System and method for concentrating a gas

Abstract

Embodiments of gas concentration systems and methods are provided. These systems and methods include configurations of hardware and software components to monitor various sensors associated with the systems and methods of concentrating gases described herein. These hardware and software components are further configured to utilize information obtained from the sensors throughout the system to perform certain data analysis tasks. Through the analysis, the system can, for example, calculate a time to failure of one or more system components, generate an alert to warn a user of an impending component failure, modify system settings to improve functionality under different environmental conditions, modify system operation to conserve energy, and / or determine an optimal settings configuration based on sensor feedback.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 052,869 (atty docket no. 12873-07041), entitled “System and Method for Concentrating Gas”, filed July 16, 2020.

[0003] This application incorporates by reference the following patent applications: U.S. Provisional Patent Application Serial No. 63 / 052,694 (atty docket no. 12873-07004) entitled "System and Method for Concentrating Gas"; U.S. Provisional Patent Application Serial No. 63 / 052,700 (atty docket no. 12873-07033) entitled "System and Method for Concentrating Gas"; U.S. Provisional Patent Application Serial No. 63 / 052,869 (atty docket no. 12873-07041) entitled "System and Method for Concentrating Gas"; U.S. Provisional Patent Application Serial No. 63 / 052,533 (atty docket no. 12873-07043) entitled "System and Method for Concentrating Gas"; and U.S. Provisional Patent Application Serial No. 63 / 052,647 (atty docket no. 12873-07043) entitled "System and Method for Managing Medical Devices". These patent applications (dock no. 12873-07044) were all filed on July 16, 2020. Background Technology

[0004] There are various applications for the separation of gas mixtures. For example, separating nitrogen from the atmosphere can provide a highly concentrated source of oxygen. These various applications include providing elevated concentrations of oxygen for medical patients and flight personnel. Therefore, it is desirable to provide systems for separating gas mixtures to provide concentrated product gases, such as breathing gases with a certain concentration of oxygen.

[0005] For example, several existing product gas or oxygen concentration systems and methods are disclosed in U.S. Patent 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 of Elyria, Ohio, and are incorporated herein by reference in their entirety.

[0006] Such systems are known to be 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 move from location to location, and typically include wheels or other mechanisms to facilitate movement. Portable systems are intended to be carried by a user, such as, for example, via a shoulder strap or similar attachment.

[0007] Failure of one or more components of these systems results in the system needing to be repaired or replaced without much prior notice. If the system cannot be quickly repaired or replaced, a suitable replacement must be found for the user. While such systems have hardware and software configured to monitor various sensors, this monitoring has been associated with the gas concentration process and limited diagnostics. Thus, it is desirable to provide systems and methods with improved data / diagnostic analysis and control capabilities, including but not limited to component failure time. SUMMARY

[0008] Gas concentration systems and methods are provided. The systems and methods can, for example, calculate a failure time of one or more system components, generate an alert to warn a user of an impending component failure, modify system settings to improve functionality under different environmental conditions, modify system operation to conserve energy, and / or determine an optimal setting configuration based on sensor feedback. Component-specific alerts can aid in diagnostics at the medical equipment provider level, increase service and repair efficiency, and save costs by reducing the probability of incorrectly replacing components.

[0009] In one embodiment, a system and method for calculating a failure time of at least one component of a gas concentration system is provided. The system and method includes determining an estimated failure time using linear regression of operating gas pressure and / or oxygen concentration time slope. In other embodiments, this determination can be made periodically to update or refresh the estimated failure time. Further, component failure can be identified by, for example, slope trends (positive or negative) of gas pressure and / or oxygen, and decay / linear regression of oxygen purity. Based on this identification, warnings, alerts, etc. can be generated to alert users and service personnel of which components are problematic.

[0010] In another embodiment, the system and method creates a baseline reading based on the altitude value. This includes determining an initial value related to the operation of the gas concentration system, including at least an average oxygen value, an average barometric pressure value, and an altitude value. In one embodiment, the system and method further includes establishing a value baseline for a given altitude value and determining if a change in altitude has occurred. If a change in altitude is determined, the system and method establishes a second value baseline for the measured altitude. If no change in altitude is determined, the system and method continues to collect data based on the values related to the operation of the gas concentration system. In another embodiment, the system and method further includes determining if a data analysis threshold has been met and, if so, performing an analysis and calculating an estimated component failure time.

[0011] In another embodiment, the system and method can further include determining a maintenance window, collecting data during the maintenance window, determining if a data analysis threshold has been met, performing a second analysis and calculating an estimated component failure time, diagnosing a failing component, and generating an alert based on the diagnosed failing component.

[0012] It is therefore an object of the present invention to determine a failure time or health of one or more components of a gas concentration system.

[0013] It is a further object to provide one or more component failure alerts or warnings based on the failure time or health analysis of at least one component.

[0014] It is a further object to provide one or more component failure or health alerts or warnings prior to a component failure.

[0015] It is a further object to provide a system and method for concentrating gas that performs a failure time or health analysis of at least one component of the system and uses that information to inform a user that maintenance is currently required or will be needed in the near future.

[0016] It is a further object to provide a system and method for concentrating gas that performs a failure time or health analysis of at least one component of the system and uses that information to inform a service or repair personnel that one or more components are currently or will be requiring service or repair in the near future.

[0017] It is a further object to provide a system and method for concentrating gas that performs a failure time 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.

[0018] These and other objects will be apparent from the drawings and description of the present invention provided below and above. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the application and, together with the general description of the application given above, and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0020] Figure 1 An embodiment of a gas concentration system and method is shown.

[0021] Figure 2 An embodiment of a pneumatic diagram of a gas concentration system and method.

[0022] Figure 3 An embodiment of a controller of an exemplary gas concentration system and method.

[0023] Figure 4 An example of an exemplary method for calculating a failure time of at least one component of a gas concentration system.

[0024] Figure 5 An example of an exemplary method for diagnosing a failure of one or more components in a gas concentration system.

[0025] Figures 6A-6B Various embodiments of methods and logic for diagnosing a failure, predicting a failure, and / or health of one or more components in a gas concentration system are illustrated.

[0026] Figure 7 A chart illustrating exemplary values of oxygen purity at extreme environmental conditions (e.g., about 10,000 feet above sea level) when modifying a transition time of an oxygen generation process.

[0027] Figure 8 An exemplary method of predicting an optimal flow rate setting of an oxygen concentration system is illustrated.

[0028] Figure 9 Another embodiment of methods and logic for diagnosing a failure, predicting a failure, and / or health of one or more components in a gas concentration system are illustrated.

[0029] Figure 10 Yet another embodiment of methods and logic for diagnosing a failure, predicting a failure, and / or health of one or more components in a gas concentration system are illustrated.

[0030] Figure 11 Still another embodiment of methods and logic for diagnosing a failure, predicting a failure, and / or health of one or more components in a gas concentration system are illustrated. DETAILED DESCRIPTION

[0031] As described herein, when one or more components are described or shown as connected, joined, affixed, coupled, attached, or otherwise interconnected, such interconnection can be direct as between the components or indirect, such as through the use of one or more intermediate components. Further, as described herein, reference to a member, component, or part does not limit the scope of encompassing components, members, elements, or parts to a single structural component, member, element, or part, but can include multiple components, members, elements, or parts.

[0032] For example, embodiments of the present application provide the ability to monitor sensors associated with the operation of an exemplary gas concentration system and utilize information obtained from the sensors throughout the system to perform certain data analysis tasks. Through analysis, the system can, for example, calculate a time to failure of one or more system components, generate an alert to warn a user of an impending component failure, modify system settings to improve functionality under different environmental conditions, modify system operation to conserve energy, and / or determine an optimal settings configuration based on sensor feedback. Each of these examples will be described in further detail herein.

[0033] By way of example, oxygen concentrators utilize pressure swing adsorption (PSA) technology to produce oxygen from air. The process is based on cyclic steps that allow the air pressure to vary from high to low and vice versa. The pressure differential is the driving force for oxygen generation and adsorbent regeneration. Some units operate based on a fixed turnaround time, where all cycles take the same duration. At the beginning of the unit's life, the sieve is fresh, and the pressure drop in the bed is at its minimum. 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 turnaround time slowly decreases as the unit's life. The adsorbent performance depends on its selectivity, nitrogen capacity, and diffusivity. Sieve regeneration is critical to the concentrator's life and the amount of oxygen produced. The absorbent becomes saturated over time with contaminants such as water vapor and carbon dioxide. This saturation is a degradation of the material's capacity because the contaminants tend to occupy sites in the zeolite structure, thus reducing the capacity to capture nitrogen. The main consequence of contamination is the impurities (nitrogen) breakthrough. The degradation of the sieve material performance directly impacts the amount of pure oxygen produced and, thus, the oxygen purity delivered to the patient. Sieve bed health degradation can result in a decrease in oxygen purity and gradually increase the rate of tank pressurization. The product tank air pressure, oxygen purity, and time feedback can be helpful in monitoring issues related to the sieve bed.

[0034] As the sieve bed's adsorption capacity decreases (due to attrition from moisture, contaminants, or wear), the amount of normal N2 that is adsorbed and captured in each cycle of the sieve bed will decrease (less bed capacity), causing excess nitrogen that is not adsorbed to breakthrough from the oxygen side of the sieve bed. This breakthrough increases the total amount of impure gas in the product tank (reservoir or accumulator), and thus increases the gas pressure in the product tank, and decreases the fraction of O2 from the total volume. The amount that leaves the product tank is typically controlled and fixed by a conservator valve timing for small volumes in portable oxygen concentrators or a flow meter in fixed oxygen concentrators. In pressure swing machines, the gas pressure for each swing is controlled, and thus the variable that changes is the rate at which the target gas pressure is reached. As the sieve bed wears, the swing time will decrease, as the greater the volume of impurities in the tank, the faster the target gas pressure is reached. To detect that the O2 degradation is due to sieve bed wear and not other faults, the gas pressure in the product tank (in time swing equipment) can be monitored. Only in the case of sieve bed wear is there a gradual increase in gas pressure coupled with a decrease in O2. The rate of gas pressure, O2 change, and time can be used as feedback for sieve bed health monitoring. Other unique component faults (e.g., pumps, valves, etc.) can also be determined by analyzing the gas pressure and / or oxygen sensor signals to generate alerts, warnings, and fault time estimates.

[0035] By way of example, one or more component-specific alerts associated with a component fault, failed component, or fault time can be determined by utilizing one or more sensor signals. For example, oxygen and / or gas pressure sensors can be used to determine sieve bed and / or compressor faults and / or predict fault times. Gas pressure signals can be used to determine main valve faults and / or predict fault times. Gas pressure waveforms into or out of the oxygen product tank coupled with low oxygen purity levels can be used to determine check valve faults and / or predict fault times. Linear regression analysis applied to gas pressure versus time slope data can determine a predicted system (or sieve bed) fault time when the operating gas pressure will exceed an acceptable value(s). Similarly, linear regression analysis applied to oxygen concentration versus time slope data can determine a predicted system (or sieve bed) fault time when the oxygen concentration will fall below an acceptable value(s). Electrical output signals of various sensors, including oxygen and gas pressure sensors, including their absence and / or out-of-range signals, can be used to determine sensor faults and / or predict fault times.

[0036] In one embodiment, the failure time is determined by using linear regression analysis to predict when a system and / or component will no longer provide sufficient 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 thereof, etc.) can be used to identify and predict the failure time of one or more components. This is because when system components such as valves, motors, pumps, screens, and sensors begin to fail or fail, they each have a unique effect or combination of effects on the overall system behavior and function, allowing the identification of (one or more) failing components and the prediction of failure time through linear regression analysis. These unique effects or combinations of effects are discussed in more detail below.

[0037] Figure 1 The illustration depicts one embodiment of an oxygen system 100, which includes component failure analysis and / or alarms. The system can be fixed, such as for use in a hospital or patient's home. The system can also be mobile or portable, such as for use by patients when they are away from home. The system can be configured to allow patients to carry it, such as, for example, via shoulder straps or via an arrangement that includes handles and wheels. Other mobility configurations are also included.

[0038] Oxygen system 100 includes a housing 102, which may be in one or more sections. Housing 102 includes multiple openings for the intake and exhaust of various gases, such as, for example, the intake of room air and the exhaust of nitrogen and other gases. Oxygen system 100 generally intakes room air, which consists primarily of oxygen and nitrogen, and separates nitrogen from the oxygen. Oxygen is stored in one or more internal or external tanks or product containers, and nitrogen is discharged back into the room air. For example, oxygen can be delivered to a patient via tubing and a nasal cannula through port 104. Alternatively, oxygen can be discharged via a replenishment port to an oxygen cylinder filling device, such as Invacare, Inc., Elyria, Ohio, USA.

[0039] Figure 2 An exemplary pneumatic block diagram of a gas concentration system 200 using pressure swing adsorption (PSA) is illustrated in one embodiment. The system may include multiple gas separation sieve beds 206a and 206b, multiple valves 204a, 204b, 204c and 204d, one or more product tanks 208a, 208b, and a retainer valve / device 218. In this embodiment, product tanks 208a and 208b are shown connected, thus acting as one product tank, but they could also be arranged to act as two product tanks. The system also includes a pressure source (compressor / pump) 203 and one or more filters 201 and silencers 202.

[0040] 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 of the gas mixture and passes one or more non-adsorbable components. Generally, the physical separation material is a molecular sieve having pores of uniform size and substantially the same molecular dimensions. These pores selectively adsorb molecules according to molecular shape, polarity, saturation, etc. In one embodiment, the physical separation medium is an aluminosilicate composition having 4 to 5 ANG (Angstrom) pores. More specifically, the molecular sieve is an aluminosilicate in sodium or calcium form, such as a 5A type zeolite. Alternatively, the aluminosilicate can have a higher silica to alumina ratio, larger pores, and affinity for polar molecules, for example, a 13x type zeolite. Zeolites adsorb nitrogen, carbon monoxide, carbon dioxide, water vapor, and other important components of air. Other types of separation media can also be used. Further, more than two sieve beds can be used. In other embodiments, the sieve beds 206a and 206b can be integrated in structure with one or more product tanks 208a and 208b, such as described in U.S. Patent No. 8,668,767, which is hereby fully incorporated by reference for this feature and other features.

[0041] In operation, as shown by the solid lines in Figure 2 During an exemplary fill cycle of the separation bed 206a, the air pressure source (pump / compressor) 203 draws indoor air through the filter 201 and to the valve 204d and the separation bed 206a, which produces oxygen at its output and into the product tank 208a, 208b through the valve 210a, as shown by the solid lines in

[0042] While the separation bed 206a is undergoing a fill cycle, the separation bed 206b can be undergoing a purge cycle to expel any nitrogen gas from a previous fill cycle. During the purge cycle, the previously pressurized separation bed 206b expels nitrogen gas through the valve 204a and out to the atmosphere through the muffler 202. The separation bed 206b is pressurized from its previous fill cycle. During the purge cycle, an amount of oxygen gas from the separation bed 206a or the product tank 208a, 208b can be fed into the separation bed 206b to pre-charge or pre-load the separation bed 206b with oxygen gas, which is controlled by the optional bleed valve 212 and fixed orifice 214 shown by the dashed lines in Figure 2

[0043] As shown in Figure 2 ​As shown by the dashed lines, once the separation bed 206a has been filled and / or the separation bed 206b has been purged, the control system 220 switches the valves 204a, 204b, 204c, and 204d so that the separation bed 206b enters a fill cycle while the separation bed 206a enters a purge cycle. In this state, the pump 203 directs room air into the separation bed 206b, which produces oxygen at its output and into the product tank 208a, 208b through the valve 210b. The separation bed 206a undergoes a purge cycle whereby it vents nitrogen and other gases to the atmosphere or room through the valve 204c and muffler 202. During the purge cycle, an amount of oxygen from the separation bed 206b or product tank 208a, 208b can be fed into the separation bed 206a to pre-load or pre-charge the separation bed 206a with oxygen, which now flows in the opposite direction compared to the previous cycle. The illustrated system also includes an exemplary air pressure equalization valve 216 that equalizes the air pressure in the two separation beds before the purge / fill cycle change.

[0044] The air pressure equalization valve 216 can allow for more efficient generation of oxygen by equalizing the air pressure between the outputs of the separation bed approaching the end of its fill cycle (e.g., 206a) and the separation bed approaching the end of its purge cycle (e.g., 206b). For example, the air pressure equalization valve 216 can be activated to equalize the air pressure between the outputs of the separation bed 206a and the separation bed 206b approaching the end of each purge / fill cycle. The operation of the air pressure equalization valve is further described in U.S. Patent Nos. 4,449,990 and 5,906,672, which are incorporated by reference in their entirety. In this manner, each separation bed 206a, 206b undergoes alternating fill and purge cycles as controlled by the control system 220 to generate oxygen.

[0045] As Figure 2 As shown in the middle, the optional reservoir valve / device 218 can be used to control the delivery of the product gas to the user 222. The reservoir valve 218 can switch between providing a concentrated product gas from the product tank 208a, 208b or venting to room air. For example, the reservoir valve 218 can 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 the sensing of the user’s inhalation and are typically determined by sensing a drop in air pressure (or increase in flow) near the user’s nose or mouth.

[0046] In this embodiment, the control system 220 can utilize various control processes to optimize the production and delivery of concentrated product gas, for example by controlling the activation, level, and relative timing of the gas pressure source 203 and valves 204a, 204b, 204c, 204d, 216, and 212. This is accomplished using one or more gas pressure sensors 224 and / or one or more oxygen concentration sensors 226. In one embodiment, the gas pressure and oxygen sensors 224 and 226 monitor the gas pressure and oxygen concentration into the product tank(s) 208A and 208(b). In other embodiments, the use of a timed cycle can be employed, where the cycle time is set at the factory. In still other embodiments, the cycle time can be determined based on the flow settings and / or sensed patient flow demand. In yet other embodiments, the cycle time can be determined during a start-up diagnostic procedure when the oxygen concentrator is turned on or powered up.

[0047] While Figure 2 While a pressure swing adsorption (PSA) cycle is illustrated, other gas concentration cycles can also be used, including vacuum swing adsorption (VSA), vacuum-pressure swing adsorption (VPSA), or other similar modes. The particular gas concentration mode is not important to the embodiments of the application described herein, so long as they are capable of producing concentrated gas, such as oxygen, for a user. Examples of the above-mentioned modes of operation are disclosed in, for example, U.S. Patent Nos. 9,266,053 and 9,120,050, which are incorporated by reference in their entirety.

[0048] Due to the mechanical nature of many system components, component wear and failure can occur. However, determining the time to failure and diagnosing which components have failed is time consuming and inefficient. Embodiments of the present invention analyze factors and component failures that can cause changes in oxygen purity and / or operating gas pressure. In one embodiment, the system and method analyzes gas 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 gas pressure over time coupled with a decrease in oxygen purity is a distinctive failure pattern associated with sieve bed wear. In other examples, operating gas pressure can be used alone to determine a predicted system or sieve bed failure time by using linear regression analysis on gas pressure versus time slope data to determine when it will increase beyond a threshold value (e.g., 34 PSI or some other value). Similarly, oxygen concentration / purity can be used alone to determine a predicted system or sieve bed failure time by using linear regression analysis on oxygen purity versus time slope data to determine when it will decrease below a threshold value (e.g., 85% or some other value). In yet another example, both gas pressure and oxygen linear regressions can be determined individually, and the predicted failure time can be set to the faster of the two determinations (e.g., the time to reach the low oxygen threshold value or the time to reach the high gas pressure threshold value), which will then trigger a warning that the sieve bed(s) need to be replaced or soon replaced.

[0049] Other distinguishable component faults can also be identified. These include stuck closed or open airside (main) valves (e.g., 204a, 204b, 204c, and / or 204d). These faults cause oxygen purity to drop (e.g., below 85%, below 73%, etc.) along with an immediate change in gas pressure on the input (or output) of the sieve bed (i.e., not gradual). Airside (main) valve leaks can be distinguished by low oxygen purity (e.g., below 85%, below 73%, etc.) out of the sieve bed and lower (out of range) sieve bed operating gas pressure. Check valve leaks can be distinguished by low oxygen purity (e.g., below 85%, below 73%, etc.) and a “v” shaped drop in product tank gas pressure. Piping leaks can be identified by low oxygen purity (e.g., below 85%, below 73%, etc.) and low (e.g., out of range) system gas pressure(s). Compressor wear can be distinguished by low oxygen purity (e.g., below 85%, below 73%, etc.) and low input and output (e.g., out of range) sieve bed gas pressures. Flow obstruction / restriction causes high system gas pressure (e.g., out of range) immediately and oxygen purity remains the same or becomes higher. Flow output setting changes can be identified by gas pressure and oxygen purity: an increase in flow setting causes gas pressure (e.g., system or product tank) to drop and oxygen purity to drop slightly, and a decrease in flow setting causes gas pressure (e.g., system or product tank) to rise and oxygen purity to rise. These and other sensed parameters can be used to diagnose component faults and / or fault times.

[0050] Figure 3A detailed view of one embodiment of a control system 220 with component failure logic is illustrated. While described herein with specific reference to an exemplary gas concentration system, it should be appreciated that the control system 220 can be readily adapted for additional systems. In certain embodiments, the control system 220 can be operatively connected to and / or in data communication with one or more sensors, such as gas pressure sensor(s) 224, oxygen sensor(s) 226, and / or an altitude sensor 312. Other sensors can also be used, including, for example, flow and / or temperature sensors. The gas pressure sensor(s) 224 can be associated with various components of an exemplary gas concentration system (e.g., gas concentration system 200) and configured to measure gas pressure in real-time or near real-time. In certain embodiments, the gas pressure sensor(s) 224 can include individual sensors configured to monitor and collect gas pressure data from multiple components. Similarly, the oxygen sensor(s) 224 can be associated with various components of an exemplary gas concentration system (e.g., gas concentration system 200) and configured to measure oxygen values in real-time or near real-time. In certain embodiments, the oxygen sensor(s) 226 can include individual sensors configured to monitor and collect gas pressure data from multiple components. The altitude sensor 312 can include an altimeter, a barometric pressure sensor, or the like, configured to measure the physical altitude of the gas concentration system. It should be appreciated that additional sensors can 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 to optimize the production and delivery of concentrated product gas by controlling the activation, level, and relative timing of the gas pressure source 203 and, in some embodiments, the valves 204a, 204b, 204c, 204d, 216, and 212 (see Figure 2 ) of the exemplary gas concentration system 200. The control system 220 can 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 can receive user inputs from a user input device, such as, for example, a computer, tablet, smart phone, or the like. In other embodiments, the user settings module 314 can receive user inputs via a control panel or the like associated with the exemplary gas concentration system (e.g., gas concentration system 200). In some embodiments, initial settings can be set by the manufacturer as “default” settings, which can be stored in memory (e.g., memory 306).

[0051] The control system 220 is also in communication with various input / output devices 316. Input and output devices include 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.), etc.), light emitting devices (e.g., lights, LEDs, etc.), speakers for audio output, microphones for audio input, cameras, etc.

[0052] In one embodiment, a gas pressure sensor is associated with the input and / or output of the sieve bed(s) 206a, 206b. The gas 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. The oxygen sensor can also be associated with the output(s) of one or more sieve beds 206a, 206b. Other components can also have gas pressure and / or oxygen sensors associated therewith.

[0053] The control system 220 includes at least logic 304 and memory 306 for component failure analysis. The logic 304 can further include one or more processors, etc., operable to perform calculations and other data analysis, such as, for example, regression analysis. It should be appreciated that the control system 220 can use additional types of data analysis performed via the logic 304, for example, linear regression (e.g., Y = bx + a), exponential trend line (e.g., Y = ae bx ), logarithmic trend line (e.g., Y = a*ln(x) + b), polynomial trend line (e.g., Y = b6x 6 +…+b1x+a), power trend line, etc. In certain embodiments, multiple data analysis can be used in combination, for example, a linear regression can be performed on a small portion of a polynomial model. In some embodiments, the control system 220 can utilize the logic 304 to perform analysis on data received from sensors associated with the gas concentration system, such as the gas pressure sensor(s) 224, the oxygen sensor(s) 226, and / or the altitude sensor(s) 312. By analyzing data received from such sensors, the control system 220 can identify and diagnose failing components before complete failure, thereby allowing for better diagnostic maintenance and repair, more efficient repair and maintenance, and cost savings related to replacing components in error (e.g., targeted repair).

[0054] Figure 4-6Figures illustrate various examples of the logic of such data analysis methods that can be performed by the controller 220. It will be appreciated that the illustrated methods and associated steps can be performed in different orders, with illustrated steps omitted, with additional steps added, or with combinations of reordered, combined, omitted, or additional steps.

[0055] In Figure 4 One embodiment of a method 400 for calculating a failure time of at least one component of a gas concentration system is shown in FIG. 4. The method 400 begins at 402, where an initial value is determined. The initial value can include an oxygen level measured by one or more oxygen sensors (e.g., oxygen sensor(s) 226), a gas pressure level measured by one or more gas pressure sensors (e.g., gas pressure sensor(s) 224), and / or an altitude measured by an altitude sensor (e.g., altitude sensor 312). It will be appreciated that the initial value can be determined at a particular time (e.g., 30 seconds after the gas concentration system is ready for operation), or, in the alternative, the initial value can include an average of several measurements taken after the gas concentration system is ready for operation. In some embodiments, block 402 additionally includes a post-warmup start check, e.g., determining a steady state of oxygen after an oxygen increase is complete rather than after oxygen reaches a predetermined readiness threshold (e.g., 85%). It is not uncommon for systems to require several cycles as a warmup or start to operate in steady state.

[0056] At block 404, a value baseline is established based on the altitude of the gas concentration system. The value baseline can include oxygen and gas pressure levels at one or more locations throughout the system (e.g., sieve beds, product tanks, valves, compressors, etc.). The altitude can be determined based on a measured altitude, or in some embodiments, can be determined based on a user setting (e.g., an altitude zone such as "high altitude" or low altitude or "sea level"). In one example: an exemplary gas concentration system can have 6 altitude zones: sea level, 4000 ft, 6000 ft, 8000 ft, 10000 ft, 13000 ft, each with a range of + / - 500 ft. Each range can have multiple combinations of various flow settings: 1, 2, 3, 4, or 5 LPM. With these settings, there can be up to 30 states. For each state, certain data points are collected to estimate a decay equation. The altitude zone can be used as feedback to increase gas pressure as needed (e.g., at lower flow settings, when fill / purge transition times become shorter, high altitude zone feedback can be used to increase time, and thus, gas pressure in the tank, to avoid valves sticking below minimum operating limits. Once the value baseline is determined at a given altitude, the baseline is stored (e.g., in memory 306) and the method 400 continues to block 406. At block 406, it is determined whether there has been a change in altitude. If there has been a change in altitude, the method 400 returns to block 404 to establish a new baseline based on the new altitude. It should be appreciated that the change in altitude can be measured, or can include a change in a user setting associated with the altitude (e.g., a change in altitude zone setting). In some embodiments, the altitude is measured during predetermined increments (e.g., every 24 hours). In certain embodiments, the change in altitude can be measured according to various altitude thresholds, e.g., according to predetermined altitude zones. In such embodiments, a change in altitude will only be determined if the measured altitude change defines an altitude range of a particular altitude zone. In some embodiments, the change in altitude can account for a hysteresis and tolerance of a given zone (e.g., + / - 500 ft).

[0057] If it is determined at block 406 that the altitude has not changed, the method continues to block 408. At block 408, data is collected and stored, such as additional oxygen and gas pressure values (e.g., in memory 306). It is appreciated that oxygen and gas pressure values for gas enrichment system components can be collected individually or in combination. For example, data can be collected over time and / or at particular time intervals for individual values and / or for sets of values. In some embodiments, data values are captured and stored according to a predetermined sampling time (e.g., every 1 hour). In some embodiments, the controller 220 can modify the sampling time based on operating conditions and / or measured values. For example, if the measured oxygen value drops below a threshold, the sampling time can be increased so as to more closely monitor changes in the measured values. In one example embodiment, if the measured oxygen value drops below 89%, the sampling time can be increased to collect samples every 10 minutes instead of every 1 hour. It is appreciated that additional sampling intervals and thresholds are contemplated and the above are provided by way of example only.

[0058] At block 410, it is determined whether a data analysis threshold has been met. The threshold for data analysis can vary depending on operating conditions, factory settings, and / or user settings. It is appreciated that additional sample sizes result in more accurate 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 continues to block 412.

[0059] At block 412, analysis of the data points is performed by the control system 220 (e.g., via logic 304) and a failure time is calculated. Various data analysis methods are contemplated herein. In some embodiments, the analysis includes performing a linear regression function (e.g., calculating the slope and intercept of oxygen and gas pressure as a function of time). An example linear regression analysis of oxygen values (O2) is expressed in Equation 1.

[0060] SumY = sum(O2) SumX = sum(Hours)

[0061] XY = O2 * Hours

[0062] XX = Hours 2

[0063] YY = O2 2

[0064] SumXX = sum(XX)

[0065] SumYY = sum(YY)

[0066] SumXY = sum(XY)

[0067] a = ((SumY * SumXX) - (SumX * SumXY)) / ((20 * SumXX) - (SumX) 2 )

[0068] b = ((20 * SumXY) - (SumX) * (SumY) / (20 * (SumXX) - (SumX) 2 )

[0069] Equation 1: Oxygen linear regression calculation

[0070] An exemplary linear regression analysis of air pressure values (air pressure) is expressed in Equation 2.

[0071] SumY = sum(Pressure)

[0072] SumX = sum(Hours)

[0073] XY = Pressure * Hours

[0074] XX = Pressure 2

[0075] XY = O2 2

[0076] SumXX = sum(XX)

[0077] SumYY = sum(YY)

[0078] SumXY = sum(XY)

[0079] a = ((SumY * SumXX) - (SumX * SumXY)) / ((20 * SumXX) - (SumX) 2 )

[0080] b = ((20 * SumXY) - (SumX) * (SumY) / (20 * (SumXX) - (SumX) 2 )

[0081] Equation 2: Gas pressure linear regression calculation

[0082] From the linear regression calculations described above, it is possible to determine a time-to-failure value. The time-to-failure can be expressed as the number of hours until one or more components of the gas concentration system fail. In certain embodiments, the linear regression calculations can be performed on components of the gas concentration system individually or in combination. In some embodiments, the linear regression calculations can 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 can be stored in memory (e.g., memory 306). The stored regression calculations can be compared or similarly analyzed to draw conclusions about and / or diagnose problems with one or more components of the gas concentration system. It will be appreciated that certain components of the gas concentration system can exhibit certain characteristics (e.g., abnormal oxygen or gas pressure values) that are indicative of degradation or failure of the component, which can result in suboptimal operation of the gas concentration system. In certain embodiments, a time-to-failure is calculated for a single component. In other embodiments, a time-to-failure is calculated for multiple components. In certain other embodiments, a time-to-failure is calculated for each component for which data is collected. By further analyzing the data, it is possible to diagnose failure of a particular component of the gas concentration system.

[0083] Figure 5 An exemplary method 500 for diagnosing failure of one or more components in a gas concentration system is illustrated. The method 500 begins at block 502, where the method 400 is performed. As described herein, the method 400 ends with performing an analysis of at least one component of the gas concentration system and calculating a time-to-failure. At block 504, a maintenance window is determined. Determining the maintenance window includes calculating a window of time before the calculated time-to-failure. This maintenance window of time can potentially allow for mitigation of a problem that, if left unchecked, would eventually result in component failure (e.g., a 30 day or 720 hour window of time before the predicted time-to-failure). In certain embodiments, the maintenance window is calculated in “moving hours,” which means that the window of time can change depending on updated data or time-to-failure.

[0084] At block 506, data is collected during the maintenance window. The data collected at block 506 can include oxygen and / or gas pressure data. At block 508, it is determined whether a data analysis threshold has been met. In some embodiments, the data analysis threshold can only require 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, a data analysis is performed and an updated time-to-failure is calculated. In certain embodiments, block 510 includes calculating a linear regression of the oxygen and / or gas 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 a failed component.

[0085] Based on analysis of the collected data and linear regression calculations, it is possible to identify a faulty component and diagnose the problem with the component. For example, if a linear regression of the gas pressure data calculated at the beginning of the maintenance window results in a negative slope, then a decay in the oxygen readings can be linked to a compressor or filter failure. Alternatively, if a linear regression of the gas pressure data at the beginning of the maintenance window produces a positive slope, then a decay in the oxygen readings indicates a sieve bed failure. As another example, if a linear regression of the gas pressure data calculated at the end of the maintenance window produces a negative slope, then a decay in the oxygen readings can be linked to a compressor or filter failure. In the alternative, if a linear regression of the gas pressure data calculated at the end of the maintenance window produces a positive slope, then a decay in the oxygen readings can be linked to a sieve bed failure.

[0086] Many additional diagnostics are contemplated herein. For example, a measured decrease in oxygen purity and an instantaneous gas pressure change can indicate an air side valve failure (e.g., stuck open / closed). Similarly, if a low oxygen purity is accompanied by a lower gas pressure on one side of the sieve bed, then it can indicate that one air side valve is leaking. When a low oxygen purity is observed along with a product tank gas pressure that is experiencing a "V" shaped drop, then it indicates a check valve leak. When a low oxygen purity is observed along with a low gas pressure, then it can indicate a line leak. When a low oxygen and low gas pressure is observed on both sides of the sieve bed, then it indicates a compressor failure. When an instantaneous gas pressure increase is observed while the oxygen purity remains constant or increases, then it indicates a flow restriction (e.g., restricted). When a gas pressure is accompanied by an instantaneous increase in oxygen purity, then it indicates a flow set change. When a gas pressure observed over time experiences a gradual increase in conjunction with a decrease in oxygen, then it indicates a sieve bed failure. It should be appreciated that the above examples are for illustrative purposes only and do not limit the scope of the embodiments.

[0087] In some embodiments, data observed from electrical signals can be used to diagnose component failures. For example, a drop in the voltage signal on the driver for each component can be used to assess whether the coil in the valve is faulty. Similarly, a voltage drop can indicate that a component in the printed circuit board (PCB) has a loose or broken wire.

[0088] It should be appreciated that block 512 can include additional data analysis (e.g., time since last maintenance, component manufacturing date, etc.) to further assist in diagnosing component failures.

[0089] After a diagnosis is made, the method continues to block 514. At step 514, an alert or warning is generated based on the diagnosis. In some embodiments, the alert includes information related to the diagnosis, such as, for example, the identified component, the latest calculated time-to-failure, the specific failure (which can include displaying an error code or message), the severity of the failure, etc. The alert can trigger certain activities associated with the gas concentration system. For example, the alert can cause a chime, a beep, or similar sound to alert a user of the detected failure. In certain embodiments, the alert is displayed on a display associated with the gas concentration system. In some embodiments, the failure alert can trigger a notification to be sent to a user’s smartphone. Similarly, a notification can also be sent to a provider responsible for the repair and maintenance of the gas concentration system. In certain embodiments, the alert information can be transmitted to a server via an internet connection or the like for storage and analysis by the provider or manufacturer. Analysis of the alert information can provide valuable information related to the operation of the gas concentration system in different environments.

[0090] Figure 6A Another exemplary method 600 for diagnosing a failure of one or more components in a gas concentration system is illustrated. The method 600 begins at block 602, where the method 400 is performed. As described herein, the method 400 ends with performing an analysis and calculating a time-to-failure for at least one component of the gas concentration system. At block 604, a maintenance window is determined. Determining the maintenance window includes calculating a window of time prior to the calculated time-to-failure that can potentially allow for mitigation of issues that, if left unchecked, would eventually result in a failure of the component (e.g., 30 days or 720 hours before failure). In certain embodiments, the maintenance window is calculated in “moving hours,” which means that the window of time can change depending on updated data.

[0091] At block 606, data is collected during the maintenance window. The data collected in block 606 can include oxygen and / or gas pressure data. At block 608, it is determined whether a data analysis threshold has been met. In some embodiments, the data analysis threshold can only require 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 610. At block 610, data analysis is performed and an updated failure time is calculated. In certain embodiments, block 610 includes calculating a linear regression of the oxygen and / or gas pressure data for one or more components of the gas concentration system. At block 612, the method diagnoses a failing component. Based on the diagnosis, the method can proceed to block 614, where a mitigation activity is performed. The mitigation activity can include any activity that engages to potentially resolve a problem with one or more components of the gas concentration system. For example, high altitudes can result in lower oxygen purity as the overall working gas pressure decreases. If these conditions exist and a related failure is detected and / or diagnosed, the oxygen concentration system (via controller 220) can modify the valve transition time, thereby optimizing oxygen purity production under given environmental conditions. Adjusting the gas pressure equalization time and transition time can be done to increase oxygen purity under extreme environmental conditions (e.g., high altitudes), high gas pressure increases due to wear and / or failure of the sieve beds, low gas pressure and / or generally low oxygen due to wear and / or failure of the compressor. For each of these scenarios, gas pressure can be maintained by changing valve timing and used as the primary feedback along with altitude. For example, an increase in transition time / gas pressure equalization time can increase system and / or component gas pressure, while a decrease in transition time / gas pressure equalization time can decrease system and / or component gas pressure. Figure 7 FIG. 3 illustrates exemplary values of oxygen purity under extreme environmental conditions (e.g., approximately sea level 10,000 ft) when modifying the transition time.

[0092] Figure 6BOne embodiment 620 of logic for analyzing sieve bed and / or compressor health is illustrated. This embodiment uses regression analysis as previously described to determine a predicted failure time, and uses moving slope analysis to identify components (e.g., sieve beds and / or compressors) that are failing or predicted to fail soon. The logic begins in blocks 622 and 644, where oxygen and air pressure sensor data is collected during warm-up or normal system operation. In blocks 624 and 648, a linear regression analysis as previously described is performed on each set of data. In block 626, the logic calculates a predicted failure time (e.g., in hours) by determining when the oxygen purity level will be at or below 83% purity (e.g., concentration) from the regression analysis. In block 638, the logic calculates a 30-day moving window prior to the predicted failure time. This window is moving because in one embodiment, the logic repeatedly calculates and updates the linear regression with the system in blocks 624 and 648. The 30-day window establishes a pre-notice prior to the predicted failure time in order to allow for scheduling of maintenance before the system suffers a component failure. In block 630, the logic checks the air pressure trend (e.g., the moving slope of the air pressure linear regression analysis in block 648) at the beginning of the 30-day window. If a positive air pressure slope trend is indicated in block 632 (e.g., the air pressure is increasing over time), the logic proceeds to blocks 634 and 636, where a decay in oxygen purity is associated with the health of the sieve bed and an alert is provided. If a negative air pressure slope trend is indicated in block 638 (e.g., the air pressure is decreasing over time), the logic proceeds to blocks 640 and 642, where a decay in oxygen purity is associated with the health of the compressor (and / or inlet filter) and an alert is provided.

[0093] In block 650, the logic again checks the air pressure trend (e.g., the moving slope of the air pressure linear regression analysis in block 648) at the end of the 30-day window. If a positive air pressure slope trend is indicated in block 652 (e.g., the air pressure is increasing over time), the logic proceeds to blocks 654 and 656, where a decay in oxygen purity is associated with the health of the sieve bed and an alert is provided. If a negative air pressure slope trend is indicated in block 658 (e.g., the air pressure is decreasing over time), the logic proceeds to blocks 660 and 662, where a decay in oxygen purity is associated with the health of the compressor (and / or inlet filter) and an alert is provided. While this embodiment illustrates checking system health at the beginning and end of the 30-day window, any appropriate interval can be used and any number of health checks can be performed. In this way, the user and / or provider are given specific advance warning of which system component(s) are predicted to fail or have failed.

[0094] Figure 9Another embodiment of a method and logic 900 for analyzing the health of system components such as the sieve bed(s) and / or predicting the time to failure is illustrated. The method and logic 900 determines the time to failure of the sieve bed(s) based on air pressure / time slope linear regression without using oxygen data (although in other embodiments, oxygen data can also be used such as Figure 6B The monitored air pressure data can be obtained from the air pressure sensor(s) 224 that monitor the air pressure at the sieve bed(s) outlet and / or product tank inlet. Other air pressure monitoring locations can also be used. The method and logic 900 uses linear regression (such as the linear regression of Equation 2) to determine when the sieve bed air pressure will reach a high threshold of 34 PSI (e.g., in hours) at which point the system will shut down and fail due to excessive sieve bed air pressure. Excessive sieve bed air pressure is indicative of the sieve bed(s) failing due to a variety of factors (e.g., dust, degradation, moisture, contamination, etc.). Preferably, an alert is generated prior to the predicted time to failure to warn that repairs are needed.

[0095] The method and logic 900 begins at block 644, which was previously described in conjunction with Figure 6B whereby air pressure sensor data associated with the sieve bed(s) is collected at various times / with various intervals. In block 648, the air pressure / time data is used to generate an air pressure linear regression based on Equation 2, as previously described in conjunction with Figure 6B In block 902, the linear regression is used to determine a predicted time to failure (e.g., in hours) of the sieve bed air pressure when it will reach a threshold of 34 PSI (other values can also be selected based on the size, capacity, and operating parameters of the system). The threshold represents an air pressure (e.g., 34 PSI) that is outside of the normal operating air pressure range of the sieve bed(s). In block 904, a 30-day window prior to failure is determined to provide an early warning of a failing component (e.g., the sieve bed(s)). The 30-day window can be a moving window that is updated each time the method and logic 900 is executed, which can be at any desired time interval (e.g., at each start-up, every 12 or 24 hours, etc.). In block 908, an alert is triggered when the system enters the 30-day window to provide a warning that the system component (e.g., the sieve bed(s)) is approaching failure. Thus, the linear regression of the air pressure / time slope data can be used to determine a predicted time to failure.

[0096] Figure 10Another embodiment of a method and logic 1000 for analyzing the health of system components, such as sieve bed(s), and / or predicting a time to failure is illustrated. The method and logic 1000 determines a time to failure of the sieve bed(s) based on oxygen purity linear regression and does not use gas pressure data (although in other embodiments, such as Figure 6B the embodiment of FIG. 1, gas pressure data can also be used therewith). The monitored oxygen purity (e.g., concentration) data can be obtained from the oxygen sensor(s) 226 that monitor the oxygen purity at the sieve bed(s) outlet and / or product tank inlet. Other oxygen monitoring locations can also be used. The method and logic 1000 uses linear regression, such as the linear regression of Equation 1, to determine when the sieve bed(s) oxygen purity will drop below a threshold of 85%, at which point the system will shut down and fail due to low oxygen purity. The low oxygen purity indicates that the sieve bed(s) will fail due to a variety of factors (e.g., dust, degradation, moisture, contamination, etc.). Preferably, an alert is generated prior to the predicted time to failure to warn that repairs are needed.

[0097] The method and logic 1000 begins at block 622, which was previously described in conjunction with Figure 6B FIG. 1, whereby oxygen purity data associated with the sieve bed(s) is collected at various times / over intervals. In block 624, the oxygen purity / time data is used to generate an oxygen purity linear regression based on Equation 1, as previously described in conjunction with Figure 6B FIG. 1. The linear regression is used in block 626 to determine a predicted time to failure (e.g., in hours) when the sieve bed(s) gas pressure will reach a purity threshold of 85% (other values can also be selected based on the size, capacity, and operating parameters of the system). The threshold represents an oxygen purity below which the system has an acceptable lower limit of oxygen purity (e.g., 85%). In block 628, a 30-day window prior to failure is determined to provide a warning of a failing component (e.g., sieve bed(s)). The 30-day window can be a moving window that is updated each time the method and logic 1000 is executed, which can be at any desired time interval (e.g., each time the system is started, every 12 or 24 hours, etc.). In block 636, an alert is triggered when the system enters the 30-day window to provide a warning that the system component (e.g., sieve bed(s)) is approaching failure. Thus, the linear regression of the oxygen purity / time slope data can be used to determine a predicted time to failure.

[0098] Figure 11 Another embodiment of a method and logic 1100 for analyzing the health of system components, such as sieve bed(s), and / or predicting a time to failure is illustrated. The method and logic 1100 determines a time to failure of the sieve bed(s) based on gas pressure / time slope Figure 9) and oxygen purity / time slope Figure 10 ) linear regression analysis based on faster failure time to determine the predicted failure time of the sieve bed(s). The method and logic 100 obtains the predicted failure time based on the gas pressure / time slope linear regression analysis from block 902( Figure 9 ) and obtains the predicted failure time based on the oxygen purity / time slope linear regression analysis from block 626( Figure 10 ) In block 1102, the two predicted failure times are compared and the predicted failure time that occurs faster is selected. The window of 30 days before failure is set based on the faster predicted failure time. In block 1104, when the system enters the 30-day window, an alarm is triggered to provide a warning that the system component (e.g., the sieve bed(s)) is approaching failure. Thus, the method and logic 1100 is based on the faster predicted failure time based on two different linear regression analyses.

[0099] An additional functionality of the present disclosure is that the control system 220 can be further used to implement additional benefits related to optimizing operation based on the flow rate settings of the oxygen concentration system. Figure 8 Method 800 is illustrated to predict flow rate based on gas pressure and altitude sensor data or settings. This allows for measuring flow rate by allowing measurement of flow rate via product tank gas pressure sensor data or feedback without the need for a dedicated flow sensor, thereby saving cost. Based on feedback associated with patient needs or requirements, the measurement of flow rate out of the product tank can be used for intelligent operation of the concentration system. Intelligent operation includes reducing energy consumption by running the pump or compressor at lower speeds, reducing system gas pressure and cycle time, etc. The accurately estimated or determined flow rate can also be displayed on the LED or LCD of the gas concentration system. Also, the system performance in terms of patient requirements or flow rate can be stored and analyzed for trends under specific settings.

[0100] Method 800 begins at block 802 when the air valve (e.g., 204b or 204d) signal changes from 0 to 1 (or from open to closed or vice versa). This valve transition indicates the transition time of the sieve bed (e.g., 206a or 206b) from high gas pressure to low gas pressure. During such a change or transition, there is no product gas (e.g., oxygen) flowing from the sieve bed to the product tank (e.g., 208a, 208b). Thus, at this time, any change in gas pressure in the product tank is due to the patient’s requirements (or outflow of product gas to the patient).

[0101] At block 804, an initial air 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, air pressure data during the collection operation is collected. Data points can be collected according to a predetermined interval (e.g., every 20 minutes or every 20 1 -minute readings; other time intervals can also be used). In one embodiment, four (4) air pressure and time readings are taken and stored in variables "Pressure(X): set_1" and "timestamp(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 to continue air pressure data collection. If the threshold has been met, the method proceeds to block 810. At block 810, an analysis is performed using the air pressure data and a regression analysis is performed. An exemplary linear regression analysis of the air pressure data is expressed in Equation 3.

[0102] Calculate the slope b of the air pressure readings:

[0103] SumY = sum(set_1)

[0104] SumX = sum(Time_1)

[0105] XY = set_1 * Time_1

[0106] XX = (Time_1) 2

[0107] YY = (set_1) 2

[0108] SumXX = sum(XX)

[0109] SumYY = sum(YY)

[0110] SumXY = sum(XY)

[0111] b = ((4*SumXY) - (SumX)*(SumY)) / (4*(SumXX) - (SumX) 2 )

[0112] Equation 3: Gas pressure decay (slope)

[0113] At block 812, it is determined whether the calculated gas pressure slope b is within a predetermined range (an average of, for example, five (5) consecutive calculated slopes b can also be used). The predetermined range can be based on an expected range of measured conditions 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 average of measured / calculated slope b) is within the expected range, the method proceeds to block 814, where the initial timing value is maintained, and the method proceeds to block 824, where a new gas pressure slope is calculated after each valve change. If the calculated gas pressure slope is outside of the expected predetermined slope range for the patient flow setting (e.g., (b <-2.5346) && (b > -2.9577), the method proceeds to block 816. At block 816, a delta between the actual and expected gas pressure slopes is calculated by determining a midpoint of the expected slope range (e.g., midpoint = (min + max) / 2), subtracting the midpoint from the calculated gas pressure decay slope (e.g., delta = midpoint - (calculated slope b)). At block 818, it is determined whether the slope b is towards the minimum (i.e., “min”) or maximum (i.e., “max”) of the expected gas pressure slope range. If the slope b is towards “min”, the method proceeds to block 820, where the delta is subtracted from all expected slope ranges for all patient flow settings. If the actual slope is towards “max”, the method proceeds to block 822, where the delta is added to all expected slope ranges for all patient flow settings. At block 824, a new gas pressure slope b is calculated for the new gas pressure and time reading according to equation 3.

[0114] In this way, when no product gas is flowing into the product tank, the decay of gas pressure in the product tank can be used to accurately measure the flow rate of product gas exiting the product tank. This allows for a simple gas pressure sensor to be used in conjunction with the logic disclosed herein to provide flow rate measurements. The flow rate measurements can be used for more efficient operation of the gas separation system, diagnostic purposes, patient demand trend analysis, and usage, among others.

[0115] Yet another additional functionality of the present disclosure is to utilize the control system 220 to save energy by utilizing gas pressure feedback to reduce the transition gas pressure on the compressor when a low flow mode is detected. For example, linear regression analysis of the gas pressure data can be used to detect different valve settings for different flow rates. At lower flow settings, power consumption can be reduced. This can increase the life of the primary components, such as the valves, compressor, and sieve bed material, by reducing the operating gas pressure of the unit. An additional advantage is that the temperature of the compressor and its output gas is reduced.

[0116] While the application has been illustrated by a description of the embodiments thereof, and while these embodiments have been described in considerable detail, it is not the intention that the application be limited thereto. Additional advantages and modifications will readily occur to those skilled in the art. Therefore, the application in its broader aspects is not limited to the specific details, representative apparatus, and illustrative examples shown and described. Accordingly, departures can be made from such details without departure from the spirit or scope of the general inventive concept.

Claims

1. A system for concentrating gas, comprising: Multiple sieve beds; Oxygen sensor; Barometric pressure sensor; The controller includes: Logic used to collect oxygen and air pressure data; The logic used to calculate linear regression of oxygen and air pressure data; Logic for determining the predicted failure time of one or more gas concentration components based on oxygen linear regression calculations and a predetermined threshold oxygen purity level. The logic used to determine the slope of the barometric linear regression calculation; and The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure.

2. The system according to claim 1, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure includes: The logic for generating a time window before predicted component failures is calculated using oxygen linear regression.

3. The system according to claim 2, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic used to determine whether the slope of the barometric linear regression calculation is positive or negative during the time window.

4. The system according to claim 3, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic used to generate the first screening bed alarm if the slope calculated by the linear regression of air pressure is positive at the start of the time window.

5. The system according to claim 3, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic used to generate a second screening bed alarm if the slope calculated by the linear regression of air pressure is positive at the end of the time window.

6. The system according to claim 3, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic is used to generate the first compressor alarm if the slope of the linear regression calculation of air pressure is negative at the start of the time window.

7. The system according to claim 3, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic is used to generate a second compressor alarm if the slope of the linear regression calculation of air pressure is negative at the end of the time window.

8. The system according to claim 3, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic is used to generate a first filter alarm if the slope calculated by the barometric linear regression is negative at the start of the time window.

9. The system according to claim 3, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic is used to generate a second filter alarm if the slope calculated by the barometric linear regression is negative at the end of the time window.

10. A health monitoring system for a gas concentrator, comprising: The controller includes: Logic used to collect oxygen and air pressure data; The logic used to calculate linear regression of oxygen and air pressure data; Logic used to determine the predicted failure time of one or more gas concentrator components based on oxygen linear regression calculations and a predetermined threshold oxygen purity level; The logic used to determine the slope of the barometric linear regression calculation; and The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure.

11. The system according to claim 10, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure includes: The logic for generating a time window before predicted component failures is calculated using oxygen linear regression.

12. The system according to claim 11, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic used to determine whether the slope of the barometric linear regression calculation is positive or negative during the time window.

13. The system according to claim 12, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic used to generate the first screening bed alarm if the slope calculated by the linear regression of air pressure is positive at the start of the time window.

14. The system according to claim 12, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic used to generate a second screening bed alarm if the slope calculated by the linear regression of air pressure is positive at the end of the time window.

15. The system according to claim 12, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic is used to generate the first compressor alarm if the slope of the linear regression calculation of air pressure is negative at the start of the time window.

16. The system according to claim 12, wherein, The logic for generating alarms for one or more components based on the slopes calculated using linear regression of oxygen and linear regression of air pressure further includes: The logic is used to generate a second compressor alarm if the slope of the linear regression calculation of air pressure is negative at the end of the time window.

17. A gas concentration system, comprising: Multiple sieve beds; Oxygen sensor; Barometric pressure sensor; The controller includes: The logic for collecting oxygen and air pressure data; The logic for calculating linear regression of oxygen and air pressure data; The logic for determining the first predicted failure time of one or more gas concentrator components based on oxygen linear regression calculation and a predetermined threshold oxygen purity level. The logic for determining the second predicted failure time of one or more gas concentrator components based on linear regression calculation of gas pressure and a predetermined threshold gas pressure level. The logic that compares the first and second predicted failure times; The logic for setting a time window based on the aforementioned comparison; and Logic for generating one or more component alerts when the system is within a time window.

18. The system according to claim 17, wherein, The logic that compares the first and second predicted failure times includes logic that determines which of the first and second predicted failure times will occur faster.

19. The system according to claim 18, wherein, The logic for setting a time window based on the comparison includes the logic for setting a time window based on the predicted failure time that occurs faster.

20. The system according to claim 17, wherein, The logic for generating one or more component alerts when the system is within a time window includes the logic for generating one or more component alerts when the system is within a 30-day time window.

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