Predictive Maintenance, an essential component of Industry 4.0

by Kneo Automation
Posted: Jun 17, 2022

What is Predictive Maintenance?

Predictive maintenance is basically the utilization of new and historical machine data to interpret and, ideally, predict performance problems before they occur. Using sophisticated machine learning and AI techniques to monitor the data generated in the modern factory, predictive analytics can minimize downtime, optimize asset performance, and improve the lifespan of machines.

Most of the maintenance programs in manufacturing are preventative. Preventative maintenance (PM) occurs at usually scheduled intervals, or when the machine goes behind prescribed production thresholds.

Preventative maintenance is a key factor for assuring asset health.PM doesn’t take into account the conditions under which an individual machine operates, the differential wear and tear of several machine parts, or other factors that might forecast failure. It frequently results in maintenance schedules that are more or less often than it requires. (You can consider the example of changing your car’s oil every 3000 miles regardless of performance).

Predictive maintenance, in contrast, utilizes the data generated by a specific machine to generate a more granular picture of part and asset life cycles. It, theoretically, takes the guesswork out of scheduling maintenance. By giving visibility into how a given machine will be used, PdM allows manufacturers to perform maintenance only when required.

The success of any predictive maintenance effort relies on the quality and quantity of the data available in a training set.

That is, you will require 1.) enough data to generate a representative sample of machine performance over time, and 2.) data that precisely reviews machine performance and utilization in local conditions.

The life span of machines is generally in the order of years, which means that data has to be gathered for an expanded period of time in order to discover the system throughout its degradation process."

A large number of production factors influence how rapidly a part or machine will reach a window of failure. Spindle speed, hours running, temperature, vibration, humidity, as well as utilization–are just a few of the parameters that discuss in unique ways and have a variable effect on machine life.

In terms of predictive maintenance, understanding how machines are utilized is equally or more essential than understanding how machines run. In order for predictive maintenance to work as efficiently as possible, you require a record of how machines are utilized on a day-to-day basis, whether they’re set precisely, whether changeovers are done accurately, and whether or not maintenance is performed exactly.

Predictive Maintenance Applications

Asset health data can be utilized by IoT-enabled predictive maintenance applications to enable a range of intelligent applications such as:

Intelligent Control for Optimizing Asset Usage

Maintenance insights can be utilized to operate automation and control functions to optimize asset usage. For instance, upon the detection of early signs of asset breakdown, a different operational mode can be activated to avoid stopping operations and prolonging the asset’s Remaining Useful Life.

Maintenance Schedules Optimization

This application integrates maintenance data about individual assets (e.g., a machinery’s RUL) with insights related to business processes (e.g., production schedules) towards creating optimized maintenance schedules. These schedules notify the best point to perform asset maintenance considering not only how to improve OEE and the utilization of the asset, but also how to improve revenue as well.

Assume the Future Condition of the Asset

Predictive maintenance parameters can be combined with powerful digital twin applications to determine the asset’s future behavior. These will help maintenance professionals to estimate alternative maintenance options and their effect on business operations.

Root Cause Analysis

It is easy to track the health status of assets over time to rectify possible causes of their performance degradation. The identification of such causes assists enterprises to increase the ways they utilize and maintain their assets.

Predictive maintenance is a transformative application of the lloT with huge benefits. Below we mentioned five advantages that can serve as a determiner for your industry:

Reduced downtime

Predictive maintenance allows technicians to recognize concerns in advance and solve concerns before equipment failure can happen, so you can:

Cut down unplanned downtime by as much as up to 30%

Schedule various service procedures at one time

Minimize costly truck rolls needed by unexpected downtime

Significant worker productivity

Predictive maintenance plans around workers’ schedules, and:

Allows near about to 83% faster service time-to-resolution

Improves uptime and intercepts productivity lags

Improves asset usage

Increased ROI

By predicting machine maintenance, service departments can produce major cost savings and improved ROI

through:

Improved first-time fix rates

Simplified maintenance costs through decreased labor, equipment as well as inventory costs

Improved Product Design

Unlocking the power of lloT data gathered via your machine’s sensors, product designers can use this key information to:

Expand asset lifespans

Increase equipment resilience and reliability

Build more effective machines in the future

Increased worker safety

An unplanned breakdown or malfunction can lead to risky working conditions for your employees. By estimating when a malfunction may happen, you can assure:

Early detection of equipment and maintenance concerns minimizes the risk of fatal failures, ignoring injury, and even death.

Technicians can bring out service before a machine becomes hazardous

To learn more about how your facility can get the advantages of a predictive maintenance program or optimize its current process, connect with KNEO Automation.