What are the primary developments in predictive maintenance that will continue past 2022? We will investigate the developing technologies that have the potential to make your current operational equipment more proactive, intelligent, and directly connected to your maintenance resources and management teams.
Introduction
The application of predictive maintenance in manufacturing is not a recent innovation, although it is undergoing constant changes. Today, computers can automatically perform many predictive maintenance tasks, resulting in significant cost savings.
McKinsey & Company found that AI-powered predictive maintenance may cut inspection expenses by 25%, increase availability by 20%, and cut yearly maintenance costs by 10%. Staying aware of current trends is a fantastic strategy for increasing the returns on investment for your company.
But to what extent will these changes happen? Which trends in 2022 do we anticipate for predictive maintenance? Our thoughts are as follows.
What is Predictive Maintenance?
Continuously monitoring an asset or system and making an educated guess about that asset’s future condition based on data amassed over some time is one definition of what is known as predictive maintenance.
It is possible to determine an asset’s propensity to fail and whether corrective actions are necessary through specialized sensing techniques such as temperature, vibration, hour meter, flow, pressure, and energy consumption.
The act or result of announcing something that has not yet occurred in advance is called “prediction.” The endeavor aims to properly define the dependability of a piece of equipment based on the collected data. That is, to evaluate how dependable this machine is in terms of its capacity to keep producing throughout the upcoming hours, days, and weeks.
The Predictive Maintenance Changes in 2022
1. Adaptation of AI for human labor enhancing
The primary function of artificial intelligence will remain to be the interpretation of data in a significantly shorter amount of time utilizing continually updated and improved algorithms. And those employed at the [organization] will apply these insights to assist their companies in scaling the technology and procedures they already have in place so that they can function at a significantly more effective and much faster rate.
Therefore, robots need not worry about being replaced by robots in predictive maintenance. They come to assist you in performing your duties more effectively, swiftly, and accurately.
2. Governmental Investments
According to FedTech, public government expenditures on AI/ML in the United States increased to nearly one billion dollars in the fiscal year 2020. This is fifty percent higher than the financial year 2018. The growth makes it one of the new technology investment areas expanding the fastest. More so, that number is only going to go up as the effects of the events of the past two years continue to reverberate across economies all over the world.
3. Further Implementation of Smart Factory Principles
IIoT World defines a “smart factory” as a facility that uses technology for executives to make intelligent decisions using insights and data. In such a scenario, preventative or predictive maintenance becomes an essential element.
Raw data can be transformed into insights that can be put to use, which in turn leads to informed decision-making when using the tools that make predictive maintenance feasible. And this is mainly accomplished through deploying machine learning, artificial intelligence support, networked sensors, and easy-to-use interfaces for human operators remotely or on the ground.
4. Increased Efficiency of Problem Detection
A significant amount of time is still lost in locating the specific failure site inside a system or piece of machinery. But things are beginning to shift in that regard. There are devices on the market that can detect the particular sensors on a system that are malfunctioning so that the appropriate repairs can be made. Because of this, it will be much simpler for an experienced repair professional to begin working on the issue effectively and promptly.
In addition, these products typically compute an estimated effect of a problem that has been found. To give leadership the ability to prioritize business-critical corrective activities above issues with a lesser impact.
Many executives use spreadsheets when implementing alternative maintenance strategies, such as preventive or corrective maintenance. These spreadsheets are used to analyze and store data, and paper Work Orders are used to validate the work completed by a team.
All of this is rendered obsolete by using technology in predictive maintenance. Maintaining manual records requires a significant investment of time and effort. However, the routine tasks of record keepers can be simplified and made more productive by incorporating Internet of Things sensors and artificial intelligence into software.
As a result, the process of data collecting becomes considerably quicker and more effective, as they can analyze thousands of bits of information in a matter of seconds and with a significantly greater level of precision. It is no longer necessary to conduct collections manually or occasionally, nor is it necessary to perform complicated interpretations or significant accounting. The technology in and of itself delivers the necessary insights for taking action.
Conclusion
In most cases, when we consider predictive technologies, we do it with the assumption that they will never become a part of our everyday lives. In fact, implementing improvement processes connected to predictive maintenance has grown simpler and more practicable. Thus, it could be much closer to your budget than you think.
There is little doubt that 2022 will see the introduction of more advanced solutions in the field of preventive analytics, together with the identification of additional use cases.
