WO2001016571A1 - Prediction of error magnitude in a pressure transmitter - Google Patents
Prediction of error magnitude in a pressure transmitter Download PDFInfo
- Publication number
- WO2001016571A1 WO2001016571A1 PCT/US2000/023402 US0023402W WO0116571A1 WO 2001016571 A1 WO2001016571 A1 WO 2001016571A1 US 0023402 W US0023402 W US 0023402W WO 0116571 A1 WO0116571 A1 WO 0116571A1
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- WIPO (PCT)
- Prior art keywords
- transmitter
- pressure
- output
- sensor
- error
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Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L27/00—Testing or calibrating of apparatus for measuring fluid pressure
- G01L27/007—Malfunction diagnosis, i.e. diagnosing a sensor defect
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
- G01D18/008—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00 with calibration coefficients stored in memory
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D3/00—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups
- G01D3/02—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups with provision for altering or correcting the law of variation
- G01D3/022—Indicating or recording apparatus with provision for the special purposes referred to in the subgroups with provision for altering or correcting the law of variation having an ideal characteristic, map or correction data stored in a digital memory
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L19/00—Details of, or accessories for, apparatus for measuring steady or quasi-steady pressure of a fluent medium insofar as such details or accessories are not special to particular types of pressure gauges
- G01L19/0092—Pressure sensor associated with other sensors, e.g. for measuring acceleration or temperature
Definitions
- the present invention relates to pressure transmitters that sense process pressures and display or transmit an output with a magnitude representative of the process pressure.
- Pressure transmitters are often installed in harsh environments that can affect the accuracy of the transmitter output. Transmitter outputs are also often corrected for present environmental conditions by a controller embedded in the transmitter, using a process called compensation. Arrangements have also been proposed to store the amplitude and duration of overpressure peaks, temperature, humidity and vibration so that an alarm is triggered when the pressure transmitter is damaged so extensively that it is near the end of its useful life as shown for example in Japanese Kokoku 2,712,701 (Kokai Hei 3 [1991] -229124) .
- pressure sensors in pressure transmitters are subject to physical changes as a result of past overpressures long before the end of the transmitter's life. These physical changes are especially likely to occur with pressure sensors that include metal components that are strained repeatedly by the overpressure. These physical changes result in degradation of the accuracy of the transmitter output, however this degradation can go unnoticed, resulting in degraded performance of the process and increased cost. This degradation is not corrected by known compensation arrangements which only address present environmental conditions. This degradation is also not identified by end-of-life alarms because degradation can occur early in the useful life of the transmitter. A transmitter is needed which can predict a present magnitude of transmitter output error due to past overpressures. With such predicted magnitude available, service personnel can evaluate the magnitude of the error and take timely action to recalibrate the pressure transmitter if the error is too high.
- a prediction of a present magnitude of transmitter output error due to past overpressures is calculated in a controller in a pressure transmitter.
- the controller calculates a predicted present magnitude of transmitter output error as a function of a record, accumulated in memory, of excessive sensor output levels and predetermined data, stored in the memory, predicting magnitudes of transmitter output error as a function of cumulative excessive sensor output levels.
- the controller generates a prediction output that can be read by service personnel or a control system for scheduling recalibration of the pressure transmitter.
- the pressure transmitter includes a pressure sensor, adapted to sense process pressure, that couples to the controller.
- the controller generates a pressure transmitter output representing the magnitude of the process pressure.
- the prediction output alerts service personnel when the pressure transmitter output may have shifted too much due to past overpressures, and the service personnel can take steps to recalibrate the pressure transmitter output .
- FIG. 1 shows a typical industrial environment for a loop powered industrial pressure transmitter
- FIG. 2 shows an embodiment of a loop powered industrial differential pressure transmitter with a predictive output predicting the magnitude of error in the transmitter's pressure output due to overpressure damage ;
- FIG. 3 shows a block diagram of a first embodiment of a pressure transmitter with an output predicting the magnitude of error in the pressure transmitter's pressure output;
- FIG. 4 shows a block diagram of a second embodiment of a pressure transmitter with an output predicting the magnitude of error in the pressure transmitter's pressure output
- FIG. 5 shows a block diagram of a third embodiment of a pressure transmitter with an output predicting the magnitude of error in the pressure transmitter's pressure output
- FIG. 6 shows a flow chart of a process of generating an output predicting the magnitude of error in a pressure transmitter's output
- FIG. 7 shows the magnitudes of present error, an error prediction output and specified error limits for a transmitter with an output predicting the magnitude of error in the pressure transmitter's output, all as a function of time.
- FIG. 8 is a diagram showing a simplified neural network.
- FIG. 9A is a diagram showing a neural network used to provide a residual lifetime estimate.
- FIG. 9B is a graph of sensor residual life versus time.
- FIG. 10 is a graph showing the output of a pressure sensor including a normal pressure range and a number of spikes due to overpressures experienced by the sensor.
- FIG. 11 is a graph showing the total strain range in percentage versus the number of cycles to failure at various hold times.
- FIG. 12 is a graph of expected lifetime versus time for a pressure sensor due to overpressures .
- process variable transmitters such as flow meter 22 in process fluid line 23, level transmitters 24, 26 on tank 28 and integral orifice flow meter 30 in process line 31 are shown electrically connected to control system 32.
- Process variable transmitters can be configured to monitor one or more process variables associated with fluids in a process plant such as slurries, liquids, vapors and gasses in chemical, pulp, petroleum, gas, pharmaceutical, food and other fluid processing plants.
- the monitored process variables can be pressure, temperature, flow, level, pH, conductivity, turbidity, density, concentration, chemical composition or other properties of fluids.
- a process variable transmitter includes one or more sensors that can be either internal to the transmitter or external to the transmitter, depending on the installation needs of the process plant.
- Process variable transmitters generate one or more transmitter outputs that represent the sensed process variable .
- Transmitter outputs are configured for transmission over long distances to a controller or indicator via communication busses 34.
- a communication buss 34 can be a 4-20 mA current loop that powers the transmitter, or a fieldbus connection, a HART (Highway Addressable Remote Transmitter) protocol communication or a fiber optic connection to a controller, a control system or a readout.
- transmitters powered by a 2 wire loop power must be kept low to provide intrinsic safety in explosive atmospheres .
- integral orifice flow meter 30 includes pressure transmitter 36 that couples along a communication bus 34 connected to it .
- Level transmitters 24, 26 also include pressure transmitters.
- Control system 32 can be programmed to display process conditions for a human operator, and can be programmed to sense the process conditions and control the process via output devices such as current to pressure converter 38 and control valve 40, for example .
- pressure transmitters at 24, 26 and 36 have pressure sensors that can be exposed to excessive pressures, called overpressures, in various pipes and tanks due to malfunctions or transient conditions and the like. These transients can occur during startup or shutdown of pumps and valves, are often not noticed by the operator, but can overpressure components in the process plant.
- Transmitter 50 includes a flange 52 for receiving a differential pressure, and one or more pressure sensors 54 (not shown) .
- Transmitter 50 is bolted to flange adapter 58.
- Flange adapter 58 connects to pressure impulse pipes connected to flange adapter unions 60 or other connection hardware.
- Circuitry 56 in transmitter 50 is electrically connected to sensor 54 and includes a controller and memory for predicting the magnitude of the error in the transmitter's pressure output 57.
- controller means any circuit or combination of circuits that can perform logic and counting functions to control the operation of a transmitter and perform the necessary steps to predict the magnitude of error.
- the controller can include, for example, a microprocessor system, an application specific integrated circuit
- ASIC a programmed gate array, a reduced instruction set computer (RISC) or other known circuits that can perform these functions.
- the steps performed in the controller to accomplish the controller's tasks can include neural networks, fuzzy logic, wavelets, autoregression, recursive filtering, adaptive self tuning, any other known algorithm for signal processing and control functions, as well as any combination of those steps.
- the controller can process the pressure sensor output using know digital signal processing techniques in the time or frequency domain including the Z transform and Fast Fourier Transform (FFT) techniques, wavelet analysis and the Discreet Wavelet Transform (DWT) as set forth in Wavelet Analysis of Vibration, Part 2: Wavelet Maps, D.E. Newland, JOURNAL OF VIBRATION AND ACOUSTICS, October 1994, Vol. 116, pg. 417. Other known techniques can be used as well.
- FFT Fast Fourier Transform
- DWT Discreet Wavelet Transform
- Wavelet analysis is a technique for transforming a time domain signal into the frequency domain which, like a Fourier transformation, allows the frequency components to be identified. However, unlike a Fourier transformation, in a wavelet transformation the output includes information related to time. This may be expressed in the form of a three dimensional graph with time shown on one axis, frequency on a second axis and signal amplitude on a third axis.
- the controller performs a ⁇ discrete wavelet transform (DWT) which is well suited for implementation in microprocessor system.
- DWT discrete wavelet transform
- the Mallet algorithm which is a two channel sub-band coder.
- the Mallet algorithm provides a series of separated or decomposed signals which are representative of individual frequency components of the original signal.
- an original sensor signal S is decomposed using a sub-band coder of a Mallet algorithm.
- the signal S has a frequency range from 0 to a maximum of fttra.
- the signal is passed simultaneously through a first high pass filter having a frequency range from
- the output from the high pass filter provides "level 1" discrete wavelet transform coefficients.
- the level 1 coefficients represent the amplitude as a function of time of that portion of the input signal which is between 1/2 f ma ⁇ and fMAx-
- the output from the 0 - 1/2 f m a ⁇ low pass filter is passed through subsequent high pass (1/4 f ma ⁇ - 1/2 f ma ⁇ ) and low pass (0 - 1/4 f ma ⁇ ) filters, as desired, to provide additional levels (beyond "level 1") of discrete wavelet transform coefficients.
- each low pass filter can be subjected to further decompositions offering additional levels of discrete wavelet transformation coefficients as desired. This process continues until the desired resolution is achieved or the number of remaining data samples after a decomposition yields no additional information.
- the resolution of the wavelet transform is chosen to be approximately the same as the signal spikes.
- Each level of DWT coefficients is representative of signal amplitude as a function of time for a given frequency range. In various embodiments, the one level of DWT coefficient correlate to over pressures in the sensor signal .
- padding is added to the signal by adding data to the sensor signal near the borders of windows used in the wavelet analysis. This padding reduces distortions in the frequency domain output.
- This technique can be used with a continuous wavelet transform or a discrete wavelet transform.
- “Padding” is defined as appending extra data on either side of the current active data window, for example, extra data points are added which extend 25% of the current window beyond either window edge .
- the padding is generated by repeating a portion of the data in the current window so that the added data "pads" the existing signal on either side. The entire data set is then fit to a quadratic equation which is used to extrapolate the signal 25% beyond the active data window.
- a Fast Fourier Transform (FFT) or other signal processing or filtering techniques can be used to identify spikes or over pressures in the sensor signal including a rule which is a simple threshold comparison or comparison of the signal to a statistical parameter such as mean or standard deviations.
- FFT Fast Fourier Transform
- the system can also be modeled using a neural network (discussed below) and compared to the actual sensor output. The residual signal can be used to detect over pressures in the sensor signal.
- a spike can also be detected using a rule, a statistical value, a trained value and a sensitivity parameter.
- a spike event occurs when the signal momentarily goes to an extreme value.
- Sensitivity to spikes in the sensor signal is controlled by adjusting a sensitivity parameter from ⁇ stored in memory 80.
- ⁇ is the acceptable trained maximum rate of change ( ⁇ P AX ) between two consecutive data points. For example, to detect any spikes that has a rate of change (ROC) from block 84 that is 30% greater than ⁇ r MA from block 78 relative to the trained value, from 80 should be set 1.30.
- FIG. 3 a block diagram of an embodiment of a pressure transmitter 70 is shown.
- a pressure sensor 72 is adapted to sense a process pressure 74.
- a controller 76 is coupled to the pressure sensor 72 and generates a transmitter output 78 representing the magnitude of process pressure.
- Transmitter output 78 can have any know form of process control output, for example an intrinsically safe 4-20 mA analog current which provides all of the electrical energization for the transmitter with digital HART or Fieldbus signals superimposed on the analog current.
- a memory 80 coupled to the controller 76, stores predetermined data 82 predicting magnitudes of transmitter output error as a function of cumulative excessive sensor output levels at 84.
- the memory 80 also stores a record 86 of cumulative excessive sensor output levels.
- the record 86 is accumulated in read/write memory that is nonvolatile such as EEPROM, while the predetermined data is stored in read only memory (ROM) .
- the controller 76 calculates a predicted present magnitude of transmitter output error as a function of the accumulated record 86 and the predetermined data 82, and generates a prediction output 88.
- the record 86 is typically representative of physical changes to the sensor resulting from overpressure .
- the record can include data on the amplitude and duration of overpressures indicated on the sensor output. The levels of what amplitude and duration of an "overpressure" will cause a shift in calibration is a function of the design of the sensor
- the predetermined data 82 predicting error magnitude can also be determined experimentally or by computer modeling and typically takes the form of either an equation or function "F (overpressure) " or a lookup table relating predicted error to levels of overpressure.
- the prediction output 88 typically represents a predicted calibration shift, such as upper and lower limits of error.
- the prediction output 88 can be independent of the present process pressure, or the prediction output can be a function of present process pressure.
- the prediction output 88 can also includes both a sensor offset error (independent of present process pressure) and a sensor gain error (proportional to present process pressure) .
- the predetermined data predicting error magnitude and the record of cumulative excess sensor output are data tend can be compared using known digital techniques for processing large amounts of data or statistics, such as neural networks, fuzzy logic, wavelet analysis, autoregression analysis, recursive filtering, adaptive self tuning, any other known algorithm for signal processing and control functions, as well as combinations of those steps.
- FIG. 4 a block diagram of another embodiment of a pressure transmitter 90 is shown in which the reference numerals used in FIG. 3 are also used to identify identical or similar elements in FIG. 4.
- the transmitter 90 further comprises a temperature sensor 92 coupled to the controller, and the predetermined data 82 predicts magnitudes of transmitter output error as a function of cumulative excessive temperature levels as well as being a function of excessive pressure levels.
- the pressure transmitter 90 further comprises a humidity sensor 94 coupled to the controller, and the predicted magnitude of transmitter output error can be further a function of humidity.
- the record 86 is a function of the magnitudes and durations of excessive sensor output levels as well as of temperature and humidity levels.
- the memory 80 can further store predetermined data 82 predicting the residual useful life of the pressure sensor.
- a block diagram of an embodiment of a pressure transmitter 100 is shown.
- a microprocessor system 102 includes a central processing unit (CPU) 104 coupled to a clock source 106.
- CPU 104 has an address and control bus
- Module 120 includes differential pressure sensor 126 that provides a pressure sensor output 128, temperature sensor 130, humidity sensor
- ROM 112 stores predetermined data predicting error magnitude as a function of cumulative output levels from the sensors 126, 130 and 132.
- the EEPROM 114 stores a record of cumulative excess sensor output levels from the sensors 126, 130 and 132.
- the microprocessor system provides both controller and memory for the transmitter. Programs are stored in ROM 112 for comparing the predetermined data predicting error magnitude, and the record of cumulative excess sensor output levels.
- Communications circuit 118 provides the transmitter output 134 and a prediction output 136 as explained above in connection with FIGS. 3 and 4.
- a power supply circuit receives power from a circuit connected to the transmitter output 134 and provides energizations for the transmitter 100.
- the transmitter is connected to a two wire loop that energizes the transmitter.
- the two wire loop can also be used to carry both the transmitter output 134 and the prediction output 136 as HART or Fieldbus signals superimposed on the energization current in the two wire loop .
- a method of predicting transmitter error is shown at 150.
- the prediction process starts at START 152 and continues on to predict the present magnitude of transmitter output error in a pressure transmitter receiving an applied process pressure and having an embedded controller.
- the controller accesses sensed process pressure from a pressure sensor.
- the controller generates a transmitter output with a magnitude representing the process pressure.
- the controller accesses predetermined data stored in a memory, the data predicting magnitudes of transmitter output error as a function of cumulative excess process pressures.
- the controller stores a record of cumulative excess process pressures in the memory.
- the controller generates a prediction output in the controller predicting a present magnitude of transmitter output error as a function of the stored record and the accessed data.
- the process returns to the beginning to repeat the process.
- the process shown in FIG. 6 can be stored as instructions on a computer readable medium and be executed by an embedded controller in a pressure transmitter to cause the pressure transmitter to generate a predictive output predicting transmitter error caused by excessive process pressure, the instructions .
- a graph shows exemplary values for various magnitudes of transmitter error as a function of time.
- the transmitter has a nominal specified range of error in the transmitter output shown between lines 180 and 182.
- the transmitter's actual present output error varies over time due to overpressures as shown at 186.
- the actual error shown at 186 is unknown to service personnel during the service life of the transmitter, unless the transmitter is taken out of service and the calibration is checked.
- the prediction output, shown at 184 is available to service personnel and predicts the positive and negative limits of error based on the calculations done in the controller. As a result, the service personnel are warned when a recalibration of the transmitter may be needed. After recalibration is performed by service personnel, the record of cumulative excess sensor output levels can be reset to zero and the transmitter can be put back in service .
- the controller performs diagnostics related to operation of pressure sensor 72 using the detected spikes.
- the timing, amplitude, width, wave shape or other parameters of the spikes can be used for diagnostics.
- the output from the diagnostics can be used to correct the sensed pressure and to provide an indication of the condition or expected lifetime of the sensor. This allows the sensor to be replaced prior to its ultimate failure. However, in the interim, prior to sensor replacement the output from the sensor can be compensated such that more accurate measurements can be obtained.
- a residual life estimate may be representative of an impending sensor failure.
- a state of health output is indicative of the remaining life of the sensor such that sensor replacement may be timed appropriately.
- An alarm signal can also be sent to control system 32 prior to sensor failure.
- controller 76 in the present invention uses empirical models or polynomial curve-fitting.
- a polynomial-like equation which has a combination of the six secondary signals as the variable terms in the polynomial, together with constants stored in memory 80 is used for computing the residual lifetime estimate. If transmitter memory is limited, the constants and/or the equation may be sent over the two wire loop to transmitter 70.
- FIG. 8 shows a typical topology of a three-layer neural network architecture implemented in controller 76 and memory 80.
- the first layer usually referred to as the input buffer, receives the information, and feeds them into the inner layers.
- the second layer in a three-layer network, commonly known as a hidden layer, receives the information from the input layer, modified by the weights on the connections and propagates this information forward. This is illustrated in the hidden layer which is used to characterize the nonlinear properties of the system analyzed.
- the last layer is the output layer where the calculated outputs (estimations) are presented to the environment .
- FIG. 9A shows a schematic for residual life estimation of pressure sensors using a neural network model.
- Spike related data is provided as an input to the neural network.
- a residual life estimate or a compensation value is provided as an output from the neural network.
- the particular spike data input to the neural network can be number of spikes, their size, the amplitude, width, shapes, frequency, statistical values related to spikes such as mean, average, rate of change, etc., or comparisons or functions of multiple spikes.
- a number of inputs to the neural network may differ depending upon the complexity of the system, and any one or combination of multiple inputs can be used. Temperature or humidity can also be used as inputs.
- FIG. 9B is a graph showing pressure sensor residual life versus time.
- an alarm signal can be provided prior to the ultimate failure of the sensor.
- the alarms signal can be calibrated relative to the estimated ultimate failure time.
- This embodiment of diagnostic circuitry implemented in controller 76 uses a set of if-then rules to reach a conclusion on the status of pressure sensor 72. These rules may be implemented in either digital or analog circuitry. The previously described spike data is monitored and present values are compared to upper and lower boundaries. The upper and lower boundaries are empirically set by extensive testing of pressure sensor 72. A decision is made based upon the comparison.
- the spike data is monitored and compared with acceptable ranges by a microprocessor.
- the spike data can also be processed using fuzzy logic techniques.
- fuzzy logic input data is processed as a function of a membership function which is selected based upon a value of the input data.
- fuzzy logic techniques are well suited for sensors which fail in a nonlinear or nonbinary fashion.
- the spike data such as the total number of spikes can be provided as an input for the fuzzy logic which can provide a continuous output indicating expected lifetime or a correction value which is used to correct the output from pressure sensor 72.
- the transmitter estimates or predicts the measured process variable during the occurrence of a spike/over pressure.
- the estimate can generate through curve fitting, linear approximation, a neural network, fuzzy logic, least squares curve fit, polynomial approximation, regression algorithm, etc., or their combination.
- the estimated process variable is provided as an output or used to calculate other process variables such as flow or level .
- the present invention provides an apparatus and technique for detecting calibration shifts of a pressure sensor in a pressure transmitter through the counting and monitoring of cyclic and continuous overpressurizations of the sensor. These calibration shifts can be due to several factors including continuous strain cyclic strain, temperature and humidity. All or some of these factors can be correlated to calibration of the sensor and the life span of the sensor using the aforementioned techniques due to the mechanical properties of the sensor material.
- FIG. 10 is a graph showing amplitude of the output from a pressure sensor versus time. As illustrated in FIG. 10, there is a normal band or operating range for the sensor output. Occasional spikes tend to greatly exceed the pressure sensor output.
- FIG. 11 is a graph of the total strain range and percentage versus cycles to failure for stainless steel. The various graphs in the figure are for different hold times of the strain. As illustrated in FIG. 11, the number of cycles to failure decreases (i.e., the life of the sensor shortens) with increased strain and with increased tensile hold time.
- One aspect of the present invention is the recognition of the relationship for the number of cycles (N) to failure as follows:
- N f(t,P,T,h) EQ. 1 where t is the duration of the pressure peak, P is the pressure during the peak, T is the temperature of the medium and h is the humidity of the medium.
- a transmitter in accordance with the present invention can continuous monitor the pressure experienced by the sensor to evaluate the number of overcycles the pressure sensor has experienced. This number can then be compared through diagnostic techniques to the theoretical failure limit of the sensor material and a warning to be issued at a time prior to failure .
- the transmitter can monitor the peak pressure and peak width and save this data in memory. During an overpressure, the peak pressure can be evaluated using a second order curvefit and using peak detection techniques. This data can be used to correct errors in the sensor output. Eventually, the sensor will fail and a warning can be provided prior to the ultimate failure.
- regression modules, fuzzy logic systems and neural network models are some of the techniques which can be estimating the residual life of the sensor.
- the output can be in the form of expected lifetime versus time as illustrated in FIG. 12.
- a threshold can be used to provide an output prior to the ultimate failure of the sensor.
- the present invention uses the relationship between the number of overpressures that the sensor has experienced to determine the calibration shift and the pressure measurement and the lifetime of the sensor.
- Typical prior art techniques have only recognized the occurrence of overpressure and not a correlation with the number of overpressures. Further, the prior art has typically failed to provide an alarm prior to the ultimate failure of the sensor.
Abstract
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Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
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AU70755/00A AU7075500A (en) | 1999-08-27 | 2000-08-25 | Prediction of error magnitude in a pressure transmitter |
JP2001520077A JP3515558B2 (en) | 1999-08-27 | 2000-08-25 | Computer readable medium having stored therein a pressure transmitter and a command to predict the magnitude of an error in the pressure transmitter |
EP00959427A EP1206689B1 (en) | 1999-08-27 | 2000-08-25 | Prediction of error magnitude in a pressure transmitter |
DE60016422T DE60016422T2 (en) | 1999-08-27 | 2000-08-25 | PREDICTING THE SIZE OF AN ERROR IN A PRESSURE TRANSMITTER |
Applications Claiming Priority (2)
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US09/384,876 US6701274B1 (en) | 1999-08-27 | 1999-08-27 | Prediction of error magnitude in a pressure transmitter |
US09/384,876 | 1999-08-27 |
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WO2001016571A1 true WO2001016571A1 (en) | 2001-03-08 |
WO2001016571A9 WO2001016571A9 (en) | 2002-09-06 |
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PCT/US2000/023402 WO2001016571A1 (en) | 1999-08-27 | 2000-08-25 | Prediction of error magnitude in a pressure transmitter |
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US (1) | US6701274B1 (en) |
EP (1) | EP1206689B1 (en) |
JP (1) | JP3515558B2 (en) |
CN (1) | CN1148571C (en) |
AU (1) | AU7075500A (en) |
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WO (1) | WO2001016571A1 (en) |
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CN101878415B (en) * | 2007-11-29 | 2012-12-05 | 罗斯蒙德公司 | Process fluid pressure transmitter with pressure transient detection |
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US10324055B2 (en) | 2015-09-30 | 2019-06-18 | Rosemount Inc. | Process variable transmitter with terminal block moisture sensor |
Also Published As
Publication number | Publication date |
---|---|
EP1206689A1 (en) | 2002-05-22 |
JP3515558B2 (en) | 2004-04-05 |
AU7075500A (en) | 2001-03-26 |
US6701274B1 (en) | 2004-03-02 |
DE60016422D1 (en) | 2005-01-05 |
DE60016422T2 (en) | 2005-11-03 |
CN1371475A (en) | 2002-09-25 |
WO2001016571A9 (en) | 2002-09-06 |
CN1148571C (en) | 2004-05-05 |
EP1206689B1 (en) | 2004-12-01 |
JP2003508742A (en) | 2003-03-04 |
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