How a Renowned Tire Manufacturing Company Used Predictive Alerts to Prevent Machine Failure & Downtime

Overview

Our client had 4-roll calender machines that frequently failed despite scheduled preventive maintenance. They needed an IoT-based solution, that remotely monitored the condition of the equipment in real time and predicted failure in advance. Smart maintenance techniques using AI/ML technologies were implemented to avoid unplanned downtime, save time and money, and increase asset lifetime.

Client Profile

Our client is a globally leading tire manufacturing company. In the passenger car segment, they supply tires to many of the leading OEMs globally and have been a pioneer in introducing tubeless radial tires in the market. In the Agri-vehicles segment, they supply tires to all the major tractor companies.

Problem

Our client uses calender machines to roll rubber sheets. The machine has many rollers that are connected to a motor that has a shaft-mounted gearbox. This gearbox allows maximum rubber sheet load and feeds the rubber sheet with reduced speed to the roller. Despite following scheduled maintenance practices, it was observed that the gearbox and motor bearings failed frequently. The rise in vibrations leading to the eventual failure of the gearbox adversely affected the quality of the rubber sheet. Monitoring the gearbox was thus vital and critical.

Solution

Sensors were installed at the bearing location of the motor drive end and non-drive end to capture tri-axial vibrations and acoustic signals in real-time. If the set limits were exceeded, then notifications were sent through emails to the registered maintenance team. Also, the sensor was able to diagnose the fault and provide appropriate recommendations or corrective actions.

The device recommended the following outcome to save money and downtime.

Diagnose and predict the gearbox and motor failures in the 4-roll calendar machine.

Optimum utilization of the 4-roll calender machine by practicing Predictive Maintenance.

Maintained consistency in the operations of the production line.

Results

After 1 month of deployment, the sensor detected a steady rise in vibrations. A corresponding rise in the fault frequency of gear mesh amplitude was also recorded. Alert notifications in the form of emails were sent to the registered local as well as the remote authorities. Along with the alerts recommended actions were also provided. Had the corrective measures not been taken, it would have certainly resulted in calender roll gear teeth breakage causing equipment failure and production downtime.