Vertical elevator overspeed automatic reset fault diagnosis method based on Gaussian mixture model

A Gaussian mixture model, automatic reset technology, applied in complex mathematical operations, elevators, transportation and packaging, etc., can solve problems such as inappropriateness, and achieve the effect of ensuring operational safety and improving fault diagnosis efficiency.

Active Publication Date: 2020-08-21
ZHEJIANG UNIV
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  • Claims
  • Application Information

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Problems solved by technology

However, considering the complexity of the elevator working environment and the manual speed adjustment of the elevator by maintenance personnel, it is not appropriate to regard the rated speed of the elevator as a constant value, and it should be regarded as a multi-modal problem

Method used

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  • Vertical elevator overspeed automatic reset fault diagnosis method based on Gaussian mixture model
  • Vertical elevator overspeed automatic reset fault diagnosis method based on Gaussian mixture model
  • Vertical elevator overspeed automatic reset fault diagnosis method based on Gaussian mixture model

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Embodiment Construction

[0038] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific examples.

[0039] Elevators are used as a kind of mechanical equipment in daily use, and their speed signals reflect the operation conditions at this time. Comparing the constant speed of the elevator at this time with the rated speed to diagnose overspeed and automatic reset faults is a common diagnostic method. The rated speed of is not appropriate as a constant value. Therefore, the present invention utilizes a Gaussian mixture model with strong fit to fit the distribution law of the elevator's uniform running speed, and then provides a fault diagnosis model. The present invention takes straight elevator overspeed and automatic reset failure as an example, as figure 1 As shown, the method of the present invention is described in detail. like figure 2 As shown in , it shows the original acceleration signal collected by the sensor and the fil...

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Abstract

The invention discloses a vertical elevator overspeed and automatic reset fault diagnosis method based on a Gaussian mixture model. For a vertical elevator, elevator overspeed and automatic reset faults are diagnosed by using theGaussian mixture model based on speed signals in the elevator movement process. The non-stationarity of elevator speed signals in an actual environment is considered, theGaussian mixture model is used for fitting an elevator speed distribution rule, and the elevator constant-speed operation speed distribution rule is obtained and used for online fault diagnosis. Effective real-time diagnosis of straight elevator overspeed and automatic reset faults is achieved, a good diagnosis effect is achieved, the safety of the manned elevator in the running process is guaranteed, and accidents are reduced.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of speed signals, in particular to a Gaussian mixture model-based fault diagnosis method for straight elevator overspeed and automatic reset faults. Background technique [0002] Straight ladder is a kind of special equipment widely used in urban buildings, and its safety and reliability are directly related to the personal safety of residents. According to the "Notice of the State Administration of Market Supervision and Administration on the National Special Equipment Safety Situation in 2017" issued by the General Administration of Quality Supervision, Inspection and Quarantine, as of the end of 2017, the total number of special equipment in the country reached 12.9652 million units, an increase of 8.31% compared with the end of 2016. The number of elevators 5.627 million units. In 2017, there were 238 special equipment accidents and related accidents nationwide, with 251 deaths and 14...

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Application Information

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IPC IPC(8): G06F17/10B66B5/04B66B5/00
CPCG06F17/10B66B5/04B66B5/0031B66B5/0037
Inventor 赵春晖郑琪李泽华
Owner ZHEJIANG UNIV
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