All machines are prone to wear and tear. Earlier, machines were not too complex, which meant fewer breakdowns. With the industrial revolution, automation is a must to handle complexity in processes. The unplanned downtime costs around $50 Billion per year to the industrial manufacturers due to equipment failure. How to cope up with this loss? Employing a more efficient equipment maintenance strategy is the solution.

Predictive maintenance (PdM) covers the gap between data and insights for industrial corporations. Being one of the most valuable applications of the Internet of Things (IoT), it has the ability to use data-driven analytics for optimizing capital equipment upkeep. The companies can store and analyze the critical outputs of the machinery with the PdM software to improve its maintenance and input parameters.

The report of CXP Group, Digital Industrial Revolution with Predictive Maintenance says, 91% of predictive maintenance manufacturers have seen lower repair time and unplanned downtime, whereas 93% see improvement of aging industrial infrastructure.

 

What is predictive maintenance (PdM)?

Many have heard of PdM but are not sure about how it is different from current practices. It is an IoT solution that utilizes data analysis tools and techniques to detect irregularities in the operations and possible defects in equipment and processes to fix them before they fail.

It is a better option as it prevents predicted problems instead of conducting maintenance at a fixed time or when an issue arises. Companies can prevent problems without unnecessarily spending on frequent maintenance. If companies know when the irregularities will happen, they conduct maintenance before the malfunction takes place. It is now possible with the intuitiveness of this IoT application.

Investing in predictive maintenance is worth it as it brings significant value to the business by reducing unnecessary maintenance costs, improving product quality and productivity through advanced analytics, and overall effectiveness of manufacturing environments.

 

How does it work?

Being one of the critical of the Internet of Things (IoT), PdM uses historical and real-time data from many parts of the operations to predict problems before they hamper the processes. While its common idea is intuitive, PdM systems depend on infinite sensor data for machine condition monitoring. Some of those measures include:

  • Temperature
  • Pressure
  • Vibration
  • Rotation speeds
  • Current
  • Chemical properties oil

With real-time monitoring of these variables, the companies can implement immediate interventions to solve issues before they arise and highlight the abnormal deviations.

Depending on the equipment, these sensors can signal future issues and formulate work orders to conduct maintenance. Here are some of the examples:

  • Increased temperatures can cause components to melt or burn. Depending on the type of equipment, it may need a solution before it causes significant damage.
  • Conducting vibration analysis can offer insights into possible breakdowns if the increased vibrations are constantly occurring as it can be a sign of component failures.
  • Oil analysis for measuring the lubricant’s properties gives an estimate of how the machine is depreciating. What’s the interesting part? Around 50 years ago, it was the domain of tribologists and machine wear experts. Now, machine oil can be automatically extracted or analyzed with the help of IoT or in a lab setting that reveals detailed wear patterns.

 

Why does it matter?

Asset management is a crucial part of manufacturing industries where advanced machinery is expensive and depreciation costs heavily. To maintain their efficiency, opting for predictive maintenance is necessary. It determines the condition of in-service equipment to predict the maintenance requirements. Major cost savings are possible with PdM, especially in improving asset management. It helps the companies to comprehend how and why asset failure happens by identifying the indications of potential problems or failure.

Where other process improvement tools and techniques like six sigma and lean management have managed to drive efficiency. But, even after using these for more than half a decade, they brought limited returns for today’s fast-paced companies.

Predictive analytics can predict future outcomes by using past data. However, please note that predictive analytics is a journey and not a destination. It begins with identifying the right set of data points, integrates with the machine to ingest real-time data, and improves the data quality through live tracking of machine failures. Data preparation and data quality are the crucial inputs. The more high-quality data is fed into the predictive model, the better accuracy.

The report of Market Research Future estimates that the global predictive maintenance market will grow to $6.3 billion by 2022 as 83% of manufacturing companies will use the PdM in the next two years. The interest in predictive maintenance has doubled in recent times because of:

  • Decrease or eliminate unscheduled equipment downtime caused due to equipment or system failure

An hour of unplanned equipment downtime can impact the revenues of the company. Since the equipment issues can be predicted in advance, their downtime can be minimized. 

  • Increased labor utilization

Equipment downtime and operation not just impact the output but also hampers the workers’ morale. It becomes challenging for them to solve problems when they arise. Predictive maintenance takes the accountability of minimizing such discrepancies. 

  • Increased production capacity

PdM comes with advanced sensors that allow facility managers to understand the root cause of the unplanned equipment failure and take necessary actions to correct the same. 

  • Reduced maintenance costs

PdM can prevent the inefficiencies of the planned equipment maintenance program. It informs technicians about the changes that should be done to the system as per symptoms.

  • Increased equipment lifespan 

Predictive maintenance minimizes machine breakdowns and ensures operation in optimum settings that significantly improves its overall useful life. 

 

Smart are those who invest in a robust and scalable full-stack IoT first solution. The PWC report says predictive maintenance in factories could:

  • Lower cost by 12%
  • Boost uptime by 9%
  • Diminish safety, health, environmental, and quality risks by 14%
  • Expand the life of an aging asset by 20%

This IoT solution is applicable to all industries where machines create crucial data and need regular maintenance or fine-tuning as per the parameters. The operations of discrete industries like consumer packaged goods (CPG), automotive, electronics, textiles, aerospace, and process industries like food and beverage, chemicals, oil and gas, and pharma can be transformed through predictive maintenance.

 

Which industries can benefit from predictive maintenance

All industries can leverage predictive maintenance. It proves to be highly valuable for machinery that produces a significant amount of data and requires maintenance. Industries such as consumer packaged goods (CPG), automotive, electronics, textiles, aerospace, food and beverage, chemicals, oil & gas, and pharma can be productively transformed with this smart technology.  

Here is the list of some of the industries where predictive maintenance is gaining importance: 

  1. Automotive: Automotive companies stand to gain significantly from a technology that reduces their equipment downtime.
  2. Airlines: PdM comes with analytical capabilities that allow airlines to ensure the safety of passengers by analyzing more data.
  3. High tech manufacturing: Operating complex equipment at optimal parameters becomes possible with PdM systems. 
  4. Transportation: Complex equipment of airways, trains, and other means of transport can be handled through predictive maintenance
  5. Oil & gas: Prevent disasters with better analytics and proactive maintenance.
  6. Ports: PdM can reduce downtime, improve service quality, and reduce waste for ports.

Implement predictive maintenance proactively with Zenatix

We believe that it is one of the most important AI use cases, especially for manufacturing companies. If you want to incorporate it to bring efficient results, you need to connect with the best IoT solutions provider. Evaluating the alternatives can be time-consuming, but it’s important for making the right assessment. Zenatix is among the top IoT companies in India that deliver state-of-the-art automation solutions that include intelligent edge hardware, an AI-powered cloud platform, and easy-to-use user dashboards. Digitally transforming the physical assets with an IoT solution can bring efficient results in the long run!


Did you find the article helpful?You might also like our solution.

Contact us