Method for remotely monitoring and predicting fault of forklift loader

Abstract

The invention provides a method for remotely monitoring and predicting the fault of a forklift loader. A pressure sensor, a temperature sensor, a vibration sensor, and a current/voltage sensor are installed on corresponding components of the forklift loader. A vehicular smart terminal acquires the status data of the corresponding sensors of the components and is proactively connected with a data center server. The data center server performs data interaction and instruction communication with the vehicular smart terminal via the Internet and stores acquired data in a corresponding data table. A data center monitor program periodically scans and compares historical data samples in a database, checks whether a fault exists in combination with an equipment maintenance log and component fatigue information, predicts components possibly generating faults, communicates with a user if finding out a abnormal condition, guides a forklift loader driver to troubleshooting the faults, prepares spare components and makes maintenance preparation, shortens detection, component preparation, transport, and maintenance time consumed between a shutdown caused by the faults and a restart as much as possible, and decreases losses caused by the faults.

Description

technical field [0001] The invention belongs to the technical field of construction machinery application. Specifically, it involves remote fault monitoring and prediction methods for forklift trucks in stone mines. According to the current remote collection of forklift truck component status data, historical sample data in the service center database, and component fatigue records, etc. are compared to judge and predict forklift trucks. Possible damage to parts, and remotely notify the user to confirm, prepare spare parts for maintenance in advance, shorten the maintenance time required for forklift trucks from component failure to work again, and minimize equipment damage. economic losses, and improve the after-sales service response capabilities of forklift truck manufacturers. Background technique [0002] Forklift trucks are construction machinery products in the loader market segment. Forklift trucks integrate machinery, electricity, instruments, hydraulics and digita...

AI Technical Summary

Problems solved by technology

[0004] The existing stone mine forklift trucks are not highly intelligent and lack fault monitoring and prediction means. Usually, the manufacturer regularly visits the user site to conduct on-site inspections to determine whether there is any damage to the forklift truck parts, or wait for the fault to occur before repairing the forklift truck. The user actively notifies the manufacturer. This method lacks real-time dynamic monitoring and prediction, and cannot timely understand the fatigue degree of the forklift truck components, so it cannot timely troubleshoot and improve the effective use time of the equipment. economic loss, but also makes manufacturers have to spend a lot of money to build a huge after-sales service network to improve after-sales emergency response capabilities

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