Elevator fault diagnosis method and device

Abstract

The invention discloses an elevator fault diagnosis method and a device, and relates to the field of mechanical equipment fault diagnosis and wireless sensor networks. (1) A multi-core fault monitoring method of global maintenance and partial identification comprises the steps of off-line training and on-line fault monitoring, wherein the off-line training is conducted on a projection matrix W with a maintained structure; a high-dimensional data sample is projected to a low-dimensional space; then a GMM (Gaussian Mixture Model) is trained; T<2>, SPE, BIP and MD monitoring statistical magnitude is computed; and fault detection and diagnosis are conducted by combining a contribution pattern. (2) The elevator fault diagnosis device acquires and transmits a signal in wired and wireless manners, and is suitable for remote monitoring of equipment in a complex environment. An application effect indicates that signal acquisition and transmission are reliable; the monitoring accuracy is high; the cost is moderate; and the method and the device have promotion values.

Description

technical field [0001] The invention relates to the field of fault monitoring and diagnosis of industrial processes and wireless sensor networks, in particular to a non-linear, non-Gaussian, multi-mode fault monitoring and diagnosis method and a fault diagnosis device. Background technique [0002] In recent years, with the rapid development of MEMS technology, wireless communication technology and sensor technology, a new mode of information acquisition and transmission - wireless sensor network technology has emerged. Compared with the traditional wired sensor monitoring method, the wireless sensor network has the advantages of rapid construction and convenient deployment, and can complete large-scale information collection and automatic monitoring of objects. In coal mine production, the main task of mine hoist is to lift coal, ore and gangue, lower materials, and lift personnel and equipment. It is one of the key production links, and its safe and normal operation has a ...

AI Technical Summary

Problems solved by technology

Although the fault detection method based on kernel PCA can solve nonlinear problems, the monitoring performance is not ideal under the condition of multiple load changes

GMM-based fault detection methods can monitor multi-modal problems, but the monitoring performance is not ideal in nonlinear and heterogeneous situations

At present, there are relatively few studies on the monitoring and diagnosis of mechanical faults for nonlinear non-Gaussian multi-mode

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