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Equipment state dynamic self-adaptive alarm method based on hidden semi-Markov model (HSMM)

A hidden semi-Markov and dynamic self-adaptive technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as large fluctuations and inaccurate alarm information

Inactive Publication Date: 2013-07-24
NANTONG UNIVERSITY
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Problems solved by technology

Such a method requires a large amount of historical operating data to build a probability model of the equipment state through the probabilistic neural network self-learning method, and directly models the original data, resulting in large fluctuations and inaccurate alarm information

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  • Equipment state dynamic self-adaptive alarm method based on hidden semi-Markov model (HSMM)
  • Equipment state dynamic self-adaptive alarm method based on hidden semi-Markov model (HSMM)
  • Equipment state dynamic self-adaptive alarm method based on hidden semi-Markov model (HSMM)

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Embodiment Construction

[0017] Hidden Semi-Markov Model is called HSMM for short. The dynamic self-adaptive alarm method for equipment state based on the hidden semi-Markov model of the present invention takes the use of bearings as an example, and mainly includes the following steps:

[0018] A. According to the historical data of bearing life, the data is divided into several segments with the same length of time, and the characteristic information is extracted for each segment as the observation value of the operating state of the tested bearing. All observation values ​​form an observation sequence, which is used as an observation sequence for HSMM training and state identification. , the number of observation sequence groups is K groups;

[0019] B. The HSMM model is trained based on the Baum-Welch algorithm, and the HSMM models of the normal state and the degraded state of the bearing are respectively established; the Baum-Welch algorithm is used to solve the HMM training problem, that is,...

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Abstract

The invention discloses an equipment state dynamic self-adaptive alarm method based on a hidden semi-Markov model (HSMM). The method is characterized by comprising the following steps of solving a degradation index (DI) curve by establishing the HSMM for history data and equipment operating data and defining a performance DI; dividing the DI into a plurality of stages by a limit error method; and solving the upper alarm threshold value and the lower alarm threshold value of the DI. In the way, the few history data are required for modeling the HSMM; the HSMM is modeled conveniently; and meanwhile, alarm threshold values of equipment operation can be acquired dynamically; and the alarm is flexible and accurate.

Description

technical field [0001] The invention relates to a dynamic adaptive alarm method for equipment state, in particular to a dynamic adaptive alarm method for equipment state based on a hidden semi-Markov model. Background technique [0002] At present, various equipment grade evaluation standards used in enterprises are absolute standards, such as ISO-2372 vibration intensity standards, etc. The alarm thresholds stipulated in these standards are static, and it is difficult to make corresponding adjustments for specific equipment in specific working environments. Adjustment. This has caused specific equipment to be repaired or replaced before or after wear and tear, so that the former causes unnecessary waste, and the latter affects production. [0003] The self-adaptive alarm technology is to change with the actual conditions of the equipment, such as working conditions, working hours, power, speed, etc., and establish dynamic evaluation rules for alarm indicators and equipment...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 王恒朱龙彪黄希徐海黎马海波
Owner NANTONG UNIVERSITY
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