The Impact of Predictive Maintenance on Asset Lifespan 

In an industrial setting, the ramifications of unplanned downtime reverberate throughout operations, leading to significant production losses and delays. Research conducted by the Aberdeen Group states that unplanned equipment downtime costs, on average, could be around $260 an hour.   

Machinery and equipment serve as the lifeblood of businesses, and their condition and performance directly have an impact on efficiency and profitability. Therefore, safeguarding these assets becomes paramount, as any disruption in their functionality can disrupt entire production processes, incur substantial financial losses, and jeopardize business continuity.  

Businesses invest their resources in acquiring and maintaining assets ranging from machinery and equipment to complex systems. Maximizing the lifespan of these assets is not only financially beneficial but also strategically imperative for sustainable operations. 

Traditionally, maintenance strategies were followed in a reactive or preventative approach, where maintenance activities are either performed in response to failures or scheduled at regular intervals. However, these methods often lead to inefficiencies, unnecessary downtime, and premature asset degradation. 

In today’s industrial landscape, IoT-based asset management is revolutionizing the way we maintain and utilize assets. By leveraging predictive maintenance, organizations can fundamentally redefine their approach to asset management, aiming to maximize utility, performance, and lifespan. 

The outcome? Reliable machinery that not only lasts longer but also consistently delivers optimal performance, free from the disruptions of unplanned downtime and performance issues. 

But what exactly is predictive maintenance?  

Predictive maintenance is an advanced maintenance strategy that utilizes data-driven techniques and predictive analytics to anticipate equipment failures before they occur. It involves monitoring the condition and performance of the assets in real-time using various technologies such as sensors, IoT devices and gateway, and data analytics.  

By analyzing historical/real-time data, performance patterns, and equipment health indicators, asset management systems can identify potential issues, predict when maintenance is required, and prescribe appropriate actions to prevent failures and optimize performance. 

According to a study published by CXP Group in 2018, about 93% of companies claim that their maintenance processes are not very efficient. This means that there is a huge gap in efficiency and performance that still needs to be bridged with technology. Does your company figure in the staggering 93% of companies? 

According to a Plant Engineering study in 2021, 44% of plants/factories spend more than 40 hours a week performing maintenance. Have you calculated the hours that your business is spending on maintenance? What if we can optimize this by using technologies such as predictive maintenance?  

A study conducted by the International Journal of Production Research found that IoT-based asset management enables businesses to optimize maintenance practices by up to 30%. By leveraging real-time data and predictive analytics, maintenance activities become more targeted, efficient, and effective, ultimately leading to extended asset lifespans. 

How Does Predictive Maintenance Enhance Asset Lifespan? 

Predicting, Preventing, and Identifying Wear & Tear: All equipment and machinery are expected to operate for long durations, under harsh environmental conditions, and several other parameters such as temperature, vibration, pressure, and other parameters. These assets deteriorate over time and cause breakdowns.  

In a smart asset management setup, sensors are deployed across the industrial equipment to continuously monitor parameters such as temperature, vibration, energy consumption, pressure, and fluid levels. Any deviations from normal operating conditions can indicate potential wear and tear or impending failure. By predicting maintenance needs accurately, businesses can avoid unexpected repairs and replacements and be able to address issues before they happen. This ensures that the equipment or machinery operates without disruptions but plays a vital role in extending the lifespan of the assets.  

Read out case study: Remote Pump Monitoring Solution

When you can extend an asset’s lifespan, it indirectly helps you to reduce the cost of operations in the long run.  

Failure Mode Analysis: IoT-based asset management incorporates failure mode analysis to understand the potential failure modes of industrial assets. By identifying the causes of failure and its root causes, maintenance teams will be better equipped to address them by developing strategies that will mitigate wear and tear and prevent failures from occurring repeatedly. 

Predictive Modeling: Utilizing historical data and machine learning algorithms, predictive maintenance systems build predictive models that forecast the future behavior of your industrial assets. These models can estimate the remaining useful life of components and predict when maintenance activities, such as part replacements or refurbishments, will be necessary to maintain optimal performance. 

Condition Monitoring and Threshold Detection Techniques: IoT-based asset management systems can also help in establishing baseline performance metrics for each asset through continuous monitoring of equipment parameters. Deviations or anomalies from these baselines can indicate abnormal operating conditions or potential faults very early. Threshold-based algorithms can also be employed to detect deviations and trigger alerts or maintenance actions when predefined thresholds are exceeded, enabling timely intervention to prevent further damage and extend equipment lifespan. 

Proactive and Dynamic Maintenance: Traditional approaches for maintenance often look to over-maintain assets by doing maintenance activities that may not be necessarily required. By implementing predictive maintenance based on the equipment condition, you can reduce unnecessary practices that are performed every time as part of the process. This may help save time and resources and changes the team’s mindset from timely repetitive traditional maintenance to proactive and dynamic maintenance. The result would be maximum uptime and a reduction in planned downtime.  

Conclusion 

In any production line, you need to take great care of your industrial assets proactively. IoT-based industrial asset management does involve initial investment costs but in the long run, helps you to attain cost savings, more reliability, improved processes, and dynamic control over the entire production process.  

Maximizing profits by minimizing downtime and improving asset lifecycle indirectly creates a massive impact on the revenue of your business. That’s why any business approaching IoT-based asset management needs to have a long-term vision of what they would like to achieve, and leverage the right technologies and the right partner who would help them achieve that. 

Remember that IoT-based asset management is not a sprint, it’s a marathon, and staying the course and refining your processes will guarantee success in the long run.