WO1998039718A1 - Systeme de diagnostic distribue - Google Patents

Systeme de diagnostic distribue Download PDF

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Publication number
WO1998039718A1
WO1998039718A1 PCT/US1998/004288 US9804288W WO9839718A1 WO 1998039718 A1 WO1998039718 A1 WO 1998039718A1 US 9804288 W US9804288 W US 9804288W WO 9839718 A1 WO9839718 A1 WO 9839718A1
Authority
WO
WIPO (PCT)
Prior art keywords
machine
data
sensor
temperature
local monitoring
Prior art date
Application number
PCT/US1998/004288
Other languages
English (en)
Other versions
WO1998039718A9 (fr
Inventor
Vojislav Divljakovic
Thomas Grudkowski
Joseph A. Kline
Austin H. Bonnet
George W. Buckley
James P. Lynch
Israel E. Alguindigue
Nancy L. Quist
Robert P. Bauer
Roland I. Hannula
Original Assignee
Emerson Electric Co.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Emerson Electric Co. filed Critical Emerson Electric Co.
Priority to AU66862/98A priority Critical patent/AU6686298A/en
Priority to EP98908960A priority patent/EP0965092A4/fr
Publication of WO1998039718A1 publication Critical patent/WO1998039718A1/fr
Publication of WO1998039718A9 publication Critical patent/WO1998039718A9/fr

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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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/406—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by monitoring or safety
    • G05B19/4063—Monitoring general control system
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/33—Director till display
    • G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/33—Director till display
    • G05B2219/33284—Remote diagnostic

Definitions

  • the present invention relates to systems and methods for diagnosing machines and, in
  • One goal of such systems and methods is to allow their users to
  • the rotational speed of the rotor often used in known systems is the rotational speed of the rotor. Often, the rotational speed is
  • diagnostic system for monitoring a plurality of machines where the system
  • each local monitoring device includes a plurality of local monitoring devices, where each local monitoring device is
  • each local monitoring device further includes a data processor
  • the exemplary system also includes a global data processor
  • the global data processor generates the set of provided parameters for each local device
  • Figure 1 illustrates an exemplary distributed diagnostic control system constructed in
  • FIGS 2A-2E illustrate in greater detail an exemplary machine and a local
  • Figure 3 generally illustrates a typical induction motor torque-speed, torque-slip
  • Figure 4 illustrates a novel circuit for determining the slip of an induction machine
  • Figure 5 generally illustrates the frequency spectrum that may be obtained through
  • Figure 6 illustrates an exemplary predictive routine in accordance with certain aspects
  • Figure 7 generally illustrates the manner in which the input data for the exemplary
  • Figure 8 illustrates the use of a local monitor device constructed according to various
  • Figure 9 provides a flow chart of the operation of a local monitor device constructed
  • Figure 10 illustrates a peak searching process that may be used by a local monitoring
  • Figures 11 A-l IC illustrate the types of loads often encountered by electric machines.
  • Figure 12 illustrates the operation of a local monitoring device constructed in
  • Figure 13 illustrates the operation of a local monitoring device constructed according
  • Figure 14 generally illustrates the use of time expansion factors in accordance with
  • the exemplary distributed diagnostic system 10 includes a plurality
  • each of the machines 11 is represented as a
  • Each of the local monitoring devices 12 collects information concerning the
  • each local local machine 11 operational status of the machine 11 with which it is associated. For example, each local
  • monitoring device 12 may collect information concerning the vibrational characteristics of the machine 11, the temperature of the stator, windings and/or bearings of the machine 11,
  • This information may be stored in data
  • the collected information concerning the various machines 11 is processed by each
  • This low-level indication may take the form of a visual
  • the local monitoring devices 12 may also pre-process some or
  • each of the local monitoring devices includes a microcontroller or
  • microprocessor (not illustrated in Figure 1) that runs software establishing a local, low-level,
  • local model may be downloaded to the local monitoring devices 12 as more fully described
  • each of the local monitoring devices 12 is adapted to
  • monitoring devices 12" are coupled to a single protocol translator 13".
  • Protocol translator 13 may be used in the system of Figure 1.
  • the protocol translators 13 simply receive information from the local
  • monitoring devices 12 using one communications protocol and converts the information such
  • the protocol translator 13 has some "intelligence" and periodically polls
  • a site processor 14 which in the exemplary system is a personal computer.
  • the site processor 14 receives and processes the collected information from the local
  • processor 14 is capable of receiving information using the same communications protocol
  • the protocol translator 13 may be eliminated.
  • the site processor 14 is a computer
  • the site processor may use this information to provide an
  • the site processor 14 is a personal computer that is running a
  • monitoring devices 12 and provides as outputs information representative of the operating
  • outputs may be used to derive the parameters used by the local monitoring devices 12 to
  • the global diagnostic program running on the personal computer 14 may include a
  • self-correcting algorithm such as a neural network, that receives information from the local
  • the site processor is adaptive it can "learn" from the information provided to it from the local
  • monitoring devices 12 can build one or more global neural networks that can predict
  • the personal computer 14 can periodically
  • the site processor 14 in addition to maintaining the global,
  • adaptive, neural network described above also performs "high" level processing of the
  • devices 12 is programmed to run a low level adaptive program similar to the higher level
  • the local adaptive program running on the site processor 14.
  • the local adaptive program running on the site processor 14.
  • each "intelligent" local monitoring device can learn from its own motor and receive information derived from an analysis of all
  • processor 14 operates on information locally acquired by the local monitoring devices 12 to
  • site processor 14 provides a
  • the "site” system disclosed above may be expanded by allowing the site processor 14
  • the data may be transferred using disk or tape, if necessary.
  • the centralized processor 15 represents a centralized
  • processor running a "super-global" adaptive program that, receives information from the site
  • processor 14 as well as information from similar site processors 14' and 14" operating in
  • site processor 14 could be a
  • site processor 14 could operate in different portions of the same plant. Alternately, site processor 14 could operate in
  • the centralized processor 15 in different parts of a given country. In either embodiment, the centralized processor 15
  • site processors 14, 14' and 14" which in turn may provide the parameters to the appropriate
  • processor 15 information may shared plant or industry wide for more effective machine
  • centralized processor 15 may be changed without departing from the present teachings.
  • wireless communication devices and/or a combination of wireless and
  • FIGS 2A-2E illustrate in greater detail an exemplary machine 1 1 and a local
  • the machine 1 1 is a
  • squirrel-cage induction machine of the type available from U.S. Electrical Motors or the
  • the machine 1 1 includes a rotating member referred to as a rotor
  • stator an outer stationary member referred to as a stator (not illustrated). Both the rotor and the stator
  • the machine 1 1 may be of conventional
  • Coupled to the motor housing 20 is a local monitoring device 12.
  • a local monitoring device 12 Coupled to the motor housing 20 is a local monitoring device 12.
  • the local monitoring device comprises one or more electronic boards (not limited
  • the device housing 22 should be capable of protecting its contents from the
  • the device housing 22 supports visual indicators 23.
  • the visual indicators comprise three lights (red, yellow, and
  • a communications link 24 extends from the local monitoring device 12 to allow the
  • local monitoring device 12 to communicate and receive information and data from outside
  • the nature of the communication link 24 will vary depending on the communication
  • the communication link 24 is a scheme employed by the local monitoring device.
  • the communication link 24 is a scheme employed by the local monitoring device. For example, the communication link 24
  • FIG. 2B illustrates in greater detail the electronics control boards housed in the
  • the electronics control boards housed in the device housing 22 include a
  • communications board 26 such as a CT Network Communications Board, that is adapted to
  • the communications board 26 is
  • the communications board 26 should include appropriate hardware,
  • communications board 26 may be adapted to communicate using wireless communication
  • the communications board 26 is also adapted to control the visual indicators 23.
  • the communications board 26 may be constructed and configured using known
  • Figure 2A includes a microprocessor or microcontroller 28 and a
  • the microprocessor 28 is a Motorola
  • MC68LC302 HC11 or HC05 type processor and the data storage device 29 comprises flash
  • memory such as a flash memory device contained within the microprocessor 28 or an
  • external flash memory device such as an AT29C256FLASH part.
  • Other external memory such as an AT29C256FLASH part.
  • EPROM and DRAM devices may be used in conjunction with the
  • control board 27 and the selection of the appropriate external memory devices will be
  • microprocessor 28 such that the microprocessor can communicate over the modem device 30.
  • the microprocessor 28 may use the modem device 30 to
  • a RF transceiver 32 is provided to allow for "wireless” communications and a HART ASIC 33 or other appropriate device (e.g., a FR 3244 transmitter) is provided to allow
  • microprocessor 28 types of communication devices that may be used with microprocessor 28 and that other
  • microprocessor communications are accomplished through the CT protocol board.
  • a dual-port memory device 40 (e.g., a dual port RAM) may be
  • FIG. 2B illustrates the use of such a device 40 in the
  • the microprocessor 28 is adapted to receive as inputs
  • Figure 2B illustrates one such exemplary sensor set including seven
  • Sensors 34a-34e are RTD transducers that are positioned appropriately with respect to
  • two of the RTD transducers 34a-34e are positioned near
  • machine housing and/or the temperature of the environment in which machine 11 is
  • RTD transducers may be used to implement the teachings contained herein.
  • RTD transducers may be used to detect and provide information concerning the
  • the microprocessor 28 includes a plurality of built-in
  • each of the RTD transducers 34a-34e comprises a RTD device and an
  • transducers 34a-34e and the microprocessor 28.
  • the microprocessor 28 also receives
  • the vibration sensor 35 may be positioned with respect to machine 11 to
  • detector 35 comprises an accelerometer, such an automotive accelerometer available from
  • the microprocessor 28 also receives as an input the
  • the flux sensor 36 should be positioned appropriately
  • the flux sensor allows for a
  • the sensors should provide enough information for reliable prediction of machine failure
  • FIG. 2C illustrates in greater detail an alternate sensor set 200 that may be used to
  • FIG. 2C a schematic for a sensor set 200 is provided. The illustrated
  • exemplary sensor set includes a number of various sensing elements that will be discussed in
  • the various sensor elements maybe
  • the components used to construct the sensors may utilize surface mount technology, although through-hole
  • the sensor set of Figure 2C includes four three-terminal temperature sensing devices
  • each of the temperature sensors is an
  • AD22100 device that provides a variable analog output that varies with the ambient
  • temperature sensor 201 is positioned to detect the ambient temperature of the electric circuit
  • Sensor 204 is positioned so as to
  • the 202 and 203 sensors are coupled to the sensor circuit board by
  • Figure 2D illustrates one such
  • an endshield 205 or other appropriate structure is
  • the endshield 205 defines a angular bearing bracket or recess 206 adapted to
  • One or more pockets 207 is formed in the structure 205 and
  • the pockets 207 are sized to receive a temperature sensor of the type used for temperature
  • a temperature sensor may be placed in recess 207, and a bearing
  • thermosensor may be placed in recess 206 such that the temperature sensor will provide an output signal
  • the temperature sensor is held in close proximity to the appropriate bearing structure such that the bearing helps to maintain
  • the depth of the recess 207 should be such that the
  • the fourth temperature sensor, sensor 204 is positioned
  • the sensor 204 is coupled.
  • the sensor 204 should be positioned to obtain a temperature
  • Small filter capacitors 208 provide some limited filtering of the analog sensors 201-
  • the sensor board is coupled.
  • the flux detecting circuit includes a magnetoresistive
  • the flux detector may be positioned to the machine housing of the
  • the flux sensor 200 should be any sensor machine to which sensor set 200 is coupled.
  • the flux sensor 200 should be any sensor machine to which sensor set 200 is coupled.
  • the flux sensor 200 should be any sensor
  • the magnetoresistive microcircuit 209 comprises a
  • resistive circuit in the form of a Wheatstone bridge having three elements of a substantially
  • terminals of the device are coupled to a known voltage supply and circuit ground, and the other two terminals are monitored to provide an indication of the strength of the magnetic
  • two terminals of the circuit 209 are coupled, respectively, to a
  • Vcc power supply and to a ground.
  • the other two terminals from the device 209 are coupled
  • the differential amplifier is configured, via a
  • differential amplifier 210 will provide an indication of the leakage flux of the machine.
  • Certain magnetoresistive circuits such as circuit 209, have a pre-set easy axis (a
  • axis can "flip,” thus changing the electrical characteristics of the circuit.
  • circuits such as circuit 20,9 have an on-chip current strap that allows for external re-flipping
  • a set/reset circuit is provided that will allow for resetting the circuit 209
  • this resetting function is accomplished as follows:
  • analog output from differential amplifier 210 is monitored by, for example, a microprocessor that converted the analog value to a digital value. If it is determined that the analog signal
  • microprocessor or other monitoring device will general a flux circuit reset signal that is
  • the set/reset circuit 21 1 will, in response, generate a
  • the sensor set 200 also includes a novel sensor circuit for detecting
  • failure sensor 212 is coupled to a three-phase machine and there are, therefore, three output
  • each of the output leads from the insulation detector is
  • detection node 216 Two current paths exist between the detection node 216 and ground. A
  • first path allows current to flow from ground, through a unidirectional current device 217 (e.g., a diode), to detection node 216.
  • a unidirectional current device 217 e.g., a diode
  • a second current path through a light-emitting diode
  • the insulation failure detection circuit 212 is constructed such that the current will
  • an insulation failure sensor is illustrated for sensing the
  • the insulated wire 219 is open of the wires that form the phase winding of the
  • the insulated wire 219 is wound about a wire
  • uninsulated wire 220 will begin to decrease. Eventually, an electrical path will be created
  • wire segment will have one insulated coating and, thus, there will be two layers of insulation separating each segment of the phase winding.
  • connection with Figure 2D may provide an indication of a potential insulation failure
  • the novel circuit set also includes an accelerometer
  • circuit 224 for detecting the acceleration/deacceleration of the electrical machine to which the
  • the accelerometer circuit comprises a
  • piezioelectric device 225 that provides an analog voltage signal having a magnitude
  • the vibration detector may be an A5100 piezioelectric sensor, available from
  • the sensor 225 should be positioned in a portion of the electric machine known to
  • vibration detector 224 can provide information concerning the
  • main control board 27 provides electrical connections to the main control board via suitable electrical connections.
  • microprocessor used to construct main control board 27 has a built-in analog-to-digital
  • a D converter an external A/D converter may be used to transform the analog signals from
  • the sensor set to digital signals of the type appropriate for input to the microprocessor 28.
  • the sensor set and the main control board together form a local monitoring
  • the specific physical structure of the local monitoring device may vary depending on
  • the local monitoring device 12 will consist of a number of appropriate sensors for
  • a power supply for the referenced circuitry will be
  • a high-level block diagram of such a local monitoring device is
  • microprocessor 28 is associated. The construction and assembly of the main control board 27
  • microprocessor 28 may be any software or firm ware required to properly operate microprocessor 28, and any software or firm ware required to properly operate microprocessor 28, may be
  • the microprocessor 28 comprises a
  • microcontroller such as a Motorola HC1 1 microcontroller in which is embedded a data
  • This program may be embedded in software or
  • firmware e.g., a EPROM or ROM
  • sensing devices utilize the model to provide local diagnostic information concerning the
  • the data acquisition and local prediction program described above may comprise two
  • the local prediction program may also include
  • the normalization of the raw data from the sensors 34a-34e, 35 and 36 may be any normalization of the raw data from the sensors 34a-34e, 35 and 36.
  • microprocessor 28 Such normalization is necessary because, the local machine model
  • Equation 1 (below) provides one example of how the raw data from the temperature
  • sensors 34a-34e, 35 and 36 may be normalized to account for load and environmental
  • Equation 1 provides an exemplary normalization equation for
  • T N (T sensor - T ambient )/L
  • T N represents the normalized temperature information
  • T ambient represents the raw temperature reading from the appropriate sensor
  • T sensor and T ambient may be obtained from appropriate sensors 34a-34e.
  • the output of flux sensor 36 may be any suitable measuring technique.
  • the output of flux sensor 36 may be any suitable measuring technique.
  • f(r) is related to the synchronous speed of the stator field f(s) by a parameter referred to as the "slip" S of the machine.
  • the slip S is expressed as a fraction of the synchronous
  • slip S will vary from a value of 1 at start-up to a value
  • Figure 3 generally illustrates a typical induction motor torque-speed, torque-slip
  • Figure 4 illustrates a novel circuit for determining the slip S of an induction machine
  • the filtered output is applied to one
  • the digital comparator 42 will compare the
  • resistor 43 and a value of logic 0 when the converse is true.
  • the output of flux sensor 36 will vary in an approximately sinusoidal fashion and,
  • the value of the filtered flux signal will periodically vary above and below the voltage
  • the output of comparator 42 will be a series of digital pulses.
  • the present inventor has recognized that, in general, the frequency associated with the
  • comparator 42 it is possible to obtain an indication of fir), which will provide an indication of
  • microprocessor 28 which monitors the pulse train according to known techniques to derive a
  • analog output from sensor 36 is converted to a digital value and the low pass filtering and
  • comparison associated with comparator 42 are accomplished through appropriate software.
  • band-pass filter 44 which will pass only signals within a selected frequency
  • the band-pass filter should be constructed to pass
  • A-D converter (which may be built-in to microprocessor 28) and a Fast Fourier
  • FFT Fast Four Transform
  • This major frequency component will be a digital signal
  • the output torque or load L of the machine 1 1 may be
  • This load value L then be used for normalization purposes using Equation 1 ,
  • L for an induction motor may be derived through a routine running on the microprocessor 28.
  • the output from the flux sensor 36 is applied to an A/D
  • sensor output are processed, through the use of a digital low pass filter and FFT or other
  • the first predetermined frequency is identified. For most applications the first predetermined frequency will be
  • the routing may also use a digital high pass filter and FFT or other appropriate techniques
  • the second predetermined frequency will be just below the first
  • the first predetermined frequency is 50 Hz.
  • second predetermined frequency may be 49 Hz.
  • predetermined frequency will generally corresponds to the synchronous stator frequency or
  • Figure 5 generally illustrates the frequency
  • Figure 5 illustrates the peak
  • routine can look for frequency peaks near or at 3 *f(s) and 7*fis). The presence of peaks at
  • the present invention may be used to normalize data from temperature sensors. Similar
  • vibration sensor 35 techniques may be used to normalize the vibrational data to filter
  • a routing running on the microprocessor 28 For example, for a
  • microprocessor 28 may collect and
  • the identified data, collected and stored by the microprocessor 28, may be used to calculate the identified data.
  • microprocessor 28 to the site processor 14 or to an appropriate protocol converter 13 for other
  • the external communication of the collected and stored information may be initiated
  • microprocessor to be collected and stored by the microprocessor are exemplary only and that other categories
  • the local monitoring device 12 may be configured to
  • the local monitoring device 12 may be
  • a unique identifier such as a serial number, which may be used to
  • the local monitoring device 12 may also be configured to be stored in a memory
  • the counter may be temporarily stored in RAM memory associated with
  • microprocessor 28 and transferred to the flash memory on a daily basis such that the flash
  • memory in the local monitoring device includes information (updated daily) relating to the
  • This data may be
  • one or more local predictive routines may use that data to provide diagnostic information concerning the
  • the predictive routine illustrated in Figure 6 may be used to receive information
  • the exemplary illustrated routine utilizes a local neural network, such as a
  • a two-layer neural network 60 is illustrated. As illustrated the
  • neural network includes three input nodes 61, 62 and 63 and six output nodes 64a-64e.
  • each of the output nodes receives as inputs
  • the neural network is a "winner-take-all" network in which the
  • output of the network is determined by the output node with the highest value.
  • Figure 7 generally illustrates the measured
  • the neural network will yield one output
  • each output is a node with a higher value that the other output nodes.
  • each output is a node with a higher value that the other output nodes.
  • node 64a-64e corresponds to an particular value of expected bearing life. For example, node
  • 64a represents an expected bearing life of 1 year, while node 64e represents an expected
  • the neural network 60 will select one output node as the "winner" and provide an
  • microprocessor 28 may be stored by microprocessor 28 for use in determining the overall health of the motor
  • the parameters of the neural network 60 are parameters of the neural network 60.
  • weighting parameters referred to herein as the "weighting parameters.”
  • the weighting parameters may be provided to the various microprocessors 28
  • weighting parameters are developed by
  • accelerated aging data e.g., data co ⁇ esponding to the t. n t.
  • appropriate neural network or predictive algorithm including a back propagation network, a
  • accelerated aging data used to train the global network may include accelerated data relating
  • the various local monitoring devices 12 is acceptable for many applications, it is limited in
  • the laboratory data is used to train the global neural network may be valid for the laboratory
  • tested motors they may not be as valid for motors manufactured using a different
  • the present invention contemplates the use of a distributed diagnostic system in
  • This field-collected data is then provided to a
  • each of the local monitoring devices 12 will include a microprocessor
  • neural network 60 running a local predictive neural network, such as neural network 60 as described above.
  • each local predictive neural network will be established using weighting parameters
  • each local monitoring device 12 will collect, pre-process and
  • monitoring devices 12 will provide this collected data (and data indicating when a machine
  • the site processor 14 will include a data processor running one or more global neural
  • the collected data co ⁇ esponding to that machine may be used by such a
  • each global neural network as a known data set for training purposes.
  • each global neural network as a known data set for training purposes.
  • site processor 14 may be collected and forwarded, along with other information, to a
  • This centralized database 15 may include one or more "super-global" neural networks that receive the relevant field-collected data and develop updated weighting parameter data for
  • a global or super-global neural network may be used to increase diagnostic capabilities. For example, a global or super-global neural network may be used to increase diagnostic capabilities. For example, a global or super-global neural network may be used to increase diagnostic capabilities. For example, a global or super-global neural network may be used to increase diagnostic capabilities. For example, a global or super-global neural network may be used to increase diagnostic capabilities. For example, a global or super-global neural network may
  • weighting parameters for such machines and provide the updated, manufacturing or material
  • super-global neural networks may be based on the Weibull law.
  • the Weibull law has been
  • a Weibull factor is used in the training of the global and
  • neural networks may receive as inputs data indicating the time spent by the machine at
  • network may indicate the expected lifetime of the machine's insulation system.
  • a neural network can receive data reflecting the past and present vibration
  • Such data can, like the bearing temperature data, be used to calculate experience of the machine.
  • Such data can, like the bearing temperature data, be used to calculate the experience of the machine.
  • a neural network may be used to predict the expected lifetime of the machine's bearings. Still further, a neural network may be used to predict the expected lifetime of the machine's bearings. Still further, a neural network may be used to predict the expected lifetime of the machine's bearings. Still further, a neural network may be used to predict the expected lifetime of the machine's bearings. Still further, a neural network may be used to predict the expected lifetime of the machine's bearings. Still further, a neural network may be used to predict the expected lifetime of the machine's bearings.
  • neural network 60 comprised a two layer network, that other more or less complicated neural
  • neural networks may be used to practice the present invention.
  • neural networks having the following features may be used to practice the present invention.
  • neural networks having the following features may be used to practice the present invention.
  • neural networks having the following features may be used to practice the present invention.
  • neural networks having the following features may be used to practice the present invention.
  • neural networks having the following features may be used to practice the present invention.
  • neural networks having the following features may be used to practice the present invention.
  • the local monitoring device 12 described herein may be advantageously used in a
  • machine/local monitoring device pair may be operated in a "birth certificate” mode in which
  • the initial quality of the machine is assessed and the base operating parameter of the machine
  • the device pair may also be operated in a "confirmation" mode to ensure
  • the local monitoring device will perform a number of "tasks" and may respond
  • device/electric machine pair may communicate with an appropriately programmed personal
  • a local monitoring device/machine pair 80 may be placed on a
  • the local monitoring device includes the sensor set illustrated in Figure
  • the communications port of the local monitoring device is coupled via an appropriate
  • the personal computer 82 is coupled to a
  • the drive 83 (which may be an converter, inductor,
  • PWM drive or other appropriate drive has an output coupled to the phase windings of the
  • a load or shaft drive device 85 may be coupled to the shaft output of the
  • Figure 9 generally illustrates a flow chart of tasks that may be implemented by the
  • serial model number register that is initially set to zero. Accordingly, upon the
  • the PC will provide at step 91 a data signal to the local
  • monitoring device assigning the local monitoring device/machine pair a specific serial
  • Flash memory may return the serial number and model number to the PC for
  • initial data acquisition may begin at Task 1.
  • the local monitoring device 80 will acquire
  • Step 92 The program
  • running in the local monitoring device may first mask the collected data with a Hanning
  • vibration data concerning the operation of the electrical machine In one example, the local
  • monitoring device will determine and store in flash memory: (i) the vibration sensor mean;
  • Step 92 from the accelerometer of Figure 2D as the machine is operated over a desired range
  • the local monitoring device (or the PC which may receive the
  • FFT fast Forieur transform
  • the process begins by analyzing the first peak of the FFT data by
  • the peak frequency value of the peak and the area under the peak is stored in
  • Step 104 FFT spectrum or the beginning of the next peak is detected at Step 104.
  • the next peak is analyzed in the same fashion and the peak frequency and area values for the various peaks are
  • only the top twenty peaks are stored in temporary memory.
  • the top twenty peaks is stored in the flash memory, the overall vibration level in a desired
  • frequency range of interest is calculated at step 98 by summing up the areas for all of the
  • the desired frequency range will vary
  • the data is first masked with a Hamming winder, analyzed using
  • a FFT is performed and the FFT spectrum is processed using the techniques described above to provide data concerning the top twenty flux peaks and the overall flux level for a
  • Task 3 After completing Task 2 the appropriate routing will implement a Task 3 in which the
  • embodiments will implement Tasks 4, 5 and 6 at Step 101.
  • the temperature data In each step, the temperature data
  • the temperature data is then statistically
  • the processor will collect temperature data from the ambient temperature
  • communications with the local monitoring device will take a number (e.g., 10 consecutive
  • the local monitoring device and machine pair may be placed on a test pad
  • birth certificate processing may be performed with the data being stored in a temporary location. If the data taken
  • a motor e ⁇ or or fault signal may be provided.
  • the birth certificate mode may be useful in monitoring the corresponding electric machine
  • the local monitoring devices of the present invention may be
  • the electric machine operates in an OF/OFF manner, where the machine is either ON
  • This ON/OFF application may
  • the local monitoring device is subjected to erratic load and speed changes.
  • readings from the sensor set of Figure 2D should be taken at the typical load of the machine.
  • Figure 12 generally illustrates the operation of the local monitoring device in the
  • this load inertia data may be used to
  • the load inertia is obtained at step 120 upon initial start-up of the machine.
  • the local monitoring device collects data from the various temperature sensors on a
  • Temp(k) (Temp(k-l)+ temp (K)+temp(k+l))/3).
  • the local monitoring device may assume that
  • the motor is in a locked rotor condition and set an appropriate alarm flag at Step 124.
  • the local monitoring device will then collect a significant
  • Harming window may then be passed through a Harming window and the resultant data may be subjected to a
  • the largest peak in the FFT spectrum between 0 and 120 Hz. may be identified and stored in the memory of the local monitoring device ⁇ as this value will correspond to the
  • Step 127 the local monitoring device will proceed to Step 127 wherein it will determine the load
  • the local monitoring device will collect select data from the sensor
  • the local monitoring device will generate
  • the first normalized temperature reading will be stored in temporary
  • the vibration sensor will be detected and, using the techniques described above, the rotational
  • the array may be analyzed by the local monitoring
  • the method for determining the load profile of the electric machine uses the method for determining the load profile of the electric machine.
  • Step 127A the local monitoring device will first attempt to whether the

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Manufacturing & Machinery (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

La présente invention concerne un système de diagnostic distribué dans lequel une pluralité de dispositifs (12, 13) de contrôle local recueille des informations locales concernant différentes machines (11) et traite cette information, selon des paramètres de diagnostic redéfinis, à des fins de diagnostic. L'information locale recueillie par la pluralité de dispositifs (12, 13) de contrôle local est fournie à un processeur global (15) qui traite globalement les informations recueillies de façon à fournir des paramètres de diagnostic mis à jour aux dispositifs (12, 13) de contrôle local.
PCT/US1998/004288 1997-03-04 1998-03-04 Systeme de diagnostic distribue WO1998039718A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
AU66862/98A AU6686298A (en) 1997-03-04 1998-03-04 Distributed diagnostic system
EP98908960A EP0965092A4 (fr) 1997-03-04 1998-03-04 Systeme de diagnostic distribue

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US3979997P 1997-03-04 1997-03-04
US60/039,799 1997-03-04

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WO1998039718A1 true WO1998039718A1 (fr) 1998-09-11
WO1998039718A9 WO1998039718A9 (fr) 1999-01-21

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AU (1) AU6686298A (fr)
WO (1) WO1998039718A1 (fr)

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EP1096456A2 (fr) * 1999-10-29 2001-05-02 Omron Corporation Systéme à senseur
EP1111550A1 (fr) * 1999-12-23 2001-06-27 Abb Ab Méthode et système pour surveiller le fonctionnement d'une machine isolée
WO2001050099A1 (fr) * 2000-01-05 2001-07-12 Reid Asset Management Company Reseau diagnostique avec module experts locaux proactifs informatises
WO2001069329A2 (fr) * 2000-03-10 2001-09-20 Cyrano Sciences, Inc. Commande d'un processus industriel au moyen d'au moins une variable multidimensionnelle
US6298308B1 (en) 1999-05-20 2001-10-02 Reid Asset Management Company Diagnostic network with automated proactive local experts
EP1162524A2 (fr) * 2000-06-06 2001-12-12 Mori Seiki Co., Ltd. Système pour l'entretien d'une machine-outil
EP1250577A1 (fr) * 1999-12-15 2002-10-23 Swantech L.L.C. Systeme reparti d'analyse d'onde de contraintes
WO2002086639A1 (fr) * 2001-04-20 2002-10-31 Rittal Gmbh & Co. Kg Systeme de surveillance d'armoire de commande
WO2002093280A1 (fr) * 2001-05-14 2002-11-21 Rosemount, Inc. Diagnostics destines a des systemes de commande et de mesure de processus industriels
FR2828945A1 (fr) * 2001-08-21 2003-02-28 Sascha Nick Systeme et procede multi-niveaux de maintenance predictive et de diagnostic a distance extensible a un tres grand nombre de machines
WO2003019377A2 (fr) * 2001-08-21 2003-03-06 Idtect Systeme et procede de diagnostic multiniveau echelonnable a distance et maintenance conditionnelle
GB2347232B (en) * 1999-02-22 2003-09-24 Fisher Rosemount Systems Inc Diagnostics in a process control system
US6889166B2 (en) 2001-12-06 2005-05-03 Fisher-Rosemount Systems, Inc. Intrinsically safe field maintenance tool
US7027952B2 (en) 2002-03-12 2006-04-11 Fisher-Rosemount Systems, Inc. Data transmission method for a multi-protocol handheld field maintenance tool
US7039744B2 (en) 2002-03-12 2006-05-02 Fisher-Rosemount Systems, Inc. Movable lead access member for handheld field maintenance tool
US7054695B2 (en) 2003-05-15 2006-05-30 Fisher-Rosemount Systems, Inc. Field maintenance tool with enhanced scripts
US7199784B2 (en) 2003-05-16 2007-04-03 Fisher Rosemount Systems, Inc. One-handed operation of a handheld field maintenance tool
WO2008132555A1 (fr) * 2007-04-26 2008-11-06 Freescale Semiconductor, Inc. Diagnostic pour dispositif à signal mixte destiné à être utilisé dans un système distribué
EP2784676A1 (fr) * 2013-03-28 2014-10-01 Eurocopter España, S.A. Superviseur de contrôle de la santé d'extension DIMA
US8874402B2 (en) 2003-05-16 2014-10-28 Fisher-Rosemount Systems, Inc. Physical memory handling for handheld field maintenance tools
CN104133734A (zh) * 2014-07-29 2014-11-05 中国航空无线电电子研究所 分布式综合模块化航空电子系统混合式动态重构系统与方法
US8898036B2 (en) 2007-08-06 2014-11-25 Rosemount Inc. Process variable transmitter with acceleration sensor
US9052240B2 (en) 2012-06-29 2015-06-09 Rosemount Inc. Industrial process temperature transmitter with sensor stress diagnostics
US9207670B2 (en) 2011-03-21 2015-12-08 Rosemount Inc. Degrading sensor detection implemented within a transmitter
DE102010029819B4 (de) * 2010-06-08 2016-09-01 Delta Electronics, Inc. Frühwarnvorrichtung zur Funktionsfähigkeitserkennung eines Servomotors und Verfahren zum Betreiben derselben
US9602122B2 (en) 2012-09-28 2017-03-21 Rosemount Inc. Process variable measurement noise diagnostic
US10261506B2 (en) 2002-12-05 2019-04-16 Fisher-Rosemount Systems, Inc. Method of adding software to a field maintenance tool
EP3499710A1 (fr) * 2017-12-15 2019-06-19 Siemens Aktiengesellschaft Procédé de surveillance du fonctionnement d'une machine tournante électrique
FR3100064A1 (fr) * 2019-08-22 2021-02-26 Safran Electrical & Power Méthode de surveillance d’au moins un roulement d’une machine électrique tournante d’un aéronef

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Cited By (49)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2347232B (en) * 1999-02-22 2003-09-24 Fisher Rosemount Systems Inc Diagnostics in a process control system
US6298308B1 (en) 1999-05-20 2001-10-02 Reid Asset Management Company Diagnostic network with automated proactive local experts
EP1096456A2 (fr) * 1999-10-29 2001-05-02 Omron Corporation Systéme à senseur
EP1096456A3 (fr) * 1999-10-29 2005-06-08 Omron Corporation Systéme à senseur
EP1250577A4 (fr) * 1999-12-15 2006-05-10 Swantech L L C Systeme reparti d'analyse d'onde de contraintes
EP1250577A1 (fr) * 1999-12-15 2002-10-23 Swantech L.L.C. Systeme reparti d'analyse d'onde de contraintes
EP1111550A1 (fr) * 1999-12-23 2001-06-27 Abb Ab Méthode et système pour surveiller le fonctionnement d'une machine isolée
US6694286B2 (en) 1999-12-23 2004-02-17 Abb Ab Method and system for monitoring the condition of an individual machine
WO2001050099A1 (fr) * 2000-01-05 2001-07-12 Reid Asset Management Company Reseau diagnostique avec module experts locaux proactifs informatises
US8352049B2 (en) 2000-03-10 2013-01-08 Smiths Detection Inc. Temporary expanding integrated monitoring network
US6985779B2 (en) 2000-03-10 2006-01-10 Smiths Detection, Inc. Monitoring system for an industrial process using one or more multidimensional variables
WO2001069329A2 (fr) * 2000-03-10 2001-09-20 Cyrano Sciences, Inc. Commande d'un processus industriel au moyen d'au moins une variable multidimensionnelle
US7313447B2 (en) 2000-03-10 2007-12-25 Smiths Detection Inc. Temporary expanding integrated monitoring network
US7272530B2 (en) 2000-03-10 2007-09-18 Smiths Detection, Inc. System for monitoring an environment
US7136716B2 (en) 2000-03-10 2006-11-14 Smiths Detection Inc. Method for providing control to an industrial process using one or more multidimensional variables
US7031778B2 (en) 2000-03-10 2006-04-18 Smiths Detection Inc. Temporary expanding integrated monitoring network
US6853920B2 (en) 2000-03-10 2005-02-08 Smiths Detection-Pasadena, Inc. Control for an industrial process using one or more multidimensional variables
AU2001247336B2 (en) * 2000-03-10 2006-02-02 Smiths Detection, Inc. Control for an industrial process using one or more multidimensional variables
US6865509B1 (en) 2000-03-10 2005-03-08 Smiths Detection - Pasadena, Inc. System for providing control to an industrial process using one or more multidimensional variables
US7912561B2 (en) 2000-03-10 2011-03-22 Smiths Detection Inc. Temporary expanding integrated monitoring network
WO2001069329A3 (fr) * 2000-03-10 2002-06-13 Cyrano Sciences Inc Commande d'un processus industriel au moyen d'au moins une variable multidimensionnelle
US6917845B2 (en) 2000-03-10 2005-07-12 Smiths Detection-Pasadena, Inc. Method for monitoring environmental condition using a mathematical model
EP1162524A2 (fr) * 2000-06-06 2001-12-12 Mori Seiki Co., Ltd. Système pour l'entretien d'une machine-outil
EP1162524A3 (fr) * 2000-06-06 2004-04-07 Mori Seiki Co., Ltd. Système pour l'entretien d'une machine-outil
WO2002086639A1 (fr) * 2001-04-20 2002-10-31 Rittal Gmbh & Co. Kg Systeme de surveillance d'armoire de commande
US6859755B2 (en) 2001-05-14 2005-02-22 Rosemount Inc. Diagnostics for industrial process control and measurement systems
WO2002093280A1 (fr) * 2001-05-14 2002-11-21 Rosemount, Inc. Diagnostics destines a des systemes de commande et de mesure de processus industriels
FR2828945A1 (fr) * 2001-08-21 2003-02-28 Sascha Nick Systeme et procede multi-niveaux de maintenance predictive et de diagnostic a distance extensible a un tres grand nombre de machines
WO2003019377A3 (fr) * 2001-08-21 2003-09-25 Idtect Systeme et procede de diagnostic multiniveau echelonnable a distance et maintenance conditionnelle
WO2003019377A2 (fr) * 2001-08-21 2003-03-06 Idtect Systeme et procede de diagnostic multiniveau echelonnable a distance et maintenance conditionnelle
US6889166B2 (en) 2001-12-06 2005-05-03 Fisher-Rosemount Systems, Inc. Intrinsically safe field maintenance tool
US7117122B2 (en) 2001-12-06 2006-10-03 Fisher-Rosemount Systems, Inc. Field maintenance tool
US7039744B2 (en) 2002-03-12 2006-05-02 Fisher-Rosemount Systems, Inc. Movable lead access member for handheld field maintenance tool
US7027952B2 (en) 2002-03-12 2006-04-11 Fisher-Rosemount Systems, Inc. Data transmission method for a multi-protocol handheld field maintenance tool
US10261506B2 (en) 2002-12-05 2019-04-16 Fisher-Rosemount Systems, Inc. Method of adding software to a field maintenance tool
US7054695B2 (en) 2003-05-15 2006-05-30 Fisher-Rosemount Systems, Inc. Field maintenance tool with enhanced scripts
US7199784B2 (en) 2003-05-16 2007-04-03 Fisher Rosemount Systems, Inc. One-handed operation of a handheld field maintenance tool
US8874402B2 (en) 2003-05-16 2014-10-28 Fisher-Rosemount Systems, Inc. Physical memory handling for handheld field maintenance tools
US8421587B2 (en) 2007-04-26 2013-04-16 Freescale Semiconductor, Inc. Diagnosis for mixed signal device for use in a distributed system
WO2008132555A1 (fr) * 2007-04-26 2008-11-06 Freescale Semiconductor, Inc. Diagnostic pour dispositif à signal mixte destiné à être utilisé dans un système distribué
US8898036B2 (en) 2007-08-06 2014-11-25 Rosemount Inc. Process variable transmitter with acceleration sensor
DE102010029819B4 (de) * 2010-06-08 2016-09-01 Delta Electronics, Inc. Frühwarnvorrichtung zur Funktionsfähigkeitserkennung eines Servomotors und Verfahren zum Betreiben derselben
US9207670B2 (en) 2011-03-21 2015-12-08 Rosemount Inc. Degrading sensor detection implemented within a transmitter
US9052240B2 (en) 2012-06-29 2015-06-09 Rosemount Inc. Industrial process temperature transmitter with sensor stress diagnostics
US9602122B2 (en) 2012-09-28 2017-03-21 Rosemount Inc. Process variable measurement noise diagnostic
EP2784676A1 (fr) * 2013-03-28 2014-10-01 Eurocopter España, S.A. Superviseur de contrôle de la santé d'extension DIMA
CN104133734A (zh) * 2014-07-29 2014-11-05 中国航空无线电电子研究所 分布式综合模块化航空电子系统混合式动态重构系统与方法
EP3499710A1 (fr) * 2017-12-15 2019-06-19 Siemens Aktiengesellschaft Procédé de surveillance du fonctionnement d'une machine tournante électrique
FR3100064A1 (fr) * 2019-08-22 2021-02-26 Safran Electrical & Power Méthode de surveillance d’au moins un roulement d’une machine électrique tournante d’un aéronef

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Publication number Publication date
EP0965092A1 (fr) 1999-12-22
AU6686298A (en) 1998-09-22
EP0965092A4 (fr) 2002-10-30

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