Over the past few years, predictive maintenance has emerged as one of the most impactful strategies and leading use cases for the Industrial Internet of Things and Industry 4.0. In simpler terms, predictive maintenance is a technique that offers data-backed analytical solutions by predicting the design life and performance characteristics of expensive manufacturing equipment. Thus, providing companies with some extra time to take appropriate measures and avoid or reduce production downtime significantly.
Before the introduction of the Internet of Things (IoT) into the production world, the stakeholders had no option but to lose a lot of their productive time scheduling maintenance and repair frequently. But with the increased applicability of AI and machine learning techniques and algorithms, things have changed drastically.
Now, with the applications of various predictive maintenance tools, manufacturers can predict maintenance tasks with utmost accuracy using big data. The predictions depend hugely on the equipment condition that is evaluated based on test data obtained through several condition monitoring sensors and techniques.
Predictive maintenance follows an analytical approach; analyzing both the real-time and historical data to evaluate machines and their components so that repairing could be done well in time.
Industries That Can Benefits From Predictive Maintenance Technology
Predictive Maintenance Technology applies to every industry
where you could obtain a notable amount of data from machines needing maintenance, regular up-gradation, or fine-tuning.
Here are eight industries that can gain immensely from Predictive Maintenance Technology-
Aviation companies can utilize this technology to solve maintenance-related flight delays and cancellation problems by closely monitoring sensor data from aeroplanes. It has proven itself a sound technique in improving aircraft health, accident and damage prevention, and enhanced passenger safety.
In this industry, the main aim is to reduce inventory costs without compromising the machine performance and aesthetics. Predictive maintenance can be instrumental in keeping up with the machine manufacturing efficiency so that companies don’t face any disruption or downtime in their integrated supply chains.
Nowadays, connected cars are in trend. Both fleet managers and customers could utilize this technology for vehicle maintenance based on performance data analysis.
You can find several everyday applications of predictive maintenance in this sector, such as equipment management in plants, complex equipment operation, upgrading workers’ safety, and much more. Major production issues, such as unplanned downtime, can be reduced significantly by analyzing useful data collected through equipment sensors.
Predictive maintenance is also quite effective in the evaluation of Overall Equipment Effectiveness (OEE).
OEE meaning – It’s a key performance indicator (KPI) useful in determining the truly productive portions of a manufacturing process.
4. Oil & Gas
The main point of concern for this industry is to lower maintenance costs while mitigating risks of environmental disasters. Several types of expensive equipment are involved both in extraction and refining. Thus stakeholders look for solutions that could avert disasters and are cost-effective.
This technology allows companies to monitor their machines’ condition and performance remotely, which could decrease their inspection expenses significantly. Sensors can be installed everywhere around the machinery to obtain data, which could further be analyzed using predictive machine learning algorithms.
Equipment on ports and harbors can deteriorate with time either due to extreme weather conditions or excessive wear and tear. Cranes being the most crucial machinery in the business of ports, their downtime would impact trade heavily. Thus predictive maintenance can be beneficial in reducing downtime and thus improving the quality of service.
6. Food & Beverage
This industry comes across distinct kinds of maintenance challenges such as complex equipment handling and strict regulatory standards where negligence can lead to serious health issues. Predictive maintenance can make a powerful impact on the functional efficiency and performance of the company’s resources. So with proper monitoring and analysis of machinery and equipment conditions, it becomes easier for companies to focus on their products rather than other factors.
7. Power & Energy
In power plants, several operations take place in one go requiring loads of machinery and equipment. In this industry, long stretches of maintenance periods could lead to abrupt depletion of funds pushing a company into recession mode if things don’t settle faster.
Thus it becomes quite crucial to take a systematic record of maintenance to curb running costs. Implementation of this technology on a bigger scale will lead to improved asset efficiency and increased profitability in the energy sector.
8. Information Technology (IT)
Computer hardware is as prone to damage and breakdown as the big factory machines. The only difference is the sensitivity of tools and equipment that makes it complicated to track the signs of failure.
Today with the use of data analytics, you can analyze patterns quite easily and make accurate decisions related to the repairing and replacement of computer hardware.
Thus, predictive maintenance allows you to safeguard your data from losses due to malfunctioning and extended service unavailability. The technology can be utilized by government agencies, hospitals, data centers, financial sectors, network and telecommunication firms, etc.
Apart from these, other discrete industries like electronics, textiles, consumer packaged goods (CPG), aerospace; and process industries such as pharmaceuticals and chemicals; can avail the benefits of the predictive maintenance technology.
While the 5G technology is already knocking at the door, it’s going to get even better, smoother, and faster for predictive maintenance to find its way through various unexplored industrial sectors. Along with the installation of sensors across the machinery, it is equally important to develop and improvise predictive models and align them in the direction of accurate maintenance predictions based on the quality of data input.
If you implement predictive maintenance technology in your business, you will witness a significant reduction in machine downtime for sure. Moreover, it assures the extension of the machinery life cycle by interpreting results based on analytical data obtained.
It’s a fact that the implementation of predictive maintenance demands a handsome amount of investment, skilled labor, and knowledge initially. But the return on the investment (ROI) aspect of the technology far outweighs the upfront costs.
It is expected that this technology will be relevant for every industry or sector which operates on huge capital expenditure, expensive & sensitive equipment & machinery, and prioritizes human safety more than anything else. Thus it won’t be incorrect to call predictive maintenance to be a revolution in Industry 4.0 generation.