System and method for predicting distance failure time of a lamp

Abstract

A system for operating a halogen lamp includes a sensor configured to detect a parameter of the lamp, and a processor to receive the parameter detected by the sensor for a data collection time period and generate a model based at least in part on the received parameter detected by the sensor. The processor uses the model to predict a time of at least one pre-failure event at which a pre-failure criterion will be met.

Description

Priority requirements

[0001] This application claims priority to U.S. nonprovisional patent application No. 18 / 597544, filed March 6, 2024, entitled “SYSTEMS AND METHODS FORPREDICTING A TIME TILL FAILURE OF A LAMP”, the entire contents of which are incorporated herein by reference. Background Technology

[0002] The field of this disclosure generally relates to systems and methods for predicting failures of electronic equipment, and more specifically, to systems and methods for predicting in advance the time to failure of a lamp.

[0003] Light, particularly halogen lamps, is used in a variety of research and technical applications by providing light to instruments such as spectrometers, which are widely used in a wide range of fields for detecting the chemical composition of samples, such as semiconductor microchip manufacturing and contaminant detection in gas production. For example, halogen lamps are used in spectrometer applications because the light they emit can have a broad spectral distribution spanning the infrared, visible, and / or ultraviolet ranges. Furthermore, analytical instruments, including halogen lamps, are used in applications requiring real-time monitoring of continuous processes. These continuous processes demand high reliability and continuous operation from the analytical instruments. Therefore, it is necessary to predict instrument failures in advance so that users can take preventative measures or schedule downtime for maintenance procedures. The failure mechanisms of halogen lamps, including filament breakage, are sudden and without warning. Currently, no known system can provide advance prediction of the time to failure for electronic equipment such as halogen lamps.

[0004] Known components and methods are inadequate in some respects in providing early predictions of the time to failure of electronic devices. Summary of the Invention

[0005] In one aspect, a system for operating a halogen lamp is provided. The system includes a sensor configured to detect parameters of the lamp, and a processor communicatively coupled to at least one memory storing instructions, which, when executed by the processor, cause the processor to receive parameters detected by the sensor during a data collection period and to generate a model based at least in part on the received parameters detected by the sensor during the data collection period. When executed by the processor, the instructions cause the processor to use the model to predict the time to at least one pre-failure event when a pre-failure criterion will be met.

[0006] On the other hand, a method for predicting the time before a light lamp fails is provided. The method includes receiving parameters detected by sensors during a data collection period and generating a model based at least in part on the received parameters detected by sensors during the data collection period. The method also includes using the model to predict the time before at least one pre-failure event when pre-failure criteria are met.

[0007] In another aspect, a non-transitory computer-readable medium is provided, embodying computer-executable instructions for implementing operation of a halogen lamp. When executed by at least one processor, the computer-executable instructions cause the at least one processor to receive parameters detected by a sensor during a data collection period, and to generate a model based at least in part on the received parameters detected by the sensor during the data collection period. The computer-executable instructions cause the at least one processor to use the model to predict the time to at least one pre-failure event when pre-failure criteria are met. Attached Figure Description

[0008] These and other features, aspects and advantages of this disclosure will become better understood when read in conjunction with the accompanying drawings, in which the same characters denote the same parts throughout the drawings.

[0009] Figure 1A This is a perspective view of an exemplary halogen lamp assembly.

[0010] Figure 1B It is used for Figure 1A A perspective view of an example filament assembly used in conjunction with known halogen lamp assemblies.

[0011] Figure 2A It is used for Figure 1A -B is a perspective view of an example faulty coil of the filament assembly of a halogen lamp assembly.

[0012] Figure 2B It is used for Figure 1A -B shows a perspective view of an example fault lead of the filament assembly of a halogen lamp assembly.

[0013] Figure 3 This is an example graph showing the relationship between current and lifespan of an exemplary electronic device.

[0014] Figure 4 This is a graph showing an example mortality curve for halogen lamp lifespan.

[0015] Figure 5 This is an exemplary graph showing the relationship between the lifespan and light output of a halogen lamp as a percentage of the design voltage.

[0016] Figure 6 This is a schematic diagram of an exemplary electronic lifetime prediction system.

[0017] Figure 7 This is an example graph showing the relationship between current and lifetime for an exemplary electronic device, illustrating different stages, including the startup stage, main stage, pre-failure stage, and final stage, as well as lifetime events.

[0018] Figure 8 This is another example graph showing the current versus lifespan relationship for several different example electronic devices, illustrating the startup phase.

[0019] Figure 9 This is another example graph showing the current versus lifespan relationship for several different example electronic devices, illustrating the startup and main phases.

[0020] Figure 10 This is another example graph showing the current-lifetime relationship for several different example electronic devices, illustrating the main stages for both the data collection curve and the model prediction curve.

[0021] Figure 11 This is another example graph showing the current-lifetime relationship for several different exemplary electronic devices, illustrating the start-up and main phases for both the data collection curve and the model prediction curve, with the main phase including the data collection sub-phase and the model sub-phase.

[0022] Figure 12 This is another example graph illustrating the relationship between current and lifespan in an example electronic device, showing the pre-failure stage.

[0023] Figure 13 This is another example graph illustrating the relationship between current and lifespan in an example electronic device, showing the final stage.

[0024] Figure 14 This is another example graph showing the relationship between current and lifetime for multiple electronic devices, illustrating the stages and lifetime events for both data collection curves and model prediction curves.

[0025] Figure 15 This is another example graph showing the relationship between current and lifetime for multiple electronic devices, illustrating the stages and lifetime events for both data collection curves and model prediction curves.

[0026] Figure 16 It is used with Figure 6 The flowchart shown is a method for operating electronic devices used in an example electronic lifetime prediction system.

[0027] Figure 17 It is used with Figure 6 The example electronic device lifetime prediction system shown is another method flowchart for operating electronic devices.

[0028] Figure 18 This is a block diagram of an example user's computer device.

[0029] Figure 19 This is a block diagram of an example server computer device. Detailed Implementation

[0030] Several terms will be used in the following specification and claims, and these terms shall be defined to have the following meanings.

[0031] The singular forms “a,” “one,” and “the” include plural references unless the context clearly indicates otherwise.

[0032] "Optional" or "optionally" means that the event or situation described below may or may not occur, and the description includes instances where the event occurs and instances where it does not occur.

[0033] Approximate language, as used herein throughout the specification and claims, may be applied to modify any permissible variation without altering the essential function associated with it. Therefore, values ​​modified by one or more terms such as “about,” “approximately,” and “substantially” are not limited to the specified precise values. In at least some cases, approximate language may correspond to the precision of the instrument measuring the value. Scope limitations may be combined and / or interchanged herein and throughout the specification and claims; such scopes have been identified and include all subscopes contained therein, unless otherwise indicated by context or language.

[0034] As used herein, the terms “processor” and “computer”, and related terms such as “processing device,” “computing device,” and “controller”, are not limited to those integrated circuits referred to in the art as computers, but broadly refer to microcontrollers, microcomputers, programmable logic controllers (PLCs), application-specific integrated circuits (ASICs), and other programmable circuits, and these terms are used interchangeably herein. In the embodiments described herein, memory may include, but is not limited to, computer-readable media such as random access memory (RAM) and computer-readable non-volatile media such as flash memory. Alternatively, optical disc-read-only memory (CD-ROM), magneto-optical disc (MOD), and / or digital versatile optical disc (DVD) may also be used. Furthermore, in the embodiments described herein, additional input channels may be, but are not limited to, computer peripherals associated with an operator interface, such as a mouse and keyboard. Alternatively, other computer peripherals may be used, which may include, for example, but are not limited to, scanners. Furthermore, in exemplary embodiments, additional output channels may include, but are not limited to, operator interface monitors.

[0035] Furthermore, as used herein, the terms “software” and “firmware” are interchangeable and include any computer program stored in memory for execution by a personal computer, workstation, client, and server.

[0036] As used herein, the term "non-transitory computer-readable medium" is intended to represent any tangible computer device implemented in any way or by any technique for short-term and long-term information storage, such as computer-readable instructions, data structures, program modules and submodules, or other data in any device. Therefore, the methods described herein can be encoded as executable instructions embodied in a tangible, non-transitory computer-readable medium, including but not limited to storage devices and / or memory devices. When executed by a processor, such instructions cause the processor to perform at least a portion of the methods described herein. Furthermore, as used herein, the term "non-transitory computer-readable medium" includes all tangible, computer-readable media, including but not limited to non-transitory computer storage devices, including but not limited to volatile and non-volatile media, and removable and non-removable media such as firmware, physical and virtual storage, CD-ROMs, DVDs, and any other digital source such as networks or the Internet, as well as undeveloped digital means, with the sole exception of transient propagation signals.

[0037] Methods for detecting lamp faults merely monitor voltage and / or current fluctuations to detect the presence of a short circuit, such as the filament coil sections fusing together, providing a very brief warning of subsequent lamp failure. For example, some methods simply compare the measured voltage to a stored reference value to detect the onset of a lamp fault without providing any prediction or early warning of any failure. In some examples, simply comparing the measured voltage to a stored reference value to detect the onset of a lamp fault may not be sufficient to predict a fault with any early warning, see, for example, [see...]. Figure 3 This illustrates the current of an exemplary lamp during its lifespan, where the current appears to have relatively minimal spikes before a failure, such as prior to the failure. For example, there are no detectable or obvious signs that a pre-failure event will occur before the pre-failure event. Therefore, some of these methods fail to predict failures with sufficient warning to allow users to prepare for lamp replacement. Furthermore, these methods do not utilize any intervention methods to delay or compensate for detected lamp failures.

[0038] This document describes exemplary embodiments of systems and methods for predicting the time to failure of electronic devices in advance. In the exemplary embodiments described herein, the prediction and warning of the time to failure of electronic devices in advance enables users to prepare for or schedule the replacement or repair of the electronic devices. In the exemplary embodiments described herein, the electronic device is implemented as a halogen lamp, for example, for real-time monitoring applications of continuous processes, and is merely an example for illustrative purposes. The systems and methods described herein can be applied to any lamp. Method aspects will become apparent in part and will be explicitly discussed in part in the following description.

[0039] Figure 1AThis is a front view of an example halogen lamp assembly 100. The halogen lamp assembly 100 includes an exhaust pipe 102 and a filament assembly 104 that is at least partially housed within the exhaust pipe 102. Figure 1B This is a front view of the filament assembly 104 removed from the exhaust pipe 102. The filament assembly 104 includes a coil 110 comprising a plurality of turns 111, and a pair of leads 112 extending from each end of the coil 110. At least a portion of the coil 110 and the leads 112 are housed within the exhaust pipe 102, and at least a portion of the leads 112 are positioned outside the exhaust pipe 102. The leads 112 may be connected to a pair of pins 114 for connecting the filament assembly 104 to a circuit, for example, to supply voltage and / or current to the filament assembly 104. In some embodiments, the exhaust pipe 102 is made of fused silica glass, and the filament assembly 104 is made of tungsten. The exhaust pipe 102 includes a sealed cavity 116 containing halogen and / or an additional or alternative suitable gas. In other embodiments, the halogen lamp assembly 100 may be made of alternative materials, and / or the cavity 116 may be filled with other suitable gases. The systems and methods described herein for predicting the time leading up to electronic device failure can be used with any other suitable type of light, or as additional or alternative electronic devices, as described below.

[0040] Lamps, such as halogen lamp assembly 100, have various failure mechanisms, such as filament assembly breakage (e.g., coil 110 or lead 112), as... Figure 2A and 2B As shown, this occurs as the tungsten filament evaporates over time. In some faults, one or more adjacent turns 111 may fuse together or contact, reducing the coil's resistance proportionally to the overall length of the coil and the number of turns. The fusion of adjacent turns and the reduction in resistance cause fluctuations in current, see, for example, [reference needed]. Figure 3 The figure shows the current fluctuations when the time is approximately 25 seconds. Although less common than filament breakage, additional and / or alternative failure mechanisms are envisioned, such as filament notching, bulb breakage, and / or filament detachment. Filament notching may occur when the lamp is operating at low temperatures, and filament detachment may occur when the lamp is exposed to vibration.

[0041] In some embodiments, a lamp may fail abruptly when power is applied to a cold filament due to filament breakage. Furthermore, halogen lamps offer limited warnings before failure; for example, halogen lamps do not blacken, and their luminous output characteristics change only slightly as the lamp ages. Similar to other incandescent lamps, the lifespan of some halogen lamps is determined by the rate of tungsten evaporation in the filament. If the filament does not have a constant temperature along its entire length, but instead has areas with much higher temperatures due to manufacturing defects such as uneven filament thickness or variations in internal structure, the filament will typically fail due to premature breakage. Even if the evaporated tungsten returns to the filament through the halogen regeneration cycle, unfortunately, the material deposits in the cooler areas of the filament, rather than the critical hot spots where filament thinning typically occurs. Therefore, predicting when any particular filament in a continuously operating lamp will fail has traditionally remained a challenge.

[0042] Figure 4 Halogen lamp components (such as) Figure 1A The example mortality curve for the halogen lamp assembly 100 shown is illustrated. The estimated lifespan of a halogen lamp can be determined by the evaporation rate of the filament. Due to slight variations in lamp manufacturing, it is unlikely that each individual lamp will have an actual lifespan that perfectly matches the estimated lifespan. Therefore, the lamp's lifespan is determined by averaging the lifespans of a large group of lamps. As shown in Figure 200, there is a 100% survival rate after 40% of the estimated lifespan, an 80% survival rate after 80% of the estimated lifespan, and a 50% survival rate after 100% of the estimated lifespan. Due to this large distribution of estimated lifespans, this information cannot be used to predict failure times or guarantee time spans for a specific lamp.

[0043] Figure 5 The example lifespan and light output are the same as those supplied to the example lamp (e.g.) Figure 1A The diagram shows the percentage relationship between the design voltage and the halogen lamp assembly 100. The design voltage can be associated with electronic ratings that are recommended by the manufacturer or specified during use for the power or voltage supplied to the lamp. As shown in Table 300, the light output and lifetime percentage depend on the design voltage percentage. Table 300 includes a low-voltage region 302 and a high-voltage region 304. Generally, operating the lamp at a lower voltage (e.g., low-voltage region 302) than operating the lamp at a voltage higher than the design voltage (e.g., high-voltage region 304) results in lower light output and a higher lifetime percentage.

[0044] In some embodiments described herein, systems and methods may utilize lifetime and light output, along with a design (also known as nominal) voltage percentage, to determine the optimal power and / or voltage level to be supplied to the lamp to increase lamp lifetime while maintaining light output suitable for the lamp's application. In some embodiments described herein, systems and methods may include selectively adjusting (e.g., reducing) the power (e.g., current and / or voltage) supplied to the lamp to extend lamp lifetime based on predicted time to lamp failure. In some embodiments included herein, systems and methods may use feedback control loops to maintain or control the supply of power, current, or voltage, e.g., at a target setpoint. For example, systems and methods may determine a voltage level of approximately 82.5% of the nominal voltage value to increase lifetime by 10 times with lower light output. Additionally, and / or alternatively, the voltage reduction may reduce the current to approximately 92% of the nominal current.

[0045] Figure 6 This is a schematic diagram of an example lifetime system 400 for predicting the time to failure of electronic device 410 and detecting lifetime events. In an exemplary embodiment, electronic device 410 may be implemented as a halogen lamp, for example, such as Figure 1A-1B The halogen lamp assembly 100 shown and described herein. In other embodiments, the electronic device 410 may be any suitable lamp, including a filament that emits light when power is supplied to it. In alternative embodiments, the electronic device 410 may be any suitable device having components capable of supplying power and capable of detecting parameters such as current, voltage, or resistance, such as, but not limited to, heaters, lamps such as incandescent or fluorescent lamps, and diodes (e.g., laser diodes). The lamp may be filled with any suitable gas, such as mercury vapor, metal halides, high-pressure sodium vapor, or low-pressure sodium vapor.

[0046] System 400 may include a computer device 420, which includes a processor 422 and at least one memory 424. System 400 includes a user interface 426 associated with or communicatively coupled to the computer device 420 for presenting information to a user, such as predicted time to failure or detected lifetime events. User interface 426 may include a display (e.g., a screen and / or lights for presenting information to the user) and one or more user inputs (e.g., buttons, keyboard, touchscreen, knobs, etc.) enabling the user to interact with computer device 420 and / or other components of system 400. In some embodiments, user interface 426 may include a speaker for emitting noise, for example, to warn the user's electronic device 410 of predicted time to failure or detected lifetime events.

[0047] System 400 includes a microcontroller and a data acquisition module 430, which are associated with or communicatively coupled to computer device 420. The microcontroller and data acquisition module 430 are communicatively coupled to a power source 432. Module 430 can transmit one or more signals to power source 432, causing power source 432 to supply power to electronic device 410. For example, power source 432 can supply power to electronic device 410, such as supplying voltage or supplying current.

[0048] The microcontroller and acquisition module 430 are also communicatively coupled to sensor 434 for detecting parameters of electronic device 410. While sensor 434 detects parameters in real time, module 430 can receive one or more signals from sensor 434. In some embodiments, user interface 426 may be used to present data such as parameters detected by sensor 434. The microcontroller and acquisition module 430 and / or sensor 434 may have any suitable data acquisition rate, such as per second, per decade, per minute, per hour, or per day, which is necessary for system 400 to function as described herein. In some embodiments, the data acquisition rate varies throughout the lifespan of the electronic device. In some embodiments, the lifespan may be days, months, or even years, depending on the specific type of electronic device 410. In some cases, the data acquisition rate may be lower in the early periods of the lifespan compared to the later periods. In an exemplary embodiment, sensor 434 includes a current sensor for detecting current in electronic device 410, such as the current in filament assembly 104 of halogen lamp assembly 100. In some embodiments, sensor 434 includes a voltage sensor for detecting voltages of electronic device 410, such as the voltage across filament assembly 104 of halogen lamp assembly 100. In other embodiments, sensor 434 includes any additional and / or alternative sensors for detecting any parameters of electronic device 410, such as temperature, optical power, and / or spectral density.

[0049] In some embodiments, power source 432 may supply power levels to electronic device 410 to achieve a constant voltage. The resistance of electronic device 410 (e.g., filament assembly 104) may vary over its lifetime. For example, the resistance may decrease when adjacent turns 111 are fused together. In some embodiments, sensor 434 is a voltage sensor, and module 430 adjusts the supplied voltage to maintain a constant voltage in electronic device 410. In some embodiments, power source 432 may supply variable power. In the embodiments described herein, the power value supplied by power source 432 is selectively adjustable (e.g., via user interface 426 or programmed into computer device 420 or microcontroller and acquisition module 430). In some embodiments, microcontroller and acquisition module 430 transmits one or more signals to power source 432 such that power source 432 ramps up (e.g., gradually increases) the supplied power during the startup phase described below, and then, after the startup phase, microcontroller and acquisition module 430 transmits one or more signals to power source 432 such that power source 432 supplies a constant value of power. In some alternative embodiments, power source 432 may supply a constant voltage to electronic device 410, or alternatively, power source 432 may supply a constant current.

[0050] In some embodiments described herein, a user can select a constant value of power supplied to electronic device 410, for example, via user interface 426. In some embodiments described herein, each electronic device 410 may be associated with a manufacturer-specified design voltage, see [link to relevant documentation]. Figure 5 In some embodiments, the microcontroller and acquisition module 430 are configured to use the design voltage to determine a suitable constant power value supplied by the power source 432 to the electronic device 410 in order to target and / or increase the lifetime of the electronic device 410. In an alternative embodiment, the microcontroller and acquisition module 430 are configured to use the design voltage as a constant power value supplied to the electronic device 410.

[0051] In the embodiments described herein, at least one of the computer device 420 and / or the microcontroller and the acquisition module 430 may be enabled to perform one or more method steps for predicting time to failure or detecting lifetime events, as will be described in further detail herein.

[0052] In some embodiments, system 400 further includes at least one supplementary electronic device 440. The supplementary electronic device 440 may be selectively communicatively coupled to power source 432, sensor 434, and / or microcontroller and acquisition module 430. In some embodiments, at least one supplementary electronic device 440 serves as a backup for electronic device 410. For example, when electronic device 410 has failed or is predicted to fail, microcontroller and acquisition module 430 may automatically shut down electronic device 410, for example, by transmitting one or more signals to power source 432, causing power source 432 to stop supplying power to electronic device 410. Simultaneously and / or subsequently, microcontroller and acquisition module 430 automatically turn on supplementary electronic device 440, for example, by transmitting one or more signals to power source 432, causing power source 432 to supply power to supplementary electronic device 440. Therefore, in some embodiments, system 400 is enabled to continuously operate at least one electronic device 410 or supplementary electronic device 440 without downtime. For example, in an exemplary embodiment, system 400 is enabled to continuously emit light from a halogen lamp without downtime.

[0053] Figure 7 Example lifetime graph 500 shows the relationship between current (in amperes) and lifetime (in time, such as days, weeks, months, or years) of an electronic device (e.g., electronic device 410). Lifetime graph 500 includes a data collection curve 502 and an exemplary model prediction curve 504 (also referred to herein as model 504). In the embodiments described herein, computer devices, such as computer device 420 and / or module 430, determine multiple phases 506 between a start time 510 of electronic device 410 (e.g., when the operating time of electronic device 410 is zero, referred to herein as startup event 510) and a failure time 512 (referred to herein as failure event). Each phase 506 includes a start occurring at a first operating time and an end occurring at a subsequent second operating time. In some embodiments, the end of phase 506 is also the start of a subsequent or consecutive phase. In the embodiments described herein, the start and / or end of phase 506 may be referred to herein as lifetime event 514.

[0054] In the embodiments described herein, phase 506 and / or lifetime event 514 may be determined by a computer device based at least in part on parameters detected by electronic device 410, such as current or voltage detected by sensor 434. In the exemplary embodiments described herein, the computer device may determine phase 506 and / or lifetime event 514 at or near the occurrence of lifetime event 514, for example, through detection commonly referred to as lifetime event 514. Additionally, and / or alternatively, the computer device may determine phase 506 and / or lifetime event 514 prior to the occurrence of lifetime event 514, for example, through prediction commonly referred to as lifetime event 514. In the exemplary embodiments described herein, the computer device may determine four different phases 506 characterized by five different events 514. In alternative embodiments, the computer device may determine any number of phases 506 and / or lifetime events 514, enabling the system and method to operate as described herein.

[0055] In the embodiments described herein, phase 506 includes a startup phase 520, a main phase 522, a pre-failure phase 524, and a final phase 526. In the exemplary embodiments described herein, three lifetime events 514 exist between a startup event 510 and a failure event 512. Lifetime events 514 include a main event 530 between startup phase 520 and main phase 522, a pre-failure event 532 between main phase 522 and pre-failure phase 524, and an impending failure event 534 between pre-failure phase 524 and final phase 526. Startup phase 520 spans from startup event 510 to main event 530. Main phase 522 spans from main event 530 to pre-failure event 532. Pre-failure phase 524 spans from pre-failure event 532 to impending failure event 534. Final phase 526 spans from impending failure event 534 to failure event 512. In the embodiments described herein, one or more of these phases 506 may be divided into two or more sub-phases. In the exemplary embodiments described herein, the main phase 522 is divided into two sub-phases, including a data collection sub-phase 540 and a modeling sub-phase 542.

[0056] The startup phase 520 occurs between startup event 510 and the subsequent main event 530, which separates startup phase 520 from the subsequent main phase 522. The computer device can determine startup event 510 by determining when sensor 434 first detects current in electronic device 410. In some embodiments, the computer device can determine startup event 510 by determining when power source 432 first supplies power to electronic device 410. In some alternative embodiments, the computer device can determine startup event 510 by determining when sensor 434 first detects voltage in electronic device 410. In some embodiments, the computer device can detect voltage or current to confirm that power source 432 (or electronic device 410) is operable. In some embodiments, the detected current may be substantially constant during startup phase 520.

[0057] In some embodiments, the computer device may determine a major event 530 by determining when a detected current changes from a substantially constant current during startup phase 520. For example, in some embodiments, the computer device may determine the slope of the detected current, and the computer device may compare the slope to a slope criterion. The computer device may determine that the major event 530 occurs when the criterion is met. In some alternative embodiments, the computer device may determine that the major event 530 occurs at a threshold time following startup event 510. In some embodiments, the computer device may determine that the major event 530 occurs at a time (e.g., a day or a week) following startup event 510. In some embodiments, the computer device may determine that the major event 530 occurs at a percentage of the expected lifetime, as provided by the manufacturer of electronic device 410. Startup event 510 may occur at a percentage of the expected lifetime between 5-10%, between 2-5%, or between 2-15%. In some embodiments, the computer device may determine that the major event 530 occurs at a percentage of the continuously applied voltage level or set point, as provided by the manufacturer of electronic device 410.

[0058] As described above, in the exemplary embodiment, the main phase 522 is divided into two sub-phases, including a data collection sub-phase 540 and a modeling sub-phase 542. During the data collection sub-phase 540, the computer device collects and / or stores an input dataset of parameters detected by the sensor 434. In the illustrated embodiment, the input dataset includes current detected during the data collection sub-phase 540. The computer device uses the input data to generate a model 504. In some embodiments, the computer device generates the model 504 by fitting a polynomial equation that models and is enabled to predict future values ​​from the input data. In some alternative embodiments, the computing device applies the input dataset to a trained lifetime model to generate the model 504. In alternative embodiments, the computer device may utilize any suitable methodology to generate the model 504 using the input data collected during the data collection sub-phase 540. In the embodiments described herein, the computer device uses any suitable methodology to determine the division between data collection sub-phase 540 and model sub-phase 542. For example, the computer device may determine the end of data collection sub-phase 540 based on a threshold time remaining from the main event 530 and / or the launch event 510. In some embodiments, the computer device may determine that the end of data collection sub-phase 540 occurs over a period of time (e.g., days, weeks, or months) following the launch event 510. In some embodiments, the computer device may determine the end of data collection sub-phase 540 by determining a percentage of the expected lifetime (as provided by the manufacturer of the electronic device 410). The percentage of the expected lifetime used for the end of data collection sub-phase 540 may be between 30% and 40% of the expected lifetime, between 20% and 30% of the expected lifetime, or between 25% and 45% of the expected lifetime.

[0059] In some embodiments, the computer device may collect and / or save input data during any suitable stage 506, including data collection sub-stage 540 and / or during modeling sub-stage 542. For example, the computer device may collect input data during modeling sub-stage 542 to generate an updated model in real time.

[0060] In some embodiments, the computer device may detect a pre-fault event 532 when it determines that a detected parameter (e.g., detected current) has changed from model 504. In some embodiments, the computer device may determine that a pre-fault event 532 has occurred when it determines that the rate of change of the detected current meets a detection criterion. In some embodiments, the computer device may determine that a pre-fault event 532 has occurred when it determines, by a detection criterion, that the detected current differs from model 504. In some embodiments, the detection criterion may include a percentage error difference between 0.1% and 0.5% between data collection curve 502 and model 504, a difference between 0.1% and 1% between data collection curve 502 and model 504, or a difference between 0.5% and 1.5% between data collection curve 502 and model 504. In some embodiments, the computer device may detect a pre-fault event 532 when it determines, by a detection criterion, that the slope of the detected parameter (e.g., detected current) has changed from a previous slope. In some embodiments, when the computer device determines, by means of a detection criterion, that the slope of a detected parameter (e.g., a detected current) changes from the slope of model 504, the computer device can detect a pre-fault event 532.

[0061] In some embodiments, the computer device may detect an impending fault event 534 when it determines that a currently detected parameter (e.g., a detected current) has changed relative to one or more previous values ​​of the detected parameter. In some embodiments, the computer device may determine that an impending fault event 534 has occurred when it determines, through a detection criterion, that the detected current differs from one or more previous values ​​of the detected current. For example, the computer device may compare the currently detected current with any previous value of the detected current (including currents detected immediately preceding the currently detected current) or with currents detected in any previous phase (e.g., during model sub-phase 542 and / or pre-fault phase 524). In some embodiments, the detection criterion may include, for example, but not limited to, a threshold change in the value between the currently detected current and a previous current value, a threshold change in the value of the current slope between a previous slope and a current slope, or a threshold change in the rate of change of the current slope between a previous detected value and a current detected value. In some embodiments, the detection criterion may include, for example, but not limited to, a threshold percentage change between 1.5% and 3%, 1% and 3.5%, or 2% and 4%. For example, when the current changes by 4% relative to the immediately preceding detected current, the computer device can detect an impending fault event 534. The threshold percentage change can be determined based on empirical data. For example, empirical data shows that an impending fault event occurs when the current decreases by a first threshold percentage change (such as 1%-3%), and a fault event occurs when the current decreases by a second threshold percentage change (such as 3%-5%).

[0062] In the embodiments described herein, the computer device detects a fault event 512 by determining when the electronic device 410 is no longer able to maintain or draw any current. Fault event 512 is associated with any fault event that causes components of the electronic device 410, such as a filament, to no longer be able to conduct current and / or apply voltage (such as when the filament breaks). In some alternative embodiments, the computer device may detect additional or alternative fault modes.

[0063] As described above, the computer device can determine (e.g., detect or predict) lifetime event 514. As described above, the computer device detects lifetime event 514 during or at the time of its occurrence based on at least one of detected parameters, model 504, and / or detection criteria. In some embodiments, the computer device predicts an estimate of the time remaining until failure event 512, at least in part, based on the detected lifetime event 514.

[0064] In an exemplary embodiment, the computer device is also capable of predicting the time to the occurrence of lifetime event 514 well before event 514 actually occurs, based on at least one of detected parameters, model 504, and / or prediction criteria. In some embodiments, the computer device predicts an estimate of the time to failure event 512 based at least in part on the predicted time to the occurrence of lifetime event 514. In some embodiments, the computer device may predict multiple different estimates of the time to failure during any stage 506. For example, the computer device may predict a first estimate of the time to failure (referred to herein as an advance estimate) during the main stage 522. In another example, the computer device may predict a second estimate of the time to failure (referred to herein as a proximity estimate) during the pre-failure stage 524.

[0065] In some embodiments, the computer device predicts the time elapsed before a pre-failure event 532 occurs. In some embodiments, the computer device predicts the pre-failure event 532 when model 504 predicts that the current will meet a prediction criterion. The prediction criterion may include the computer device determining that the current will decrease by a threshold percentage relative to an earlier current value (e.g., the current value at or near the major event 530, or the current value at the initiation event 510). The threshold percentage may be between 2-3%, 2-4%, 1-5%, or 3-6%. In the embodiments described herein, the computer device may use the predicted time elapsed before the pre-failure event 532 to predict an estimate of the time elapsed before the failure event 512. In the embodiments described herein, the computer device may predict the time elapsed before the pre-failure event 532 and / or an estimate of the time elapsed before the failure event 512 occurs (e.g., during the major phase 522). The computer device may estimate the time elapsed before the failure event 512 by adding a period of time to the predicted time elapsed before the failure event 532. In some embodiments, the computer device may estimate the time to failure event 512 using a predicted time to failure event 532 and a percentage of the initial current value (e.g., the current value at or near major event 530, or at initiation event 510). In the embodiments described herein, the system computer device uses model 504 to predict the time to failure event 532 well before it actually occurs. For example, the system computer device may predict failure event 532 during major phase 522 (e.g., during data collection sub-phase 540 or during modeling sub-phase 542). In some embodiments, the system computer device may predict failure event 532 at the end of data collection sub-phase 540.

[0066] In some embodiments, the computer device predicts the time until an impending failure event 534 will occur. In the embodiments described herein, the computer device may use the predicted time until the impending failure event 534 to predict an estimate of the time until failure event 512. In the embodiments described herein, the computer device may predict the time until the impending failure event 534 and / or the estimate of the time until failure event 512 before the impending failure event 534 occurs (e.g., during the main phase 522 and / or the pre-failure phase 524). The computer device may estimate the time until failure event 512 by adding a period of time to the predicted time until the impending failure event 534. The computer device may estimate the time until the impending failure event 534 occurs by determining when model 504 meets a prediction criterion. In some embodiments, the prediction criterion includes when the computer device determines that a predicted parameter of model 504 has decreased relative to a previous parameter value. For example, the prediction criterion may include the computer device determining that the predicted current of model 504 has decreased by a threshold amount relative to a previously detected current (e.g., a previously detected current at or near the main event 530). In some embodiments, the prediction criteria include model 504 predicting that the current will decrease by 5%-6%, 4%-6%, or 3%-7% relative to the current at or near the major event 530.

[0067] In some alternative embodiments, the computer device utilizes a training dataset to construct a lifetime model for predicting the time to failure. For example, the lifetime model can be trained using a training dataset that includes parameters detected by sensor 434 throughout the lifetime of multiple electronic devices 410. Alternatively, the lifetime model can be trained using a training dataset that includes parameters detected by sensor 434 for multiple different electronic devices 410 during one or more phases 506. The trained lifetime model can be configured to accept one or more model inputs to generate one or more model outputs. In some embodiments, model inputs may include the aforementioned input data and / or additional or alternative data, such as data associated with electronic device 410 (e.g., manufacturing data such as design voltage and / or predicted lifetime curves), power supply, safety factor, desired or specified brightness, etc. In some embodiments, model outputs may include, for example, a prediction of the time to failure, such as an advance prediction of the time to failure and / or a near-term prediction of the time to failure. In some embodiments, model outputs may include a prediction of the time to occurrence of lifetime event 514. In some embodiments, the lifetime model can be retrained using an updated training dataset that includes newly detected parameters / data by sensor 434.

[0068] In some embodiments, the computer device may continuously monitor the current during any stage 506 of its lifespan to detect current changes that may indicate a failure event is about to occur. For example, in some rare cases, the electronic device 410 may fail very early, such as during the major stage 522.

[0069] Figure 8 Another example of a lifespan chart 500 shows the relationship between the current (in amperes) of multiple electronic devices 410 and their lifespan (in time, such as days, weeks, months, or years), illustrating the startup phase 520.

[0070] Figure 9 Another example lifespan chart 500 shows the relationship between the current (in amperes) of multiple electronic devices 410 and their lifespan (in time, such as days, weeks, months, or years), illustrating the startup phase 520 and the main phase 522.

[0071] Figure 10 Another example of a lifetime graph 500 shows the relationship between the current (in amperes) and lifetime (in time, such as days, weeks, months or years) of multiple electronic devices 410, showing the main phase 522, which includes a data collection sub-phase 540 and a model sub-phase 542 for both the data collection curve 502 and the model prediction curve 504.

[0072] Figure 11 Another example of a lifetime graph 500 shows the relationship between the current (in amperes) of multiple electronic devices 410 and their lifetime (in time, such as days, weeks, months, or years), illustrating a startup phase 520 and a main phase 522, which includes a data collection sub-phase 540 and a model sub-phase 542 for both the data collection curve 502 and the model prediction curve 504.

[0073] Figure 12 Another example of a lifespan chart 500 shows the relationship between the current (in amperes) of electronic device 410 and its lifespan (in time, such as days, weeks, months, or years), illustrating the pre-failure stage 524.

[0074] Figure 13 Another example lifespan chart 500 shows the relationship between the current (in amperes) of the exemplary electronic device 410 and its lifespan (in time, such as days, weeks, months, or years), illustrating the final stage 526.

[0075] Figure 14 Another example lifetime graph 500 shows the relationship between the current (in amperes) of multiple electronic devices 410 and lifetime (in time, such as days, weeks, months or years), showing phases 506 and lifetime events 514 for data collection curve 502 and model prediction curve 504.

[0076] Figure 15 Another example lifetime graph 500 shows the relationship between the current (in amperes) of multiple electronic devices 410 and lifetime (in time, such as days, weeks, months or years), showing phases 506 and lifetime events 514 for data collection curve 502 and model prediction curve 504.

[0077] Figure 16 It is used with Figure 4 The illustrated method flowchart is an exemplary method 600 for the operation of electronic device 410 used in conjunction with the system (e.g., predicting and / or detecting phase 506 and lifetime event 514, or control parameters). One or more method steps of method 600 can be performed or implemented by any suitable computer device (e.g., computer device 420 and / or module 430). Unless otherwise stated, any part of method 600 and method 700 can be performed in any order, and any part of method 600 can be performed in combination with any part of method 700.

[0078] Method 600 may include a computer device detecting a startup event 510 of electronic device 410. Detecting startup event 510 may include the computer device determining that electronic device 410 is turned on, for example, using detected parameters / data received from sensor 434. For example, detection 602 may include the computer device detecting or receiving initially detected current or voltage from sensor 434. In some embodiments, method 600 includes the computer device turning on electronic device 410. Turning on may include the computer device transmitting one or more signals to power source 432, causing power source 432 to initially supply power to electronic device 410. In some embodiments, method 600 includes resetting data, clearing data cache, etc., before causing power source 432 to initially supply power to electronic device 410.

[0079] Method 600 includes a computer device detecting a major event 530. Detecting a major event 530 may include the computer device determining that a detection parameter of the electronic device 410 has stabilized; for example, the computer device may detect that the detection parameter has maintained a substantially constant value, such as a substantially constant current. In some embodiments, detecting a major event 530 may include the computer device determining that the major event 530 occurs at a percentage of the expected lifespan provided by the manufacturer of the electronic device 410. In some embodiments, the computer device may determine that the major event 530 occurs at a percentage of a continuously applied voltage level or set point provided by the manufacturer of the electronic device 410. In some embodiments, detecting a major event 530 may include the computer device determining that the major event 530 occurs at a time, for example, a percentage of the expected lifespan provided by the manufacturer. In some embodiments, detecting a major event 530 may include the computer device determining that a period of time has elapsed since the detection of the initiation event 510, for example, several days or weeks.

[0080] Method 600 includes a computer device collecting sensor data received from sensor 434 during a data collection sub-phase 540. Data collection may include the computer device collecting data over a period of time following a startup event 510.

[0081] In rare cases, electronic device 410 may fail prematurely, such as an early failure event during the main phase 522, which can be detected by rapid changes or rates of change in current, similar to the detection of pre-failure event 532 or impending failure event 534. Method 600 may include detecting changes or rates of change in current at any time during the lifespan of the computer device to detect early failure events.

[0082] Method 600 includes a computer device generating a 608 model 504 using collected data. Generating the 608 model 504 may include the computer device fitting a polynomial equation to the collected data. In some embodiments, generating the 608 model 504 may include the computer device training or adjusting a lifetime model using data collected for the plurality of electronic devices 410 during a data collection period and / or throughout the lifetime of the plurality of electronic devices 410, and then applying input data, such as the collected data, such that the lifetime model generates model 504 as output.

[0083] Method 600 includes a computer device predicting 610 the time elapsed before a pre-failure event 532 occurs (e.g., predicting the time elapsed before or at the time of an event in the electronic device 410 that occurs before or at the time of the pre-failure event 532). In some embodiments, prediction 610 includes the computer device determining the pre-failure event 532 when model 504 predicts that the current will meet a prediction criterion. In some embodiments, the prediction criterion may include the computer device determining that the current will decrease by a threshold percentage compared to an earlier current value, such as a current value at or near the time of a major event 530 or a startup event 510. For example, in some embodiments, the threshold percentage may be between 0.5-3%, between 1-2%, between 0.5-3.5%, or between 1-3%.

[0084] Method 600 includes a computer device predicting 612 the time remaining until an impending failure event 534 (e.g., predicting the time of an event of electronic device 410 occurring before or at the impending failure event 534). In some embodiments, method 600 includes the computer device predicting the time remaining until the impending failure event 534. Prediction 612 may include the computer device using model 504 to predict the time remaining until the impending failure event 534. Prediction 612 may include the computer device determining when model 504 meets prediction criteria. In some embodiments, prediction criteria include when the computer device determines that a predicted parameter of model 504 will decrease compared to a previous parameter value. For example, in some embodiments, the computer device may predict 612 that the impending failure event 534 will occur when the computer device determines, by detection criteria, that the detected current is different from one or more previous values ​​of the detected current. For example, the computer device may compare the currently detected current with any previous value of the detected current, including current detected immediately before the currently detected current, or current detected in any previous phase, such as during model sub-phase 542 and / or pre-failure phase 524. In some embodiments, the detection criteria may include, for example, but not limited to, a threshold change in the value between the currently detected current and a previous current value, a threshold change in the slope, or a threshold change in the rate of change of the slope. In some embodiments, the detection criteria may include, for example, but not limited to, a threshold percentage change between 1.5% and 3%, 1% and 3.5%, or 2% and 4%. In some embodiments, the prediction criteria include a computer prediction device using model 504 to predict that the current will decrease from the previous current at or near the time of the major event 530 by 1% and 2%, 1% and 3%, or 2% and 5%.

[0085] In some embodiments, method 600 includes a computer device predicting 614 a first estimate of the time remaining until a failure event 512. Prediction 614 may include the computer device estimating the time remaining until the failure event 512 by adding a period of time to the predicted time remaining until the impending failure event 534 or the predicted time remaining until the pre-failure event 532. In the embodiments described herein, the computer device may predict the time remaining until the impending failure event 534 and / or the estimate of the time remaining until the failure event 512 before the failure event 534 occurs, for example during the main phase 522 and / or the pre-failure phase 524. In the embodiments described herein, the computer device may predict the time remaining until the pre-failure event 532 and / or the estimate of the time remaining until the failure event 512 before the failure event 532 occurs, for example during the main phase 522.

[0086] Method 600 includes a computer device transmitting 616 one or more warning messages indicating a first estimate of the time remaining until a failure event 512. Transmission 616 may include the computer device transmitting one or more signals to a user interface 426, causing the user interface 426 to display the warning messages. Transmission 616 of the warning messages may occur after: prediction 610 of a pre-failure event 532, prediction 612 of at least the time remaining until an impending failure event 534, and / or after prediction 614 of a first estimate of the time remaining until failure event 512. The warning messages may indicate the predicted time remaining until any of the pre-failure event 532, the impending failure event 534, and / or failure event 512.

[0087] In some embodiments, method 600 includes a computer device adjusting parameters of electronic device 410. Adjustment 618 may include the computer device transmitting one or more signals to power source 432 to adjust (e.g., increase or decrease) the amount of power supplied to electronic device 410. In some embodiments, adjustment 618 includes the computer device transmitting one or more signals to power source 432 to reduce the amount of power supplied to electronic device 410. In some embodiments, the computer device adjusting the parameters of electronic device 618 is automatically triggered by a predicted or detected event, or triggered by comparing a predicted or detected event with a triggering criterion. For example, a triggering criterion may include the computer device comparing a predicted time to failure with a threshold time amount. In some embodiments, method 600 includes the computer device reducing the power delivered to electronic device 410 from startup phase 510 to extend the lifespan of electronic device 410. In some embodiments, the computer device causes power source 432 to supply between 90-99%, 80-99%, or 85-95% of rated power. In some embodiments, the computer device causes the power source 432 to supply between 45-55%, 40-50%, or 50-60% of the rated power.

[0088] Figure 17 It is used with Figure 6 A flowchart of another exemplary method 700 for the operation of electronic device 410 used with the system shown (e.g., predicting and / or detecting phase 506 and lifetime event 514, or control parameters). One or more method steps of method 700 can be performed by any suitable computer device, such as computer device 420 and / or module 430.

[0089] Method 700 includes computer device detection 702 of a pre-fault event 532. Detection 702 may include the computer device determining that a detected parameter (e.g., detected current) has changed relative to model 504. In some embodiments, detection 702 may include the computer device determining, via detection criteria, that the detected current differs from model 504. Detection criteria may include a percentage error difference between 2% and 5% between data collection curve 502 and model 504, a difference between 1% and 4% between data collection curve 502 and model 504, or a difference between 2% and 3% between data collection curve 502 and model 504.

[0090] Method 700 includes a computer device prediction 704 of a second estimate of the time remaining until a failure. Prediction 704 may include the computer device estimating the time remaining until a failure event 512 by adding a period of time to the detected pre-failure event 532.

[0091] Method 700 includes a computer device transmitting 706 a second warning message indicating a second estimate of time remaining until a fault and / or a detected pre-fault event 532. Transmission 706 may include the computer device transmitting one or more signals to a user interface 426, causing the user interface 426 to display the second warning message.

[0092] Method 700 includes a computer device detecting 708 an impending failure event 534. In some embodiments, detection 708 includes the computer device determining that a currently detected parameter (e.g., a detected current) has changed relative to a plurality of previous values ​​of the detected parameter. In some embodiments, the computer device may detect an impending failure event 534 occurring when the computer device determines that the detected current differs from a plurality of previous values ​​of the detected current. For example, the computer device may compare the currently detected current with any previous value of the detected current, including current detected immediately before the currently detected current, or current detected during any previous phase (e.g., during model sub-phase 542 and / or pre-failure phase 524). In some embodiments, the detection criteria may include, for example, but not limited to, a threshold change in the value between the currently detected current and a previous current value, a threshold change in the slope, or a threshold change in the rate of change of the slope.

[0093] Method 700 includes a third estimate of the time remaining until a failure is predicted by the computer device 710. Prediction 710 may include the computer device estimating the time remaining until a failure event 512 by adding a period of time to the detected impending failure event 534.

[0094] Method 700 may include a computer device sending 712 a third warning message indicating a third estimate of the time remaining until a fault and / or a detected impending fault event 534. Sending 712 may include the computer device sending one or more signals to a user interface 426, causing the user interface 426 to display the third warning message.

[0095] Method 700 may include computer device detection 714 fault event 512. Detection may include the computer device determining when electronic device 410 is no longer able to transmit current and / or voltage. For example, the computer device may compare the current (if any) to a minimum current threshold. When the computer device determines that the detected current is less than the minimum threshold, the computer device detects fault event 512.

[0096] In some alternative embodiments, method 700 may include, after detecting fault event 512 or after a third estimate of the time remaining until fault is predicted 716, the computer device activating supplementary electronics 440 716. Activating supplementary electronics 440 716 may include the computer device sending one or more signals to power source 432, causing power source 432 to supply power to supplementary electronics 440.

[0097] At least part of the methods described herein can be implemented in any suitable computer device 800 and the software implemented therein. Figure 18 This is a block diagram of an example computer device 800. In an example embodiment, the computer device 800 includes a user interface 804 that receives at least one input from a user. The user interface 804 may include a keyboard 806 that enables the user to input relevant information. The user interface 804 may also include, for example, a pointing device, a mouse, a stylus, a touch-sensitive panel (e.g., a touchpad and a touchscreen), a gyroscope, an accelerometer, a position detector, and / or an audio input interface (e.g., including a microphone).

[0098] Furthermore, in the example embodiment, the computer device 800 includes a presentation interface 817 that presents information to a user, such as detected or predicted events and / or sensed parameters, such as current. The presentation interface 817 may also include a display adapter 808 coupled to at least one display device 810. More specifically, in the example embodiment, the display device 810 may be a visual display device, such as a cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED) display, and / or an "electronic ink" display. Alternatively, the presentation interface 817 may include an audio output device (e.g., an audio adapter and / or a speaker) and / or a printer.

[0099] Computer device 800 also includes processor 814 and memory device 818. Processor 814 is coupled to user interface 804, presentation interface 817, and memory device 818 via system bus 820. In example embodiments, processor 814 communicates with a user, for example, by prompting the user through presentation interface 817 and / or receiving user input through user interface 804. The term "processor" generally refers to any programmable system, including systems and microcontrollers, reduced instruction set computers (RISC), complex instruction set computers (CISC), application-specific integrated circuits (ASIC), programmable logic circuits (PLC), and any other circuit or processor capable of performing the functions described herein. The examples above are for illustrative purposes only and are therefore not intended to limit the definition and / or meaning of the term "processor" in any way.

[0100] In an example embodiment, memory device 818 includes one or more devices that enable information (e.g., executable instructions and / or other data) to be stored and retrieved. Furthermore, memory device 818 includes one or more computer-readable media, such as, but not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), solid-state drive, and / or hard disk. In an example embodiment, memory device 818 stores, but is not limited to, application source code, application object code, configuration data, additional input events, application state, assertion statements, verification results, and / or any other type of data. In an example embodiment, computer device 800 may also include a communication interface 830 coupled to processor 814 via system bus 820. Furthermore, communication interface 830 is communicatively coupled to data acquisition device.

[0101] In an example embodiment, processor 814 can be programmed by encoding operations using one or more executable instructions and providing those executable instructions in memory device 818. In an example embodiment, processor 814 is programmed to select multiple measurements received from a data acquisition device.

[0102] In operation, a computer executes computer-executable instructions embodied in one or more computer-executable components stored on one or more computer-readable media to implement aspects of the invention described and / or illustrated herein. In the embodiments of the invention illustrated and described herein, the order in which operations are performed or fulfilled is not required unless otherwise specified. That is, operations may be performed in any order unless otherwise specified, and embodiments of the invention may include more or fewer operations than disclosed herein. For example, it is contemplated that a particular operation be performed before, simultaneously with, or after another operation within the scope of various aspects of the invention.

[0103] At least a portion of the methods 600 and / or 700 described herein can also be implemented using server computer device 1001. Figure 19 An example configuration of server computer device 1001 is shown. Server computer device 1001 also includes a processor 1005 for executing instructions. For example, instructions may be stored in memory 1030. Processor 1005 may include one or more processing units (e.g., in a multi-core configuration).

[0104] The processor 1005 is operatively coupled to the communication interface 1015, enabling the server computer device 1001 to communicate with a remote device or another server computer device 1001. For example, the communication interface 1015 can receive data from the system 12 via the Internet.

[0105] Processor 1005 may also be operatively coupled to storage device 1034. Storage device 1034 is hardware suitable for any computer operation of storing and / or retrieving data. In some embodiments, storage device 1034 is integrated within server computer device 1001. For example, server computer device 1001 may include one or more hard disk drives as storage device 1034. In other embodiments, storage device 1034 is external to server computer device 1001 and can be accessed by multiple server computer devices 1001. For example, storage device 1034 may include multiple storage units in a redundant array of independent disks (RAID) configuration, such as hard disks and / or solid-state drives. Storage device 1034 may include a storage area network (SAN) and / or network attached storage (NAS) system.

[0106] In some embodiments, processor 1005 is operatively coupled to storage device 1034 via storage interface 1020. Storage interface 1020 is any component capable of providing processor 1005 with access to storage device 1034. Storage interface 1020 may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and / or any component that provides processor 1005 with access to storage device 1034.

[0107] At least one technical effect of the systems and methods described herein includes (a) advance prediction of the time to failure of electronic devices; (b) imminent prediction of the time to failure of electronic devices; (c) advance detection and prediction of the time to failure of electronic devices; and (d) accurate prediction of the time to failure of electronic devices.

[0108] Example embodiments of systems and methods for lamp malfunctions have been described in detail above. These systems and methods are not limited to the specific embodiments described herein, but rather, components of the system and / or operation of the method may be utilized independently and separately from other components and / or operations described herein. Furthermore, the described components and / or operations may also be defined in or used in combination with other systems, methods, and / or devices, and are not limited to practice only with the systems described herein.

[0109] As used herein, elements or steps described in the singular and beginning with the words “a” or “one” should be understood to not exclude a plurality of elements or steps, unless such exclusion is explicitly stated. Furthermore, references to “example” or “an example” disclosed herein are not intended to exclude the existence of additional examples that also include the described features. Moreover, within the scope of the terms “comprising,” “including,” “having,” “containing,” and variations thereof used herein, these terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word, without excluding any additional or other elements.

[0110] Although specific features of various embodiments of the invention may be shown in some figures and not in others, this is merely for convenience. Features in any figure may be referenced and / or claimed in conjunction with any features in any other figure according to the principles of the invention.

[0111] This written description uses examples to disclose the invention, including the best mode, and also enables any person skilled in the art to practice the invention, including making and using any device or system and performing any combined methods. The patentable scope of the invention is defined by the claims, but may include other examples that would occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that are indistinguishable from the literal language of the claims, or if they include equivalent structural elements that are not substantially different from the literal language of the claims.

Claims

1. A system for operating a halogen lamp, the system comprising: A sensor configured to detect parameters of the halogen lamp; as well as A processor, communicatively coupled to at least one memory storing instructions, which, when executed by the processor, cause the processor to: Parameters detected by the sensor are received during the data collection period; The model is generated at least in part based on received parameters detected by the sensor during the data collection period; as well as Using the model, predict the time until at least one pre-failure event is met before the pre-failure criteria are satisfied.

2. The system of claim 1, wherein the processor is further configured to Using the model, the time elapsed before the pre-failure criterion is met is predicted for the pre-failure event, wherein the processor determines that the pre-failure criterion is met when the model predicts that the parameter will deviate from its previous value by a threshold amount.

3. The system of claim 2, wherein the threshold amount is between 1% and 6%.

4. The system according to any one of the preceding claims, wherein the data collection time period corresponds to the main phase of the halogen lamp, and during the main phase, the parameter decreases over time.

5. The system of claim 4, wherein the processor is further configured to: The start of the main phase is determined in the following way: Detecting key events using sensor data; and The time corresponding to the main event is selected as the start of the main phase.

6. The system according to any one of the preceding claims, wherein the processor is further configured to: A model is generated, at least in part, based on the received parameters, which are detected by the sensor during the data collection period, by fitting a polynomial equation to the received parameters.

7. The system according to any one of the preceding claims, wherein the processor is configured to predict the time to failure by adding a time amount to the predicted pre-failure event.

8. The system of claim 7, wherein the processor is configured to predict the time to failure based at least in part on the predicted time to the pre-failure event.

9. The system according to any one of the preceding claims, wherein the processor is further configured to: The pre-fault event is detected by comparing the current value of the detected parameter with the previously detected parameter value; and The updated time to failure is predicted, at least in part, based on the detected pre-failure events.

10. The system according to any one of the preceding claims, wherein the processor is further configured to: An impending failure event is detected by comparing the current value of the detected parameter with the previously detected parameter value; and The time to failure is predicted, at least in part, based on the detected impending failure events.

11. The system of claim 10, wherein the processor is further configured to: The transmission will display a message on the user interface indicating at least one of the predicted time to failure or the predicted time to imminent failure.

12. The system according to any one of the preceding claims further includes a voltage sensor, wherein the processor is further configured to: Feedback from the voltage sensor is used to control the voltage across the halogen lamp.

13. The system according to any one of the preceding claims, wherein the sensor is a current sensor and the detected parameter is the current of the halogen lamp.

14. A method for predicting the time of distance failure of a lamp, the method comprising: Receive parameters detected by sensors during the data collection period; The model is generated at least in part based on the received parameters, which are detected by the sensor during the data collection period. as well as Using the model, predict the time until at least one pre-failure event is met before the pre-failure criteria are satisfied.

15. The method of claim 14, further comprising: Using the model, the time from which the pre-failure criterion will be met is predicted, wherein the pre-failure criterion is met when the model predicts that the parameter will deviate from its previous value by a threshold amount.

16. The method of claim 15, wherein the threshold amount is between 1% and 6%.

17. The method according to any one of claims 14 to 16, wherein the data collection time period corresponds to the main phase of the lamp, and during the main phase, the parameter decreases over time.

18. The method according to any one of claims 14 to 17, further comprising: Sensor data is used to determine the start of the startup phase.

19. The method according to any one of claims 14 to 18, further comprising: Adjust the voltage across the lamp to a user-defined level.

20. A non-transitory computer-readable medium having embodied computer-executable instructions for implementing operation of a halogen lamp, wherein when executed by at least one processor, the computer-executable instructions cause the at least one processor to: Receive parameters detected by sensors during the data collection period; The model is generated at least in part based on the received parameters, which are detected by the sensor during the data collection period; as well as Using the model, predict the time until at least one pre-failure event is met before the pre-failure criteria are satisfied.

Images