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How Predictive Maintenance is changing the Art of Maintenance?

Updated: Jan 22

Predictive maintenance, a key aspect of Industry 4.0

Maintenance is a challenging process: Ensure that the machine remains available and reduce repair resource consumption while ensuring that the quality of the product is maintained. It was previously difficult to address all these aspects. The advent of Industry 4.0, on the other hand, has provided new possibilities for predictive maintenance.

A key part of Industry 4.0 includes sensors and cyber-physical systems that allow physical processes to be digitized and integrated with IT-based data-driven automation and control operations. Digital twins, flexible automation, and predictive maintenance are a few aspects of Industry 4.0's industrial applications.

As part of Industry 4.0, huge quantities of digital data are collected regarding the health of machinery and equipment incorporate in maintenance. Data collection is enabled by deploying a variety of sensors, including vibration sensors, sound sensors, temperature sensors, energy usage sensors, and thermal cameras.

What Is Predictive Maintenance?

Every day, we use various machines, but without proper maintenance, they will eventually break down. You can forecast when machines will break down if you use predictive maintenance. By planning ahead, you can prevent unplanned downtime, manage inventories better, manage inventories more effectively, and extend the life of your equipment. By using predictive maintenance, you can anticipate future failures as well as pinpoint problems in complex machinery and determine what needs to be replaced.

In predictive maintenance, system data triggers repair notices. Teams conduct predictive maintenance when sensors, PLCs, and other Internet of Things (IoT) applications indicate that a part or component is failing or is at or near the point of failure. The distinction between predictive maintenance and time-and-usage-based preventive maintenance is that predictive maintenance relies on the real-time component condition, whereas preventive maintenance depends on predetermined standards for component life.

Maintaining your equipment with predictive maintenance gives you more control and relies on less speculation. In order to maximize equipment performance, longevity, and repeatability, factories should combine preventive and predictive maintenance practices.

PdM refers to the practice of conducting maintenance to prevent problems from occurring instead of conducting maintenance on a fixed schedule or when a problem occurs.

Why predictive maintenance is Important?

Industry such as manufacturing, which has costly machinery and depreciation is a significant expense, needs to effectively manage assets. Saving money through predictive maintenance is possible.

Effective asset management, in essence, comes down to predictive maintenance. Despite being older strategies aimed at increasing efficiency, six sigma and lean management offer limited results for businesses today after being used for more than a decade.

Understanding how and why assets fail is essential to developing a Predictive Maintenance approach, along with recognizing the warning signs of impending problems. Manufacturing environments can also benefit from predictive maintenance by improving productivity, quality, and efficiency.

With predictive maintenance, all three can be achieved: safety compliance, proactive corrective action, and longer asset life. The asset can be prevented from failing by looking ahead and recognizing when it is likely to fail and undertaking preventive maintenance, schedule adjustments, and repairs.

Predictive Maintenance has the advantages like- A reduction or elimination of unscheduled equipment downtime due to equipment or system failure; an increase in labour efficiency; an increase in production capacity; a reduction in maintenance costs; and an increase in equipment lifespan.

How does Predictive Maintenance Work?

Predictive Maintenance Workflow
Predictive Maintenance Workflow - source google

Although predictive maintenance is a simple concept, it relies on a variety of sensor data to maintain machine health. Predictive maintenance consists of scheduling work based on the current conditions of an asset. The process of knowing the exact condition of complex assets, on the other hand, does not come easily.

It tracks the condition of assets and alerts technicians to upcoming equipment breakdowns by using three primary components

A condition-monitoring system sends real-time performance data and machine health information

The Internet of Things enables machines, software solutions, and cloud technology to communicate. This allows huge amounts of data to be collected and analyzed

With all that processed data, predictive data models can create failure predictions.

Some of those sensors measure:

  • Temperature

  • Pressure

  • Vibration

  • Rotation speeds

  • Current

  • Chemical properties oil

These sensors can provide an indication of future issues, which creates maintenance work orders for machines whose values are above or below normal. Based on what you plan to track and which assets will be on your PdM, you can choose the sensors you want.