the evaluation values extracted for each of the facilities. This makes it possible to evaluate the state of equipment without setting a threshold.
  • a diagnostic method is a diagnostic method using a terminal device and a diagnostic device capable of communicating with the terminal device, wherein the terminal device requests diagnosis of the state of each of a plurality of facilities. and an output step of outputting for comparison the evaluation values for each of the facilities extracted by the extracting step of the diagnostic device, wherein the diagnostic device outputs the plurality of facilities at the request of the terminal device a measurement result acquisition step of acquiring a measurement value of the current supplied to each; an analysis step of frequency-analyzing the measurement value; and a frequency component value of a predetermined frequency from the analysis result of the frequency analysis. and an extraction step of extracting as an evaluation value to be shown.
  • the program according to the tenth aspect provides the computer 900 with a step of acquiring a measured value of current supplied to each of a plurality of pieces of equipment, a step of frequency-analyzing the measured value, and an analysis of the frequency analysis.
  • a step of extracting a frequency component value of a predetermined frequency from the results as an evaluation value indicating the state of the equipment, and a step of outputting a comparison result of the evaluation values extracted for each equipment are executed.
  • the diagnostic target can be diagnosed appropriately.

Abstract

L'invention concerne un système de diagnostic capable de diagnostiquer l'état d'une installation sans valeur de seuil. Le système de diagnostic comprend : une unité d'acquisition de résultat de mesure qui acquiert une valeur mesurée d'un courant électrique fourni à chacune d'une pluralité d'installations ; une unité d'analyse qui effectue une analyse de fréquence de la valeur mesurée ; une unité d'extraction qui extrait une valeur de composante de fréquence d'une fréquence prédéterminée à partir du résultat d'analyse de l'analyse de fréquence, en tant que valeur d'évaluation indiquant un état d'une installation ; et une unité de sortie qui délivre en sortie la valeur d'évaluation pour chaque installation d'une manière comparable.
PCT/JP2022/005064 2021-02-19 2022-02-09 Système de diagnostic, procédé de diagnostic, et programme WO2022176730A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010288352A (ja) * 2009-06-10 2010-12-24 Nippon Steel Corp 設備の異常診断方法
WO2017154091A1 (fr) * 2016-03-08 2017-09-14 株式会社日立製作所 Dispositif de diagnostic de machine rotative et procédé de diagnostic
WO2018109993A1 (fr) * 2016-12-15 2018-06-21 三菱電機株式会社 Dispositif de diagnostic d'anomalie pour mécanisme de transmission de puissance, et procédé de diagnostic d'anomalie pour mécanisme de transmission de puissance
WO2020039661A1 (fr) * 2018-08-23 2020-02-27 三菱電機株式会社 Dispositif de diagnostic d'anomalie
WO2020208743A1 (fr) * 2019-04-10 2020-10-15 三菱電機株式会社 Dispositif, procédé et système de diagnostic d'anomalies servant à un équipement de moteur électrique

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010288352A (ja) * 2009-06-10 2010-12-24 Nippon Steel Corp 設備の異常診断方法
WO2017154091A1 (fr) * 2016-03-08 2017-09-14 株式会社日立製作所 Dispositif de diagnostic de machine rotative et procédé de diagnostic
WO2018109993A1 (fr) * 2016-12-15 2018-06-21 三菱電機株式会社 Dispositif de diagnostic d'anomalie pour mécanisme de transmission de puissance, et procédé de diagnostic d'anomalie pour mécanisme de transmission de puissance
WO2020039661A1 (fr) * 2018-08-23 2020-02-27 三菱電機株式会社 Dispositif de diagnostic d'anomalie
WO2020208743A1 (fr) * 2019-04-10 2020-10-15 三菱電機株式会社 Dispositif, procédé et système de diagnostic d'anomalies servant à un équipement de moteur électrique

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