Predictive maintenance method and system and storage medium

Abstract

The embodiment of the invention discloses a predictive maintenance method and system and a storage medium. The method comprises the steps of: receiving configuration information of a target monitoring assembly by a user through a station management man-machine interaction interface, wherein the configuration information comprises stations, assembly names, data acquisition schemes and protocols and acquired data characteristic variables; based on the configuration information, respectively collecting data of the at least one target monitoring component, and performing unified management on the collected data; and when the configuration information further comprises a fault alarm threshold and a fault code for a data characteristic variable, monitoring the data of the target monitoring component collected in real time based on the fault alarm threshold, and when it is continuously monitored that the data of one data characteristic variable reaches the corresponding fault alarm threshold within a set time, sending out a fault alarm and providing corresponding fault information according to the fault code. According to the technical scheme in the embodiment of the invention, predictive maintenance with a low knowledge threshold can be realized.

Description

technical field [0001] The invention relates to the field of automobile industry, in particular to a predictive maintenance method, system and computer-readable storage medium. Background technique [0002] In the automotive industry, due to the large number of equipment suppliers and types, and the variety of production processes and links, the knowledge requirements for automation equipment and maintenance personnel are higher. Therefore, traditional predictive maintenance systems cannot quickly perform predictive maintenance on workshop or factory-level equipment. Predictive diagnosis of fault types requires significant R&D and experimental costs. [0003] Furthermore, even if predictive maintenance analysis of equipment is possible based on cutting-edge IT and IoT technologies, the results are difficult to use in actual automotive factories because the results depend on different equipment mechanical characteristic data and production processes on site, Such as differen...

AI Technical Summary

Problems solved by technology

[0002] In the automotive industry, due to the large number of equipment suppliers and types, and the variety of production processes and links, the knowledge requirements for automation equipment and maintenance personnel are higher. Therefore, traditional predictive maintenance systems cannot quickly perform predictive maintenance on workshop or factory-level equipment. Predictive diagnosis of fault types requires significant R&D and experimental costs

[0003] In addition, even if predictive maintenance analysis of equipment can be performed based on cutting-edge IT and IoT technologies, the results are difficult to use in actual automotive factories because these results depend on different equipment mechanical characteristic data and production processes on site, Such as different equipment structures, different flexible production loads and different station processes, etc.

These complex problems make the predictive maintenance business largely rely on professional data experts and vibration experts or domain-specific technical experts, and the reusability of core algorithms is limited due to the complexity of data samples

[0004] In short, the important issues to promote predictive maintenance in the whole plant include: Question 1: How to understand the data of different workstations and equipment with low knowledge threshold

Question 2: How to promote the basic analysis of the key equipment of the whole plant at low cost

Question 3: How to smoothly hand over predictive maintenance to field workers with low technical threshold

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