Method for predicting operating life

Abstract

A computer-implemented method of predicting operational life of a device includes sensing at least one real-time workload parameter indicative of a current workload; sensing at least one real-time condition parameter indicative of a current condition; determining an effective age of the device using the device health model; and determining a remaining operational life for output based on the effective age of the device.

Description

Technical Field

[0001] The present disclosure relates to prediction of the operational life of equipment, and in particular to equipment having moving parts. Background Art

[0002] It is often more difficult to determine the health of a complete device than of its individual components. In such cases, refurbishment or replacement of the device can only be done based on the manufacturer's recommended design life of the device.

[0003] The manufacturer's recommended design life is usually determined without considering the actual load conditions, operating hours and operating environment of the specific equipment. Some equipment may have a lighter average working load or lower average operating hours, and therefore their service life may be longer. Premature refurbishment of such equipment will cause unnecessary service interruption and waste. On the other hand, some equipment in poor condition may be too worn out to reach the design life, but may not be refurbished in time, resulting in frequent equipment failures and interruptions. These equipment may also be a hidden danger of potential safety issues.

[0004] It is an object of the present disclosure to address or at least partially ameliorate some of the above-mentioned limitations of current approaches. Summary of the invention

[0005] The features and advantages of the present disclosure will be set forth in the following description, and in part will be obvious from the description, or may be learned by practicing the principles disclosed herein. The features and advantages of the present disclosure may be realized and obtained by the instruments and combinations particularly pointed out in the appended claims.

[0006] According to a first aspect of the present disclosure, there is provided a computer-implemented method for predicting an operating life of a device, the method comprising: sensing at least one real-time workload parameter indicating a current workload; sensing at least one real-time condition parameter indicating a current condition; determining an effective age of the device using a device health model; and determining a remaining operating life for an output based on the effective age of the device.

[0007] Workload parameters may include the operating hours of the equipment during a predefined period of time.

[0008] The workload parameters may include energy sensor output or user counts over a predefined time period.

[0009] The energy sensor output may indicate one or more outages, faults, or idling within a predefined time period as an additional real-time condition parameter.

[0010] The energy sensor output may provide additional real-time condition parameters compared to the baseline energy sensor output.

[0011] The condition parameter may include vibration sensor outputs from a plurality of vibration sensors arranged at different locations of the equipment.

[0012] The condition parameter may include the number of times the vibration sensor output exceeds a threshold within a predefined time period.

[0013] The device health model may be configured to calculate a lifetime health index (LHI), wherein the LHI is an exponential function of effective age.

[0014] The coefficients of the exponential function can be determined by fitting the curve based on measurements from other devices.

[0015] The LHI may be calculated by normalizing the real-time workload parameter and the real-time condition parameter and forming a linear combination.

[0016] The method may include outputting a warning signal based on a short remaining operational life.

[0017] The method may include indicating an alert regarding current conditions based on the real-time condition parameter.

[0018] The equipment may be an escalator or a moving walk.

[0019] According to a second aspect of the present disclosure, a computer-readable medium is provided, which is configured to store instructions that, when executed by a processor, cause the processor to: sense at least one real-time workload parameter indicating a current workload; sense at least one real-time condition parameter indicating a current condition; determine an effective age of a device using a device health model; and determine a remaining operating life for an output based on the effective age of the device.

[0020] According to a third aspect of the present disclosure, there is provided a data processing module for predicting the operating life of a device, the data processing module comprising: at least one real-time workload sensor, the at least one real-time workload sensor being configured to sense a workload parameter indicating a current workload; at least one real-time condition sensor, the at least one real-time condition sensor being configured to sense a condition parameter indicating a current condition; and a processor being configured to determine an effective age of the device using a device health model, and to determine a remaining operating life for output based on the effective age of the device.

[0021] Workload parameters may include the operating hours of the equipment during a predefined period of time.

[0022] The workload sensor may include an energy sensor or a counter configured to output a user count over a predefined time period.

[0023] The energy sensor output further indicates one or more outages, faults, or idle speeds within a predefined time period as additional real-time condition parameters.

[0024] The energy sensor output may provide additional real-time condition parameters compared to the baseline energy sensor output.

[0025] The condition sensor may include a plurality of vibration sensors arranged at different locations of the device output.

[0026] The condition parameter may include the number of times the vibration sensor output exceeds a threshold within a predefined time period.

[0027] The device health model may be configured to calculate a lifetime health index (LHI), wherein the LHI is an exponential function of effective age.

[0028] The coefficients of the exponential function can be determined by fitting the curve based on measurements from other devices.

[0029] The LHI may be calculated by normalizing the real-time workload parameter and the real-time condition parameter and forming a linear combination.

[0030] The processor may be configured to output an alarm signal based on the remaining operational life.

[0031] The processor may be configured to output an alert based on the real-time condition parameter indicating the relevant current condition.

[0032] The equipment may be an escalator or a moving walk. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to describe the manner in which the above and other advantages and features of the present disclosure can be obtained, a more particular description of the principles briefly described above will be presented by reference to specific embodiments of the present disclosure shown in the accompanying drawings. It should be understood that these drawings depict only exemplary embodiments of the present disclosure and, therefore, should not be considered to limit the scope of the present disclosure. The principles herein will be described and explained with additional specificity and detail through the use of the accompanying drawings.

[0034] Preferred embodiments of the present disclosure will be further explained in detail below by way of examples and with reference to the accompanying drawings, in which:

[0035] Figure 1 A schematic diagram of a data processing device according to an embodiment is shown.

[0036] Figure 2 A schematic diagram of the device and components of a data processing device is shown.

[0037] Figure 3 A schematic diagram showing the arrangement of vibration sensors on a device.

[0038] Figure 4A A diagram representing a health degradation model is shown.

[0039] Figure 4B A diagram representing the application of a health degradation model on an example device is shown.

[0040] Figure 5 A flow chart of a method for predicting the operating life of a device according to an embodiment is shown. DETAILED DESCRIPTION

[0041] Various embodiments of the present disclosure are discussed in detail below. Although specific implementations are discussed, it should be understood that this is done for illustrative purposes only. Those skilled in the relevant art will recognize that other components and configurations may be used without departing from the scope of the present disclosure.

[0042] With reference to the accompanying drawings, Figure 1 2 shows a data processing module 1 according to an embodiment. The data processing module 1 comprises at least one real-time workload sensor 100 , at least one real-time condition sensor 200 and a processor 300 .

[0043] The real-time workload sensor 100 is configured to sense a workload parameter indicating a current workload. The workload parameter may include the operating hours of the device within a predefined time period. Alternatively or additionally, the workload parameter may include the number of users or passengers of the device within a predefined time period.

[0044] In some examples, the workload sensor 100 may include an energy sensor. In some examples, the workload sensor 100 may include a counter configured to output a user count within a predefined time period. In some examples, the workload sensor 100 may be configured to measure per minute or per second, or at any suitable sampling rate determined by an operator.

[0045] In some examples, workload sensor data may be collected on an hourly or daily basis to calculate workload parameters. Alternatively, data may be collected in real time, or over any suitable time period. In some examples, data may be collated by service day (e.g., 4:00 a.m. - 4:00 a.m.) rather than calendar day.

[0046] Figure 2 The device and components of the data processing module 1 are shown.

[0047] The device may be an escalator or moving walkway. As shown in the figure, the device may include steps, trusses, railings and handrails, a motor, a control unit, a gear box, a drive sprocket with a main drive shaft, and a tensioning bracket.

[0048] The energy sensor may be connected to the control unit and / or the motor, or may be attached to any component on the drive train. In some examples, the measured minute-by-minute energy consumption may be processed to determine daily fixed and variable energy losses. For example, an algorithm and / or estimation function may be used to process the energy consumption. An estimation function may be applied to calculate a workload, such as a daily passenger count, using variable losses.

[0049] The real-time condition sensor 200 is configured to sense a condition parameter indicative of a current condition. In some examples, the condition sensor 200 may be sampled daily, twice daily, four times daily, hourly, or at any suitable sampling interval as desired. The condition parameter may be evaluated daily or weekly, monthly or yearly, or at any suitable interval determined by an operator.

[0050] The condition sensor 200 may include a plurality of vibration sensors arranged at different locations of the device output.

[0051] In some examples, the energy sensor output may further indicate one or more outages, faults, or idling within a predefined time period as an additional real-time condition parameter.

[0052] Figure 3 The arrangement of vibration sensors on the device is shown. In some examples, these vibration sensors can be arranged near major components, such as the motor, drive sprocket and tension bracket (e.g., for chain drive equipment) and gear box of the escalator.

[0053] As shown in the figure, the escalator can be monitored by an arrangement of 8 vibration sensors, which are located at the motor outer bearing, the motor inner bearing, the gearbox drive end bearing, the gearbox, the outer bearing, the main shaft bearing at the drive end, the main shaft bearing at the non-drive end, the right tension bracket bearing, and the left tension bracket bearing.

[0054] The vibration sensor may be configured to measure acceleration and velocity. The conditional parameter may include the number of times the vibration sensor output exceeds a threshold value within a predefined time period. Alternatively, in some examples, the conditional parameter may include coefficients of a fitted model that characterize the relationship between the number of times the vibration sensor output exceeds a threshold range.

[0055] In some examples, the energy sensor output may provide additional real-time condition parameters compared to the baseline energy sensor output. For example, the energy sensor may be used to identify a fault or interruption in the operation of the equipment. Faults may include motor failure (overheating, defective bearings), gearbox failure, machine brake failure, failure of moving parts (e.g., steps and handrails), etc.

[0056] The processor 300 is configured to determine the effective age of the device using the device health model.The processor 300 is further configured to determine the remaining operational life 20 for the output based on the effective age of the device.

[0057] In this way, the data processing module 1 can be used to generate an operating life 20 specific to the device and integrate the workload of the device and the current condition of the device. In this way, the operating life 20 generated by the data processing module 1 can be more accurate than the design life provided by the device manufacturer. This method can reduce unnecessary interruptions and costs caused by premature replacement of lightly used equipment in good condition, and can reduce a large number of failures and potential hazards caused by not replacing heavily used equipment in poor condition.

[0058] The equipment health model may be configured to calculate a lifetime health index LHI, wherein the LHI is an exponential function of the effective age. The LHI may be calculated by normalizing the real-time workload parameter and the real-time condition parameter and forming a linear combination.

[0059] In some examples, LHI can be calculated monthly, quarterly, biennially, annually, or at any other suitable time for evaluating equipment. Calculation of LHI for a period of time can use workload parameters and condition parameters determined for the period of time.

[0060] In some examples, the workload parameter may be evaluated over the lifetime of the device. For example, the workload parameter may be based on the number of operating hours determined over the previous year multiplied by the age of the device. Alternatively, the workload parameter may be based on the number of passengers determined over the previous year multiplied by the age of the device. Alternatively, the workload parameter may be based on the energy consumption determined over the previous year multiplied by the age of the device.

[0061] Where data is available, workload parameters are based on actual lifetime parameters such as energy use, number of passengers or operating hours, for example if each year of the equipment's lifetime has been recorded.

[0062] In some examples, the workload parameter may be a combination of the above factors and / or additional factors. Each factor may be standardized / normalized as needed for combination. A weighted or unweighted combination of factors may be used to calculate the workload parameter.

[0063] The condition parameter may be based on the number of vibration alarms in the previous year. Alternatively or additionally, the condition parameter may be based on the number of faults in the previous year. In some examples, each fault may be rated according to severity.

[0064] In some examples, the conditional parameter may be a combination of the above factors and / or additional factors. Each factor may be standardized / normalized as needed for combination. The conditional parameter may be calculated using a weighted or unweighted combination of factors.

[0065] In some examples, the processor 300 may be configured to output an alarm based on the real-time condition parameter indicating a worrying current condition. For example, if the vibration level continuously or repeatedly exceeds a predetermined threshold set for the device, the alarm may alert the user.

[0066] The LHI may be calculated as the workload parameter and the condition parameter added together. In some examples, either or both of the workload parameter and the condition parameter may be weighted.

[0067] Figure 4A is an exemplary graph representing a health degradation model according to an embodiment. The graph shows LHI calculated for a plurality of equipment, which is plotted relative to the actual age of each equipment. In this example, each equipment is an escalator.

[0068] The curve shows a fitted exponential degradation model, which is described by the following equation:

[0069] LHI i =a*exp(b*Age i )

[0070] The coefficients of the exponential function can be determined by fitting the curve based on measurements from other devices. As indicated by the rising curve, a higher LHI indicates a shorter remaining useful life.

[0071] Figure 4B Two graphs representing the application of the health degradation model on example devices are shown. The actual age of both devices is 18.7 years.

[0072] The first graph shows the LHI calculated for the first device. As shown, the LHI is above the curve, i.e., higher than expected for the actual age. The LHI is used together with the above formula to calculate the effective age, and it can be seen from the graph that the effective age is over 35 years old. By subtracting the effective age from the standard total operating life, the remaining operating life 20 can be reduced accordingly. In some examples, the processor 300 can be configured to output an alarm signal based on the remaining operating life 20. For example, if the remaining operating life is short (i.e., below a fixed threshold, such as 1 year remaining), and / or if the remaining operating life is shorter than expected (e.g., more than 3 years below the expected remaining operating life), an alarm signal can be output.

[0073] In contrast, the second graph shows the LHI calculated for the second device. As shown, the LHI is below the curve, i.e., below what would be expected for the actual age. The LHI can be used with the above formula to calculate the effective age, which can be seen from the graph to be approximately 13 years. The remaining operating life 20 can be calculated as the total operating life minus the effective age. Therefore, the remaining operating life 20 of the second device is greater than the remaining operating life of the first device.

[0074] Figure 5 A method for predicting the operating life of a device according to an embodiment is shown. The method starts at step S01.

[0075] In step S02, at least one real-time workload parameter is sensed, the real-time workload parameter indicating a current workload.

[0076] In step S03, at least one real-time condition parameter is sensed, the real-time condition parameter indicating the current condition.

[0077] In step S04, the effective age of the device is determined using the device health model.

[0078] In step S05, the remaining operating life is determined based on the effective age of the equipment for output.

[0079] The method ends at step S06.

[0080] The above embodiments are described by way of example only. Many variations are possible without departing from the scope of the present disclosure as defined in the appended claims.

[0081] For clarity of explanation, in some cases, the present technology may be presented as including various functional blocks including functional blocks constituted by devices, device components, steps or routines in methods implemented in software, or a combination of hardware and software.

[0082] The method according to the above example can be implemented using computer executable instructions, which are stored in a computer readable medium or can be obtained from a computer readable medium in other ways. Such instructions may include, for example, instructions and data, which enable or otherwise configure a general-purpose computer, a special-purpose computer or a special-purpose processing device to perform a specific function or function group. The part of the computer resources used can be accessed through a network. Computer executable instructions can be, for example, binary files, intermediate format instructions, such as assembly language, firmware or source code. Examples of computer readable media that can be used to store instructions, information used and / or information created in the method process according to the described example include disks or optical disks, flash memories, universal serial bus (USB) devices provided with non-volatile memory, networked storage devices, etc.

[0083] The apparatus for implementing the method according to these disclosures may include hardware, firmware and / or software, and may take any of a variety of form factors. Typical examples of such form factors include laptop computers, smart phones, small personal computers, personal digital assistants, etc. The functions described herein may also be implemented in peripheral devices or add-on cards. As a further example, such functions may also be implemented on circuit boards of different chips, or may be implemented on different processes executed in a single apparatus.

[0084] Instructions, media for transmitting such instructions, computing resources for executing such instructions, and other structures for supporting such computing resources are the means for providing the functionality described in these disclosures.

[0085] Although various examples and other information are used to explain aspects within the scope of the appended claims, limitations to the claims should not be implied based on specific features or arrangements in such examples, as those of ordinary skill in the art will be able to derive a variety of implementations using these examples. Further, and although a subject matter may have been described in language specific to examples of structural features and / or method steps, it should be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or actions. For example, such functionality may be distributed differently or performed in components other than those identified herein. Instead, the described features and steps are disclosed as examples of system components and methods within the scope of the appended claims.

Claims

1. A computer-implemented method for predicting the operational life of a device, the method comprising: sensing at least one real-time workload parameter indicative of a current workload; sensing at least one real-time condition parameter indicative of a current condition; using a device health model to determine an effective age of the device; as well as A remaining operational life for the output is determined based on the useful age of the device.

2. The computer-implemented method of claim 1, wherein: The workload parameters include operating hours of the device within a predefined time period.

3. The computer-implemented method of claim 1 or claim 2, wherein: The workload parameters include energy sensor output or user counts over a predefined time period.

4. The computer-implemented method of claim 3, wherein: The energy sensor output further indicates one or more outages, faults, or idle speeds within a predefined time period as an additional real-time condition parameter.

5. A computer-implemented method as claimed in claim 3 or claim 4, wherein: The energy sensor output provides an additional real-time condition parameter compared to the baseline energy sensor output.

6. A computer-implemented method as claimed in any preceding claim, wherein: The condition parameters include vibration sensor outputs from a plurality of vibration sensors arranged at different locations on the equipment.

7. The computer-implemented method of claim 6, wherein: The condition parameter includes a number of times the vibration sensor output exceeds a threshold within a predefined time period.

8. A computer-implemented method as claimed in any preceding claim, wherein: The equipment health model is configured to calculate a lifetime health index (LHI), wherein the LHI is an exponential function of the effective age.

9. The computer-implemented method of claim 8, wherein: The coefficients of the exponential function are determined by fitting a curve based on the measurement results of other devices.

10. A computer-implemented method as claimed in claim 8 or claim 9, wherein: The LHI is calculated by normalizing the real-time workload parameter and the real-time condition parameter and forming a linear combination.

11. The computer implemented method of any preceding claim, further comprising outputting a warning signal based on a short remaining operational life.

12. The computer-implemented method of any preceding claim, further comprising indicating an alert regarding current conditions based on the real-time condition parameter.

13. A computer-implemented method as claimed in any preceding claim, wherein: The equipment is an escalator or a moving walkway.

14. A computer readable medium configured to store instructions which, when executed by a processor, cause the processor to perform the method of any preceding claim.

15. A data processing module for predicting the operating life of a device, the data processing module comprising: at least one real-time workload sensor configured to sense a workload parameter indicative of a current workload; at least one real-time condition sensor configured to sense a condition parameter indicative of a current condition; as well as A processor is configured to determine an effective age of the device using a device health model and to determine a remaining operational life for an output based on the effective age of the device.

16. The data processing module according to claim 15, wherein: The workload parameters include operating hours of the device within a predefined time period.

17. A data processing module according to claim 15 or claim 16, wherein: The workload sensor comprises an energy sensor or a counter configured to output a user count within a predefined time period.

18. The data processing module according to claim 17, wherein: The energy sensor output further indicates one or more outages, faults, or idle speeds within a predefined time period as an additional real-time condition parameter.

19. A data processing module according to claim 17 or claim 18, wherein: The energy sensor output provides an additional real-time condition parameter compared to the baseline energy sensor output.

20. A data processing module as claimed in any one of claims 15 to 19, wherein the condition sensor comprises a plurality of vibration sensors arranged at different locations of the device output.

21. The data processing module according to claim 20, wherein: The condition parameter includes a number of times the vibration sensor output exceeds a threshold within a predefined time period.

22. The data processing module according to any one of claims 15 to 21, wherein: The equipment health model is configured to calculate a lifetime health index (LHI), wherein the LHI is an exponential function of the effective age.

23. The data processing module according to claim 22, wherein: The coefficients of the exponential function are determined by fitting a curve based on the measurement results of other devices.

24. A data processing module according to claim 22 or claim 23, wherein: The LHI is calculated by normalizing the real-time workload parameter and the real-time condition parameter and forming a linear combination.

25. The data processing module according to any one of claims 15 to 24, wherein: The processor is further configured to output an alarm signal based on the remaining operational life.

26. The data processing module according to any one of claims 15 to 25, wherein: The processor is further configured to output an alert based on the real-time condition parameter indicating a current condition of interest.

27. The data processing module according to any one of claims 15 to 26, wherein: The equipment is an escalator or a moving walkway.

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