Complex transmission chain predictive maintenance system and method based on industrial Internet of Things

Abstract

The invention discloses a complex transmission chain predictive maintenance system based on the industrial Internet of Things. Hardware equipment of the system comprises a high-frequency sensor network, a high-frequency signal processing device and intelligent hardware, wherein the high-frequency sensor network is used for collecting working signals in real time or periodically, the high-frequencysignal processing device is used for preprocessing the working signals, and the intelligent hardware carries out data processing at a local end by utilizing a wavelet characteristic extraction algorithm and uploads the data to a cloud end; and the cloud is used for verifying and storing the data, analyzing the data by utilizing an intelligent algorithm with feature extraction, and finally outputting a corresponding fault feature state. An exclusive data processing device is adopted for related high-frequency signals, processing capacity required by a core processing unit is dispersed, and thestability of the system is improved; aiming at the characteristics of complex vibration signals, high-frequency data dimension reduction is completed on the edge side through feature extraction, an equipment state degradation analysis side cloud data architecture is constructed through cooperative use of a cloud algorithm, and predictive maintenance of a complex transmission chain mechanism is realized.

Description

technical field [0001] The invention relates to the field of online status monitoring and fault diagnosis, and specifically relates to an equipment predictive maintenance system and method for complex transmission chain mechanisms using an industrial Internet of Things architecture. Background technique [0002] For the complex transmission chain mechanism, the failure mechanism is complicated due to the many factors affecting the rear load. For the core transmission chain mechanism, it is often the key to the entire system. If it can accurately monitor its operating status and predict its future operating status, and at the same time effectively avoid failures, especially delay the deterioration of progressive failures, it can efficiently realize the predictive maintenance of the organization. [0003] Existing predictive maintenance systems for this type of organization have considerable limitations: [0004] On the one hand, the analysis and diagnosis functions of the p...

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Problems solved by technology

[0003] Existing predictive maintenance systems for this type of organization have considerable limitations:

[0006] 1. The complexity of the types of fault signals

The mechanism model of this type of system is complex, the types of information involved are diverse, and the signals have multiple time scales, such as low-frequency temperature signals related to environmental information, high-frequency vibration signals related to mechanical mechanisms, and high-frequency currents related to electric drag voltage signal;

[0007] 2. It is difficult to extract fault signal features

Due to the complex transmission structure of this type of system and the specificity of the fault state, it is difficult to extract the fault type, so the signal for the degradation process is more complicated;

[0008] 3. Limitations of fault analysis methods

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