An Exponential Model Method for Remaining Life Prediction of Mechanical Equipment Based on Trapezoidal Noise Distribution
An exponential model and noise distribution technology, which is applied in mechanical bearing testing, mechanical component testing, machine/structural component testing, etc., can solve the problems of model prediction accuracy and reliability reduction, and achieve the goal of improving prediction accuracy and reliability Effect
Active Publication Date: 2020-02-18
XI AN JIAOTONG UNIV
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Problems solved by technology
Therefore, the assumption of the noise term of the traditional exponential model does not match the actual situation, resulting in a decrease in the prediction accuracy and reliability of the model
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Embodiment
[0100] Embodiment: The present invention is verified by taking the experimental data of accelerated life of a rolling bearing as an example.
[0101] The accelerated life test of rolling bearings is completed on the PRONOSTIA test bench. By loading the bearings with air pressure, the bearings can work under high load conditions, and the bearings can be degraded from normal state to complete failure within a few hours. During the experiment, the bearing speed was 1800rpm and the load was 4kN. The acceleration sensor is used to sample the bearing vibration signal, the sampling frequency is 25.6kHz, the data length is 2560, the duration of each sampling is 0.1s, and the sampling interval is 10s. When the vibration amplitude exceeds 20g, the bearing fails completely. The whole life vibration signal of the experimental bearing is as follows: figure 2 shown.
[0102] Extract the effective value from the vibration signal as the health status indicator, and determine the fitting s...
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The invention discloses a prediction method for remaining life of mechanical equipment through an exponential model of trapezoidal noise distribution. According to the method, first, the exponential model of trapezoidal noise distribution is established; second, vibration signals of bearings, gears or rotors in the mechanical equipment are monitored in real time and collected, and health state indicators are extracted from the vibration signals to determine a fitting start moment; and last, parameter estimation is performed on a degradation model, and a random sampling method is adopted to give remaining life estimation and probability distribution of a rolling bearing. Through the method, the problem that in a traditional exponent prediction model, noise item assumption is not consistent with the actual condition is solved; original triangular noise distribution is changed into trapezoidal noise distribution, that is, an initial value of a noise item of the exponential model is increased; moreover, health state indicator noise at a smooth running stage is used as the initial value of the noise item of the model, and it is verified that the exponential model of trapezoidal noise distribution has higher precision and reliability in terms of remaining life prediction compared with a traditional exponential model by the adoption of bearing accelerated life experimental data.
Description
technical field [0001] The invention relates to the technical field of equipment remaining life prediction, in particular to a method for predicting the remaining life of mechanical equipment using an exponential model of trapezoidal noise distribution. Background technique [0002] With the rapid development of manufacturing technology and the continuous expansion of human exploration of natural fields, many devices have become more and more complex. Due to the complexity of the machinery and the influence of various operating factors (such as wear, external impact, load, and operating environment), the performance and health of these devices will inevitably degrade, resulting in final failure. For actual engineering equipment, once an accident caused by failure occurs, the resulting loss of personnel and property or even environmental damage is often immeasurable. Therefore, how to effectively evaluate the operating status of mechanical equipment and prevent accidents cau...
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IPC IPC(8): G06F30/20G01M13/00G01M13/04
CPCG01M13/00G01M13/045G06F30/367
Inventor 雷亚国李宁波李乃鹏闫涛林京
Owner XI AN JIAOTONG UNIV