Escalator fault diagnosis method based on sequential probability

Abstract

The invention discloses an escalator fault diagnosis method based on the sequential probability. The escalator fault diagnosis method based on the sequential probability comprises the steps that vibration signals of key components of an escalator are collected through accelerator sensors mounted on the components of the escalator in vertical direction and the horizontal direction; the vibration signals are treated, and the peak values of the processed signals are used for characteristic values, namely indicator data of the actual operating state of the escalator; the indicator data are calculated through a sequential probability ratio algorithm to obtain a likelihood ratio; and the likelihood ratio is compared with the threshold value of a monitoring node, and fault diagnosis of an escalator mechanical system is conducted. According to the escalator fault diagnosis method based on the sequential probability, a wavelet packet algorithm is used for achieving smoothening and noise reduction of escalator data at different frequencies; and for the escalator in different operating states, the characteristic parameters are calculated through the sequential probability ratio algorithm, andthus differences caused by different operating states of the escalator are eliminated. In the detection process, the number of samples used for diagnosis does not need to be preset, the influence ofsample deficiencies or sample redundancy on the diagnosis result is avoided, the checking efficiency and accuracy are improved, and safety operation of the escalator is ensured.

Description

technical field [0001] The invention relates to the technical field of sequential probability ratio verification algorithm and equipment fault diagnosis, in particular to a sequential probability-based fault diagnosis method for elevators and escalators. Background technique [0002] With the rapid development of science and technology and the improvement of automation level, the safety and reliability of mechanical systems are becoming more and more prominent. It is more and more important to find faults accurately and timely, identify fault types and make evaluations. As an important means of transportation in high-rise buildings, elevators play an extremely important role in the national economy and people's daily life. Therefore, the study of elevator fault diagnosis is of great significance to the safety of people's lives. [0003] At present, vibration diagnosis is the most widely used signal for mechanical status monitoring and fault diagnosis. The analysis and proce...

AI Technical Summary

Problems solved by technology

[0004] In the fault feature analysis, the information representing the fault feature is analyzed and extracted from the original signal from the sensor, and the method of extracting the fault feature mainly adopts the time domain frequency domain analysis method, and this processing method is based on the linearity of the analyzed system. However, since all motions are nonlinear in nature, the signal analysis of nonlinear systems is very limited, and it is difficult to explain the relationship between a fault phenomenon and multiple possible fault causes. Non-linear artificial neural networks are gradually incorporated into fault characteristics. In the analysis of nonlinear systems, data analysis and index extraction can be performed on nonlinear systems, but the use of artificial nervous systems requires a large number of representative samples for learning, and relies on complete sample values. When the samples are insufficient, errors will occur.

[0005] In terms of fault identification, expert systems are often used to reason and judge based on the knowledge and experience provided by experts in a certain field, to simulate the decision-making process of experts, and to make judgments or inferences on the types, causes and components of mechanical equipment faults. Therefore, this The identification of various types of fault information is heavily dependent on efficient and accurate expert systems and complete knowledge bases. For complex systems, due to incomplete expert knowledge, narrow working scope, poor reasoning ability and many other problems, it leads to wrong judgments.

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