Patent Description:
<CIT> discloses an apparatus and associated systems and methods relating to automated learning of a baseline differential pressure (dP) characteristic to monitor the performance of a field-installed gas flow meter by comparing on-line dP measurements to the learned baseline dP characteristic. In an exemplary embodiment, a first baseline dP characteristic may be learned in a first mode over a first predetermined period of time according to a first set of learning criteria, and a second baseline dP characteristic may be learned in a second mode over a second predetermined period of time according to a second set of learning criteria. The first period of time may be substantially shorter than the second period of time. The first set of criteria may be substantially more relaxed than the second set of criteria. During the second mode, meter performance degradation may be diagnosed by comparing measured dP against the first baseline dP characteristic.

<CIT> discloses an apparatus and associated systems, methods and computer program products relate to monitoring the performance of an operating gas meter by automatic and substantially continuous differential pressure (dP) measurement. Measured dP may be compared against a baseline dP characteristic to determine if the measured dP exceeds a threshold value above a baseline dP characteristic. If the threshold is exceeded, then the system may generate a signal to request repair or replacement of the meter. After installation, some embodiments collect dP data over time and/or over a range of flow rates to automatically learn a baseline dP characteristic under installation conditions. A system may switch from a default baseline dP characteristic to a learned baseline dP characteristic. Some embodiments may further correct volume or flow rate signals for line pressure and/or temperature. Further embodiments provide a passive apparatus to protect a dP sensor against transients in line pressure and/or differential pressure.

The subject matter herein relates to improvements to electronics on gas meters (and other flow meters) to better monitor health of the device. Of particular interest herein are embodiments that can perform diagnostics at low flow rates. These embodiments may employ hardware and algorithms that permit diagnostics for flow at least as low as <NUM>% of the maximum flow rate across the device. This feature addresses a propensity of end users (e.g., utilities) to "oversize" flow meters on their distribution systems. This decision may frustrate diagnostics in the field because flow rate rarely exceeds the "lower limit" flow rate for effective diagnostics to occur on the device (e.g., <NUM>% of maximum flowrate). As noted below, the proposed design collects data at lower flow rates, including differential pressure (DP) data. This feature is useful on positive displacement meters, which may use counter-rotating impellers as its metering mechanics, because changes in DP correlate well with performance of impellers. The data is useful, then, as basis to identify bearing wear or contamination that may impact volume measurement accuracy because gas can leak around the impellers. Another benefit is that the proposed design may gather data at more advantageous times, like during periods of identifiable stable flow, while at the same time extending useable life of the on-board battery or other in-situ power supply.

Reference is now made briefly to the accompanying drawings, in which:.

Where applicable, like reference characters designate identical or corresponding components and units throughout the several views, which are not to scale unless otherwise indicated. The embodiments disclosed herein may include elements that appear in one or more of the several views or in combinations of the several views. Moreover, methods are exemplary only and may be modified by, for example, reordering, adding, removing, and/or altering the individual stages.

The discussion now turns to describe various features found in the drawings above. These features may form part of a gas meter that employs algorithms designed to gather data at particularly advantageous times, including differential pressure (DP) data. For years, DP testing was done by an end user (or operator) in the field with a portable manometer. The operator would collect data to compare with a baseline DP characteristic for the device, typically a curve that plots DP measurements against a range of flow rates. This baseline was often the result of testing done at a factory as part of the assembly process. Eventually advances in technology became available to develop baseline characteristics on-board the device in the field. These capabilities may aggregate data over a period of time, like one (<NUM>) year, so that the device can "learn" the baseline value(s) or "constants. " The technology also provides processing capabilities sufficient to analyze the data according to mathematical models of behavior of the gas meter. These models may predict or diagnose conditions on the device, for example, by relating DP measurements to health of the gas meter. Applicable models may vary as necessary.

These capabilities are not without their limitations. Electronics necessary to process data for purposes of diagnostics consume a good deal of energy. This feature poses a problem in the field because of the finite life-span of on-board batteries. The proposed design is beneficial because the gas meter uses less energy for diagnostics, while at the same time providing DP measurements (and other data) for analysis that is more likely to prove fruitful in diagnosing problems, if any should prevail on the device. Other embodiments are within the subject matter of this disclosure, in accordance with the appended claims.

<FIG> depicts a perspective view of an example of a flow meter in the form of a gas meter <NUM>. This example may have a meter body <NUM>, typically a cast or machined metal housing with a "pass-through" flow path <NUM> that terminates at flanged ends (e.g., a first flanged end <NUM> and a second flanged end <NUM>). Covers <NUM>, <NUM> may attach to opposite sides of the metal housing <NUM>. The covers <NUM> allow access to an interior cavity where mechanics reside in the flow path <NUM>. The gas meter <NUM> may also include a differential pressure (DP) unit <NUM> to monitor differential pressure across these mechanics. The DP unit <NUM> may direct fluid from the flow path <NUM> to an electronics unit <NUM> that is useful to generate, collect, and process data. Circuitry in the electronics unit <NUM> may include a DP sensor (not shown) that can measure differential pressure from the inputs from the DP unit <NUM>. This DP sensor can activate and de-activate as necessary to gather DP data.

<FIG> depict schematic diagrams of the cross-section of an example of the gas meter <NUM> of <FIG>. Positive-displacement type devices may include mechanics <NUM>, like a pair of lobed-impellers that precisely mesh with one another. The impellers <NUM> counter-rotate in response to flow of fluid <NUM> through the flow path <NUM>. The diagrams reflect configurations of the impellers <NUM> that occur during one complete "revolution" of the impellers <NUM>. Each configuration exhausts a precise amount or volume of fluid <NUM> from the flow path <NUM> to conduit <NUM> that attaches downstream of the device.

<FIG> depicts an exemplary plot of differential pressure (DP) across the impellers <NUM>. Curve A represents measured values of DP (or "measured DP"). During stable flow, measured DP may exhibit a sinusoidal wave form with a period that corresponds with the "exhaust" configurations. Curve B represents the average of the measured DP values (or "average DP"). This value may be useful in the mathematical models for diagnostics because it negates variations or fluctuations in measured DP. In this regard, the methods herein take advantage of the sinusoidal wave form to manage operation of the DP sensor that generates the measured DP data. This feature provides better "good" data to determine average DP, which in turn improves accuracy and reliability of the on-board diagnostics. As an added benefit, these methods may save energy because the DP sensor only operates at times at which it will secure this "good" data, and not in any haphazard, random, or pre-determined pattern of operation that would be less likely to guarantee data that is suitable for diagnostics.

<FIG> depicts a flow diagram of an exemplary method <NUM> for functionality on-board the gas meter <NUM> to operate the DP sensor to gather data and to perform diagnostics and other health-related functions. These diagrams outline stages that may embody executable instructions for one or more computer-implemented methods or processes. The executable instructions may instantiate a computer program, software, firmware, or like compilation of machine-readable instructions. The stages in these methods may be altered, combined, omitted, or rearranged in some embodiments.

The method <NUM> may enable the electronics unit <NUM> to perform diagnostics for flow below the "lower limit" flow rate. The method <NUM> may include, at stage <NUM>, gathering data during a period of stable flow through the gas meter. The method <NUM> may also include, at stage <NUM>, monitoring a timer. In one implementation, the method <NUM> may include a stages for a learning mode, for example, at stage <NUM>, monitoring a learning period and, at stage <NUM>, calculating constants for a mathematical model of gas meter behavior. At stage <NUM>, the method <NUM> may include determining diagnostic values at expiration of the timer. The method <NUM> may further include, at stage <NUM>, determining a relationship between diagnostic values. The method <NUM> may also include, at stage <NUM>, assigning an operating condition and, at stage <NUM>, generating an output in accordance with the operating condition.

At stage <NUM>, the method <NUM> may gather data during a period of stable flow through the meter body <NUM>. The period of stable flow likely corresponds with constant (or relatively constant) demand downstream of the gas meter <NUM>. This demand results in a flow of fuel gas that exercises the impellers <NUM>. The flow may cause the impellers <NUM> to counter-rotate, which in turn may generate the motion signal SM as a series of consistent or repeatable pulses. Notably, while periods of stable flow may occur fairly frequently, it is not often that the flow reaches the flowrate necessary to implement any diagnostics. It could be weeks or months before flowrate has characteristics that are appropriate for DP measurements. The method <NUM> is beneficial because it avoids exhausting battery power by collecting data unnecessarily during these potentially long periods of delay between stable flow.

At stage <NUM>, the method <NUM> may monitor a timer to regulate use of the differential pressure sensor <NUM>. This timer may embody certain stages that increment some value of time (e.g., seconds). These stages may also activate or monitor electronic hardware, including discrete or solid-state devices, for this purpose as well. In one implementation, the method <NUM> may include one or more stages for operating the timer, for example, stages for activating the timer, incrementing the timer, and resetting the timer (where necessary). The method <NUM> may reset the timer in response to changes in stability of the flow, e.g., where the flow becomes unstable. The unstable flow may cause the timer to reset. The method <NUM> may concomitantly deactivate the DP sensor as well. During stable flow, the timer may continue to increment until it reaches a maximum value that measures the extent of activation desired for the DP sensor. These features can regulate regulates power draw, for example, by limiting the activation time of the DP sensor.

At stages <NUM>, <NUM>, the method <NUM> may operate in a "learning mode. " This mode may occur early in the lifetime of the gas meter <NUM>, for example, soon after it is put into service. The method <NUM> may continue in the learning mode for a specified period of time. This period may last for one (<NUM>) year after installation or, for example, long enough for the device to collect data sample sets from all seasons that the gas meter <NUM> experiences in the field; however this disclosure does contemplate that the sample period may be shorter or longer as desired. In one implementation, the learning mode may result in a value for one or more constants of the mathematical model of gas meter behavior. Exemplary values or constants may correspond with Equations (<NUM>), (<NUM>), and (<NUM>) below: <MAT> <MAT> <MAT> where α is the constant, DPav is average DP, DPi is measured DP for a single data set i, n is the number of measured DP values, m is the number of calculated α values, and T, P, and SG are parameters or conditions of the fluid (e.g., T is temperature, P is pressure, and SG is specific gravity), and Q is flow rate. All values are associated with "k" conditions, which are preferably stable for pressure, specific gravity (or other parameters of gas composition), and temperature. In one implementation, one or more of these parameters (e.g., T, P, SG, etc.) may be of a pre-determined or "un-measured" value. An end user (e.g., operator, technician, etc) may input these values into a user interface or other software generated input screen or they may pre-populate a memory or storage device. Some embodiments may include sensors that measure these parameters in "real-time," effectively concomitantly with operation of the gas meter <NUM>, particularly during periods of stable flow through the device.

At stage <NUM>, the method <NUM> may use the constant α and other data to determine values that are useful to diagnose heath of the gas meter <NUM>. These "diagnostic" values may relate to measured DP across the impellers <NUM>. In one implementation, the diagnostic values may correspond with Equation (<NUM>) below: <MAT> where DPav is the average DP, DPi are the measured DP values in the data sample, and p is the number of data points in the sample set. The diagnostic values may also correspond with Equation (<NUM>) below: <MAT> where DP<NUM> is a base value of DP, α is the constant, P is pressure, Q is flow rate, SG is specific gravity, and T is temperature.

At stage <NUM>, the method <NUM> may determine the relationship between diagnostic values. This stage may include stages for comparing the average DP (DPav) to the base DP (DPb). The result may indicate that DPav is greater than, less than, or the same as DPb. Notably, values for DPav may correspond with consecutive measurements, either that are taken during the period of stable flow or taken during the requisite "test period" noted above. The values for DPb, on the other hand, relate to the constant α or values collected over a longer period of time. These values characterize operation as means to establish whether a device malfunction or other operating condition is present on the gas meter <NUM>.

At stage <NUM>, the method <NUM> may assign the operating condition. Examples of the operating condition may require the end user to perform various tasks, including periodic or regular maintenance, repair, or replace the gas meter <NUM>. In some implementations, the operating condition may depend on one or more coefficients with values that can weight one or both of the average DP (DPav) or the base DP (DPb), as desired. This coefficient value may correspond with a pre-determined criteria, for example, criteria that indicates a device malfunction, as shown in Equation (<NUM>) below: <MAT> where b is the coefficient for purposes of this example. In one implementation, the stages may include a "false" positive detection that ensures that the operating condition actually prevails on the device. This detection may require multiple assignments, or "events," of the operating condition to occur in the algorithm. If below a threshold, for example, the method <NUM> may return to stage <NUM> to continue data collection and analysis.

At stage <NUM>, the method <NUM> may generate the output in accordance with the operating condition. The output may embody any number of audio or visual cues to alert the end user about the condition of the gas meter <NUM>. The subject matter of these cues may correspond with the severity of the operating condition, for example, an LED may illuminate or an alarm may sound on the gas meter <NUM> in response to maintenance or repair, respectively. For more serious malfunctions, the device may go inactive or enter into a reduced function mode that prevents certain (or all) functionality of the gas meter <NUM>. Any of these specific responses may combine with others as well. In one implementation, the gas meter <NUM> may also generate a signal that encodes data, for example, an email or text message, that will resolve on a computing device or system, like an end user's laptop, smartphone, or tablet.

<FIG> depicts a flow diagram of an example of the method <NUM> with additional stages for diagnostics. These stages may occur once the gas meter completes the learning mode (at stages <NUM>, <NUM> above). At stages <NUM> and <NUM>, the method <NUM> may include stages for checking for stable flow. These stage may include stages for monitoring pulses consistent with the counter-rotating impellers <NUM>, for example, by measuring a time period between consecutive pulses or comparing the time period between consecutive pulses to a maximum allowable value. The stages may also count consecutive pulses that meet the maximum allowable value and, where necessary, assume that flow is stable when the pulse count reaches a certain reasonable threshold level (e.g., <NUM> consecutive pulses).

If flow is stable, the method <NUM> may continue with further data collection functions. At stage <NUM>, the method <NUM> may include stages for determining various test parameters for collecting data on the gas meter <NUM>. These test parameters may include flow rate Q which may be calculated based on the distance between adjacent pulses from the counter-rotating impellers <NUM>. Another test parameter may include the period T of the sinusoidal wave form of the measured DP data. The period T may relate to the flow rate Q. For example, assuming the flow rate Q is <NUM> CF/hr on a gas meter <NUM> with a displacement of <NUM> CF, each full revolution of the impellers <NUM> takes <NUM> sec. The period T and frequency f of the sinusoidal wave form may be calculated according to Equations (<NUM>) and (<NUM>) below: <MAT> <MAT> Still another test parameter is the "test time" for activating the DP sensor to collect the measured DP data. Test time relates to the period T of the sinusoidal wave form, preferably as multiple of the period T (e.g., 2T, 4T, etc.). This feature ensures that the operation of the DP sensor starts and stops at similar places (albeit spaced apart over time) on the sinusoidal curve A (<FIG>) discussed above.

The method <NUM> may use the test time (and other test parameters) with other functions. At stage <NUM>, the method <NUM> may include stages for starting the timer (to count down the test time). The method <NUM> may continue, at stage <NUM>, activating the DP sensor and, when necessary, other sensors that measure temperature T, pressure P, and specific gravity (SG) of the fluid (at stage <NUM>). In one implementation, the method <NUM> may include, at stages <NUM>, <NUM> checking (again) for stable flow and, if not stable, may include at stage <NUM> resetting the timer, at stage <NUM> deactivating the sensors <NUM>, and at stage <NUM> deactivating the DP sensor. These stages may conclude any future data gathering until flow becomes stable again (at stage <NUM>) Alternatively, if flow is stable and the timer has "expired" or reached the "test time" (at stage <NUM>), then the method <NUM> may continue, at stage <NUM>, validating the test cycle. In this way, the method <NUM> can continue operation with confidence that any collected data was gathered during stable flow and is "good" data to provide accurate diagnostics pursuant to the mathematical models for gas meter behavior discussed above.

<FIG> depicts a flow diagram of an example of the method <NUM>. This example may continue, at stage <NUM>, determining whether the test cycle is valid or complete and, if so, calculating average DP (DPav) (at stage <NUM>) and calculating base DP (DPb) (at stage <NUM>). The method <NUM> may also continue, at stage <NUM>, comparing DPav and DPb to inform the remaining stages of diagnosis herein. At stage <NUM>, determining whether the relationship between DPav and DPb indicates a "lock-up" operating condition, which occurs on the gas meter <NUM> when the impellers <NUM> can no longer rotate in the meter body <NUM>. If so, the method <NUM> may continue, at stage <NUM>, incrementing an event counter that aggregates the occurrence of the lock-up condition and, at stage <NUM>, determining whether the event counter has exceeded a pre-determined limit. Values for the pre-determined limit may be set to avoid false positives, which may cause the method <NUM> to return to stage <NUM> (<FIG>), resetting the system for purposes of gathering a new sample set of data during stable flow. If affirmative, the method <NUM> may include, at stage <NUM>, setting a "current condition" for the operating condition and (where applicable) activate the output discussed above. This current condition may reflect "lock-up" of the impellers <NUM>, malfunction of the DP sensor, or other indication (e.g., "DP alarm") that there is problems (or potential problems) on the device. When the lock-up condition is not met, the method <NUM> may continue at stage <NUM>, resetting the event counter. The method <NUM> may further include, at stage <NUM> determining whether the relationship between DPav and DPb indicates the "fault" condition and, further continue at stage <NUM>, <NUM>, <NUM> to provide an appropriate response to the same. As also shown, if the fault condition is not met, the method <NUM> may also reset the counter (at stage <NUM>) and continue, at stage <NUM>, determining whether the relationship between the DPav and DPb indicates the "alarm" condition. This condition may give way to stages <NUM>, <NUM>, <NUM> as appropriate. Further, if none of the conditions are met, the method <NUM> may continue at stage <NUM>, resetting the counter, and return to stage <NUM> to continue the data collection process, for example, during the next period of stable flow or test time period.

<FIG> schematically depicts an example of the gas meter <NUM> of <FIG>. As shown, the electronics unit <NUM> may include a processing unit <NUM> with a pair of processors (e.g., a first processor <NUM> and a second processor <NUM>). The processors <NUM>, <NUM> may each have their own "computing structure" with a memory <NUM> and a dedicated power supply <NUM>, as desired. This computing structure may take the form of a fully-integrated micro-processor. Executable instructions <NUM> may reside on the memory <NUM>. A buss structure <NUM> may couple the processing unit <NUM> with a sensor unit <NUM> to allow exchange of data and information, shown generally as signals (in analog or digital formats), including request data SR and measurement data SM. The sensor unit <NUM> may include various sensors that generate the data signals SR, SM. The sensors may include the DP sensor <NUM> that couples with the DP unit <NUM>. Other sensors may include motion sensors <NUM> or fluid condition sensors <NUM>. The sensors <NUM>, <NUM> may provide data that relates to flow of fluid <NUM> through conduit <NUM>. In one implementation, measurement signals SM from the motion sensors <NUM> may correspond with rotation of the impellers <NUM> in response to flow of fluid <NUM> in the flow path <NUM>. The fluid condition sensors <NUM> may generate measurement signals SM that describe temperature T, pressure P, or specific gravity SG.

<FIG> schematically depicts an example of the gas meter <NUM> of <FIG>. The electronics unit <NUM> may include a communication unit <NUM> with antenna <NUM> that may send and receive signals, including incoming signals SI and outbound signals So. Examples of the antenna <NUM> may operate with various wireless formats, including long-range and short-range formats. In one implementation, these formats are useful to exchange data with remote devices, for example, a mobile device <NUM> (via Bluetooth®) or a remote server <NUM> via a network <NUM>.

<FIG> and <FIG> schematically depict an application of the gas meter <NUM> of <FIG>. This application deploys processors <NUM>, <NUM> individually as a volume processor <NUM> and a diagnostics processor <NUM>. The volume processor <NUM> is configured, through executable instructions <NUM>, to request (R) data from various components of the sensor unit <NUM>. In one implementation, requests R<NUM> and R<NUM> are made at a known time-constant interval (e.g., thirty (<NUM>) seconds) to obtain data D<NUM> and D<NUM> from the temperature and pressure sensors P. Request R<NUM> obtains data D<NUM> from the specific gravity sensor SG. The device may collect data D<NUM> from the motion sensor <NUM> without the need for any request; but this does not always have to be the case. The volume processor <NUM> can use this data (also through executable instructions <NUM>) to generate a volume output O<NUM>, for example, values for volumetric flow. In one example, the volume processor <NUM> may also generate a parameter output O<NUM> that passes parameter values (e.g., temperature, pressure, specific gravity (or other parameters of gas composition), flow rate, etc.) onto other parts of the device or into the network <NUM> (<FIG>). It may benefit the design for volume processor <NUM> to have additional capabilities to "correct" the values to take into consideration localized temperature and pressure parameters, as well. As also shown, request R<NUM> causes the DP sensor <NUM> to transmit data D<NUM> to the diagnostics processor <NUM>. This device can use DP data in combination with other parameter data D<NUM> to generate diagnostics output O<NUM>. As best shown in <FIG>, the processors <NUM>, <NUM> may forego any data processing in lieu of a "cloud" based system that delivers outputs O<NUM>, O<NUM>, typically raw data for DP, pressure, temperature, and specific gravity (or other parameters of gas composition), to a diagnostic server <NUM> for data processing that delivers the diagnostic output O<NUM> to a back office system <NUM>.

<FIG> and <FIG> schematically depicts another application of the gas meter <NUM> of <FIG>. Here, the diagnostics processor <NUM> manages requests R<NUM> for data D<NUM> from the DP sensor <NUM>. The diagnostics processor <NUM> also receives data D<NUM> from the motion sensor <NUM>. In one implementation, this device also acquires parameter data D<NUM> in response to request R<NUM>, also on an intermitted schedule (e.g., thirty (<NUM>) seconds). Like <FIG> above, the processors <NUM>, <NUM> may deploy as part of a "cloud" based system to deliver raw data to remotely located diagnostic server <NUM>.

In light of the foregoing, the embodiments herein may enhance diagnostics. A technical effect is to influence operation of the DP sensor, causing it to activate and deactivate at opportune times so as to gather better "good" data and save energy.

Claim 1:
A method, comprising:
monitoring a flow of fluid across impellers (<NUM>) on a gas meter (<NUM>);
identifying a period of stable flow;
operating a differential pressure sensor during the period of stable flow;
calculating diagnostic values from data from the differential pressure sensor; and
generating an output that corresponds with an operating condition that relates to a relationship between the diagnostic values,
characterized in that the differential pressure sensor is operated by:
- calculating a fixed test time, which is a multiple of a period T of a sinusoidal wave that describes the flow of fluid across the impellers (<NUM>);
- activating the differential pressure sensor at the beginning of the fixed test time;
- deactivating the differential pressure sensor at the end of the fixed test time.