Patent ID: 12190647

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section.

One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details.

During the operation of a mechanical device, one or more parts can degrade over time due to wear and tear. Predicting the lifetime of the parts prior to an operational failure can enable users to avoid faulty operations. Additionally, predicting part failure can direct maintenance operations that can extend the useful operation of the parts. Traditionally, algorithms to predict the part failure have been developed; however, predictive failure algorithms are based on standardized tests that fail to account for the specific operating characteristics, and/or operating history, of the given mechanical device.

Various embodiments of the present invention can be directed to computer processing systems, computer-implemented methods, apparatus and/or computer program products that facilitate the efficient, effective, and autonomous (e.g., without direct human guidance) predicting of mechanical device malfunctions at various temperature ranges. For example, one or more embodiments described herein can monitor the temperature experienced by one or more parts during operation in addition to an amount of vibration experienced by the one or more parts at the temperature. Based on the monitored temperature and amount of vibration, various embodiments described herein can regard updating an initial predictive failure algorithm to more accurately account for the operating condition of the mechanical device. In various examples, the mechanical device can be an oven, wherein temperature changes induced by operation of the oven can influence how vibration levels of an operating oven part impact a malfunction prediction.

The operational lifetime of a mechanical part can vary based on one or more contexts of its operation. Part manufactures can employ one or more stress tests to determine a function of operation parameters that predicts a malfunction of the mechanical part. However, the effects of the operation parameters on the malfunction prediction can be vary based on one or more parameter correlations not accounted for by the stress test, and thereby not included in the initial predictive failure analysis. In various embodiments described herein, a correlation between temperature and vibration experienced by the mechanical part can influence the malfunction prediction. For instance, vibration levels experienced by a mechanical part can be markedly different at various operating temperature ranges. Thereby, vibration level thresholds that can be indicative of a future failure can also be markedly different at various operating temperatures. A predictive failure analysis that does not adjust for how the vibration levels are interpreted based on the operating temperature inherently integrates an amount of inaccuracy to any predictions regarding an operating temperature outside the predefined temperature range. Thus, the accuracy of a predictive failure analysis can be improved by accounting for the correlation between experienced vibration and temperature for a given part. Further, this improved predictive analysis can be particularly relevant to mechanical devices prone to experience temperature fluctuations, such as ovens and/or furnaces.

The computer processing systems, computer-implemented methods, apparatus and/or computer program products employ hardware and/or software to solve problems that are highly technical in nature (e.g., predict failure analyses), that are not abstract and cannot be performed as a set of mental acts by a human. For example, an individual, or a plurality of individuals, cannot continuously, or near continuously, monitor the condition of mechanical parts during their operation without interfering with the function of the associate mechanical device.

Also, one or more embodiments described herein can constitute a technical improvement over conventional predictive failure analyses by incorporating the effects of an interdependency between vibration levels and operating temperatures experienced by mechanical parts. Additionally, various embodiments described herein can demonstrate a technical improvement over conventional predictive failure analyses by adjusting the impact that observed vibration levels have on the predictive failure analysis of an mechanical part based on operating temperature. Further, one or more embodiments described herein can have a practical application by accurately predicting one or more malfunctions associated with a mechanical part prone to experience temperature fluctuations during its operational history. For instance, various embodiments described herein can facilitate a predictive failure analysis associated with one or more oven parts based on a correlation between vibration levels experienced by the oven parts and the operating temperature of the oven parts.

FIG.1illustrates a block diagram of an example, non-limiting system100that can perform one or more predictive failure analyses. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. Aspects of systems (e.g., system100and the like), apparatuses or processes in various embodiments of the present invention can constitute one or more machine-executable components embodied within one or more machines (e.g., embodied in one or more computer readable mediums (or media) associated with one or more machines). Such components, when executed by the one or more machines (e.g., computers, computing devices, virtual machines, etc.) can cause the machines to perform the operations described.

As shown inFIG.1, the system100can comprise one or more servers102, one or more networks104, input devices106, and/or mechanical devices108. The server102can comprise status component110. The status component110can further comprise communication component112. Also, the server102can comprise or otherwise be associated with at least one memory114(e.g., one or more volatile or non-volatile memory devices). The server102can further comprise a system bus116that can couple to various components such as, but not limited to, the status component110and associated components, memory114and/or a processor118. While a server102is illustrated inFIG.1, in other embodiments, multiple devices of various types can be associated with or comprise the features shown inFIG.1. Further, the server102can communicate with one or more cloud computing environments.

The one or more networks104can comprise wired and wireless networks, including, but not limited to, a cellular network, a wide area network (WAN) (e.g., the Internet) or a local area network (LAN). For example, the server102can communicate with the one or more input devices106and/or mechanical devices108(and vice versa) using virtually any desired wired or wireless technology including for example, but not limited to: cellular, WAN, wireless fidelity (Wi-Fi), Wi-Max, WLAN, Bluetooth technology, a combination thereof, and/or the like. In various embodiments, the various components of system100can communicate with each other via one or more satellitia and/or cellular networks104. For example, the one or more networks104can be one or more wireless cellular networks comprising cellular data technology that can facilitate communication between: the server102and the input devices106(and vice versa), the server102and the mechanical devices108(and vice versa), and/or the input devices106and the mechanical devices108(and vice versa). For instance,FIG.1depicts an exemplary embodiment in which the one or more networks104can be wireless cellular networks (e.g., represented by the tower icon) and/or direct electrical connections (e.g., represented by the double arrow lines); however, the architecture of the one or more networks104is not so limited. In various embodiments, the one or more networks104can include any of the wired or wireless technology described herein. Further, although in the embodiment shown the status component110can be provided on the one or more servers102, it should be appreciated that the architecture of system100is not so limited. For example, the status component110, or one or more components of status component110, can be located at another computer device (e.g., another server device, a client device, etc.).

The one or more input devices106can comprise one or more computerized devices, which can include, but are not limited to: personal computers, desktop computers, laptop computers, cellular telephones (e.g., smart phones), computerized tablets (e.g., comprising a processor), smart watches, keyboards, touch screens, mice, a combination thereof, and/or the like. The one or more input devices106can be employed to enter one or more predictive failure algorithms into the system100, thereby sharing (e.g., via a direct connection and/or via the one or more networks104) said data with the server102. For example, the one or more input devices106can send data to the communications component112(e.g., via a direct connection and/or via the one or more networks104). Additionally, the one or more input devices106can comprise one or more displays that can present one or more outputs generated by the system100to a user. For example, the one or more displays can include, but are not limited to: cathode tube display (“CRT”), light-emitting diode display (“LED”), electroluminescent display (“ELD”), plasma display panel (“PDP”), liquid crystal display (“LCD”), organic light-emitting diode display (“OLED”), a combination thereof, and/or the like.

In various embodiments, the one or more input devices106and/or the one or more networks104can be employed to input one or more settings and/or commands into the system100. For example, in the various embodiments described herein, the one or more input devices106can be employed to operate and/or manipulate the one or more mechanical devices108and/or the server102and/or associate components. Additionally, the one or more input devices106can be employed to display one or more outputs (e.g., displays, data, visualizations, and/or the like) generated by the server102and/or associate components. Further, in one or more embodiments, the one or more input devices106can be comprised within, and/or operably coupled to, a cloud computing environment.

In one or more embodiments, the one or more input devices106can be employed to enter one or more predictive failure algorithms into the system100. For example, the one or more predictive failure algorithms can be received by the communications component112and stored in the one or more memories114. For instance, the one or more predictive failure algorithms can be stored in one or more algorithm databases120. The one or more predictive failure algorithms can regard one or more mechanical parts122of the one or more mechanical devices108. In various embodiments, the status component110can employ the one or more predictive failure algorithms to determine an operating status of the one or more mechanical parts122. For example, the status component110can employ the one or more predictive failure algorithms to determine the likelihood of an imminent malfunction of the one or more mechanical parts122. In various embodiments, each predictive failure algorithm comprised within the algorithm database120can be associated with a respective mechanical part122. Alternatively, in one or more embodiments, a predictive failure algorithm can be employed by the status component110with regards to multiple types of mechanical parts122. For instances, a single predictive failure algorithm can be employed by the status component110with respect to a group of mechanical parts122, wherein the mechanical parts122of the group share one or more commonalities that render the mechanical parts122applicable to the parameter relationships characterized by the predictive failure algorithm.

In various embodiments, the one or more input devices106can further be employed to control, and/or otherwise operate, the one or more mechanical devices108. For example, the one or more input devices106can be employed to define one or more settings that guide operation of the mechanical devices108. For instance, the one or more input devices106can be employed to activate or deactivate one or more mechanical parts122of the mechanical devices108, set a run time for the one or more mechanical devices108, and/or the like.

Further, the one or more input devices106can be employed to enter into the system100one or more values for one or more parameters of the one or more predictive failure algorithms. For instance, the one or more predictive failure algorithms can include parameters regarding the operation of the one or more mechanical parts122. Example parameters regarding the operation can include, but are not limited to: the number of times the one or more mechanical parts122have been activated; the average run time of the one or more mechanical parts122, an amount of work performed by the one or more mechanical parts122, a combination thereof, and/or the like. In one or more embodiments, the one or more input devices106can be employed to manually enter the one or more parameter values to facilitate execution of the predictive failure algorithm.

In various embodiments, the one or more input devices106can generate a log of settings and/or commands sent to the one or more mechanical devices108so as to provide the parameter values autonomously. For example, the one or more input devices106can log each activation and/or deactivation of the mechanical parts122instructed by the input devices106. Based on the log, the one or more input devices106can determine operation parameters such as, for example, the number of operations performed by the one or more mechanical parts122and/or the cumulative runtime of the one or more mechanical parts122. Thereby, the one or more input devices106can share the operation parameters with the status component110to facilitate the execution of one or more predictive failure algorithms that utilize the operation parameters in the malfunction analysis.

In various embodiments, the one or more memories114can also store one or more threshold databases123. The threshold databases123can include one or more threshold schemes that define vibration thresholds associated with one or more temperature ranges for a respective mechanical parts122. For example, each mechanical part122can be associated with a threshold scheme. Further, each threshold scheme can include a plurality of temperature ranges. Each temperature range can be associated with a vibration threshold. The vibration threshold can define the amount of vibration experienced during normal operation of the respective mechanical part122when operating within the respective temperature range. In one or more embodiments, wherein the mechanical part122is experiencing an amount of vibration that is greater than the defined vibration threshold associated with the current operating temperature of the mechanical part122, the mechanical part122may be experiencing a malfunction or may experience a malfunction imminently. Alternatively, in one or more embodiments, wherein the mechanical part122is experiencing an amount of vibration that is less than the defined vibration threshold associated with the current operating temperature of the mechanical part122, the mechanical part122may be experiencing a malfunction or may experience a malfunction imminently.

In one or more embodiments, a threshold scheme can be associated with a plurality of mechanical parts122. Alternatively, each mechanical part122can be associated with respective threshold schemes. The one or more input devices106can be employed to enter the one or more threshold databases123into the system100and/or to update the one or more threshold databases123.

AlthoughFIG.1depicts the one or more input devices106separate from the one or more mechanical devices108, the architecture of the system100is not so limited. Embodiments in which the one or more input devices106are a part of the one or more mechanical devices108are also envisaged. Further, the one or more input devices106can communicate commands, settings, and/or data with the one or more mechanical devices108via one or more direct electrical connections and/or networks104.

The one or more mechanical devices108can be devices, apparatuses, and/or systems that employ one or more principals of mechanical engineering to perform a defined task. Example mechanical devices108that can be analyzed by the system100can include, but are not limited to: ovens, furnaces, a combination thereof, and/or the like. In various embodiments, the one or more mechanical devices can include one or more mechanical parts122and/or sensors124. The one or more mechanical parts122can be various mechanical components comprised within the mechanical devices108that can facilitate operation of the mechanical devices108and/or performance of the defined task. Example mechanical parts122can include, but are not limited to: motors, fans, bearings (e.g., friction reducing devices such as ball bearings, roller bearings, plane bearings, sleeve bushings, and/or the like), rotaries, belts, pullies, clutches, bushings, gears, fasteners, chains, sprockets, rods, seals, springs, blowers, exhaust systems (e.g., exhaust manifolds), conveyers, industrial hardware, heating elements, a combination thereof, and/or the like.

The one or more sensors124can monitor, measure, and/or otherwise observe one or more characteristics of the one or more mechanical parts122during operation of the one or more mechanical devices108. The one or more characteristics can serve as parameters in the one or more predictive failure algorithms. For example, the one or more sensors124can measure at least the temperature of the one or more mechanical parts122during operation of the one or more mechanical devices108. Additionally, the one or more sensors124can measure an amount of vibration experienced by the one or more mechanical parts122during operation of the one or more mechanical devices108. Example types of sensors124can include, but are not limited to: thermometers (e.g., liquid or gas-filled thermometers, bimetal thermometers, electronic thermometers, thermistors, infrared thermometers, laser thermometers, and/or the like), vibration sensors (e.g., displacement sensors, velocity sensors, accelerometers, strain gauges, gyroscopes, laser displacement sensors, capacitive displacement sensors, vibration meters, vibration data loggers, and/or the like), a combination thereof, and/or the like.

In various embodiments, the one or more sensors124can be positioned adjacent to, and/or in contact with, the one or more mechanical parts122. The one or more sensors124can monitor the one or more mechanical parts122throughout operation of the mechanical device108, in response to a request generated by the status component110and/or the input devices106, at defined time intervals, and/or in response to one or more triggering events. Additionally, the one or more sensors124can share one or more measurements and/or observations (e.g., temperature values and/or amounts of vibration) with the status component110via one or more direct electrical connections and/or networks104.

FIG.2illustrates a diagram of an example, non-limiting mechanical device108, wherein the mechanical device108can be embodied as an oven and/or furnace in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity.FIG.2depicts the mechanical device108as an oven to exemplify features of the various embodiments described herein; however, as described herein, other types of mechanical devices108are also envisaged. Additionally, the structure of the oven depicted inFIG.2is also exemplary, and alternate oven structures comprising mechanical parts122and/or sensors124are also envisaged. For example, the one or more mechanical devices108can be various types of ovens, including but not limited to: baking ovens, curing ovens, drying ovens, cleanroom ovens, batch production ovens, continuous production ovens, a combination thereof, and/or the like.

The mechanical parts122of an oven can be exposed to a range of temperature fluctuations due to the inherent heating and cooling operations. As the temperature of the mechanical parts122changes the amount of vibration exhibit by the mechanical parts122while operating can also change. In various embodiments described herein, the status component110can adjust a predictive failure algorithm associated with the given mechanical part122so as to account for the impact temperature has on how the mechanical part122vibrates.

As shown inFIG.2, the exemplary oven can have an have an insulated body202that can house one or more mechanical parts122. Additionally, one or more mechanical parts122can be positioned outside the insulated body202. For example, the oven depicted inFIG.2can include one or more mechanical parts122such as fans204, motors206, exhaust devices208, and/or heating elements210. The one or more heating elements210can heat the ambient air adjacent to one or more heating chambers212. Additionally, the one or more fans204can circulate the heated air around and/or into the one or more heating chambers212. For example, the one or more fans can generate one or more horizontal, vertical, uniform, and/or non-uniform flows of heated air within the insulated body202and/or within the one or more heating chambers212. Further, the one or more fans204can be operably coupled to one or more motors206(e.g., positioned outside of the insulated body202, as shown inFIG.2) that can drive operation of the fans204. Moreover, one or more exhaust devices208can export heated air from the insulated body202to facilitate one or more cool-downs of the oven and/or achieve a set temperature within the one or more heating chambers212.

During operation of the one or more mechanical devices108, the one or more mechanical parts122can experience varying temperature changes. For example, during operation of the exemplary oven shown inFIG.2, the one or more mechanical parts122(e.g., fans204, motors206, exhaust devices208, and/or heating elements210) can be heated and/or cooled. For instance, the one or more heating elements210can achieve a variety of temperatures as the heating elements210generate and/or dissipate heat. Likewise, the one or more fans204can experience elevated temperatures during operation due at least in part to contact with the heated air. Similarly, the one or more exhaust devices208can experience elevated temperatures during operation due at least in part to heated air being dispelled out of the insulated body202. Additionally, the one or more motors206can experience elevated temperatures due to residual heat escaping from the insulated body202, heated air exhausted from the insulated body202, and/or ambient temperatures from the environment surrounding the insulated body202. In one or more embodiments, one or more motors206(e.g., driving mechanical parts122and/or components other than the one or more fans204) can be positioned inside the insulated body202and/or can experience elevated temperatures due to at least the heat generated by the one or more heating elements210.

Additionally, during operation of the one or more mechanical devices108, the one or more mechanical parts122can experience varying amounts of vibration. A defined amount of vibration can be expected during normal operation of the mechanical parts122. However, abnormal amounts of vibration can be an indication of an existing malfunction or an imminent malfunction. For example, the one or more predictive failure algorithms can determine that a mechanical part122is experiencing a malfunction or is prone to experience a malfunction in the near future based on the amount of vibration exceeding a defined vibration threshold that characterizes normal operating amounts of vibration.

Further, the amount of vibration expected to be experienced during normal operation of the mechanical parts122can vary based on the temperature of the mechanical parts122. Thereby, a mechanical part122can be associated with respective vibration thresholds for respective temperature ranges. For example, at a first temperature, an amount of vibration experienced by a mechanical part122can be determine to be within a range associated with normal operation; whereas at a second temperature, the same amount of vibration can be determined to be outside the range associated with normal operation. Thus, the temperature of the mechanical parts122can affect how much vibration the mechanical parts122experiences during normal operation, and thereby can affect how a predictive failure algorithm determines the presence and/or imminence of a malfunction.

Moreover, amount of vibration experienced during normal operation can vary between respective mechanical parts122. Likewise, the vibration thresholds associated with respective temperature ranges can vary between mechanical parts.122. For example, the amount of vibration experienced during normal operation of a fan204at a given temperature can be different than the amount of vibration experienced during normal operation of a motor206at the given temperature. Additionally, the type of mechanical device108housing the mechanical part122can influence the amount of normal operating vibration. For example, mechanical devices108performing different tasks can be subjected to different amounts of vibration during normal operation.

As shown inFIG.2, the mechanical device108(e.g., an oven) can include the one or more sensors124positioned adjacent to, in contact with, and/or comprised within the one or more mechanical parts122. As described herein, the one or more sensors124can measure the temperature and/or vibration experienced by the mechanical parts122. In one or more embodiments, each mechanical part122can be observed by one or more respective temperature sensors124and vibration sensors124. In some embodiments, a plurality of mechanical parts122can be observed by a shared sensor124. For example, a single temperature sensor124can measure the temperature of multiple mechanical parts122in a defined proximity to the temperature sensor124.

FIG.3illustrates a diagram of the example, non-limiting system100further comprising temperature component302in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the temperature component302can determine the temperature of the one or more mechanical parts122based on one or more temperature measurements performed by the one or more sensors124. The temperature component302can communicate with the one or more sensors124via a direct electrical connection and/or the network104(e.g., via communications component112).

In one or more embodiments, the one or more sensors124can collect temperature data characterizing the temperature of the one or more mechanical parts122in accordance with one or more of the following temperature collection schemes. In a first exemplary temperature collection scheme, the one or more sensors124can measure the temperature of the mechanical parts122continuously, or near continuously. In a second exemplary temperature collection scheme, the one or more sensors124can measure the temperature of the mechanical parts122in response to a triggering event. For example, a triggering event can be initiating operation of the one or more mechanical parts122, the vibration of the mechanical parts122exceeding a defined threshold, and/or a temperature request generated by the temperature component302. Following the triggering event, the one or more sensors124can measure the temperature of the one or more mechanical parts122: continuously, or near continuously; in accordance with a defined time interval (e.g., a defined interval of seconds, minutes, hours, and/or the like); and/or in response to a second triggering event.

Additionally, the one or more sensors124can share the temperature data with the temperature component302in accordance with one or more of the following temperature sharing schemes. In a first exemplary temperature sharing scheme, the one or more sensors124can directly share the (e.g., stream) the temperature data to the temperature component302. In a second exemplary temperature sharing scheme, the one or more sensors124can share the temperature data with the temperature component302in response to the measured temperature exceeding one or more defined temperature thresholds. For instances, one or more temperature thresholds can define a plurality of temperature ranges, wherein the one or more sensors124can share the temperature data with the temperature component302in response to the measured temperature transitioning from one temperature range to another. In a third exemplary temperature sharing scheme, the one or more sensors124can share the temperature data with the temperature component302in accordance with one or more time intervals (e.g., a defined interval of seconds, minutes, hours, and/or the like). Additionally, the one or more sensors124can share the temperature data with the temperature component302in response to one or more temperature requests generated by the temperature component302.

In various embodiments, the temperature component302can further generate and/or update one or more temperature databases304. As shown inFIG.3, the one or more temperature databases304can be stored in the one or more memories114. The one or more temperature databases304can include one or more temperature logs that include the measured temperatures associated with the mechanical parts122. In one or more embodiments, the temperature database304can include a temperature log for each mechanical part122. Additionally, the temperature component302can populate the temperature data collected from the one or more sensors124in the one or more temperature databases304. For example, the temperature component302can populate the temperature log associated with a mechanical part122with the temperature data characterizing the mechanical part122in response to the temperature data being received from the one or more sensors124. As the temperature component302collects additional temperature data from the one or more sensors124, the temperature component302can update the temperature logs of the mechanical parts122characterized by the additional temperature data with the temperature data. In various embodiments, the temperature component302can time-stamp the temperature data in populating the one or more temperature logs included in the one or more temperature databases304.

FIG.4illustrates a diagram of the example, non-limiting system100further comprising vibration component402in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the vibration component402can determine the amount of vibration associated with the one or more mechanical parts122based on one or more vibration measurements performed by the one or more sensors124. The vibration component402can communicate with the one or more sensors124via a direct electrical connection and/or the network104(e.g., via communications component112).

In one or more embodiments, the one or more sensors124can collect vibration data characterizing the amount of vibration being experienced by the one or more mechanical parts122in accordance with one or more of the following vibration collection schemes. In a first exemplary vibration collection scheme, the one or more sensors124can measure amount of vibration associated with (e.g., being experienced by) the mechanical parts122continuously, or near continuously. In a second exemplary vibration collection scheme, the one or more sensors124can measure the amount of vibration associated with (e.g., being experienced by) the mechanical parts122in response to a triggering event. For example, a triggering event can be initiating operation of the one or more mechanical parts122, the temperature of the mechanical parts122exceeding a defined threshold, and/or a vibration request generated by the vibration component402. Following the triggering event, the one or more sensors124can measure the amount of vibration associated with (e.g., being experienced by) the one or more mechanical parts122: continuously, or near continuously; in accordance with a defined time interval (e.g., a defined interval of seconds, minutes, hours, and/or the like); and/or in response to a second triggering event.

Additionally, the one or more sensors124can share the vibration data with the vibration component402in accordance with one or more of the following vibration sharing schemes. In a first exemplary vibration sharing scheme, the one or more sensors124can directly share the (e.g., stream) the vibration data with the vibration component402. In a second exemplary vibration sharing scheme, the one or more sensors124can share the vibration data with the vibration component402in response to the measured vibration exceeding one or more defined vibration thresholds. For instances, one or more vibration thresholds can define a plurality of vibration ranges, wherein the one or more sensors124can share the vibration data with the vibration component402in response to the measured amount of vibration transitioning from one vibration range to another. In a third exemplary vibration sharing scheme, the one or more sensors124can share the vibration data with the vibration component402in accordance with one or more time intervals (e.g., a defined interval of seconds, minutes, hours, and/or the like). Additionally, the one or more sensors124can share the vibration data with the vibration component402in response to one or more vibration requests generated by the vibration component402.

In various embodiments, the vibration component402can further generate and/or update one or more vibration databases404. As shown inFIG.4, the one or more vibration databases404can be stored in the one or more memories114. The one or more vibration databases404can include one or more vibration logs that include the measured amounts of vibration associated with the mechanical parts122. In one or more embodiments, the vibration database404can include a vibration log for each mechanical part122. Additionally, the vibration component402can populate the vibration data collected from the one or more sensors124in the one or more vibration databases404. For example, the vibration component402can populate the vibration log associated with a mechanical part122with the vibration data characterizing the mechanical part122in response to the vibration data being received from the one or more sensors124. As the vibration component402collects additional vibration data from the one or more sensors124, the vibration component402can update the vibration logs of the mechanical parts122characterized by the additional vibration data with the vibration data. In various embodiments, the vibration component402can time-stamp the vibration data in populating the one or more vibration logs included in the one or more vibration databases404.

FIG.5illustrates a diagram of the example, non-limiting system100further comprising baseline component502in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the baseline component502can determine one or more vibration baseline values associated with respective mechanical parts122at respective temperature ranges. In one or more embodiments, the baseline component502can generate and/or alter the threshold schemes of the threshold database123based on the one or more vibration baseline values.

The baseline component502can monitor the temperature database304and/or the vibration database404to track the operational data of the one or more mechanical parts122. For example, the baseline component502can track the amount of vibration experienced by a mechanical part122(e.g., and logged into the vibration database404) over time to generate a statistical baseline determination. In one or more embodiments, the baseline component502can determine a moving average of the vibration data for a respective mechanical part122for each temperature range defined by the one or more temperature thresholds to generate the one or more vibration baseline values. The baseline component502can analyze the temperature and/or vibration data for the entire recorded history of the mechanical part122to determine the one or more vibration baseline values. Alternatively, the baseline component502can analyze the temperature and/or vibration data for a defined historic time period (e.g., a default time period, such as the most recent thirty days, and/or a time period defined by the one or more input devices106).

The statistical vibration baseline value generated by the baseline component502can be based on the historic operational data (e.g., temperature data and/or vibration data stored in the temperature database304and/or vibration database404) and can characterize an amount of vibration expected to be experienced by the mechanical part122during a normal operating condition at a given temperature range. Thereby, the baseline component502can analyze the historic operational data of a mechanical part122to determine the amount of vibration associated with standard operating condition of the mechanical part122at a given temperature range.

The baseline component502can further determine the one or more vibration thresholds for the threshold schemes of the threshold database123based on the vibration baseline value determinations. For example, the one or more vibration thresholds can be a defined amount of deviation from the associate vibration baseline value for the given temperature range. For instance, the vibration threshold associated with a given temperature range in a threshold scheme can be defined by the baseline component502as an amount of vibration experienced in excess of the vibration baseline value associated with the given temperature range. In another instance, the vibration threshold associated with a given temperature range in a threshold scheme can be defined by the baseline component502as amount of vibration not experienced by the mechanical part122in comparison to the vibration baseline value associated with the given temperature range.

In various embodiments, the baseline component502can update one or more vibration baseline values in accordance with a schedule or a triggering event. For example, the baseline component502can update the one or more vibration baseline values in response to one more new entries of operational data into the temperature database304and/or the vibration database404. In another example, the baseline component502can update the one or more vibration baseline values in accordance with a fixed schedule (e.g., set via the one or more input devices106). For instance, the baseline component502can generate one or more measurement requests that command the temperature component302and/or the vibration component402to collect new temperature data and/or vibration data to be added to the historic record and facilitate an updated vibration baseline value determination. In various embodiments, the vibration baseline values for a mechanical part122can change over time. For example, as the mechanical part122ages, the amount of acceptable vibration associated with standard operating condition can change in comparison to the mechanical part's122first use. For instance, the mechanical part122can be altered, adapted, and/or tuned over time through operation of the mechanical device108.

In one or more embodiments, the same type and/or model mechanical part122can be comprised within a plurality of mechanical devices108in the system100. In such cases, the baseline component502can analyze operational data of respective mechanical parts122of the given type and/or model across the mechanical devices108to generate a vibration baseline value for the mechanical part122type and/or model. Further, the baseline component502can use the vibration baseline value associated with the type and/or model to configure the threshold scheme for a mechanical part122that is of the given type and/or model. Thereby, the baseline component502can incorporate the operational data of other mechanical parts122of the same type and/or model as a given mechanical part122in determining the one or more vibration baseline values for the given mechanical part122. Thus, where a mechanical part122is new, and thereby the operational data is limited, the baseline component502can determine the vibration baseline value based on the operational data of other mechanical parts122of the same type and/or model. Additionally, by aggregating operational data of a plurality of mechanical parts122of the same type and/or model, the baseline component502can employ a larger dataset in determining the one or more vibration baseline values and thereby increase an accuracy of the one or more vibration baseline values.

FIG.6illustrates a flow diagram of an example, non-limiting computer-implemented method600that can be implemented by the baseline component502in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the baseline component502can generate the one or more vibration baseline values in accordance with computer-implemented method600.

At602, the computer-implemented method600can comprise sampling the temperature and/or vibration data of the mechanical part122. For example, the baseline component502can sample operational data characterizing the mechanical part122from the temperature database304and/or the vibration database404. In various embodiments, the operational data can be timestamped to facilitate an association between temperature data and vibration data at a given moment.

At604, the computer-implemented method600can comprise identifying vibration data samples associated with a given temperature range. For example, the baseline component502can link a vibration data sample to a corresponding temperature data sample based on at least the timestamps. Thereby, the baseline component502can further identify vibration data samples corresponding to temperature data samples that are within a defined temperature range. The temperature range can be defined, for example, via the one or more threshold schemes of the threshold database123and/or can be defined via the one or more input devices106.

At606, the computer-implemented method600can comprise determining a moving average for the vibration data samples identified at604. The moving average can characterize a statistical baseline analysis. At608, the computer-implemented method600can comprise storing (e.g., within one or more memories114) the moving average as one or more vibration baseline values. At610, the computer-implemented method600can comprise calculating one or more vibration thresholds based on a deviation from the one or more vibration baseline values. In one or more embodiments, the baseline component502can implement computer-implemented method600to determine the one or more vibration baseline values based on historic operational data of the mechanical parts122. Further, the one or more vibration baseline values can characterize a standard amount of vibration experienced by the given mechanical part122at the given temperature range during standard operating condition.

In various embodiments, the baseline component502can repeat computer-implemented method600with regards to each temperature range defined in the threshold scheme associated with the given mechanical part122. As described herein, the baseline component502can also repeat computer-implemented method600to update the one or more vibration baseline values in response to a defined scheduled and/or triggering event.

FIG.7illustrates a diagram of the example, non-limiting system100further comprising correlation component702in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the correlation component702can correlate a predictive failure algorithm from the algorithm database120with a vibration threshold from the threshold database123, temperature data from the temperature database304, and vibration data from the vibration database404for one or more target mechanical parts122.

As described herein, a mechanical part122can be associated with a predictive failure algorithm from the algorithm database120, a threshold scheme from the threshold database123, a temperature log from the temperature database304, and/or a vibration log from the vibration database404. For a given mechanical part122, an associate predictive failure algorithm can define how vibration and temperature relate to each other, and/or other parameters, in performing a predictive failure analysis. The correlation component702can match the temperature and vibration data of mechanical part122to the predictive failure algorithm associated with the mechanical part122to facilitate execution of the predictive failure analysis. For example, the correlation component702can extract the latest temperature data from temperature log associated with the mechanical part122and/or the latest vibration data from the vibration log associated with the mechanical part122. Additionally, the correlation component702can extract the appropriate vibration threshold from the threshold scheme associated with the mechanical part122based on the extracted latest temperature data.

In one or more embodiments, the correlation component702can analyze the time-stamp of the latest temperature data, vibration data, and/or vibration baseline values for the mechanical part122of interest. Wherein the temperature data, vibration data, and/or vibration baseline value is older than a defined age threshold, the correlation component702can instruct the temperature component302, the vibration component402, and/or the baseline component502to collect more recent temperature data, vibration data, and/or vibration baseline values for the correlation with the predictive failure algorithm. The age threshold can be a defined interval of time (e.g., an interval of seconds, minutes, hours, days, and/or like), and can be set, for example, by the one or more input devices106. For example, wherein the age threshold is one minute and the most recent temperature data was collected and/or logged 2 minutes ago, the correlation component702can instruct the temperature component302to collect new temperature data from the one or more sensors124for the target mechanical part122. In another example, wherein the age threshold is one minute and the most recent vibration data was collected and/or logged 2 minutes ago, the correlation component702can instruct the vibration component402to collect new vibration data from the one or more sensors124for the target mechanical part122.

In various embodiments, the correlation component702can correlate the predictive failure data (e.g., temperature data, vibration data, threshold data, predictive failure algorithm) for one or more target mechanical parts122in response to one or more execution requests. For example, the one or more execution requests can be generated by one or more associate components of the status component110(e.g., temperature component302, and/or vibration component402) and/or the one or more input devices106. The execution request can initiate execution of the associate predictive failure algorithm, and thereby can initiate one or more correlations by the correlation component702. For instance, the temperature component302can generate an execution request in response to the measured temperature of a mechanical part122transitioning from one temperature range to another (e.g., wherein the temperature ranges can be delineated by the threshold scheme associated with the target mechanical part122). In another instance, the vibration component402can generate an execution request in response to the measured amount of vibration experienced by the target mechanical part122transitioning outside of a vibration range (e.g., wherein the vibration range can be delineated by the threshold scheme associated with the target mechanical part122). In a further instance, the one or more input devices106can be employed to generate an execution request on-demand. In one or more embodiments, the correlation component702can perform one or more correlations periodically in accordance with one or more schedules (e.g., defined by the one or more input devices106).

FIG.8illustrates a diagram of the example, non-limiting system100further comprising analysis component802in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the analysis component802can analyze one or more target mechanical parts122by executing the associate predictive failure algorithm with the associate predictive failure data, such as the temperature data, vibration data, and/or threshold data correlated (e.g., by the correlation component702) with the predictive failure algorithm.

In one or more embodiments, the analysis component802can update the predictive failure algorithm associated with the one or more target mechanical parts122with the temperature, vibration, and/or threshold data characterizing operation of the target mechanical parts122based on the correlations made by the correlation component702. By executing the predictive failure algorithm with the updated data, the analysis component802can determine whether the one or more target mechanical parts122are experiencing a malfunction and/or are predicted to experience a malfunction in the near future.

In various embodiments, the analysis component802can update and execute the predictive failure algorithm in response to one or more execution requests generated by the associate components of the status component110and/or by the one or more input devices106, as described herein. Additionally, the analysis component802can update and execute the predictive failure algorithm in accordance with one or more defined schedules (e.g., set by the one or more input devices106). In one or more embodiments, the analysis component802can update and execute the predictive failure algorithm with respect to a first target mechanical part122in response to a malfunction determination resulting from execution of a predictive failure algorithm with respect to a second target mechanical part122. For example, the malfunction, or imminent malfunction, of the second mechanical part122can be indicative of a problem in the mechanical device108that can lead to the malfunction, or imminent malfunction, of one or more other mechanical parts122(e.g., the first mechanical part122). Thereby, the analysis component802can update and execute predictive failure algorithms with regards to a plurality of the mechanical parts122so as to elaborate upon the collective operating status of the mechanical device108.

In one or more embodiments, the one or more predictive failure algorithms associated with the one or more target mechanical parts122can be initially calibrated for a default operating temperature. For example, the one or more predictive failure algorithms can assume that the operating temperature of the mechanical part122is a predefined temperature, such as room temperature. As described herein, the one or more mechanical parts122can be subjected to a variety of operating temperatures, which may differ from the predefined temperature used to initially calibrate the predictive failure algorithm. The analysis component802can compare the predefined temperature of the pre-calibrated predictive failure algorithm with the temperature data of the mechanical part122during operation (e.g., the operating temperature of the mechanical part122).

Wherein the operating temperature of the mechanical part122and the predefined temperature of the pre-calibrated predictive failure algorithm are within the same temperature range delineated by the threshold scheme correlated to the mechanical part122(e.g., by correlation component702), the analysis component802can execute the pre-calibrated predictive failure algorithm with the correlated temperature and/or vibration data. Wherein the operating temperature of the mechanical part122and the predefined temperature of the pre-calibrated predictive failure algorithm are within different temperature ranges, as delineated by the threshold scheme correlated to the mechanical part122(by correlation component702), the analysis component802can update the predictive failure algorithm with the correlated vibration threshold (e.g., the vibration threshold associated with the threshold scheme temperature range that includes the operating temperature of the mechanical part122). Thereby, the analysis component802can re-calibrate the predictive failure algorithm to account for vibration variances related to temperature. Subsequently, the analysis component802can execute the re-calibrated predictive failure algorithm using the correlated temperature and/or vibration data.

FIG.9illustrates a diagram of the example, non-limiting system100further comprising notification component902in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In various embodiments, the notification component902can generate one or more generate one or more notifications regarding results of the predictive failure algorithm executed by the analysis component802.

In one or more embodiments, the one or more notifications can be generated describe one or more determinations made by the analysis component802. For example, the one or more notifications can describe whether the analysis component802determined that the one or more target mechanical parts122are in normal operating condition, are experiencing a malfunction, and/or are predicted to experience a malfunction imminently. Further, the notification component902can store the one or more notifications (e.g., in the one or more memories114) for later review. For example, the notification component902can generate a warning notification based on a determination by the analysis component802that the mechanical part122is likely to experience a malfunction in the near future. For instance, the analysis component802can execute the predictive failure algorithm to predict a trend in the future operational data of the mechanical part122that markedly deviates from one or more vibrational baseline values, whereupon the notification component902can generate a warning notification to alert a user of the system100to the mechanical part's122predicted malfunction. In another example, the notification component902can generate an alarm notification based on a determination by the analysis component802that the mechanical part122is currently, or imminently, experiencing a malfunction. In one or more embodiments, the notification component902can also share the one or more notifications with the one or more input devices106(e.g., via a direct electrical connection and/or the one or more networks104). Thereby, an operating of the one or more mechanical devices108can employ the one or more input devices106to review one or more notifications generated by the status component110regarding the operating condition of one or more mechanical parts122included in the mechanical device108.

FIG.10illustrates a flow diagram of an example, non-limiting computer-implemented method1000that can facilitate analyzing the operating status of one or more mechanical parts122in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity.

At1002, the computer-implemented method1000can comprise measuring (e.g., via temperature component302and/or vibration component402), by a system100operatively coupled to a processor118, a temperature and vibration of one or more mechanical parts122during operation. In accordance with various embodiments described herein, the one or more mechanical parts122can be comprised within one or more mechanical devices108. For instance, the one or more mechanical parts122can include one or more fans204, motors206, exhaust devices208, and/or heating elements210comprised within an oven. Also comprised within the one or more mechanical devices108can be one or more sensors124that can measure the temperature and/or amount of vibration experienced by the mechanical parts122during operation of the mechanical device108. In accordance with various embodiments described herein, the temperature component302and/or vibration component402can collect the temperature data and/or vibration data measured by the sensors124and update one or more temperature logs and/or vibration logs associated with the mechanical parts122.

At1004, the computer-implemented method1000can comprise updating (e.g., via correlation component702and/or analysis component802), by the system100, one or more predictive failure algorithms by altering a vibration threshold based on the measured temperature. The vibration threshold can characterize an amount of vibration associated with a standard operating condition (e.g., normal operating condition) of the one or more mechanical parts122. In accordance with the various embodiments described herein, the amount of vibration associated with normal operation of a mechanical part122can vary depending on the operating temperature of the mechanical part122. For example, the operating temperatures can influence the amount of vibration experienced during normal operation of the mechanical parts122. Thereby, an amount of vibration indicative of a mechanical part122malfunction at a first temperature can be indicative of a normal operating condition at a second temperature, and vice versa.

In various embodiments, each of the one or more mechanical parts122can be characterized by one or more threshold schemes (e.g., comprised within threshold database123), which can delineate one or more vibration thresholds associated with respective operating temperature ranges. Based on the operating temperature measured at802, the correlation component702can identify the appropriate vibration threshold associated with the measured operating temperature for the target mechanical part122. Further, the analysis component802can update one or more predictive failure algorithms (e.g., identified and/or extracted from the algorithm database120) with the identified vibration threshold; thereby calibrating the predictive failure algorithm to account for how the operating temperature of the mechanical parts122influence the amount of vibration experienced during normal operation. For instance, the altering the vibration threshold can include populating a vibration threshold parameter value of the predictive failure algorithm that was previously un-populated (e.g., wherein the predictive failure algorithm was not pre-calibrated based on a default temperature), or replacing an initial vibration threshold parameter value of the predictive failure algorithm with the identified vibration threshold (e.g., identified based on the operating temperature of the mechanical part122in accordance with the associate threshold scheme).

At1006, the computer-implemented method1000can execute (e.g., via analysis component802), by the system100, the updated predictive failure algorithm to determine whether the one or more mechanical parts122are experiencing a malfunction currently or imminently. For example, the analysis component802can execute the updated predictive failure algorithm in response to an execution request generated by the temperature component302, vibration component402, and/or input devices106. In various embodiments, the analysis component802can execute the updated predictive failure algorithm in accordance with a defined schedule. The results of the execution at806can guide one or more maintenance operations on the mechanical device108.

FIG.11illustrates a flow diagram of an example, non-limiting computer-implemented method1100that can facilitate analyzing the operating status of one or more mechanical parts122in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. In accordance with various embodiments, the computer-implemented method1100can exemplify one or more features of the system100and/or computer-implemented method1000.

At1102, the computer-implemented method1100can comprise collecting (e.g., via temperature component302and/or vibration component402), by a system100operatively coupled to a processor118, temperature and vibration data regarding one or more mechanical parts122during operation. For example, the temperature component302and/or the vibration component402can collect the temperature and/or vibration data from one or more sensors124in response to one or more execution requests, triggering events, and/or schedules in accordance with one or more embodiments described herein. In various embodiments, the one or more mechanical parts122can be comprise within a mechanical device108prone to experience temperature fluctuations, such as an oven.

At1104, the computer-implemented method1100can comprise identifying (e.g., via correlation component702), by the system100, a vibration threshold associated with the one or more mechanical parts122based on the temperature data collected at902. For example, each mechanical part122can be associated with a respective threshold scheme included in the threshold database123. The threshold scheme can include a plurality of vibration thresholds, each associated with a respective temperature range. Thereby, the correlation component702can compare the operating temperature data of the one or more mechanical parts122with the temperature ranges included in the threshold scheme to identify the vibration threshold associated with the operating temperature data.

At1106, the computer-implemented method1100can comprise identifying (e.g., via correlation component702), by the system100, a predictive failure algorithm for analyzing the operating status of the one or more mechanical parts122. For example, the correlation component702can identify the predictive failure algorithm from a plurality of predictive failure algorithms included within an algorithm database120.

At1108, the computer-implemented method1100can comprise determining (e.g., via analysis component802), by the system100, whether the collected temperature and a pre-defined temperature used to calibrate the predictive failure algorithm are within the same temperature range. The collected temperature (e.g., collected at1102) can be the operating temperature of the one or more mechanical parts122. As described herein, the predictive failure algorithms included in the algorithm database120can be initially calibrated based on a pre-defined temperature (e.g., room temperature). The analysis component802can analyze a threshold scheme (e.g., included in threshold database123) to identify the defined temperature ranges of the one or more mechanical parts122. Further, the analysis component802can determine which of the defined temperature ranges includes the collected temperature and which of the defined temperature ranges includes the pre-defined calibration temperature of the predictive failure algorithm. Wherein the collected temperature and the pre-defined calibration temperature are within the same defined temperature range (e.g., a “YES” determination), the computer-implemented method1100can skip step1110and proceed directly to step1112. In contrast, wherein the collected temperature and the pre-defined calibration temperature are within in different defined temperature ranges (e.g., a “NO” determination), the computer-implemented method1100can proceed to step1110.

At1110, the computer-implemented method1100can comprise updating the predictive failure algorithm with the identified vibration threshold. For example, one or more vibration thresholds initially included in the predictive failure algorithm can be replaced with the vibration threshold identified at1104. The one or more initial vibration thresholds can be based on the pre-defined calibration temperature, and thereby be associated with a defined temperature range that is outside the operating temperature collected at1102. Thereby, the one or more initial vibration thresholds can inaccurately characterize permissible amounts of operating vibration as compared to the vibration threshold identified at1104. Thus, the analysis component802can improve the accuracy of the predictive failure algorithm with regards to the particular operating temperature being experienced by the mechanical part122by updating the predictive failure algorithm with the identified vibration threshold (e.g., in accordance with the associate vibration scheme).

At1112, the computer-implemented method1100can comprise executing (e.g., via analysis component802), by the system100, the predictive failure algorithm. For example, the non-updated predictive failure algorithm can be executed at1112in response to a “YES” determination at1108, or the updated predictive failure algorithm can be executed at1112in response to a “NO” determination at1108and the updating at1110. By executing the predictive failure algorithm, the analysis component802can determine whether the one or more mechanical parts122are experiencing a malfunction and/or predict whether the one or more mechanical parts122are expected to experience a malfunction imminently. At1114, the computer-implemented method1100can comprise generating (e.g., via notification component702), by the system100, one or more notifications that can report the results of the execution at1112. In accordance with various embodiments described herein, the one or more notifications can be shared with one or more input devices106to alert an operator of the mechanical device108to the operational status of the one or more mechanical parts122.

In order to provide additional context for various embodiments described herein,FIG.12and the following discussion are intended to provide a brief, general description of a suitable computing environment1200in which the various embodiments of the embodiment described herein can be implemented. While the embodiments have been described above in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the embodiments can be also implemented in combination with other program modules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, Internet of Things (“IoT”) devices, distributed computing systems, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. For example, in one or more embodiments, computer executable components can be executed from memory that can include or be comprised of one or more distributed memory units. As used herein, the term “memory” and “memory unit” are interchangeable. Further, one or more embodiments described herein can execute code of the computer executable components in a distributed manner, e.g., multiple processors combining or working cooperatively to execute code from one or more distributed memory units. As used herein, the term “memory” can encompass a single memory or memory unit at one location or multiple memories or memory units at one or more locations.

Computing devices typically include a variety of media, which can include computer-readable storage media, machine-readable storage media, and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media or machine-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media or machine-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable or machine-readable instructions, program modules, structured data or unstructured data.

Computer-readable storage media can include, but are not limited to, random access memory (“RAM”), read only memory (“ROM”), electrically erasable programmable read only memory (“EEPROM”), flash memory or other memory technology, compact disk read only memory (“CD-ROM”), digital versatile disk (“DVD”), Blu-ray disc (“BD”) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, solid state drives or other solid state storage devices, or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.

Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

With reference again toFIG.12, the example environment1200for implementing various embodiments of the aspects described herein includes a computer1202, the computer1202including a processing unit1204, a system memory1206and a system bus1208. The system bus1208couples system components including, but not limited to, the system memory1206to the processing unit1204. The processing unit1204can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures can also be employed as the processing unit1204.

The system bus1208can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory1206includes ROM1210and RAM1212. A basic input/output system (“BIOS”) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (“EPROM”), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer1202, such as during startup. The RAM1212can also include a high-speed RAM such as static RAM for caching data.

The computer1202further includes an internal hard disk drive (“HDD”)1214(e.g., EIDE, SATA), one or more external storage devices1216(e.g., a magnetic floppy disk drive (“FDD”)1216, a memory stick or flash drive reader, a memory card reader, etc.) and an optical disk drive1220(e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.). While the internal HDD1214is illustrated as located within the computer1202, the internal HDD1214can also be configured for external use in a suitable chassis (not shown). Additionally, while not shown in environment1200, a solid state drive (“SSD”) could be used in addition to, or in place of, an HDD1214. The HDD1214, external storage device(s)1216and optical disk drive1220can be connected to the system bus1208by an HDD interface1224, an external storage interface1226and an optical drive interface1228, respectively. The interface1224for external drive implementations can include at least one or both of Universal Serial Bus (“USB”) and Institute of Electrical and Electronics Engineers (“IEEE”) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer1202, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to respective types of storage devices, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, whether presently existing or developed in the future, could also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein.

A number of program modules can be stored in the drives and RAM1212, including an operating system1230, one or more application programs1232, other program modules1234and program data1236. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM1212. The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems.

Computer1202can optionally comprise emulation technologies. For example, a hypervisor (not shown) or other intermediary can emulate a hardware environment for operating system1230, and the emulated hardware can optionally be different from the hardware illustrated inFIG.12. In such an embodiment, operating system1230can comprise one virtual machine (“VM”) of multiple VMs hosted at computer1202. Furthermore, operating system1230can provide runtime environments, such as the Java runtime environment or the .NET framework, for applications1232. Runtime environments are consistent execution environments that allow applications1232to run on any operating system that includes the runtime environment. Similarly, operating system1230can support containers, and applications1232can be in the form of containers, which are lightweight, standalone, executable packages of software that include, e.g., code, runtime, system tools, system libraries and settings for an application.

Further, computer1202can be enable with a security module, such as a trusted processing module (“TPM”). For instance with a TPM, boot components hash next in time boot components, and wait for a match of results to secured values, before loading a next boot component. This process can take place at any layer in the code execution stack of computer1202, e.g., applied at the application execution level or at the operating system (“OS”) kernel level, thereby enabling security at any level of code execution.

A user can enter commands and information into the computer1202through one or more wired/wireless input devices, e.g., a keyboard1238, a touch screen1240, and a pointing device, such as a mouse1242. Other input devices (not shown) can include a microphone, an infrared (“IR”) remote control, a radio frequency (“RF”) remote control, or other remote control, a joystick, a virtual reality controller and/or virtual reality headset, a game pad, a stylus pen, an image input device, e.g., camera(s), a gesture sensor input device, a vision movement sensor input device, an emotion or facial detection device, a biometric input device, e.g., fingerprint or iris scanner, or the like. These and other input devices are often connected to the processing unit1204through an input device interface1244that can be coupled to the system bus1208, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc.

A monitor1246or other type of display device can be also connected to the system bus1208via an interface, such as a video adapter1248. In addition to the monitor1246, a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc.

The computer1202can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s)1250. The remote computer(s)1250can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer1202, although, for purposes of brevity, only a memory/storage device1252is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (“LAN”)1254and/or larger networks, e.g., a wide area network (“WAN”)1256. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer1202can be connected to the local network1254through a wired and/or wireless communication network interface or adapter1258. The adapter1258can facilitate wired or wireless communication to the LAN1254, which can also include a wireless access point (“AP”) disposed thereon for communicating with the adapter1258in a wireless mode.

When used in a WAN networking environment, the computer1202can include a modem1260or can be connected to a communications server on the WAN1256via other means for establishing communications over the WAN1256, such as by way of the Internet. The modem1260, which can be internal or external and a wired or wireless device, can be connected to the system bus1208via the input device interface1244. In a networked environment, program modules depicted relative to the computer1202or portions thereof, can be stored in the remote memory/storage device1252. It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used.

When used in either a LAN or WAN networking environment, the computer1202can access cloud storage systems or other network-based storage systems in addition to, or in place of, external storage devices1216as described above. Generally, a connection between the computer1202and a cloud storage system can be established over a LAN1254or WAN1256e.g., by the adapter1258or modem1260, respectively. Upon connecting the computer1202to an associated cloud storage system, the external storage interface1226can, with the aid of the adapter1258and/or modem1260, manage storage provided by the cloud storage system as it would other types of external storage. For instance, the external storage interface1226can be configured to provide access to cloud storage sources as if those sources were physically connected to the computer1202.

The computer1202can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, store shelf, etc.), and telephone. This can include Wireless Fidelity (“Wi-Fi”) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.

What has been described above include mere examples of systems, computer program products and computer-implemented methods. It is, of course, not possible to describe every conceivable combination of components, products and/or computer-implemented methods for purposes of describing this disclosure, but one of ordinary skill in the art can recognize that many further combinations and permutations of this disclosure are possible. Furthermore, to the extent that the terms “includes,” “has,” “possesses,” and the like are used in the detailed description, claims, appendices and drawings such terms are intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim. The descriptions of the various embodiments have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.