Predicting a time of failure of a device
a technology of a device and which is applied in the direction of program control, instruments, testing/monitoring control systems, etc., can solve the problems of not traditionally providing information about a time of failure, existing devices are typically prone to failure, and devices are becoming increasingly complex
Inactive Publication Date: 2014-07-03
IBM CORP
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AI Technical Summary
Benefits of technology
The disclosure describes a method, system, and computer storage media for predicting when a device will fail. This can be useful for identifying devices that are approaching the end of their lifespan and preventing failures that could cause damage or downtime. The technology involves receiving laboratory test results, generating physical measurements, and determining the difference between the test results and the physical measurements. A predicted time of failure is then determined based on this difference and outputted to the device. The technical effect of this technology is to improve the reliability and efficiency of devices by identifying potential failures and allowing for timely replacement or maintenance.
Problems solved by technology
Devices are becoming increasingly complex.
As a result, existing devices are typically prone to failures.
This allows for assessing the presence or the absence of failure, but does not traditionally provide information about a time of failure.
However, a major issue with the conventional failure prediction methods based on stress tests is the fact that the stress tests are performed with fixed parameters under controlled environments.
Those particular situations do not necessarily reflect the conditions in which the devices will operate; therefore the outcome of the stress tests is typically insufficient in assessing the actual status of devices in use (particularly when the devices are used in environments which are different from those in which the stress tests were conducted (that is, the change in operating condition adversely affects the predicted time of failure)).
Method used
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[0020]For the purposes of description, the term “real-time” is intended to refer to cause and effect occurring approximately contemporaneously in time (e.g., without significant time lag between cause and effect but not necessarily instantaneously).
[0021]For the purposes of description, the term “sensor” is intended to refer to a mechanism for acquiring one or more real-time measurements of one or more physical parameters. In various examples, a sensor may acquire measurements for temperature, current, voltage, acceleration, tension and / or electro-magnetic field.
[0022]For the purposes of description, the term “device” is intended to refer to any mechanism operating while using physical components (e.g., electrical, electronic, acoustic, hydraulic and / or mechanical components). Various examples of a “device” include (but are not limited to) the following: a smart phone (a mobile phone, a cellular phone); a tablet; a laptop computer; a desktop computer; a computer system, a household ...
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The present disclosure relates generally to the field of predicting a time of failure of a device. In various examples, predicting a time of failure of a device may be implemented in the form of systems, methods and / or algorithms.
Description
BACKGROUND[0001]The present disclosure relates generally to the field of predicting a time of failure of a device.[0002]In various examples, predicting a time of failure of a device may be implemented in the form of systems, methods and / or algorithms.DESCRIPTION OF RELATED ART[0003]Devices are becoming increasingly complex. Various devices change dynamically in response to their mobility, changing execution environments and frequency of use. As a result, existing devices are typically prone to failures.[0004]Certain existing failure prediction methods are based on, for example, on-line monitoring that considers both the actual state and the estimated (modeled) state of a system. Typically, those methods incorporate measurements of actual system parameters in order to evaluate the difference between the observed and the estimated (modeled) state which gives a residual. Then, the residual is monitored for the purpose of detecting significant deviation, that is, a failure indicator of ...
Claims
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Login to View More IPC IPC(8): G06F17/00
CPCG06F17/00G06F11/008G06F11/22G06F11/3058G05B19/4065G05B23/0283G06F11/3495
InventorBA, AMADOU
OwnerIBM CORP