Investigating the cause of equipment failure

Industrial robots, semiconductor manufacturing equipment, CNC machine tools, metal processing machines, and other equipment and devices in factories are manufactured based on an centered operation system from a controller as a brain called a programmable logic controller (PLC). Each device’s data is given a work ID, time stamp, and other information. However, this production system makes it difficult to determine the reason why the equipment stopped. Because when something is wrong with the device, an instruction layer receives only the result either if it works or not.

A newly developed YRM-X controller captures both operation data and results from the motion of the motors of each device, so that it can be used to determine when, with which device, and how it was processed, thereby enhancing traceability until product completion. In addition, real-time data on the same time frame, such as quality data, can be obtained when a device is connected, so that in the event of a device failure, the cause of the problem can be determined accurately.

Other solutions

  • Production Production
  • Quality Quality
  • Maintenance Maintenance

Shorter installation time

AI picking

AI Picking

By utilizing the AI technology “Alliom” developed by Yaskawa Group, the installation time to actual operation is drastically shortened, and the accuracy to actual machine can also be improved.

Production Production

Flexible production

High Variety and Variable Quantity Production

High Variety and Variable Quantity Production

By using digital data to manage automated production lines, setup can be prepared automatically without manual intervention, enabling high variety and variable quantity production from a minimum of one unit.

Production Production
Autonomous distributed manufacturing

Autonomous Distributed Manufacturing

Digital data such as the torque value, vibration value, and temperature of the servomotor is absorbed into the controller, and the robot can think for itself how to move.

Production Production

Accuracy improvement

 Accuracy improvement of defect cause analysis Yaskawa case

Accuracy Improvement of Defect Cause Analysis < Yaskawa Case >

By “visualizing” the operation status of equipment/devices with Yaskawa Cockpit, it is possible to identify the root cause by comparing the normal value and abnormal value of the data in the factor analysis for defects in production.

Quality Quality

Quality inspection

Automated product quality assessment with AI

Automated Product Quality Assessment with AI

When the quality inspection process is labor-saving, the use of an image judgement service that utilizes AI technology such as deep learning makes it possible to automatically determine complex No Good patterns with the same level of accuracy as humans.

Quality Quality

Failure prediction

Predictive failure diagnosis of equipment

Predictive Failure Diagnosis of Equipment

To reduce downtime to zero by performing planned maintenance in anticipation of equipment failure due to wear, etc., in response to concerns that production may become impossible due to the sudden shutdown or something else.

Maintenance Maintenance

Recovery support

Faster Recovery Simulation

Faster Recovery Simulation

The planning technology that Yaskawa developed automatically generates optimal paths, enabling simulation in a few minutes and dramatically reducing engineering time for recovery from sudden stop.

Maintenance Maintenance