WO2002003158A1 - Systeme de diagnostic et de gestion d'un equipement d'installation, appareil de gestion et appareil de diagnostic - Google Patents
Systeme de diagnostic et de gestion d'un equipement d'installation, appareil de gestion et appareil de diagnostic Download PDFInfo
- Publication number
- WO2002003158A1 WO2002003158A1 PCT/JP2001/005807 JP0105807W WO0203158A1 WO 2002003158 A1 WO2002003158 A1 WO 2002003158A1 JP 0105807 W JP0105807 W JP 0105807W WO 0203158 A1 WO0203158 A1 WO 0203158A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- equipment
- information
- management data
- facility
- monitoring unit
- Prior art date
Links
- 238000004458 analytical method Methods 0.000 claims abstract description 132
- 238000012544 monitoring process Methods 0.000 claims abstract description 87
- 238000012545 processing Methods 0.000 claims abstract description 79
- 238000001514 detection method Methods 0.000 claims abstract description 35
- 238000004891 communication Methods 0.000 claims abstract description 31
- 230000005856 abnormality Effects 0.000 claims abstract description 28
- 238000003745 diagnosis Methods 0.000 claims description 123
- 238000000034 method Methods 0.000 claims description 28
- 230000002159 abnormal effect Effects 0.000 claims description 25
- 238000012423 maintenance Methods 0.000 claims description 24
- 230000008569 process Effects 0.000 claims description 22
- 238000007405 data analysis Methods 0.000 claims description 14
- 230000006872 improvement Effects 0.000 claims description 12
- 230000000246 remedial effect Effects 0.000 claims 1
- 238000007726 management method Methods 0.000 description 63
- 238000010586 diagram Methods 0.000 description 21
- 238000005259 measurement Methods 0.000 description 10
- 238000012935 Averaging Methods 0.000 description 9
- 239000011159 matrix material Substances 0.000 description 8
- 230000005540 biological transmission Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 6
- 238000007689 inspection Methods 0.000 description 6
- 238000009434 installation Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000001066 destructive effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000000491 multivariate analysis Methods 0.000 description 3
- 238000005096 rolling process Methods 0.000 description 3
- 230000036962 time dependent Effects 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000007797 corrosion Effects 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000003921 oil Substances 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 241001201614 Prays Species 0.000 description 1
- 230000009118 appropriate response Effects 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000010687 lubricating oil Substances 0.000 description 1
- 238000005461 lubrication Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- 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
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
- G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
-
- 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
-
- 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
- G05B2223/00—Indexing scheme associated with group G05B23/00
- G05B2223/06—Remote monitoring
Definitions
- Equipment equipment diagnosis system management device and diagnosis device
- the present invention relates to a system for managing and diagnosing the state of equipment.
- the present invention has been made to solve the above-described problems, and has as its object to grasp and manage the minimum operation status of equipment by continuous measurement by a device or by measurement by a person to perform various kinds of data analysis.
- the information is immediately transmitted to the equipment diagnosis center, which is a specialized technical group.
- the advanced analysis and diagnosis unit performs advanced analysis and diagnosis processing on the information and promptly notifies the equipment management side of the best information to respond to the equipment that is determined to be abnormal.
- the advanced analysis and diagnosis unit uploads the equipment management data analysis program to the equipment monitoring unit on the user side, so that raw information with a large amount of information can be sent to the equipment diagnosis unit. Send to It is to St. provide equipment diagnosis system analysis can at the user side without. Disclosure of the invention
- a typical configuration of the equipment diagnostic system is a equipment state detection means attached to the equipment and detecting a state of the equipment, and an equipment state detection detected by the equipment state detection means.
- An equipment management data processing unit that processes and outputs information
- an equipment state determination unit that performs level determination on information output from the equipment management data processing unit with respect to a management reference value and outputs the information.
- a facility monitoring unit that collects, processes, and outputs information related to equipment that has been level-determined and output from the equipment state determination unit, and a corresponding equipment device that performs advanced analysis on the information output from the equipment monitoring unit. It has an advanced analysis and diagnosis unit that specifies the cause of the abnormality and measures to improve it, and transmits the specified result to the equipment monitoring unit.
- the equipment monitoring unit and the advanced analysis diagnosis unit are configured to be able to communicate with each other via a communication network, and upload the equipment management data analysis program from the advanced analysis diagnosis unit to the equipment monitoring unit. .
- the user who manages the equipment receives the equipment state detection information detected by the equipment state detection means on the equipment management data.
- the equipment status judgment unit makes a level judgment against the management reference value, and the equipment monitoring unit collects and processes the relevant information of the equipment whose level was judged, and specializes it through the communication network. It is output to the equipment diagnosis center, which is a technical group.
- the advanced analysis and diagnosis unit which has received the information output from the equipment monitoring unit on the user side, performs advanced analysis on the information and identifies the cause of the abnormality in the equipment and the measures to be taken to improve it.
- the specified result is transmitted to the equipment monitoring unit on the user side via the communication network.
- the altitude analysis and diagnosis unit on the equipment diagnosis sensor side also performs the secondary processing.
- the program for facility management data analysis is uploaded to the facility monitoring unit on the user side via the communication network, and the advanced analysis is performed again on the user side.
- a program for analyzing equipment management data is uploaded to the user side, and the user performs an altitude analysis again, thereby generating a huge amount of information such as equipment state detection information detected by the equipment state detection means.
- FIG. 1 is a block diagram showing the configuration of the equipment diagnostic system according to the present invention.
- FIG. 2 is a block diagram showing a configuration on the user side of the equipment diagnostic system according to the present invention.
- FIG. 3 is a diagram showing a configuration example of a communication network between the equipment monitoring unit and the advanced analysis diagnosis unit.
- Fig. 4 is a block diagram showing the configuration of the equipment state detection means and the altitude analysis diagnosis unit.
- Fig. 5 is a block diagram showing the configuration of the advanced analysis diagnosis unit. It is.
- FIG. 6 is a diagram showing a detailed configuration of the altitude data analysis unit.
- FIG. 7 is a diagram illustrating the relationship between a signal processing recipe and a group of equipment management data analysis programs.
- FIG. 1 is a block diagram showing the configuration of the equipment diagnostic system according to the present invention.
- FIG. 2 is a block diagram showing a configuration on the user side of the equipment diagnostic system according to the present invention.
- FIG. 3 is a diagram showing a configuration example of
- FIG. 8 is a diagram showing an example of a display screen of the equipment monitoring unit.
- FIG. 9 is a diagram showing an example of a display screen of the equipment monitoring unit.
- FIG. 10 is a diagram showing an example of a diagnostic result list sent from the altitude analysis diagnostic unit.
- FIG. 11 is a diagram showing an example of a primary diagnosis result sent from the advanced analysis diagnosis unit.
- FIG. 12 is a diagram showing an example of a secondary diagnosis result sent from the advanced analysis diagnosis unit.
- FIG. 13 is a diagram showing the relationship between the cause and the result diagnosed by the advanced analysis diagnosis unit.
- FIG. 14 is a diagram showing an example of output that has been subjected to secondary processing using a facility management data analysis program developed by a user.
- FIG. 15 is a diagram showing an example of a criterion for judging abnormal vibration of a rolling bearing.
- FIG. 1 is a block diagram showing a configuration of an equipment diagnostic system according to the present invention
- FIG. 2 is a block diagram showing a configuration of a user of the equipment diagnostic system according to the present invention
- FIG. Fig. 4 shows an example of the configuration of a communication network between the advanced analysis and diagnosis unit
- Fig. 4 is a block diagram showing the configuration of the equipment status detection means and the advanced analysis and diagnosis unit
- Fig. 5 is the configuration of the advanced analysis and diagnosis unit.
- FIG. 6 is a diagram showing a detailed configuration of the altitude data analysis unit
- FIG. 7 is a diagram illustrating a relationship between a signal processing recipe and a facility management data analysis program group.
- FIG. 8 and 9 show examples of the display screen of the equipment monitoring unit.
- Fig. 10 shows an example of a list of diagnostic results sent from the advanced analysis diagnostic unit.
- Fig. 11 shows the advanced analysis.
- Fig. 12 shows an example of the primary diagnosis result sent from the diagnosis unit.
- Fig. 12 shows an example of the secondary diagnosis result sent from the advanced analysis diagnosis unit.
- Fig. 13 shows the diagnosis performed by the advanced analysis diagnosis unit.
- Fig. 14 shows the relationship between the cause and effect, Fig. 14 shows an example of output that has been subjected to secondary processing using a facility management data analysis program applied to the user, and
- Fig. 15 shows rolling.
- FIG. 4 is a diagram illustrating an example of a bearing abnormal vibration determination criterion.
- A is a factory on the user side B where a number of equipment such as rotating devices are installed, and C is a distance from the user side B. This is the equipment diagnostic center with a specialized technical group that is familiar with the diagnostic services of the equipment located.
- factory A rotating equipment such as a fan 1a and a pump 1b and many other equipment 1 that perform various functions are installed, and various equipment 1 detect the status of the equipment 1
- the equipment state detectors 2a and 2b which are composed of various sensor elements and the like, which serve as equipment state detection means, are installed.
- the equipment status detectors 2a and 2b send daily equipment status detection information on the equipment 1 to the equipment management data processing section 3a of the monitoring device 3, and the equipment management data is sent to the evening processing section 3a.
- the processed equipment status information is transmitted to the equipment status determination unit 3b.
- the equipment state determination unit 3b determines the level of the information output from the equipment management data processing unit 3a with respect to a preset management reference value and outputs the information.
- Equipment management data The evening processing unit 3a and the equipment state determination unit 3b are installed at the site around the equipment 1 in the factory A.
- the vibration raw waveform data is collected by the equipment status detectors 2a and 2 such as the vibration sensor provided in the equipment 1, and the vibration management waveform data is filtered by the equipment management data processing unit 3a.
- OZA value output of the averaging process
- the state of the equipment 1 is primarily determined by comparing the values of the equipment state parameters, such as the values, with a preset threshold.
- the information determined by the equipment status determination unit 3b is transmitted to the equipment monitoring unit 5, which collectively manages the equipment 1 of the entire factory A, and the equipment monitoring unit 5 determines the level from the equipment status determination unit 3b. Collects, processes, outputs, and saves the relevant information of equipment 1 that was output. That is, the equipment monitoring unit 5 saves the equipment state parameters as trend management data and collects and manages equipment-related information such as the specifications and history of the equipment 1.
- the equipment monitoring unit 5 of the user B and the advanced analysis diagnosis unit 6 of the equipment diagnosis center C are configured to be able to communicate with each other via a communication network 10 such as a network, an in-net, a public line, or the like. Accordingly, the equipment monitoring unit 5 collects and processes information on the corresponding equipment 1 determined to be abnormal by the equipment state determination unit 3b, and transmits the information to the equipment diagnosis center C via the communication network 10.
- the equipment monitoring unit 5 collects and manages the trend management data collected and managed by the equipment monitoring unit 5 together with the value of the equipment status parameter, which is the primary processing result.
- the equipment-related information such as the specifications and history of the equipment 1 is transmitted to the equipment diagnosis center C via the communication network 10.
- the information transmitted from the equipment monitoring unit 5 of the user side B is received by the advanced analysis diagnosis unit 6 of the equipment diagnosis center C, and output from the equipment monitoring unit 5 in the advanced analysis diagnosis unit 6.
- the information is automatically analyzed to identify the cause of the abnormality of the corresponding equipment 1 and the remedy, and the specified result is transmitted to the equipment monitoring unit 5.
- the advanced angle muting diagnosis unit 6 evaluates the diagnostic information sent from the user B and automatically determines the abnormal part, the cause of the abnormality, the remaining life, the countermeasure (improvement method), etc.
- An automatic diagnosis unit 6a is provided for performing a dynamic diagnosis.
- the advanced analysis diagnosis section 6 arbitrarily converts the raw waveform signals detected by the equipment state detectors 2a and 2b into a single wavelet (wavelet).
- SDP Symmetrized Dot Patterns
- visual analysis technology that plots raw waveform signals detected by the equipment condition detectors 2a and 2b into target coordinates
- the dimensionality of dimensional features, such as vibration and sound is made dimensionless to characterize the features of the signal, and the like.
- a program for advanced analysis such as multivariate analysis which is an analysis technology that pursues the cause using multiple signals with noise, is sent to the equipment monitoring unit 5 on the user B side for detailed analysis, and the accuracy of abnormality detection And automatically analyze the analysis results.
- the advanced data analysis unit 6b includes the above-mentioned SDP file lla, Java blade file llb, FFT file 11c, dimensionless sign parameter parameter Lld, multivariate analysis file lle, and other analysis files 11f are provided for each signal processing recipe.Based on the information output from the equipment monitoring unit 5, the advanced analysis diagnosis unit 6 diagnoses and adds If it is necessary to make a diagnosis, a predetermined equipment management program is extracted from the equipment management data analysis program group 12 shown in FIGS. 6 and 7, and an SDP file of each signal processing recipe is extracted. ll a, e-blade fife Ub,? D file 11 (;, dimensionless sign parameter file 11d, multivariate analysis file lie, and other analysis files 11f. Uploaded to equipment monitoring unit 5 on Side B.
- the advanced analysis and diagnosis unit 6 prays for the change tendency from the change over time sent from the equipment state determination unit 3b of the user B.
- the analysis result is sent to the automatic diagnosis unit 6a.
- the output sent to the automatic diagnosis unit 6a is sent to the life prediction unit to the advanced analysis diagnosis unit 6, and is managed by the trend management unit 6c where the life prediction analysis is performed and the trend management unit 6c.
- Life prediction is carried out based on the time-dependent change data, and the life prediction is calculated by a unique formula obtained from past diagnosis results, and the analysis result is sent to the automatic diagnosis unit 6a.
- Life prediction unit 6d Is provided.
- the advanced analysis diagnostic section 6 detects the characteristic frequency from the precise diagnostic information by the fast Fourier transform (FFT), which is a typical frequency analysis method, and compares the characteristic frequency with the normal precise diagnostic information.
- FFT fast Fourier transform
- the advanced analysis diagnosis section 6 selects the most appropriate improvement method based on the specifications of the applicable equipment 1 and the contents of the diagnosis based on the improvement method data constructed based on the past diagnosis and improvement.
- the improvement method selection unit 6f which uses the results as the diagnosis results of the automatic diagnosis unit 6a, manages the specifications, maintenance plans, maintenance results, etc. of the equipment 1 on the user side B, and performs automatic analysis based on this information. It has a maintenance information section 6 g that implements and contributes to concrete measures and improvement methods.
- Equipment monitoring unit 5 on user side B and equipment diagnosis center The advanced analysis and diagnosis unit 6 is connected to a communication network 10 such as a network, a network, and a public line.
- a communication network 10 such as a network, a network, and a public line.
- Various types of information such as equipment status detection information and diagnostic reports exchanged between each other are converted to electronic files and transmitted and received by e-mail or the like.
- Reference numeral 9 in FIG. 3 denotes a file installed between the external network and the internal network. This is to prevent leakage, falsification, destruction, etc. If security is ensured, a configuration without the fire wall 9 may be used.
- a dedicated line, a communication satellite, or the like is used as another communication network 10 for connecting the equipment monitoring unit 5 on the user side B and the advanced analysis diagnosis unit 6 on the equipment diagnosis center C. Is also good.
- the information sent from the equipment monitoring unit 5 of the user B is advanced analyzed by the advanced analysis and diagnosis unit 6, and the result is returned to the equipment monitoring unit 5 of the user B. Based on the information, the equipment monitoring unit 5 has an error. The best treatment is notified to the relevant equipment 1 determined as, and can be dealt with immediately.
- rotary equipment vibration diagnostics include on-line equipment and portable equipment diagnostic measuring instruments as standard diagnostics and precision diagnostics.
- An oil diagnostic device is used for the oil diagnosis.
- the standard diagnosis is to judge whether the equipment is normal or abnormal based on the vibration level of the equipment and changes over time, and to easily perform the cause, location, degree, life expectancy, etc. Is to analyze in detail the events that cannot be determined by the standard diagnosis by frequency analysis or the like.
- non-destructive inspection using UT Ultra Sonic; ultrasonic wave
- corrosion diagnosis using infrared force melody are performed.
- equipment status detection means in tank bottom plate diagnosis For example, a non-destructive inspection of the tank bottom plate using UT is performed, and as a means of detecting the state of equipment in general stationary equipment, a non-destructive inspection using UT and a corrosion diagnosis using an infrared camera are performed.
- the daily inspection system shown in the upper right of Fig. 4 is implemented daily by operators. This is implemented by inputting the inspection information of the plant that is being performed to the mobile terminal at the time of on-site inspection, and realizing data management over a personal computer. It mainly consists of process information (temperature, pressure during operation, etc.) ), Five senses information such as leaks around the equipment and abnormal noise.
- the equipment status detectors 2a and 2b consisting of sensor elements etc. attached to the equipment 1 always detect various status conditions such as vibration, temperature, pressure, lubricating oil component, sound, current, voltage, etc.
- the equipment status detectors 2a and 2b are not directly attached to the equipment 1 without the equipment condition detectors 2a and 2b, the portable operator measures various information when traveling around the equipment 1. It may be a diagnostic equipment for portable equipment.
- the equipment state detectors 2a and 2b attached to many rotating equipments in the factory A provide equipment state detection information of the rotating equipment. Is transmitted to the equipment management data processing unit 3a.
- the equipment management data processing unit 3a performs signal processing such as filtering and speed conversion on the signal received as primary processing, and further performs peak processing and frequency analysis to perform vibration of the corresponding rotating equipment. It outputs a judgment signal composed of the acceleration over-all value, acceleration peak value, speed over-all value, etc. necessary for diagnosing the situation (primary processing output), and sends it to the equipment status judgment section 3b. Send.
- a management reference value prepared in advance at the equipment diagnosis center side C is input to the equipment state determination unit 3b, and the determination signal, which is information output from the equipment management data processing unit 3a, is transmitted to the equipment state determination unit 3b.
- Judge the level of the rotating equipment by comparing it with the control standard value. Level judgment is usually roughly classified into “normal” and “abnormal”, and “abnormal” is further classified into “caution” and “danger”.
- the determined determination signal is recorded in the equipment monitoring unit 5.
- the equipment status determination unit 3b determines that the status is “abnormal”, that is, “caution” or “danger”, the measurement data, the history data, and the specified information such as measurement data of the rotating equipment that is currently operating in a different place with the same model as the applicable rotating equipment, and transmitted from the equipment status judgment unit 3b
- the recorded judgment signal (primary processing output result) is recorded, and such information is collected in an electronic file and attached to an e-mail. Automatically sends an e-mail to the advanced analysis diagnosis unit 6 via the Also, in the case of “Normal”, an e-mail is automatically sent periodically (for example, once a day).
- the data may be downloaded from the equipment monitoring unit 5 from the equipment diagnosis center C using the monitoring screen in the homepage format.
- the equipment monitoring unit 5 attaches and outputs an abnormality data in the case of an abnormality in addition to the presence or absence of the abnormality, and sends the result to the advanced analysis diagnosis unit 6.
- the equipment state determination unit 3b has a function of checking whether the information determined as “abnormal” is a transient phenomenon due to disturbance, and this is a function of the transient phenomenon. Only the information determined to be “abnormal” due to reasons other than the above is transmitted to the advanced analysis / diagnosis unit 6.
- FIG. 8 and Fig. 9 are examples of images displayed on the equipment monitoring unit 5 of the user B and sent to the advanced analysis diagnosis unit 6, and Fig. 8 shows a list of measurement data for the relevant rotating equipment.
- FIG. 9 is a graph showing the change over time of the measurement points of a specific rotating device among the corresponding rotating devices.
- the distinction between normal “ ⁇ ”, caution “ ⁇ ”, and danger “X” is recorded.
- the vertical axis indicates the vibration value (mm / sec)
- the horizontal axis indicates the date
- the channels 1 to 32 in the selection field 7b on the screen in Fig. 8 By selecting the number 7c and the period type 7d and clicking the graph display button 7e, the time-dependent change graph shown in Fig. 9 is displayed.
- the advanced analysis diagnosis unit 6 determines that the sent caution is “ ⁇ ” and the danger is “X”. Advanced analysis of the information on the rotating equipment that was selected, extracted and specified necessary items such as the cause, optimal countermeasures, future maintenance plans, etc., and connected them to the equipment monitoring section 5 in the factory A on the user side B. Reply by e-mail via communication network 10 such as Internet or public line. It should be noted that, besides e-mail, facsimile transmission or documents may be sent by mail or the like.
- the altitude analysis and diagnosis unit 6 shown in FIG. The diagnosis result is returned to the equipment monitoring unit 5.
- Fig. 10 shows an example of an image sent from the advanced analysis and diagnosis unit 6 relating to an extruder, which is an example of a rotating device.Selecting and clicking the ⁇ ⁇ '' mark described in the primary diagnosis result column 8a, As shown in Fig. 11, a primary diagnosis result in which "cause” and “measures" are commented by text is attached.
- the diagnosis results are expressed in a definitive way by avoiding expressions that might make the user B confused as much as possible, and with specific expressions so that countermeasures can be taken immediately. .
- the advanced keratodiagnostics unit 6 determines that it is necessary to analyze and analyze the information on the rotating device that has been determined to be caution “mm” and danger “X” with higher accuracy when performing advanced analysis. Requests the equipment monitoring unit 5 to extract necessary information further, additionally analyzes new information transmitted from the equipment monitoring unit 5, and sends it from the advanced analysis diagnosis unit 6 to FIG. A diagnostic result similar to that shown is returned to the equipment monitoring unit 5.
- the equipment monitoring unit 5 of the user B and the advanced analysis diagnosis unit 6 of the equipment diagnosis center C are connected by a communication network 10 such as a network, an Internet network or a public telephone, and can communicate in two directions. Therefore, User B sends an e-mail until the customer understands the diagnosis result of the rotating device. It is now possible to request explanations from the equipment diagnosis center C by talking.
- a communication network 10 such as a network, an Internet network or a public telephone
- the Advanced Analysis and Diagnosis Unit 6 stores the specifications and dimensions of the various equipment and components that make up the various equipment 1 for each equipment 1 installed in the factory A in various fields. , Date of manufacture, equipment specification information on various specification items (for example, rotation speed, shaft diameter, operating temperature, etc.), maintenance history information such as installation date, operation history, repair history, etc. The history of measurement values obtained when inspecting and diagnosing the equipment 1 is recorded and accumulated.
- each type of equipment 1 it is classified according to the size, load condition, installation environment, etc. of the equipment 1, and the vibration state, remaining life, etc. of the equipment 1 under optimal operating conditions are calculated statistically and theoretically
- the causes of abnormalities that occurred in the past and the abnormal phenomena that occurred in the past, their countermeasures, etc. are systematically organized, recorded, and accumulated.
- the altitude analysis and diagnosis unit 6 when the vibration analysis state of a predetermined rotating device is diagnosed by the altitude analysis and diagnosis unit 6, the altitude analysis and diagnosis unit 6 includes a rotation speed, a shaft diameter, and a load state of a population of the rotation devices in the field to which the rotating device belongs. Since various information such as lubrication state, installation state, etc. has already been input, it is possible to grasp the appropriate vibration state of the population of rotating equipment.
- the values of the appropriate vibration state are adopted as the management reference values, and the vibration state of the predetermined rotation device is determined by comparing the measured value of the vibration state of the target predetermined rotation device. Can be done.
- the horizontal axis is the DN value (shaft diameter X number of rotations) and the vertical axis is the vibration calorie speed value. If the DN value of the equipment is known, looking at its position along the vertical axis, the graphs for normal, caution, danger, etc. will be the respective management thresholds. This standard is based on a compilation of diagnostic performance data.
- the advanced analysis diagnostic section 6 records and accumulates all kinds of information and data on various types of equipment 1 arranged in many factories A, and based on such information and data.
- the theoretical formula is assembled so that the general tendency can be calculated by comparing the result of the theoretical formula with the actual state of the equipment 1 while sequentially correcting the coefficient of the theoretical formula and improving the accuracy of the theoretical formula.
- Various settings can be It is possible to construct predictive maintenance of various equipment 1 as well as equipment diagnosis of equipment 1.
- FIG. 13 shows a part of a diagnostic knowledge matrix stored in the advanced analysis / diagnosis unit 6 for deriving a diagnosis result of a predetermined blower body.
- the configuration of the diagnostic knowledge matrix table is classified according to the model (for example, a fan or a compressor) that constitutes each equipment 1 and is different from each other.
- the abscissa of the diagnostic knowledge matrix table shown in Fig. 13 categorizes the abnormal phenomena that may occur over a number of items, and the ordinate shows the time when the abnormality occurs, the location where the abnormality occurred, and the abnormality mode. , Abnormal changes with time, and the component configuration of the equipment 1 are composed of many items, and the corresponding items are marked with “”.
- markings are not only made statistically and empirically, but also based on the theoretical calculations described above. Then, as described above, when information on the predetermined transmission unit is transmitted from the equipment monitoring unit 5 to the advanced analysis diagnosis unit 6, the items on the vertical axis in the diagnostic knowledge matrix table of FIG. 13 are automatically set. Marking is given. .
- the result of the marking is obtained by combining the diagnostic knowledge matrix table as a population of the main body and the information of the predetermined main body of the transmission J which has already been constructed in the advanced analysis diagnosis section 6.
- the comparison, comparison, and calculation are performed, and the same diagnosis result of the predetermined blower body as shown in FIG. 10 is derived.
- the diagnostic knowledge matrix table makes sure that the textual knowledge that describes the cause of the abnormality, countermeasures, maintenance plans, etc. is linked to the diagnostic knowledge matrix table. Since it is constructed in the same way, the information of the predetermined transmission body and the diagnostic knowledge matrix table are compared, compared, and operated to obtain the same predetermined transmission J3 ⁇ 4 body as shown in Fig. 11 and Fig. 12. The diagnostic results as comments are automatically compounded and synthesized based on sentence knowledge.
- the equipment management data processing unit 3 a Since only the prepared signal processing can be performed, it can be used for diagnosis only for the equipment status parameters that are the processing results. Also effective signal processing for certain phenomena
- the output signals of the equipment status detectors 2a and 2b are processed by the equipment management data processing unit 3a, and the equipment status determination unit 3b does not immediately determine the equipment status. If it is necessary to perform various advanced arithmetic processing on the raw waveforms of the output signals of the detectors 2a and 2b and lead it to the equipment state determination unit 3b, the equipment management data necessary for advanced analysis processing
- the analysis program is provided in the advanced data analysis unit 6b, and the equipment management data analysis program is uploaded to the user B by remote processing from the equipment diagnosis center C to be processed.
- the output signals of the equipment status detectors 2a and 2b are processed on the user side B via a communication network 10 such as a network, an in-net network, or a public line, and the processing result is output to the equipment diagnostic sensor C.
- a communication network 10 such as a network, an in-net network, or a public line
- the processing result is output to the equipment diagnostic sensor C.
- the automatic diagnosis unit 6a issues an instruction to generate a secondary processing program. Is sent to the advanced data analysis unit 6b, and the necessary equipment management data analysis program is selected and extracted from the equipment management data analysis program group 12 as appropriate, and each equipment management data for secondary processing is extracted.
- the evening analysis program is generated by electronic filer.
- Fig. 6 and Fig. 7 show an example of creating an FFT file lie from a signal processing recipe.
- FFT fast Fourier transform
- a typical frequency analysis method First, after performing the averaging process, further perform the waveform cutout process using the window function, and then perform the analysis process.
- the facility management data analysis program group 12 includes various averaging processing programs 12a such as an average averaging process (Average) s RMS (square root of variance) and various window functions such as a hanning window and a hamming window. (Windows) Various analysis processing programs such as program 12b, Fourier transform, Wavelet (Wavelet) 12c, etc. are aggregated for each program according to various functions, and various programs are further The equipment management data analysis program group 12 stores the necessary conditions for operation and the output format of the program.
- averaging processing programs 12a such as an average averaging process (Average) s RMS (square root of variance) and various window functions such as a hanning window and a hamming window.
- Windows Various analysis processing programs such as program 12b, Fourier transform, Wavelet (Wavelet) 12c, etc. are aggregated for each program according to various functions, and various programs are further
- the equipment management data analysis program group 12 stores the necessary conditions for operation and the output format of the program.
- the equipment management data analysis program group 12 is an object that integrates the data itself and the processing for handling the data (a small).
- the advanced data analysis unit 6b receives the instruction to generate the secondary processing program from the automatic diagnosis unit 6a, and the advanced data analysis unit 6b stores the contents of the signal processing recipe 11. Based on this, the best objects are selected and combined, and a secondary processing program is automatically generated.
- each object executes the processing possessed by the object, updates and refers to the data overnight, and exchanges messages with the object having other functions, thereby linking the objects. Do.
- Interaction between objects can be processed in a distributed manner because the objects themselves do not need to know where the other object exists. This makes it possible to realize an environment in which the necessary analysis processing is timely uploaded to the user B by connecting the objects according to the purpose by converting the signal processing into an object.
- the averaging process is a random waveform in which the periodic oscillation of period T is composed of a distorted waveform that includes harmonics in addition to the basic waveform, and a random waveform that is composed of frequency components with multiple periods, and represents the amplitude level of these waveforms. This is the process to be performed.
- the time averaging process (Average) and RMS (square root of variance) are expressed by the following formulas, respectively.
- the periodicity becomes a problem at the joints of the waveforms.
- high-frequency components generated from the joints of the cycles are also analyzed together. Since this high-frequency is not a component in the original waveform, By multiplying the waveform by a function that attenuates gently at both ends, and performing a digital Fourier transform, you can observe the spectrum from which the high frequencies generated at the joints have been removed.
- a function that gently attenuates both ends for waveform extraction used for such a purpose is called a window function (Time Windows).
- Window functions have been devised, and they are used depending on the purpose of analysis and the difference in waveform characteristics.
- Typical window functions are a Hamming window (suitable for analysis of frequency components that are close) and a Hanning window (suitable for analysis of waveform components that are not very close).
- the Fourier Transform is a typical frequency analysis for transforming a complex signal into a group of a large number of sine waves, and a wavelet is a wavelet that arbitrarily converts a raw waveform signal into a wavelet. This is a time-frequency analysis that decomposes into).
- the degree data analysis section 6b selects and combines various optimum programs from the facility management data analysis program group 12.
- the FFT when the FFT is selected as the secondary processing, when generating the FFT file 11c, the time averaging processing (Average) is performed from the averaging processing program 12a according to the variation form of the target signal and the frequency band.
- the Hamming window is selected from the window function program 12b, the Fourier transform is selected from the analysis processing program 12c, and the necessary conditions and programs for the relationship definition, connection, and operation of the program are selected.
- the secondary processing program is generated by matching the output format and the like, and is converted into an electronic file (li e ').
- the electronic file 11c 'containing the facility management data analysis program which is the secondary processing program generated by the advanced data analysis unit 6b of the equipment diagnosis center C, is as shown in Fig. 2.
- Processing unit 3 Send to a.
- the equipment management data processing unit 3a processes the equipment status detection information detected by the equipment status detectors 2a and 2b, which are the equipment status detection means, using the predetermined equipment management data analysis program sent.
- the information output from the equipment management data processing section 3a is output to the equipment state determination section 3b after determining the level with respect to the management reference value, and the equipment state determination section 3b determines the level.
- the equipment monitoring unit 5 collects and processes the relevant information of the equipment 1 output and output it, and outputs it to the advanced analysis diagnosis unit 6 of the equipment diagnosis center C via the communication network 10.
- Fig. 14 shows the secondary processing in the equipment management data processing unit 3a by the FFT equipment management data analysis program applied to the user side B for the measurement point of channel number "13" in Fig. 8.
- the analysis results shown in Fig. 14 are sent to the equipment diagnosis center C via the communication network 10 in the same manner. Is parsed.
- the advanced analysis and diagnosis unit 6 which receives the information output from the equipment monitoring unit 5, Further analysis is performed to identify the cause of the abnormality of the equipment 1 concerned and the measures to be taken for improvement, and the identification result is transmitted to the equipment monitoring unit 5 via the communication network 10 and, if necessary, the equipment monitoring unit 5 Step 5 is repeated, and the process of developing the facility management data analysis program is repeated.
- the equipment monitoring unit 5 includes a maintenance information database 13 serving as a maintenance information section of the equipment 1 shown in FIG. 2, an operation information database 14 serving as an operation information section, and an illustration serving as an external information section. At least one of maintenance information, operation information, and external information is collected and processed from at least one of the external information data and output to the advanced analysis and diagnosis section 6 of the equipment diagnosis center C I have.
- the contents of secondary processing include a maintenance information database that stores information such as maintenance plans, equipment specifications, and maintenance histories in user B13, process information, production planning, It also includes the operation information database 14, which stores information such as quality information, and the processing to collect external information such as manufacturer's design specifications and product information and output it to the advanced analysis and diagnosis unit 6 of the facility diagnosis center-Side C.
- the present invention Since the present invention has the above-described configuration and operation, a worker in the factory on the user side collects various data by grasping and managing the operation status of the equipment on a daily basis, When the information corresponding to the abnormal level is extracted from the collected information, the information is promptly sent to the equipment diagnosis center, which is a specialized technical group, via a network, in-net or public network.
- the equipment diagnosis center can use the advanced analysis and diagnosis section to perform advanced analysis on the information and quickly return the best information on the equipment determined to be abnormal to the user side. Based on this, the appropriate response directed by the technical group can be implemented promptly.
- the advanced analysis / diagnosis unit at the equipment diagnosis center will further monitor the equipment at the user side as secondary processing.
- the equipment management data analysis program is deployed to the department via a communication network, and the user performs the altitude analysis again.
- the program for analyzing equipment management data is uploaded to the user side, and the user performs the altitude analysis again, so that an enormous amount of data such as equipment state detection information detected by the equipment state detection means can be obtained. There is no need to send the information to the equipment diagnosis center, and only a small amount of information with only analysis results is sent to the equipment diagnosis center, reducing the burden of information transfer on the communication network.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Testing And Monitoring For Control Systems (AREA)
Description
Claims
Priority Applications (6)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/332,007 US7085684B2 (en) | 2000-07-04 | 2001-07-04 | System for diagnosing a facility apparatus |
EP01947820A EP1298510A4 (en) | 2000-07-04 | 2001-07-04 | SYSTEM FOR DIAGNOSING A PLANT, MANAGEMENT DEVICE AND DIAGNOSTIC DEVICE |
KR10-2003-7000040A KR100522342B1 (ko) | 2000-07-04 | 2001-07-04 | 설비 기기 진단 시스템, 관리 장치 및 진단 장치 |
AU2001269438A AU2001269438A1 (en) | 2000-07-04 | 2001-07-04 | System for diagnosing facility apparatus, managing apparatus and diagnostic apparatus |
US11/330,354 US7143011B2 (en) | 2000-07-04 | 2006-01-12 | System for diagnosing facility apparatus, managing apparatus and diagnostic apparatus |
US11/330,266 US7139681B2 (en) | 2000-07-04 | 2006-01-12 | System for diagnosing facility apparatus, managing apparatus and diagnostic apparatus |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2000201907A JP2002023841A (ja) | 2000-07-04 | 2000-07-04 | 設備機器診断システム |
JP2000-201907 | 2000-07-04 | ||
JP2000-262500 | 2000-08-31 | ||
JP2000262500A JP4428838B2 (ja) | 2000-08-31 | 2000-08-31 | 設備機器診断システム |
Related Child Applications (4)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10332007 A-371-Of-International | 2001-07-04 | ||
US10/332,007 A-371-Of-International US7085684B2 (en) | 2000-07-04 | 2001-07-04 | System for diagnosing a facility apparatus |
US11/330,354 Division US7143011B2 (en) | 2000-07-04 | 2006-01-12 | System for diagnosing facility apparatus, managing apparatus and diagnostic apparatus |
US11/330,266 Division US7139681B2 (en) | 2000-07-04 | 2006-01-12 | System for diagnosing facility apparatus, managing apparatus and diagnostic apparatus |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2002003158A1 true WO2002003158A1 (fr) | 2002-01-10 |
Family
ID=26595318
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/JP2001/005807 WO2002003158A1 (fr) | 2000-07-04 | 2001-07-04 | Systeme de diagnostic et de gestion d'un equipement d'installation, appareil de gestion et appareil de diagnostic |
Country Status (6)
Country | Link |
---|---|
US (1) | US7143011B2 (ja) |
EP (1) | EP1298510A4 (ja) |
KR (1) | KR100522342B1 (ja) |
CN (1) | CN1235105C (ja) |
AU (1) | AU2001269438A1 (ja) |
WO (1) | WO2002003158A1 (ja) |
Families Citing this family (25)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7515977B2 (en) * | 2004-03-30 | 2009-04-07 | Fisher-Rosemount Systems, Inc. | Integrated configuration system for use in a process plant |
JP4732304B2 (ja) * | 2006-11-07 | 2011-07-27 | 株式会社小野測器 | 回転計及び回転計用プログラム |
GB2459064B (en) * | 2007-02-25 | 2011-09-07 | Network Technologies Ltd | Drilling collaboration infrastructure |
JP4735592B2 (ja) * | 2007-04-11 | 2011-07-27 | ダイキン工業株式会社 | 群管理装置及び群管理システム |
FI20075546L (fi) * | 2007-07-17 | 2009-01-18 | Pettis Oy | Ylläpitojärjestelmä |
CN101393543A (zh) * | 2007-09-18 | 2009-03-25 | 西门子公司 | 一种故障分析和诊断的方法及系统 |
US20090198409A1 (en) * | 2008-01-31 | 2009-08-06 | Caterpillar Inc. | Work tool data system |
DE102008060011A1 (de) | 2008-11-25 | 2010-05-27 | Pilz Gmbh & Co. Kg | Sicherheitssteuerung und Verfahren zum Steuern einer automatisierten Anlage |
KR101010717B1 (ko) * | 2009-11-10 | 2011-01-24 | 한국동서발전(주) | 상태기반 발전소 운전 및 정비 관리 시스템 |
US8670939B2 (en) * | 2009-12-18 | 2014-03-11 | Electronics And Telecommunications Research Institute | Apparatus and method of providing facility information |
JP5627477B2 (ja) | 2011-01-20 | 2014-11-19 | 三菱重工業株式会社 | プラント安全設計支援装置及びプラント監視保守支援装置 |
JP5843669B2 (ja) * | 2012-03-14 | 2016-01-13 | アズビル株式会社 | 整備対象バルブ選定装置および選定方法 |
EP2909826A4 (en) * | 2012-10-19 | 2016-11-16 | Roadroid Ab | METHOD AND SYSTEM FOR MONITORING A ROAD CONDITION |
JP5803986B2 (ja) * | 2013-06-19 | 2015-11-04 | 栗田工業株式会社 | 設備管理システム |
KR101616072B1 (ko) | 2014-07-08 | 2016-04-28 | 주식회사 글로비즈 | 모니터링 방법 및 시스템 |
US10281909B2 (en) | 2013-07-10 | 2019-05-07 | Globiz Co., Ltd. | Signal measuring/diagnosing system, and method for applying the same to individual devices |
KR101569279B1 (ko) * | 2014-04-29 | 2015-11-13 | 엔셀 주식회사 | 센서, 액추에이터, 및 말단제어기의 정보 분산 통합에 의한 설비의 접속, 진단 및 제어 시스템 및 방법 |
KR101583705B1 (ko) | 2014-05-27 | 2016-01-11 | 주식회사 글로비즈 | 다대역 주파수 모니터링 방법 |
US9719881B2 (en) * | 2014-08-29 | 2017-08-01 | Verizon Patent And Licensing Inc. | Scalable framework for managing civil structure monitoring devices |
KR101913474B1 (ko) * | 2015-10-28 | 2018-11-02 | 현대오토에버 주식회사 | 자동화 설비 cms의 제어 방법 |
JP6460137B2 (ja) * | 2017-03-06 | 2019-01-30 | オムロン株式会社 | 制御装置、制御方法、およびプログラム |
KR102046371B1 (ko) * | 2017-10-19 | 2019-11-19 | 한국수자원공사 | 수력발전용 어플 기반 고장복구 시스템 |
KR102385100B1 (ko) * | 2017-12-22 | 2022-04-13 | 삼성디스플레이 주식회사 | 인프라 설비 가동 데이터 자동분석방법 및 시스템 |
JP7280703B2 (ja) * | 2019-01-31 | 2023-05-24 | 住友重機械工業株式会社 | 診断システム |
KR101996375B1 (ko) * | 2019-03-06 | 2019-07-03 | (주)와이제이솔루션 | 기능 확장이 용이한 이상 예측 수처리 제어시스템 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0262606A (ja) * | 1988-08-29 | 1990-03-02 | Fanuc Ltd | Cncの診断方式 |
JPH0511834A (ja) * | 1991-07-01 | 1993-01-22 | Amada Co Ltd | 機械のリモート診断システム |
JPH08137540A (ja) * | 1994-11-11 | 1996-05-31 | Toto Ltd | 機器の故障診断方法及び装置 |
JPH10222220A (ja) * | 1997-02-12 | 1998-08-21 | Mitsubishi Electric Corp | リモート診断システム |
JPH11119815A (ja) * | 1997-10-17 | 1999-04-30 | Nakamura Tome Precision Ind Co Ltd | Nc工作機械の故障診断方法及び装置 |
JPH11252670A (ja) * | 1998-03-05 | 1999-09-17 | Omron Corp | 遠方監視制御システム及びセンサ端末装置 |
Family Cites Families (29)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP0436312A3 (en) * | 1989-12-14 | 1993-06-09 | Westinghouse Electric Corporation | Diagnostic expert system monitor |
US5311562A (en) * | 1992-12-01 | 1994-05-10 | Westinghouse Electric Corp. | Plant maintenance with predictive diagnostics |
US5533413A (en) | 1994-06-30 | 1996-07-09 | Yokogawa Electric Corporation | Equipment diagnosis system |
US5845230A (en) | 1996-01-30 | 1998-12-01 | Skf Condition Monitoring | Apparatus and method for the remote monitoring of machine condition |
US6489884B1 (en) * | 1996-01-30 | 2002-12-03 | Skf Condition Monitoring | Apparatus and method for the remote monitoring of machine condition |
TWI249760B (en) * | 1996-07-31 | 2006-02-21 | Canon Kk | Remote maintenance system |
US5963884A (en) | 1996-09-23 | 1999-10-05 | Machine Xpert, Llc | Predictive maintenance system |
US6041287A (en) | 1996-11-07 | 2000-03-21 | Reliance Electric Industrial Company | System architecture for on-line machine diagnostics |
US6199018B1 (en) | 1998-03-04 | 2001-03-06 | Emerson Electric Co. | Distributed diagnostic system |
US6192325B1 (en) | 1998-09-15 | 2001-02-20 | Csi Technology, Inc. | Method and apparatus for establishing a predictive maintenance database |
JP2000210800A (ja) | 1999-01-27 | 2000-08-02 | Komatsu Ltd | 産業機械のモニタ方法およびその装置 |
US6505145B1 (en) * | 1999-02-22 | 2003-01-07 | Northeast Equipment Inc. | Apparatus and method for monitoring and maintaining plant equipment |
FI990715A (fi) | 1999-03-31 | 2000-10-01 | Valmet Corp | Tuotantolaitoksen huoltojärjestely |
US6298308B1 (en) | 1999-05-20 | 2001-10-02 | Reid Asset Management Company | Diagnostic network with automated proactive local experts |
US6411678B1 (en) | 1999-10-01 | 2002-06-25 | General Electric Company | Internet based remote diagnostic system |
CA2314573C (en) | 2000-01-13 | 2009-09-29 | Z.I. Probes, Inc. | System for acquiring data from a facility and method |
US6421571B1 (en) | 2000-02-29 | 2002-07-16 | Bently Nevada Corporation | Industrial plant asset management system: apparatus and method |
US6594621B1 (en) | 2000-03-06 | 2003-07-15 | James H. Meeker | System and method for determining condition of plant |
EP1164550B1 (en) | 2000-06-16 | 2008-12-03 | Ntn Corporation | Machine component monitoring, diagnosing and selling system |
JP3612472B2 (ja) * | 2000-06-22 | 2005-01-19 | 株式会社日立製作所 | 遠隔監視診断システム、及び遠隔監視診断方法 |
US6556956B1 (en) * | 2000-06-30 | 2003-04-29 | General Electric Company | Data acquisition unit for remote monitoring system and method for remote monitoring |
US20020022969A1 (en) | 2000-07-07 | 2002-02-21 | Berg Marc Van Den | Remote automated customer support for manufacturing equipment |
JP2002023839A (ja) | 2000-07-13 | 2002-01-25 | Hitachi Ltd | 機器管理システムと機器管理方法及び監視装置、データベース装置とデータベースクライアント装置並びに記録媒体 |
US6618692B2 (en) * | 2000-09-20 | 2003-09-09 | Hitachi, Ltd. | Remote diagnostic system and method for semiconductor manufacturing equipment |
US6721689B2 (en) * | 2000-11-29 | 2004-04-13 | Icanon Associates, Inc. | System and method for hosted facilities management |
US6795798B2 (en) * | 2001-03-01 | 2004-09-21 | Fisher-Rosemount Systems, Inc. | Remote analysis of process control plant data |
EP1366398A2 (en) | 2001-03-01 | 2003-12-03 | Fisher-Rosemount Systems, Inc. | Automatic work order/parts order generation and tracking |
US6795799B2 (en) * | 2001-03-07 | 2004-09-21 | Qualtech Systems, Inc. | Remote diagnosis server |
US20020177978A1 (en) * | 2001-04-16 | 2002-11-28 | Obenhoff Ryan E. | Digital data acquisition system for manitoring and remote testing of gas and steam turbine performance parameters |
-
2001
- 2001-07-04 EP EP01947820A patent/EP1298510A4/en not_active Withdrawn
- 2001-07-04 CN CNB018121853A patent/CN1235105C/zh not_active Expired - Lifetime
- 2001-07-04 KR KR10-2003-7000040A patent/KR100522342B1/ko active IP Right Grant
- 2001-07-04 AU AU2001269438A patent/AU2001269438A1/en not_active Abandoned
- 2001-07-04 WO PCT/JP2001/005807 patent/WO2002003158A1/ja not_active Application Discontinuation
-
2006
- 2006-01-12 US US11/330,354 patent/US7143011B2/en not_active Expired - Lifetime
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH0262606A (ja) * | 1988-08-29 | 1990-03-02 | Fanuc Ltd | Cncの診断方式 |
JPH0511834A (ja) * | 1991-07-01 | 1993-01-22 | Amada Co Ltd | 機械のリモート診断システム |
JPH08137540A (ja) * | 1994-11-11 | 1996-05-31 | Toto Ltd | 機器の故障診断方法及び装置 |
JPH10222220A (ja) * | 1997-02-12 | 1998-08-21 | Mitsubishi Electric Corp | リモート診断システム |
JPH11119815A (ja) * | 1997-10-17 | 1999-04-30 | Nakamura Tome Precision Ind Co Ltd | Nc工作機械の故障診断方法及び装置 |
JPH11252670A (ja) * | 1998-03-05 | 1999-09-17 | Omron Corp | 遠方監視制御システム及びセンサ端末装置 |
Also Published As
Publication number | Publication date |
---|---|
US20060116836A1 (en) | 2006-06-01 |
KR100522342B1 (ko) | 2005-10-19 |
CN1440523A (zh) | 2003-09-03 |
US7143011B2 (en) | 2006-11-28 |
EP1298510A1 (en) | 2003-04-02 |
KR20030014417A (ko) | 2003-02-17 |
CN1235105C (zh) | 2006-01-04 |
EP1298510A4 (en) | 2005-01-26 |
AU2001269438A1 (en) | 2002-01-14 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2002003158A1 (fr) | Systeme de diagnostic et de gestion d'un equipement d'installation, appareil de gestion et appareil de diagnostic | |
US7139681B2 (en) | System for diagnosing facility apparatus, managing apparatus and diagnostic apparatus | |
JP4428838B2 (ja) | 設備機器診断システム | |
CN105809255B (zh) | 一种基于物联网的火电厂旋转机械健康管理方法及系统 | |
Crabtree et al. | Survey of commercially available condition monitoring systems for wind turbines | |
US7606673B2 (en) | Rotating bearing analysis and monitoring system | |
US6801864B2 (en) | System and method for analyzing vibration signals | |
WO2017134983A1 (ja) | 設備診断装置、設備診断方法及び設備診断プログラム | |
JP2000259222A (ja) | 機器監視・予防保全システム | |
US20200284694A1 (en) | Machine Monitoring | |
Palem | Condition-based maintenance using sensor arrays and telematics | |
WO2004040465A1 (en) | System and method for remote diagnosis of distributed objects | |
JP2013061945A (ja) | 状態監視システムおよびその方法 | |
KR102301201B1 (ko) | IoT 센서 기반 회전 기기의 정밀 상태 진단 장치 및 방법 | |
KR20210081145A (ko) | 진동과 소음신호를 이용한 기계결함진단장치 및 그 신호를 이용한 빅데이터 기반의 스마트 센서 시스템 | |
US10341167B2 (en) | Electronic volume corrector with cloud enabled health monitoring of associated gas distribution equipment | |
JP2010027076A (ja) | 設備機器診断方法 | |
JP2002023841A (ja) | 設備機器診断システム | |
CN102759905B (zh) | 用于表征过程控制设施完整性的方法和装置 | |
US10156844B1 (en) | System and method for new equipment configuration and sound monitoring | |
KR20000036962A (ko) | 통신망을 이용한 설비 진단 서비스 제공 방법과 시스템 및그 방법이 저장된 기록매체 | |
Salokangas et al. | MIMOSA for Condition-based Maintenance. | |
TWI777681B (zh) | 用於電動機之振動監測系統 | |
EP1549918B1 (en) | Monitoring and diagnosting a technical installation using purely mechanically activated signalling means | |
JP2018040605A (ja) | 回転機器の監視装置 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AK | Designated states |
Kind code of ref document: A1 Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG US UZ VN YU ZA ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: A1 Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GW ML MR NE SN TD TG |
|
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
WWE | Wipo information: entry into national phase |
Ref document number: 018121853 Country of ref document: CN |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2001947820 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 1020037000040 Country of ref document: KR Ref document number: 10332007 Country of ref document: US |
|
WWP | Wipo information: published in national office |
Ref document number: 1020037000040 Country of ref document: KR |
|
WWP | Wipo information: published in national office |
Ref document number: 2001947820 Country of ref document: EP |
|
REG | Reference to national code |
Ref country code: DE Ref legal event code: 8642 |
|
WWG | Wipo information: grant in national office |
Ref document number: 1020037000040 Country of ref document: KR |
|
WWW | Wipo information: withdrawn in national office |
Ref document number: 2001947820 Country of ref document: EP |