Machine Anomaly Detection And Diagnostics Solutions

Helped the Forging Plant Operator Increase the Plant's Availability

Background

The mist collector machine is a critical equipment in the forging plant operation. It minimizes the adverse effects of exposure to metalworking fluids and helps shops comply with indoor air quality standards. It also improves in-process part quality by keeping work surfaces clean while reducing maintenance and housekeeping costs.

Problem Statement

Electrical motor unit failure in the mist collector machine is a common cause. It affects the availability of the forging unit. A time-based maintenance strategy is insufficient to avoid unplanned breakdowns. Real-time monitoring with early fault detection is critical to increasing the reliability and availability of the mist collector machine in the forging shop.

Our Solution

A global automotive company predicted failure of the mist collector machine in forging division and saved 8 hours of downtime and lakhs of rupees in repair and rebuild time

Deployment

VIBit Edge IoT sensors were installed on the Motor DE & NDE bearings of the mist collector.

In order to track the health of the machine, VIBit collects tri-axial vibration and temperature readings every 10 seconds and transmits them to the iEdge 360 cloud using the Plant's Wi-Fi and 4G communications.

Machine anomaly detection and diagnostics solution, VIBit and iEdge 360 IoT platform, deployed to monitor real-time health of the Mist collector. It provides early detection of an anomaly using various AI/ML-based models.

The customer success team and subject matter experts analyze machine parameters and various charts and provide correction and preventive action to the forging plant maintenance team.

Early detection of the electrical motor foundation stiffness and bearing wear issue saved unplanned downtime and production losses in the forging unit.

Insights

Overall vibration, RMS velocity (3-direction) and high alerts were detected on Motor DE (Drive End) bearing.

AI/ML in Action

Auto-diagnostics & recommendation algorithm detected a high structural looseness/bearing fault risk and provided recommendations to inspect the machine.

Inferences Customer Success and Subject Matter Team Expertise 

The customer success team and subject matter experts started observing vibration trends, FFT & time waveform as the severity of risk was critical. The time waveform had been showing bearing-related issues, and FFT showed structure stiffness as the cause of the bearing damage.

Monitor dominant frequency Check the stiffness of the motor foundation.

Inspect the motor bearing defects

Impact

The customer maintenance team started maintenance work order planning based on recommendations so inspection of the machine and repair could be carried out during non-production hours. They replaced the bearing during the routine maintenance activity to avoid unplanned downtime.

Benefits

Shift unplanned maintenance to planned

Avoided catastrophic machine failure and production losses

Real-time monitoring provides machine health status