Data

Aug 06, 2023

By Sponsored Content | May 1, 2023

Making decisions based on data, not projections or expected results, can produce far more accurate and efficient outcomes regarding your equipment. Sensor data can be collected and turned into information useful far beyond the narrow scope of part presence or absence. This information can be used to help users objectively make critical decisions such as when to order materials; employee, production, and maintenance scheduling; determining if and when production lines need upgrades; and more.

Take, for instance, a metal-stamping operation with limited data intelligence capabilities. Components can be added to existing equipment to collect and interpret previously inaccessible information to understand machine availability, performance, and quality, which are the essentials of OEE. Quality and performance can be monitored by photoelectric sensors counting good parts exiting the press, then comparing that number to the total number of parts produced. Machine availability is easily monitored via current-monitoring sensors installed on the motor power cables.

Current-monitoring data also creates the ability to track energy usage on equipment. By combining the current measurement with the supply voltage and monitoring this result over time, plant managers can more accurately understand the costs of running their equipment. Not only can this data show which equipment is the most cost effective to operate, but also it can inform future equipment decisions via comparing the costs of maintaining and running existing equipment versus the costs of new equipment that uses less energy.

Traditionally, large manufacturers have a vibration expert analyze motors and bearings on a monthly or quarterly basis, tracking readings and trying to proactively correct issues before failures occur. Other companies conduct maintenance at scheduled intervals based on estimated service needs, or simply replace equipment when it fails. Other companies are leveraging data for predictive maintenance, which involves machine learning analyzing data from vibration sensors to determine when maintenance is needed, and alerting operators so they can proactively address issues before equipment breaks down.

Another example is using data to improve the efficiency and uptime of compressed air systems. By adding vibration, temperature, and current sensors to the compressor motor, along with pressure and dewpoint sensors downstream, data may be analyzed over time to track trends. If sensors recognize a motor temperature starting an abnormal rise, a technician can be dispatched to the compressor immediately to check on it. Or, if the motor begins to run more often and system pressure varies too much, it may be a sign of an air leak. Technicians can track down the problem immediately and avoid excess energy consumption and expense.

The ability to collect, analyze, and use data to enhance production efficiency and make the most of finite resources can take automation not just to the next level, but to levels beyond.