Systems and methods for concentrating gases - Patents.com

Abstract

To provide a suitable system and method for concentrating gas.SOLUTION: A gas concentration system and method includes a configuration of hardware and software components for monitoring various sensors related to a system and a method for concentrating gas as described herein. The hardware and software components are further constituted so as to utilize information acquired from the sensors through the entire system and execute a certain data analysis task. Through an analysis, the system for example calculates time until malfunction regarding one or more system components, generates an alarm for warning a user of imminent component malfunction, corrects a system setting for improving functionality under different environmental conditions, corrects system operation for saving energy, and / or can determine an optimum setting configuration on the basis of a sensor feedback.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 052,869, filed July 16, 2020, entitled “System and Method for Concentrating Gas” (Attorney Docket No. 12873-07041).

[0002] This application is a continuation of the following provisional patent applications: U.S. Provisional Patent Application No. 63 / 052,694, entitled "System and Method for Concentrating Gas" (Attorney Docket No. 12873-07004), U.S. Provisional Patent Application No. 63 / 052,700, entitled "System and Method for Concentrating Gas" (Attorney Docket No. 12873-07033), U.S. Provisional Patent Application No. 63 / 052,869, entitled "System and Method for Concentrating Gas" (Attorney Docket No. 12873-07041), U.S. Provisional Patent Application No. 63 / 052,533, entitled "System and Method for Concentrating Gas" (Attorney Docket No. 12873-07043), all filed on July 16, 2020, and U.S. Provisional Patent Application No. No. 63 / 052,647 (Attorney Docket No. 12873-07044), entitled "Microfluidic Devices," which is incorporated by reference. [Background technology]

[0003] Various applications exist for the separation of gaseous mixtures. For example, the separation of nitrogen from atmospheric air can provide a source of high concentration oxygen. These various applications include the provision of high concentration oxygen for medical patients and flight personnel. Therefore, it is desirable to provide a system for separating a gaseous mixture to provide a concentrated product gas, such as a breathing gas with a concentration of oxygen.

[0004] For example, several existing product gas or oxygen concentration systems and methods are disclosed in U.S. Pat. Nos. 4,449,990, 5,906,672, 5,917,135, 5,988,165, 7,294,170, 7,455,717, 7,722,700, 7,875,105, 8,062,003, 8,070,853, 8,668,767, 9,132,377, 9,266,053, and 10,010,696, which are commonly assigned to Invacare Corporation (Elyria, Ohio) and are hereby incorporated by reference in their entireties.

[0005] Such systems are known to be either stationary, transportable, or portable. Stationary systems are intended to remain in one location, such as, for example, a user's bedroom or living room. Transportable systems are intended to be moved from location to location and often include wheels or other mechanisms to facilitate movement. Portable systems are intended to be carried with the user, such as, for example, via a shoulder strap or similar attachment.

[0006] Failure of one or more components of these systems results in the system needing to be either repaired or replaced without much advance notice. If the system cannot be repaired or replaced quickly, a suitable alternative must be found for the user. Such systems have hardware and software configured to monitor various sensors, but this monitoring is associated with the gas concentration process and limited diagnostics. It is therefore desirable to provide systems and methods with improved data / diagnostic analysis and control capabilities, including, but not limited to, time to component failure. Summary of the Invention [Means for solving the problem]

[0007] Gas concentration systems and methods are provided that may, for example, calculate time to failure for one or more system components, generate alarms to warn users of impending component failure, modify system settings to improve functionality in different environmental conditions, modify system operation to conserve energy, and / or determine optimal setting configurations based on sensor feedback. Component-specific alarms can aid in diagnosis at the medical device provider level, increase the efficiency of service and repair, and save costs by reducing the probability of components being replaced erroneously.

[0008] In one embodiment, a system and method for calculating time to failure for at least one component of a gas enrichment system is provided. The system and method includes using an operating pressure and / or oxygen enrichment time slope linear regression to determine an estimated time to failure. In other embodiments, this determination can be performed periodically to update or refresh the estimated time to failure. Additionally, component failures can be identified, for example, by pressure and / or oxygen slope trends (positive or negative) and oxygen purity decay / linear regression. Based on this identification, warnings, alarms, and the like can be generated to alert users and service personnel regarding the problematic component.

[0009] In another embodiment, the system and method creates a baseline reading based on the altitude value. This includes determining initial values ​​related to the operation of the gas enrichment system, including at least an average oxygen value, an average pressure value, and an altitude value. In one embodiment, the system and method further includes establishing a baseline of values ​​for a given altitude value and determining whether a change in altitude has occurred. If a change in altitude is determined, the system and method establishes a second baseline of values ​​for the measured altitude. If no change in altitude is determined, the system and method proceeds to collect data based on values ​​related to the operation of the gas enrichment system. In another embodiment, the system and method further includes determining whether a data analysis threshold has been met, and if so, performing an analysis and calculating an estimated time to component failure.

[0010] In another embodiment, the system and method may further include determining a maintenance window, collecting data during the maintenance window, determining if a data analysis threshold is met, performing a second analysis and calculating an estimated time to component failure, diagnosing the failed component, and generating an alert based on the diagnosed failed component.

[0011] It is therefore an object of the present invention to determine the time to failure or health of one or more components of a gas concentration system.

[0012] It is another object to provide one or more component failure alarms or warnings based on a time-to-failure or health analysis of at least one component.

[0013] It is another object to provide one or more component failure or health alarms or warnings prior to a component failure.

[0014] It is another object to provide a system and method for concentrating gas that performs a time-to-failure or health analysis of at least one component of the system and uses that information to inform a user that inspection is currently or will be required in the near future.

[0015] It is another object to provide a system and method for concentrating gas that performs a time-to-failure or health analysis of at least one component of the system and uses that information to notify inspection or repair personnel that one or more components currently or soon will require inspection or repair.

[0016] It is another object to provide a system and method for concentrating gas that performs a time-to-failure or health analysis of at least one component of the system and uses that information to modify the performance of the system and method to extend the useful life of the component or system.

[0017] These and other objects will become apparent from the drawings and description provided above and below herein. The present specification also provides, for example, the following items: (Item 1) 1. A system for concentrating a gas, comprising: A plurality of sheave beds; An oxygen sensor; A pressure sensor; A controller, logic for collecting oxygen and pressure data; logic for calculating a linear regression on the oxygen and pressure data; logic for determining a predicted time to failure for one or more gas concentrating components based on an oxygen linear regression calculation and a predetermined threshold oxygen purity level; Logic for determining the slope of the pressure linear regression calculation; logic for generating one or more component alarms based on a slope of said oxygen linear regression calculation and said pressure linear regression calculation; A controller comprising: A system comprising: (Item 2) Logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation includes: logic for generating a time window before a predicted failure of said component using said oxygen linear regression calculation; Item 2. The system of item 1, comprising: (Item 3) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for determining whether the slope of said pressure linear regression calculation is positive or negative during said time window; 3. The system according to item 2, comprising: (Item 4) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a first sieve floor alarm if the slope of the pressure linear regression calculation is positive at the beginning of the time window; 4. The system of item 3, comprising: (Item 5) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a second sieve floor alarm if the slope of the pressure linear regression calculation is positive at the end of the time window. 4. The system of item 3, comprising: (Item 6) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a first compressor alarm if a slope of the pressure linear regression calculation is negative at a beginning of the time window. 4. The system of item 3, comprising: (Item 7) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a second compressor alarm if the slope of the pressure linear regression calculation is negative at the end of the time window. 4. The system of item 3, comprising: (Item 8) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a first filter alarm if the slope of the pressure linear regression calculation is negative at a beginning of the time window. 4. The system of item 3, comprising: (Item 9) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a second filter alarm if the slope of the pressure linear regression calculation is negative at the end of the time window. 4. The system of item 3, comprising: (Item 10) 1. A health monitoring system for a gas concentrator, comprising: A controller, logic for collecting oxygen and pressure data; logic for calculating a linear regression on the oxygen and pressure data; logic for determining a predicted time to failure for one or more gas concentrator components based on the oxygen linear regression calculation and a predetermined threshold oxygen purity level; Logic for determining the slope of the pressure linear regression calculation; logic for generating one or more component alarms based on a slope of said oxygen linear regression calculation and said pressure linear regression calculation; A controller comprising: A system comprising: (Item 11) Logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation includes: logic for generating a time window before a predicted failure of said component using said oxygen linear regression calculation; Item 11. The system of item 10, comprising: (Item 12) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for determining whether the slope of said pressure linear regression calculation is positive or negative during said time window; Item 12. The system of item 11, comprising: (Item 13) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a first sieve floor alarm if the slope of the pressure linear regression calculation is positive at the beginning of the time window; Item 13. The system of item 12, comprising: (Item 14) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a second sieve floor alarm if the slope of the pressure linear regression calculation is positive at the end of the time window. Item 13. The system of item 12, comprising: (Item 15) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a first compressor alarm if a slope of the pressure linear regression calculation is negative at a beginning of the time window. Item 13. The system of item 12, comprising: (Item 16) The logic for generating one or more component alarms based on a slope of the oxygen linear regression calculation and the pressure linear regression calculation further comprises: logic for generating a second compressor alarm if the slope of the pressure linear regression calculation is negative at the end of the time window. Item 13. The system of item 12, comprising: (Item 17) 1. A gas concentrating system comprising: A plurality of sheave beds; An oxygen sensor; A pressure sensor; A controller, logic for collecting oxygen and pressure data; logic for calculating a linear regression on said oxygen and pressure data; logic for determining a first predicted time to failure for one or more gas concentrator components based on an oxygen linear regression calculation and a predetermined threshold oxygen purity level; logic for determining a second predicted time to failure for the one or more gas concentrator components based on the pressure linear regression calculation and a predetermined threshold pressure level; logic for comparing the first and second predicted times to failure; logic for setting a time window based on said comparison; logic for generating one or more component alarms when the system is within the time window; A controller comprising: A system comprising: (Item 18) 20. The system of claim 17, wherein the logic for comparing the first and second predicted times to failure comprises logic for determining which of the first and second predicted times to failure is likely to occur sooner. (Item 19) 20. The system of claim 18, wherein logic for setting a time window based on the comparison comprises logic for setting the time window based on a predicted time to earlier failure. (Item 20) 20. The system of claim 17, wherein the logic for generating one or more component alarms when the system is within the time window comprises logic for generating one or more component alarms when the system is within a 30 day time window. [Brief description of the drawings]

[0018] In the accompanying drawings, which are incorporated in and form a part of this specification, embodiments of the present invention are illustrated and, together with the general description of the invention given above and the detailed description given below, serve to illustrate the principles of the invention.

[0019] [Figure 1] FIG. 1 illustrates one embodiment of a gas concentration system and method.

[0020] [Diagram 2] FIG. 2 is one embodiment of a pneumatic block diagram of a gas concentration system and method.

[0021] [Diagram 3] FIG. 3 is one embodiment of a controller for the exemplary gas concentration system and method.

[0022] [Figure 4] FIG. 4 is one embodiment of an exemplary method for calculating the time to failure for at least one component of a gas concentration system.

[0023] [Diagram 5] FIG. 5 is an example of an exemplary method for diagnosing a fault in one or more components in a gas concentration system.

[0024] [Figure 6A] 6A-6B illustrate various embodiments of methods and logic for diagnosing the failure, predicted failure, and / or health of one or more components in a gas enrichment system. [Figure 6B]6A-6B illustrate various embodiments of methods and logic for diagnosing the failure, predicted failure, and / or health of one or more components in a gas enrichment system.

[0025] [Figure 7] FIG. 7 is a chart illustrating example values ​​of oxygen purity in extreme environmental conditions (eg, 10,000 feet above sea level) when correcting for deviation times of the oxygen generation process.

[0026] [Figure 8] FIG. 8 illustrates an example method for predicting optimal flow rate settings for an oxygen concentrator system.

[0027] [Figure 9] FIG. 9 illustrates another embodiment of a method and logic for diagnosing the failure, predicted failure, and / or health of one or more components in a gas enrichment system.

[0028] [Figure 10] FIG. 10 illustrates yet another embodiment of a method and logic for diagnosing the failure, predicted failure, and / or health of one or more components in a gas enrichment system.

[0029] [Figure 11] FIG. 11 illustrates a further embodiment of a method and logic for diagnosing the failure, predicted failure, and / or health of one or more components in a gas enrichment system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0030] explanation As described herein, when one or more components are described or shown as being connected, joined, affixed, coupled, attached, or otherwise interconnected, such interconnection may be direct, as between the components, or may be indirect, such as through the use of one or more intermediate components. Also, as described herein, reference to a member, component, or portion is not intended to be limited to a single structural member, component, element, or portion, but can include a collection of components, members, elements, or portions.

[0031] Embodiments of the present invention provide the ability to monitor sensors associated with the operation of an exemplary gas concentration system, for example, and utilize information obtained from sensors throughout the system to perform certain data analysis tasks. Through analysis, the system may, for example, calculate time to failure for one or more system components, generate alarms to alert a user to impending component failure, modify system settings to improve functionality in different environmental conditions, modify system operation to conserve energy, and / or determine optimal setting configurations based on sensor feedback. Each of these examples will be described in further detail herein.

[0032] As an example, oxygen concentrators utilize pressure swing adsorption (PSA) technology to generate oxygen from air. The process is based on cycle steps that allow the pressure to transition from high to low (and vice versa). The pressure difference is the driving force for oxygen generation and adsorbent regeneration. Some units operate on a fixed transition time basis, where all cycles take the same duration. At the beginning of the unit life, the sieves are new and the pressure drop across the bed is minimal. Other units operate under breakthrough conditions, where the end of the adsorption time is determined by the amount of impurities detected in the product tank. In those units, the transition time slowly decreases over the life of the unit. Adsorbent performance depends on its selectivity, nitrogen capacity, and diffusivity. Molecular sieve regeneration is essential to the concentrator life and the amount of oxygen produced. The absorber becomes saturated over time with contaminants such as water vapor and carbon dioxide. This saturation degrades the material capacity, as the contaminants tend to occupy sites in the zeolite structure, reducing its capacity to trap nitrogen. The primary consequence of contamination is the breakthrough of impurities (nitrogen gas). Degradation of sieve material performance directly impacts the amount of pure oxygen produced and therefore the oxygen purity delivered to the patient. Degradation of sieve bed integrity can cause a decrease in oxygen purity and a gradual increase in the rate of pressurizing the tank. Product tank pressure, oxygen purity, and time feedback can help monitor problems related to the sieve bed.

[0033] As the adsorption capacity in the sieve bed decreases (due to wear from moisture, contaminants, or abrasion), the normal N2O that is adsorbed and trapped in the sieve bed each cycle decreases. 2 The amount of nitrogen adsorbed will decrease (less bed volume) and excess nitrogen that was not adsorbed will break through from the oxygen side of the sieve bed. This breakthrough increases the total amount of impurity gas in the product tank (reservoir or accumulator) and therefore increases the pressure in the product tank and removes O from the total volume. 2Reduce the fraction. The amount coming out of the product tank is usually controlled and fixed by conserva valve timing for trace amounts in portable oxygen concentrators or a flow meter in stationary oxygen concentrators. In a pressure shift machine, the pressure of each shift is controlled, therefore the variable that changes is the rate at which that target pressure is reached. As the sieve bed wears, the shift time will decrease due to reaching the target pressure sooner with a greater volume of impurities in the tank. 2 The pressure in the product tank (at the time shift device) can be monitored to detect that the drop is due to sieve bed wear and not some other fault. 2 The gradual increase in pressure combined with a decrease in pressure, O, is present only in the case of sieve bed wear. 2 The rate of change and time can be used as feedback for sieve bed health monitoring. Other unique component failures (e.g., pumps, valves, etc.) can also be determined through analysis of the pressure and / or oxygen sensor signals and generate alarms, warnings, and time-to-failure estimates.

[0034] As an example, one or more component specific alarms associated with component failure, failing components, or time to failure can be determined by utilizing one or more sensor signals. For example, oxygen and / or pressure sensors can be used to determine sieve bed and / or compressor failure and / or predict time to failure. Pressure signals can be used to determine main valve failure and / or predict time to failure. Pressure waveforms into and out of oxygen product tanks can be used in combination with low oxygen purity levels to determine check valve failure and / or predict time to failure. Linear regression analysis applied to pressure vs. time slope data can determine a predicted time to system (or sieve bed) failure when operating pressure will exceed an acceptable value. Similarly, linear regression analysis applied to oxygen concentration vs. time slope data can determine a predicted time to system (or sieve bed) failure when oxygen concentration will fall below an acceptable value. Electrical output signals (including their absence and / or out of range signals) of various sensors, including oxygen and pressure sensors, can be used to determine sensor failure and / or predict time to failure.

[0035] In one embodiment, the time to failure is determined by using linear regression analysis to predict the time that the system and / or components will no longer provide proper output or operating parameters. For example, linear regression analysis of system pressure and / or oxygen data (e.g., high, low, decaying over time, rising over time, combinations of the above, etc.) can be used to identify and predict the time to failure of one or more components. This means that system components such as valves, motors, pumps, sieve beds, and sensors, as they begin to fail or fail, each cause a unique effect or combination of effects on overall system behavior and function, allowing for the identification of the failing component and the predicted time to failure through linear regression analysis. These unique effects or combinations of effects are discussed in more detail below.

[0036] Illustrated in FIG. 1 is one embodiment of an oxygen system 100 that includes component failure analysis and / or alarms. The system may be stationary, such as for use in a hospital or in the patient's home. The system may also be ambulatory or mobile, such as for use by the patient when the patient is away from home. The system can be configured in a manner that allows the patient to carry the system, such as through an over-the-shoulder strap or an arrangement whereby the system includes a handle and wheels. Other mobile configurations are also included.

[0037] The oxygen system 100 includes a housing 102, which may be in one or more compartments. The housing 102 includes multiple openings for the inhalation and exhalation of various gases, such as, for example, the inhalation of room air and the exhalation of nitrogen and other gases. The oxygen system 100 generally inhales room air, which is mostly composed of oxygen and nitrogen, and separates the nitrogen from the oxygen. The oxygen is stored in one or more internal or external storage or product tanks, and the nitrogen is exhaled back to room air. For example, oxygen gas may be exhaled through port 104 to the patient through tubing and a nasal cannula. Alternatively, oxygen gas may be exhaled through an auxiliary port to an oxygen cylinder filling device, such as the HOMEFILL® manufactured by Invacare Corp. (Elyria, Ohio, USA).

[0038] 2 illustrates one embodiment of an exemplary pneumatic block diagram for a gas concentration system 200 using pressure swing adsorption (PSA). The system can include a plurality of gas separation sieve beds 206a and 206b, a plurality of valves 204a, 204b, 204c, and 204d, one or more product tanks 208a, 208b, and a conserva valve / device 218. In this embodiment, the product tanks 208a, 208b are shown connected, so they act as one product tank, but may also be arranged to act as two product tanks. The system also includes a compressor / pump 203, one or more filters 201, and a muffler 202.

[0039] The sieve beds 206a and 206b are filled with a physical separation medium or material. The separation material selectively adsorbs one or more adsorbable components and allows one or more non-adsorbable components of the gaseous mixture to pass through. Generally, the physical separation material is a molecular sieve with pores of uniform size and essentially identical molecular dimensions. These pores selectively adsorb molecules according to molecular shape, polarity, saturation, and the like. In one embodiment, the physical separation medium is an alumina silicate composition with pores of 4-5 ANG. (Angstroms). More specifically, the molecular sieve is a sodium or calcium form of alumina silicate, such as 5A type zeolite. Alternatively, the alumina silicate may have a higher silicon to aluminum ratio, larger pores, and an affinity for polar molecules, e.g., 13x type zeolite. Zeolites adsorb nitrogen, carbon monoxide, carbon dioxide, water vapor, and other significant components of air. Other types of separation media may also be used. Also, more than two sieve beds can be used. In other embodiments, the sieve beds 206a and 206b can be structurally integrated with one or more product tanks 208a and 208b, as described in U.S. Patent No. 8,668,767, which is incorporated by reference in its entirety for this and other features.

[0040] In operation, as shown by the solid lines in FIG. 2, during an exemplary fill cycle of separation bed 206a, pump / compressor 203 draws room air through filter 201, valve 204d and separation bed 206a, which produces oxygen at its output through valve 210a into product tanks 208a, 208b. Pump / compressor 203 delivers up to about 32 pounds per square inch of air to the sieve bed during the fill phase. Other operating pressure ranges include about 15-34 pounds per square inch. Valves 210a and 210b may be check valves or any other similarly functioning valves that allow only one-way flow.

[0041] While separation bed 206a is undergoing a fill cycle, separation bed 206b may undergo a purge cycle to vent any nitrogen gas from the previous fill cycle. During the purge cycle, previously pressurized separation bed 206b vents nitrogen gas through valve 204a and through muffler 202 to the atmosphere. Separation bed 206b is pressurized from its previous fill cycle. During the purge cycle, an amount of oxygen from separation bed 206a or product tanks 208a, 208b can be pumped into separation bed 206b to preload or precharge separation bed 206b with oxygen, as controlled by optional bleed valve 212 and fixed orifice 214, shown in dashed lines in FIG. 2.

[0042] As shown by the dashed lines in Figure 2, once separation bed 206a is filled and / or separation bed 206a is purged, control system 220 switches valves 204a, 204b, 204c, and 204d such that separation bed 206b enters a fill cycle while separation bed 206a enters a purge cycle. In this state, pump 203 directs room air into separation bed 206b, which produces oxygen at its output through valve 210b into product tanks 208a, 208b. Separation bed 206a undergoes a purge cycle whereby it exhausts nitrogen and other gases out valve 204c and muffler 202 to the atmosphere or room. During the purge cycle, an amount of oxygen from separation bed 206b or product tanks 208a, 208b can be pumped into separation bed 206a to preload or precharge separation bed 206a with oxygen now flowing in the opposite direction compared to the previous cycle. The illustrated system also includes an exemplary pressure equalization valve 216, which equalizes the pressure in the two separation beds prior to the purge / fill cycle change.

[0043] The pressure equalization valve 216 can enable more efficient generation of oxygen by equalizing pressure between the output of a separation bed (e.g., 206a) near the end of its fill cycle and a separation bed (e.g., 206b) near the end of its purge cycle. For example, the pressure equalization valve 216 may be activated to equalize pressure between the output of separation bed 206a and separation bed 206b near the end of each purge / fill cycle. U.S. Patent Nos. 4,449,990 and 5,906,672 (incorporated by reference herein in their entireties) further describe the operation of the pressure equalization valve. In this manner, each separation bed 206a, 206b is periodically subjected to alternating fill and purge cycles as controlled by the control system 220, thereby generating oxygen.

[0044] 2, an optional conserver valve / device 218 may be used to control the delivery of product gas to a user 222. The conserver valve 218 may switch between providing concentrated product gas from the product tanks 208a, 208b or venting to room air. For example, the conserver valve 218 may be used to selectively provide various continuous or pulsed flows of concentrated oxygen product gas in amounts and times determined by the control system 220. The times are typically based on sensing an inhale by the user, typically determined by sensing a drop in pressure or (increased flow) proximate to the user's nose or mouth.

[0045] In this embodiment, control system 220 may utilize various control processes to optimize the generation and delivery of concentrated product gas, for example, by controlling the activation, levels, and relative timing of pressure source 203 and valves 204a, 204b, 204c, 204d, 216, and 212. This is accomplished through the use of one or more pressure sensors 224 and / or oxygen concentration sensors 226. In one embodiment, pressure and oxygen sensors 224 and 226 monitor the pressure and oxygen concentration entering product tanks 208A and 208(b). In other embodiments, the use of timed cycles can be employed, with cycle times set at the factory. In further embodiments, cycle times can be determined from flow settings and / or sensed patient flow demand. In still further embodiments, cycle times can be determined during a start-up diagnostic procedure when the oxygen concentrator is turned on or powered on.

[0046] 2 illustrates a pressure swing adsorption (PSA) cycle, other gas concentration cycles may also be used, including vacuum swing adsorption (VSA), vacuum-pressure swing adsorption (VPSA), or other similar modes. The particular gas concentration modes are not critical to the embodiments of the invention described herein, so long as they are capable of producing concentrated gases, such as oxygen, to a user. Examples of the above operating modes are disclosed, for example, in U.S. Patent Nos. 9,266,053 and 9,120,050, which are incorporated by reference in their entireties.

[0047] Due to the mechanical nature of many of the system components, component wear and failure may occur. However, determining time to failure and diagnosing which component has failed is time consuming and inefficient. An embodiment of the present invention analyzes factors that may change oxygen purity and / or operating pressure and component failure. In one embodiment, the present system and method analyzes pressure and / or oxygen sensor data to determine sieve bed wear and predict time to failure. In one example, a gradual increase in separation or sieve bed operating pressure combined with a decrease in oxygen purity over time is a distinct failure mode associated with sieve bed wear. In another example, operating pressure can be used alone to determine a predicted time to system or sieve bed failure by using a linear regression analysis on the pressure vs. time slope data and determining when it will increase above a threshold value (e.g., 34 PSI or some other value). Similarly, oxygen concentration / purity can be used alone to determine a predicted time to system or sieve bed failure by using a linear regression analysis on the oxygen purity vs. time slope data and determining when it will decrease below a threshold value (e.g., 85% or some other value). In yet other embodiments, both the pressure and oxygen linear regressions can be determined separately and the predicted time to failure can be set to be the earlier of the two determinations (e.g., the time to reach the low oxygen threshold or the time to reach the high pressure threshold), which would then trigger a warning that the sieve bed needs to be replaced or replaced soon.

[0048] Other distinct component failures can also be identified. These include air-side (main) valves (e.g., 204a, 204b, 204c, and / or 204d) getting stuck closed or open. These failures result in reduced oxygen purity (e.g., below 85%, below 73%, etc.) along with an immediate (i.e., not gradual) pressure change to the sieve bed input (or output). An air-side (main) valve leak can be clearly identified by low oxygen purity (e.g., below 85%, below 73%, etc.) exiting the sieve bed and a lower (out of range) sieve bed operating pressure. A check valve leak can be clearly identified by low oxygen purity (e.g., below 85%, below 73%, etc.) and a "v" shaped reduction in product tank pressure. A tubing leak can be clearly identified by low oxygen purity (e.g., below 85%, below 73%, etc.) and a low (e.g., out of range) system pressure. Compressor wear can be clearly identified by low oxygen purity (e.g., below 85%, below 73%, etc.) and low input and output (e.g., out of range) sieve bed pressures. An obstruction / restriction to flow causes an immediate high system pressure (e.g., out of range) and oxygen purity that remains the same or becomes higher. Flow output setting changes can be identified by pressure and oxygen purity, i.e., an increase in flow setting causes a decrease in pressure (e.g., system or product tank) and a slight decrease in oxygen purity, and a decrease in flow setting causes an increase in pressure (e.g., system or product tank) and an increase in oxygen purity. These and other sensed parameters can be used to diagnose component failure and / or time to failure.

[0049] FIG. 3 illustrates a detailed view of one embodiment of the control system 220 with component failure logic. Although described herein with specific reference to an exemplary gas enrichment system, it should be understood that the control system 220 may be readily adapted for use with additional systems. In an embodiment, the control system 220 may be operatively connected to and / or in data communication with one or more sensors, such as pressure sensor 224, oxygen sensor 226, and / or altitude sensor 312. Other sensors may also be used, including, for example, flow and / or temperature sensors. The pressure sensor 224 may be associated with various components of the exemplary gas enrichment system (e.g., gas enrichment system 200) and configured to measure pressure in real-time or near real-time. In an embodiment, the pressure sensor 224 may comprise an individual sensor configured to monitor and collect pressure data from multiple components. Similarly, the oxygen sensor 224 may be associated with various components of the exemplary gas enrichment system (e.g., gas enrichment system 200) and configured to measure oxygen values ​​in real-time or near real-time. In an embodiment, the oxygen sensor 226 may comprise an individual sensor configured to monitor and collect pressure data from multiple components. The altitude sensor 312 may comprise an altimeter, barometric pressure sensor, or the like, configured to measure the physical altitude of the gas enrichment system. It is understood that additional sensors may be operatively connected to and / or in data communication with the control system 220. In some embodiments, the control system 220 is configured to implement a control scheme that optimizes the production and delivery of enriched product gas by controlling the activation, levels, and relative timing of the pressure source 203 and, in some embodiments, the valves 204a, 204b, 204c, 204d, 216, and 212 (see FIG. 2). The control system 220 may additionally be operatively connected to and / or in data communication with a user settings module 314. The user settings module 314 is configured to communicate various user settings to the control system 220.In some embodiments, the user settings module 314 may receive user input from a user input device, such as, for example, a computer, tablet, smartphone, or the like. In other embodiments, the user settings module 314 may receive user input via a control panel or the like associated with the exemplary gas enrichment system (e.g., gas enrichment system 200). In some embodiments, the initial settings may be set by the manufacturer as "default" settings that may be stored in a memory, for example, memory 306.

[0050] The control system 220 also communicates with various input / output devices 316. The input and output devices include push buttons on the oxygen concentrator housing, wireless devices (e.g., tablets, smartphones, laptops, remote servers, RFID tags, readers, writers, etc.), devices connected through one or more communication ports (e.g., serial bus ports (e.g., USB, etc.), memory card slots (e.g., SD, etc.), light emitting devices (e.g., lamps, LEDs, etc.), speakers for audio output, microphones for audio input, cameras, etc.

[0051] In one embodiment, a pressure sensor is associated with the input and / or output of the sieve beds 206a, 206b. A pressure sensor can further be associated with the input and / or output of one or more product tanks 208a, 208b. Similarly, an oxygen sensor can be associated with the input and / or output of one or more product tanks 208a, 208b. An oxygen sensor can also be associated with the output of one or more sieve beds 206a, 206b. Other components can have pressure and / or oxygen sensors associated with them as well.

[0052] The control system 220 comprises at least logic 304 for component failure analysis and a memory 306. The logic 304 may further comprise one or more processors or the like operable to perform calculations and other data analysis, such as, for example, regression analysis. Additional types of data analysis may be performed, such as, for example, linear regression (e.g., Y=bx+a), exponential approximation (e.g., Y=ae bx ), logarithmic trend line (e.g., Y=a×ln(x)+b), polynomial trend line (e.g., Y=b6x 6 It should be understood that a linear regression such as α, β, β, β+α, β, β+ ...

[0053] 4-6 illustrate various examples of logic relating to such data analysis methods that can be implemented by the controller 220. It should be understood that the methods and associated steps illustrated may be performed in a different order, illustrated steps may be omitted, additional steps may be added or reordered, combined, omitted, or involve combinations of additional steps.

[0054] In FIG. 4, one embodiment of a method 400 for calculating the time to failure for at least one component of a gas enrichment system is shown. The method 400 begins at 402, where an initial value is determined. The initial value may comprise an oxygen level as measured by one or more oxygen sensors (e.g., oxygen sensor 226), a pressure level as measured by one or more pressure sensors (e.g., pressure sensor 224), and / or an altitude as measured by an altitude sensor (e.g., altitude sensor 312). It should be understood that the initial value may be determined at a specific time (e.g., 30 seconds after the gas enrichment system is ready for operation), or alternatively, the initial value may comprise an average of several measurements taken after the gas enrichment system is ready for operation. In some embodiments, block 402 additionally includes a post-warm-up start-up check, e.g., determining the steady state of oxygen after it has finished increasing, rather than after it has reached a predefined readiness threshold (e.g., 85%). It is not uncommon for the system to require several cycles as a warm-up or start-up to steady state operation.

[0055] At block 404, a baseline of values ​​is established based on the altitude of the gas enrichment system. This baseline of values ​​may comprise oxygen and pressure levels at one or more locations throughout the system (e.g., sieve beds, product tanks, valves, compressors, etc.). The altitude may be determined based on a measured altitude, or in some embodiments, based on a user setting (e.g., altitude zones such as "high altitude" or low or "sea level"). In one example, an exemplary gas enrichment system has six altitude zones, namely, sea level, 4,000 feet, 6,000 feet, 8,000 feet, 10,000 feet, and 13,000 feet, each with a ±500 foot range. Each range can have multiple combinations with various flow settings, namely, 1, 2, 3, 4, or 5 LPM. At these settings, up to 30 states can exist. For each state, a data point is collected to estimate the decay equation. The altitude zone can be used as feedback to increase pressure if necessary (e.g., at lower flow settings when the fill / purge transition times are shorter, a high altitude zone feedback can be used to increase the time, and therefore the pressure, in the tank to avoid the valve getting stuck below the minimum operating limit). Once a baseline of values ​​has been determined at a given altitude, the baseline is stored (e.g., in memory 306) and method 400 proceeds to block 406. At block 406, it is determined whether there is a change in altitude. If there is a change in altitude, method 400 returns to block 404 and establishes a new baseline based on the new altitude. It should be appreciated that the change in altitude may comprise a change in a user setting (e.g., a change in altitude zone setting) that may be measured or associated with the altitude. In some embodiments, the altitude is measured during predefined increments (e.g., every 24 hours). In certain embodiments, the change in altitude may be measured according to various thresholds of altitude, for example, according to predefined altitude zones.In such an embodiment, a change in altitude will be determined only if the measured altitude changes the range of altitudes that define a specific altitude zone. In some embodiments, the altitude change may take into account hysteresis and tolerance for a given zone (e.g., ±500 feet).

[0056] If no elevation change is determined in block 406, the method proceeds to block 408. In block 408, data, e.g., additional oxygen and pressure values, are collected and stored (e.g., in memory 306). It should be understood that oxygen and pressure values ​​may be collected for components of the gas enrichment system individually or in combination. For example, data may be collected for individual values ​​and / or over time and / or for a set of valves at a time interval. In some embodiments, data values ​​are captured and stored according to a predetermined sample time (e.g., hourly). In some embodiments, the sample time may be modified by controller 220 based on operating conditions and / or measurements. For example, if the measured oxygen value falls below a threshold, the sample time may be increased to more closely monitor changes in the measurements. In one exemplary embodiment, if the measured oxygen value falls below 89%, the sample time may be increased to collect samples every 10 minutes instead of every hour. It should be understood that additional sample intervals and thresholds are envisioned and the above is provided by way of example only.

[0057] At block 410, it is determined whether a data analysis threshold has been met. The threshold for data analysis may vary according to operating conditions, factory settings, and / or user settings. It should be understood that additional sample sizes result in more precise analysis. If the threshold has not been met, the method returns to step 408 to collect additional data points. Once a sufficient number of data points have been collected, the method proceeds to block 412.

[0058] At block 412, analysis of the data points is performed by the control system 220 (e.g., via logic 304) and the time to failure is calculated. Various methods of data analysis are contemplated herein. In some embodiments, the analysis includes performing a linear regression function (e.g., calculating the slope and intercept of oxygen and pressure as a function of time). Oxygen values ​​(O 2 An exemplary linear regression analysis for ) is presented in Equation 1: [ka] [ka]

[0059] An exemplary linear regression analysis for pressure values ​​(pressure) is presented in Equation 2. [ka]

[0060] From the linear regression calculations above, it is possible to determine a time-to-failure value. The time-to-failure may be expressed as the number of hours until one or more components of the gas enrichment system fail. In some embodiments, the linear regression calculations may be performed on the components of the gas enrichment system individually or in combination. In some embodiments, the linear regression calculations may be calculated multiple times as updated data is collected, for example, after a sufficient number of data points have been collected for data analysis (e.g., every 20 new data points). Each linear regression calculation may be stored in a memory (e.g., memory 306). The stored regression calculations may be compared or similarly analyzed to draw conclusions about and / or diagnose problems associated with one or more components of the gas enrichment system. It should be understood that certain components of the gas enrichment system may exhibit certain characteristics (e.g., abnormal oxygen or pressure values) indicative of degradation or failure of the component that may lead to suboptimal operation of the gas enrichment system. In some embodiments, the time-to-failure is calculated for a single component. In other embodiments, the time-to-failure is calculated for multiple components. In certain other embodiments, the time to failure is calculated for all components for which data is collected. Through further analysis of the data, it is possible to diagnose failure of specific components of the gas concentration system.

[0061] FIG. 5 illustrates an example method 500 for diagnosing failure of one or more components in a gas enrichment system. Method 500 begins at block 502, where method 400 is performed. As described herein, method 400 concludes with performing an analysis and calculating a time to failure for at least one component of the gas enrichment system. At block 504, a maintenance window is determined. Determining the maintenance window includes calculating a time window prior to the calculated time to failure. This maintenance window can potentially allow for mitigation of a problem that would ultimately lead to failure of the component if left unchecked (e.g., a 30 day or 720 hour window prior to the predicted time to failure). In an embodiment, the maintenance window is calculated in "moving time", meaning that the time window can change depending on updated data or time to failure.

[0062] At block 506, data is collected during the maintenance window. The data collected at block 506 may comprise oxygen and / or pressure data. At block 508, it is determined whether a data analysis threshold is met. In some embodiments, the data analysis threshold may require only a single measurement taken at the beginning of the maintenance window. Once a sufficient amount of data has been collected, the method proceeds to block 510. At block 510, data analysis is performed and an updated time to failure is calculated. In an embodiment, block 510 includes calculating a linear regression on the oxygen and / or pressure data for one or more components of the gas concentration system (e.g., see equations 1 and 2 above). At block 512, the method diagnoses the failed component.

[0063] Based on the analysis of the collected data and the linear regression calculation, it is possible to identify the faulty component and diagnose the problem with the component. For example, if the linear regression of the pressure data calculated at the beginning of the maintenance window results in a negative slope, the decay of the oxygen readings can be associated with a compressor or filter failure. Alternatively, if the linear regression of the pressure data calculated at the beginning of the maintenance window results in a positive slope, the decay of the oxygen readings is indicative of a sieve bed failure. As another example, if the linear regression of the pressure data calculated at the end of the maintenance window results in a negative slope, the decay of the oxygen readings can be associated with a compressor or filter failure. Alternatively, if the linear regression of the pressure data calculated at the end of the maintenance window results in a positive slope, the decay of the oxygen readings can be associated with a sieve bed failure.

[0064] Many additional diagnostics are contemplated herein. For example, a measured decrease in oxygen purity and an immediate pressure change can indicate an air-side valve failure (e.g., stuck open / closed). Similarly, if low oxygen purity is accompanied by lower pressure on one side of the sieve bed, this can indicate that one of the air-side valves is leaking. When low oxygen purity is observed along with the product tank pressure undergoing a "V" shaped decrease, this indicates a check valve leak. When low oxygen purity is observed along with low pressure, this can indicate a tubing leak. When low oxygen and low pressure are observed on both sides of the sieve bed, this indicates a compressor failure. When an immediate pressure increase is observed while oxygen purity remains the same or increases, this indicates an obstruction (e.g., restriction) to flow. When pressure increases immediately with oxygen purity, this indicates a flow setting change. When the observed pressure undergoes a gradual increase combined with a decrease in oxygen over time, this indicates a sieve bed failure. It should be understood that the above examples are provided for illustrative purposes only and are not intended to be a limitation on the scope of the present embodiments.

[0065] In some embodiments, component failures may be diagnosed using data observed from the electrical signals. For example, a drop in the voltage signal on each component's driver can be used to evaluate whether a coil in the valve is bad. Similarly, a drop in voltage may indicate that a component has a loose or cut wire in the printed circuit board (PCB).

[0066] It is to be appreciated that block 512 may include additional data analysis (eg, time since last maintenance, component manufacture date, etc.) to further assist in diagnosing the component failure.

[0067] After the diagnosis is performed, the method proceeds to block 514. At step 514, an alert or warning is generated based on the diagnosis. In some embodiments, the alert comprises information related to the diagnosis, such as, for example, the identified component, the latest calculated time to failure, the specific failure (which may include displaying an error code or message), the severity of the failure, etc. The alert may trigger some action associated with the gas enrichment system. For example, the alert may cause a chime, buzzer, or similar sound to alert a user to the detected failure. In some embodiments, the alert is displayed on a display associated with the gas enrichment system. In some embodiments, a failure alert may trigger a notification to be sent to a user's smartphone. Similarly, the notification may also be sent to a provider responsible for servicing and maintenance of the gas enrichment system. In some embodiments, the alert information may be transmitted to a server via an internet connection or equivalent for storage and analysis by the provider or manufacturer. Analysis of the alert information can provide useful information related to the operation of the gas enrichment system in different environments.

[0068] 6A illustrates another example method 600 for diagnosing failure of one or more components in a gas enrichment system. Method 600 begins at block 602, where method 400 is performed. As described herein, method 400 concludes with performing an analysis and calculating a time to failure for at least one component of the gas enrichment system. At block 604, a maintenance window is determined. Determining the maintenance window includes calculating a time window prior to the calculated time to failure, which may allow for mitigation of a problem that would potentially lead to a component failure if left unchecked (e.g., 30 days or 720 hours to failure). In an embodiment, the maintenance window is calculated in "moving time," meaning that the time window may change depending on updated data.

[0069] At block 606, data is collected during the maintenance window. The data collected at block 606 may comprise oxygen and / or pressure data. At block 608, it is determined whether a data analysis threshold is met. In some embodiments, the data analysis threshold may require only a single measurement taken at the beginning of the maintenance window. Once a sufficient amount of data is collected, the method proceeds to block 610. At block 610, data analysis is performed and an updated time to failure is calculated. In an embodiment, block 610 includes calculating a linear regression on the oxygen and / or pressure data for one or more components of the gas enrichment system. At block 612, the method diagnoses the failed component. Based on the diagnosis, the method may proceed to block 614 and mitigation actions are implemented. Mitigation actions may comprise any actions engaged in to potentially resolve a problem with one or more components of the gas enrichment system. For example, high altitudes may cause lower oxygen purity due to reduced overall operating pressure. If these conditions exist and associated faults are detected and / or diagnosed, the oxygen concentrator system (via controller 220) can modify the valve shift times, thereby optimizing oxygen purity production given the environmental conditions. Adjusting the pressure equalization time and shift times can be done to increase oxygen purity in extreme environmental conditions (e.g., high altitude), high pressure increases as a result of sieve bed wear and / or failure, low pressure as a result of compressor wear and / or failure, and / or low oxygen in general. For each of these situations, pressure can be maintained by modifying valve timing and used as primary feedback along with altitude. For example, increasing the shift time / pressure equalization time can increase system and / or component pressure, while decreasing the shift time / pressure equalization time can decrease system and / or component pressure. FIG. 7 illustrates example values ​​of oxygen purity in extreme environmental conditions (e.g., 10,000 feet above sea level) when modifying the shift times.

[0070] FIG. 6B illustrates one embodiment 620 of logic for analyzing the health of a sieve bed and / or a compressor. This embodiment uses regression analysis as described above to determine a predicted time to failure and a moving slope analysis to identify components (e.g., sieve bed and / or compressor) that are failing or predicted to fail soon. The logic begins at blocks 622 and 644 where oxygen and pressure sensor data is collected during warm-up or normal system operation. At blocks 624 and 648, a linear regression analysis as described above is performed on each set of data. At block 626, the logic calculates a predicted time to failure (e.g., in hours) by determining when the oxygen purity level will be at or below 83% purity (e.g., concentration) according to the regression analysis. At block 638, the logic calculates a 30-day moving window preceding the predicted time to failure. The window is moving, in one embodiment, as the logic repeatedly calculates and updates the linear regression at blocks 624 and 648 with use of the system. The 30-day window establishes advance notification in advance of the predicted failure time to allow service to be scheduled before the system suffers a component failure. At block 630, the logic checks the pressure trend (e.g., the moving slope of the pressure linear regression analysis at block 648) at the start of the 30-day window. If a positive pressure slope trend is indicated at block 632 (e.g., pressure is increasing over time), the logic proceeds to blocks 634 and 636 where the decay in oxygen purity is associated with the health of the sieve bed and an alarm is provided. If a negative pressure slope trend is indicated at block 638 (e.g., pressure is decreasing over time), the logic proceeds to blocks 640 and 642 where the decay in oxygen purity is associated with the health of the compressor (and / or inlet filter) and an alarm is provided.

[0071] At block 650, the logic again checks the pressure trend (e.g., the moving slope of the pressure linear regression analysis at block 648) at the end of the 30-day window. If a positive pressure slope trend is indicated at block 652 (e.g., pressure is increasing over time), the logic proceeds to blocks 654 and 656, where the decay in oxygen purity is associated with the health of the sieve bed and an alarm is provided. If a negative pressure slope trend is indicated at block 658 (e.g., pressure is decreasing over time), the logic proceeds to blocks 660 and 662, where the decay in oxygen purity is associated with the health of the compressor (and / or inlet filter) and an alarm is provided. Although the present embodiment illustrates checking the system health at the beginning and end of the 30-day window, any suitable interval can be used and any number of health checks can be performed. In this manner, the user and / or provider is given specific advance warning of which system components are predicted to fail or have failed.

[0072] FIG. 9 illustrates another embodiment of a method and logic 900 for analyzing the health and / or predicted time to failure of a system component such as a sieve bed. The method and logic 900 determines the time to failure of the sieve bed based on a pressure / time slope linear regression without the use of oxygen data (although in other embodiments such as that of FIG. 6B, oxygen data can also be used in conjunction therewith). The monitored pressure data can be obtained from pressure sensor 224, which monitors the pressure at the sieve bed outlet and / or the inlet to the product tank. Other pressure monitoring locations can be used as well. The method and logic 900 uses a linear regression (such as that of Equation 2) to determine when (e.g., in hours) the sieve bed pressure will reach a high threshold of 34 PSI, at which point the system will shut down and fail due to excessive sieve bed pressure. Excessive sieve bed pressure indicates that the sieve bed is failing due to any number of factors (e.g., dusting, deterioration, moisture, contamination, etc.). An alarm is preferably generated prior to the predicted downtime to warn that service is required.

[0073] The method and logic 900 begins at block 644, described above in connection with FIG. 6B, whereby pressure sensor data associated with the sieve bed is collected at various times / intervals. At block 648, the pressure / time data is used to generate a pressure linear regression based on Equation 2, as described above in connection with FIG. 6B. The linear regression is used at block 902 to determine a predicted time (e.g., in hours) to failure when the sieve bed pressure will reach a threshold of 34 PSI (other values ​​may also be selected based on the size, capacity, and operating parameters of the system). The threshold represents a pressure (e.g., 34 PSI) that is beyond the normal operating pressure range of the sieve bed. At block 904, a 30-day window prior to failure is determined to provide advance warning of a failing component (e.g., the sieve bed). The 30-day window may be a moving window that is updated each time the method and logic 900 is performed, which may be at any desired time interval (e.g., every 12 or 24 hours, upon each start-up, etc.). If the system enters the 30 day window, an alarm is triggered in block 908 to provide a warning that a system component (e.g., a sieve bed) is close to failure. Thus, a linear regression of the pressure / time slope data can be used to determine a predicted time to failure.

[0074] FIG. 10 illustrates another embodiment of a method and logic 1000 for analyzing the health and / or predicted time to failure of a system component such as a sieve bed. The method and logic 1000 determines the time to failure of the sieve bed based on an oxygen purity linear regression and without the use of pressure data (although in other embodiments such as that of FIG. 6B, pressure data can also be used in conjunction therewith). The monitored oxygen purity (e.g., concentration) data can be obtained from an oxygen sensor 226 that monitors the oxygen purity at the sieve bed outlet and / or the inlet to the product tank. Other oxygen monitoring locations can be used as well. The method and logic 1000 uses a linear regression (such as that of Equation 1) to determine when (e.g., in hours) the sieve bed oxygen purity will fall below a threshold of 85%, at which point the system will shut down and fail due to low oxygen purity. Low oxygen purity indicates that the sieve bed is failing due to any number of factors (e.g., dusting, deterioration, moisture, contamination, etc.). An alarm is preferably generated prior to the predicted downtime to warn that service is required.

[0075] The method and logic 1000 begins at block 622, described above in connection with FIG. 6B, whereby oxygen purity data associated with the sieve bed is collected at various times / intervals. At block 624, the oxygen purity / time data is used to generate an oxygen purity linear regression based on Equation 1, as described above in connection with FIG. 6B. The linear regression is used at block 626 to determine a predicted time (e.g., in hours) to failure when the sieve bed pressure will reach a threshold purity value of 85% (other values ​​may also be selected based on the size, capacity, and operating parameters of the system). The threshold represents an oxygen purity below a lower limit of acceptable oxygen purity for the system (e.g., 85%). At block 628, a 30-day window prior to failure is determined to provide advance warning of a failing component (e.g., sieve bed). The 30-day window may be a moving window that is updated each time the method and logic 1000 is performed, which may be at any desired time interval (e.g., every 12 or 24 hours, upon each start-up, etc.). In block 636, if the system enters the 30 day window, an alarm is triggered to provide a warning that a system component (e.g., a sieve bed) is close to failure. Thus, a linear regression of the oxygen purity / time slope data can be used to determine the predicted time to failure.

[0076] FIG. 11 illustrates another embodiment of a method and logic 1100 for analyzing the health and / or predicted time to failure of a system component such as a sieve bed. The method and logic 1100 determines a predicted time to failure of a sieve bed based on a time to earlier failure based on a pressure / time slope (FIG. 9) and an oxygen purity / time slope (FIG. 10) linear regression analysis. The method and logic 100 obtains a predicted time to failure based on a pressure / time slope linear regression analysis from block 902 (FIG. 9) and a predicted time to failure based on an oxygen purity / time slope linear regression analysis from block 626 (FIG. 10). In block 1102, the two predicted times to failure are compared and the predicted time to failure that will occur sooner is selected. A 30-day window before failure is set based on the predicted time to earlier failure. In block 1104, when the system enters the 30-day window, an alarm is triggered to provide a warning that a system component (e.g., a sieve bed) is close to failure. Thus, the method and logic 1100 bases the predicted time to earlier failure on two different linear regression analyses.

[0077] It is an additional feature of the present disclosure that the control system 220 may further be utilized to achieve additional benefits associated with optimizing operation based on flow rate settings for the oxygen concentrator system. FIG. 8 illustrates a method 800 for predicting flow rate based on pressure and altitude sensor data or settings. This allows for cost savings by allowing measurement of flow rate through product tank pressure sensor data or feedback instead of requiring a dedicated flow sensor. Measurement of flow exiting the product tank can be used for smart operation of the concentrate system based on feedback associated with patient needs or demands. Smart operation includes reducing energy consumption, such as by starting pumps or compressors at slower speeds, reducing system pressure and cycle times. The accurately estimated or determined flow rate can also be displayed on an LED or LCD of the gas concentrator system. Also, system performance in terms of patient demand or flow rate can be stored and analyzed for trends at specific settings.

[0078] The method 800 begins at block 802 when an air valve (e.g., 204b or 204d) signal goes from 0 to 1 (or from open to closed, or vice versa). This valve transition indicates a transition time from high to low pressure for the sieve bed (e.g., 206a or 206b). During such change or transition, no product gas (e.g., oxygen gas) is flowing from the sieve bed to the product tank (e.g., 208a, 208b). Thus, at this point, any change in pressure in the product tank is due to patient demand (or the flow of product gas to the patient).

[0079] At block 804, an initial pressure value is determined and stored (e.g., in memory 306). In some embodiments, the initial value is determined after a predetermined wait time. Implementing a wait time before recording the initial value can prevent recording a potentially misleading value due to initial check valve leakage. At block 806, pressure data during operation is collected. Data points may be collected according to a predetermined interval (e.g., every 20 minutes or every 20 one-minute readings (other time intervals can also be used)). In one embodiment, four (4) pressure and time readings are obtained and stored in variables "Pressure(X):set_1" and "time stamp(Y):Time_1" (see also Equation 3 below). At block 808, it is determined whether a data analysis threshold has been met. If the data analysis threshold has not been met, the method returns to block 806 and continues pressure data collection. If the threshold has been met, the method proceeds to block 810. At block 810, an analysis is performed and a regression analysis is performed using the pressure data. An exemplary linear regression analysis on the pressure data is represented in Equation 3. [ka] [ka]

[0080] At block 812, it is determined whether the calculated pressure slope b is within a predetermined range (e.g., an average of five (5) consecutive calculated slopes b can also be used). The predetermined range may be an expected range based on the measured condition factors (e.g., for a 5 LPM flow setting, the predetermined or expected range = (b < -2.5346) & & (b > -2.9577)). If the measured / calculated slope b (or the average of the measured / calculated slopes b) is within the expected range, the method proceeds to block 814 and the initial timing values ​​are maintained and the method proceeds to block 824 and a new pressure slope is calculated after each valve change. If the calculated pressure slope is outside the expected predetermined range of slopes for the patient flow setting (e.g., (b<-2.5346) && (b>-2.9577), the method proceeds to block 816. In block 816, the difference between the actual pressure slope and the expected pressure slope is calculated by determining the midpoint of the expected range of slopes (e.g., midpoint=(min+max / 2)) and subtracting that midpoint from the calculated pressure decay slope (e.g., difference=midpoint-(calculated slope b)). In block 818, if slope b is outside the expected pressure slope range, the method proceeds to block 818. It is determined whether the slope b is trending towards the minimum (i.e., "min") or maximum (i.e., "max") of the range. If the slope b is trending towards the "min", the method proceeds to block 820 and a difference is subtracted from all expected slope ranges for all patient flow settings. If the actual slope is trending towards the "max", the method proceeds to block 822 and a difference is added for all expected slope ranges for all patient flow settings. In block 824, a new pressure slope b is calculated according to Equation 3 for the new pressure and time readings.

[0081] In this manner, the pressure decay in the product tank when no product gas is flowing into the product tank can be used to accurately measure the flow rate of product gas out of the product tank. This allows a simple pressure sensor to be used in conjunction with the logic disclosed herein to provide a flow rate measurement. The flow rate measurement can be used for more efficient start-up of the gas separation system, for diagnostic purposes, patient demand trending and use, etc.

[0082] Yet another additional feature of the present disclosure is to utilize pressure feedback and control system 220 to save energy by lowering the offset pressure to the compressor when a low flow mode is detected. For example, different valve settings for different flow rates can be detected using linear regression analysis of the pressure data. Under lower flow rate settings, power consumption can be reduced. This can increase the life of key components such as valves, compressors, and sieve bed materials by lowering the operating pressure of the unit. An additional benefit is reduced temperatures for the compressor and its output gas.

[0083] The present invention has been illustrated by the description of its embodiments, and while the embodiments have been described in considerable detail, it is not the intention of the description to restrict or in any way limit the scope of the appended claims to such details. Additional advantages and modifications will readily occur to those skilled in the art. Thus, the present invention in its broader aspects is not limited to the specific details, representative apparatus, and illustrative examples shown and described. Thus, departures may be made from such details without departing from the spirit or scope of the general inventive concept.

Claims

1. collecting oxygen and pressure data from a sensor of the gas concentrator; calculating a linear regression for the oxygen and pressure data; determining a predicted time to failure for one or more gas concentrator components based on the oxygen linear regression and a predetermined threshold oxygen purity level; determining the slope of the oxygen linear regression and the slope of the pressure linear regression; generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression; A method comprising:

2. generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression; using said oxygen linear regression to generate a time window that is a period of time that ends before a predicted failure of said component; The method of claim 1 , comprising:

3. Generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression further comprises: determining whether the slope of the pressure linear regression is positive or negative during the time window; The method of claim 2 , comprising:

4. Generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression further comprises: generating a sieve floor alarm if the slope of the pressure linear regression is positive at the beginning of the time window. The method of claim 3 , comprising:

5. and generating one or more component alarms based on the slope of the oxygen linear regression and the slope of the pressure linear regression, further comprising: generating a sieve floor alarm if the slope of the pressure linear regression is positive at the end of the time window. The method of claim 3 , comprising:

6. Generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression further comprises: generating a compressor alarm if the slope of the pressure linear regression is negative at the beginning of the time window. The method of claim 3 , comprising:

7. Generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression further comprises: generating a compressor alarm if the slope of the pressure linear regression is negative at the end of the time window. The method of claim 3 , comprising:

8. Generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression further comprises: generating a filter alarm if the slope of the pressure linear regression is negative at the beginning of the time window. The method of claim 3 , comprising:

9. Generating one or more component alarms based on a slope of the oxygen linear regression and a slope of the pressure linear regression further comprises: generating a filter alarm if the slope of the pressure linear regression is negative at the end of the time window. The method of claim 3 , comprising:

10. collecting oxygen and pressure data; calculating a linear regression for the oxygen and pressure data; determining a first predicted time to failure for one or more gas concentrator components based on the oxygen linear regression and a predetermined threshold oxygen purity level; determining a second predicted time to failure for the one or more gas concentrator components based on the pressure linear regression and a predetermined threshold pressure level; comparing the first and second predicted times to failure; setting a time window having a duration ending before an earlier of the first predicted time to failure or the second predicted time to failure of the one or more gas concentrator components based on the comparison; generating one or more component alarms when the system is within the time window; and A method comprising:

11. 11. The method of claim 10, wherein comparing the first and second predicted times to failure comprises determining which of the first and second predicted times to failure will occur sooner.

12. The method of claim 11 , wherein setting a time window based on the comparison comprises setting the time window based on a predicted time to earlier failure.

13. 11. The method of claim 10, wherein the logic for generating one or more component alarms when the system is within the time window comprises generating one or more component alarms when the system is within a 30 day time window.

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